Features description.
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Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Emphasis is placed on evolutionary algorithms and swarm intelligence as computational means to efficiently balance the tradeoff between optimality of the produced solutions and the complexity derived from their estimation. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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The rise of Taylorism, standardisation, electrical systems, electronic systems and computing, and now, quantum computing, has given scheduling a whole World of importance.
\r\n\tFrom practice to a mathematical and technological application, scheduling has become another form of art: an algorithmic art, declined in as many OS and hardware constraints, from embedded systems onboard an aircraft or a spacecraft to databases in all financial and Internet servers.
\r\n\tThey have become ubiquitous so that a large part of our civilisational development is supported by their reliability, redundancy, and optimisation capacity. Like all of our civilisational assets, they are benefiting from scientific breakthrough in computational sciences such as evolutionary algorithms, Artificial Intelligence, and quantum computing. If not by using it, by being in need of adapting to the next generation of computing. Space development is also bringing new challenges, especially in redundancy and reliability.
Varistor is an electrical device based on semiconductor materials used for protection against voltage spikes in the electric network, against overvoltage in electronic circuits of low voltage and electrical power systems [1,2].
Due to the high energy absorption, the ceramic varistors become many helpful in protecting electrical circuits, and their electrical properties are highly dependent on their microstructure. The development of devices ever more technological and brings the need for electrical protection due to the sensitivity equipment. The use of varistors as voltage protectors in electronic equipment is very simple: the varistor is directly connected in parallel to the power line of equipment, and in case of an increase in the electrical current on energy network, the varistor rapidly increases the conductivity, allowing the current flow toward the ground. For electrical appliances operating with few voltages, the varistors ceramics are called low-voltage varistors [1–3].
The first varistor ceramics were developed in 1930. They were constituted from compact silicon carbide (SiC) partially sintered and were designed by the System Bell Labs to replace selenium rectifiers that were used in the protection of telephone systems [4]. Over time, the processing of varistors has undergone successive improvements, and in 1968, Matsuoka [3] developed varistors based on zinc oxide with manganese and cobalt as a dopant to improve the electrical properties. One of the disadvantages of using ZnO-based varistors are the large amount of dopant added to ceramic matrix for its electric modification and consequently to its high chemical instability that leads to degradation of the varistor. Castro et al. [5] reported that the trapping of electrons, ion migration and oxygen adsorption are included as ZnO varistor degradation mechanisms. The exposure of ZnO varistors to high temperatures and oxidizing atmospheres leads to excess interstitial ions
The SnO2-based varistors were introduced by Pianaro et al. [7] as an alternative to the ZnO varistors commercial, presenting nonlinear electrical characteristics similar to ZnO varistors. The SnO2-based system shows more advantages, for example, their simpler microstructure and no formation of secondary phases require a lower concentration of agents modifiers to promote the varistor characteristics and densification and higher chemical and thermal resistances. The use of M2+ ion as dopant improved significantly the densification of the varistor, the addition of M5+ ion promoted electrical conductivity, and the M3+ ion influenced on nonlinearity coefficient.
The electrical properties of varistor ceramics are governed by potential barriers located in the grain boundaries. Potential barriers were formed by the addition of dopants elements to generate defects on crystal network, which segregate to the grain boundary region by diffusion during sintering. The presence of these barriers promotes the large-capacity power absorption and its flow when subjected to electrical overvoltage [4,8].
In denominated “smart ceramics,” the ceramic varistor acts as variable resistors, with resistive behavior at low voltages and conductive behavior starting from a specific voltage value, known as the breakdown voltage (
These electrical responses featuring the varistor ceramics as main elements in the manufacture of devices for electrical protection equipment subjected to both low and medium voltages apply directly as of the electro-electronics components (telephony system, computers, medical devices, automotive electronics, industrial automation systems, alarms, transformers, etc.) and for the high voltages used as part of lightning protection devices installed in the terminals of the power substations [11].
The varistor characteristic associated to quality is the nonlinear coefficient (
where
Electric field (
The pre rupture region is also named as linear region and has an ohmic behavior when the material is under operation normal tension. The varistor acts as a resistor in this case with a small amount of current (known as leakage current) passing through the material due to the action of the potential barrier formed at grain boundary and preventing the electronic conduction between the grains. The conductivity in this region is of thermionic emission type, i.e., the electrical conduction is strongly dependent of temperature, thus being possible to retrieve information about the resistivity of the material [8,16,17].
The rupture region showed nonlinear behavior, i.e., non-ohmic behavior between the applied voltage and the current that the material is submitted. The conductivity of the material increases with a small variation in the applied voltage, indicating the varistor efficiency that starts to act as a conductor from a specific breakdown electric field (
In the post rupture region, the ohmic behavior between the current and the applied voltage is observed once again and is characterized by high current density. The electric conduction in this region is controlled by the impedance of the grains [2,8].
The
The varistor efficiency determined by the breakdown region can be evaluated by the
The electric field and the current density are obtained from the measurements of the electric current (I) generated when the sample is submitted to a potential difference (V), according to Eqs. 4 and 5 [18,19]:
The potential barrier is the determining factor on the electrical properties of varistors. Several models have been proposed to better understand the potential barrier formed in the grain boundary region [20,21].
Gupta et al. [22] proposed the first potential barrier model for ZnO-based varistor of the Schottky-type with an intergranular layer acting as insulator between the grains. In this model, negative charge densities (formed by Zn vacancies) were trapped between the grain boundary being balanced by two depletion layers that are positively charged. Leite et al. [23] proposed the accumulation of oxygen species adsorbed as new origin of negative defects [22,23].
Based on the ZnO potential barriers model, Bueno et al. [14] suggested a modification for formation of potential barrier in SnO2 varistors systems, whereas the sides of the barrier are in contact since there is no precipitated phase in the grain boundary, as observed Figure 2. In this model, the oxygen adsorbed species in the grain boundary region generate the negative charges defects, counterbalanced by the positive defects in the depletion layer. Pianaro et al. [1] proposed a potential barrier model, which has a large presence of negative charges on the SnO2 surface generated by tin vacancies (
Potential barrier model to ZnO varistors proposed by Gutpa et al. [
In this model, the electrons are emitted and pass through the potential barriers particularly due to the action of temperature distorting the energy band diagram, near the interface. This distortion modifies the potential barrier favoring the thermal emission. The equation that describes this behavior is [24,25]
where
where
The emission of the Poole–Frenkel type assumes the formation of coulombian centers in the grain–intergranular layer interface region. The relationship that describes this type of emission is on Equation 9, where the external electric field variations are more relevant than for issue of Schottky type [26]:
where
The processing by mixing oxides is widely used at the industrial scale for the production of varistor ceramics mainly due to its low cost, consisting basically of an initial powder mix and wet milling followed by drying, deagglomeration of powder, forming pellets/bulks, and sintering. The varistor synthesis with large amounts of chemical additives and/or impurities resulting from the process can lead to non-densifying sintering mechanisms. This mean that impurities may accumulate on the material surface and increase the mass flow on the surface or forming more unstable compounds that can evaporate and condense on the surface, favoring grain growth without decreasing pore size. The advancement in ceramic materials process technology aims to find low-cost methods and the viability of the process on an industrial scale. Among the processes available in the literature for the production of ceramics, techniques can be cited as coprecipitation, sol-gel, dehydration by rapid cooling (freeze drying), combustion method, and polymeric precursor method known as the Pechini method [28–31].
Schematic representation of reactions developed in the polymeric precursor method (Pechini method) [
The polymeric precursor method involves a complexation reaction of metal ions by an organic complexing agent as carboxylic acid. The metal ions are complexed into carboxylic sites forming a metal carboxylate, which is sequentially polymerized with ethylene glycol, as shown in Figure 3, citric acid is often used as the complexing agent. This process shows advantages such as low temperature of synthesis and high control of stoichiometry, and allows the obtention of powder with nanometric particles. The immobilization of metal ion in organic matrix reduces the segregation of the metal during the decomposition of the polymer at high temperatures, thus ensuring a homogeneous composition [31]. The ceramic powders are obtained by controlled calcination of the resin until total oxide formation.
Another method widely used for controlled synthesis of multifunctional ceramics is the sol-gel, that is used for the synthesis of a colloidal suspension where the dispersed phase is a solid and the dispersion medium is liquid, and is called sol. Therefore, there is the formation of a dual phase material: a solid body that is occupied by a solvent, i.e., moist gel. The initiator compounds, commonly called precursors, consist of a metal surrounded by many connections and typically are inorganic salts or organic compounds. The two precursors undergo two chemical reactions at sol preparation: hydrolysis and condensation, which resulted from the addition of an acid or base catalyst to form small solid particles or clusters in a liquid (aqueous solvent) [32,33]. The sol-gel method provides homogenous mixtures of cations on an atomic scale and also allows the preparation of ceramic powders with high surface area and films or gels fibers, which have high technological importance. The method has advantages over other conventional methods such as high purity, resin calcination at low temperatures, and synthesis of oxides with defined and controlled properties [32–34].
Also, the controlled precipitation method (CPM) can be used to prepare precursor powders. In this case, the solution containing the cation of interest is added to another solution containing a precipitating agent that can be a base or anion (ammonia, urea, and oxalic acid). In this way, the final product precipitate is separated by filtration, washed, dried, and calcined to obtain the oxide. The precipitation process has a complex mechanism, which is dependent on the degree of saturation of the ion to be used. The process starts by formation of cluster from chemical species in the solution, known as nucleation process. Reaching the ion solubility limits the growth stage of formed centers and finally the formation of precipitates [35].
To check the influence of the chemical synthesis route the electrical properties of the SnO2-based varistors, Mosquera et al. [36] carried out the synthesis of tin oxide by controlled precipitation and polymeric precursor (Pechini) methods that’s offering the strict control of the chemical purity and the particle size of the raw material. The system SnO2.Co3O4.Nb2O5.TiO.Al2O3, with 1 mol% Co3O4, 0.05 mol% Nb2O5, and 1 mol% TiO2 and variations of 0.05 (named SCNT05A), 0.1 (named SCNT1A), and 0.2 mol% (named SCNT2A) of Al2O3 were prepared. Following synthesis, the materials were submitted to heat treatment at 600°C/1 h (controlled precipitation method, CPM) and 600°C/2 h (Pechini method, PCH) to eliminate organic matter and obtain the full formation of the oxide. The use of dopants in both methods resulted in no change in the SnO2-crystal structure or formation of secondary phases due to have been added small amounts of dopants (Figure 4). The SEM micrographs indicated the influence of the addition of the aluminum grain growth control. The Pechini method showed smaller grains and more porous samples.
SEM for sintered samples at 1350°C, obtained by CPM and PCH (a) 0.05% Al2O3 and (b) 0.1% Al2O3. XRD for varistor system whit 0.2% Al2O3 synthesized by CPM and PCH [
The aluminum concentration also influenced on the electrical properties, as shown in Figure 5, mainly in the breakdown electric field variation that had been related to decreasing of grain size. The samples showed nonlinear coefficient (
log J
Sintering is the processing step that aims to confer mechanical strength to ceramic or metal powders, shaped by pressing or deposited as films. The process occurs by coalescence of the particles in solid or liquid phase to form a more dense mass. The sintering is an irreversible process and results in decrease of the total free energy of the system. Mathematically, the equation related to total energy of the system is
where Δ
For the decrease of free energy of the system, there is a force that induces microstructural changes, replacing the contact points between the particles by grain boundaries, closing the pores, densifying, and making the material a hard solid. In addition to the system power source, the sintering mechanisms are also a contributing factor induced by driving forces. Figure 6 shows the possible forces involved in the sintering process: surface free energy, applied external pressure, and chemical reaction [38].
The three main drivers for solid densification: surface free energy, applied pressure, and chemical reaction [
Diagram flow of vacancies on the surface. The atoms flow is opposite to the vacancy [
The surface energy is related to the surfaces curve and characterized by vacancies and gaps. The surfaces energy is the main force that sinters the material by mass flow through the region of higher concentration to a lower concentration region where vacancies and gaps, as shown in Figure 7.
The variation of free energy during sintering is represented by Eq. 11:
where the free energy variation depends on the variation on interfacial energy as a function of the surface area. The surface tension solid–solid (
In polycrystalline materials, the mass transport ways that are responsible for sintering are diffusion via crystal lattice, surface diffusion, volume diffusion, plastic flow, and evaporation–condensation. Figure 8 shows all mass transport paths arrive at the point of contact between two particles [38].
Mass transport mechanism solid and viscous sintering [
In Figure 8, the first three mechanisms do not lead to an alignment of the mass centers of the particles and therefore are non-densifying mechanisms. Thus, the mechanisms that start on the volume of material to the neck that increase in the neck and decrease the distance between the particles are densifying mechanisms [39].
The sintering mechanisms occur by three successive or simultaneously stages divided into initial, intermediate, and final stages. In some cases, there is the zero stage, which corresponds to particle rearrangement stage for subsequent joining by spot contact called necks [40]. The initial stage consists of particles rounding, formation of necks with low grain growth, and significant reduction in surface area and porosity. This stage progresses until the point where the necks interfere with each other. This stage corresponds to the point where the dihedral angle of equilibrium is reached. For the system with the green density of ~60%, this corresponds to a linear shrinkage of 3% to 5% [36]. It is possible to develop a general equation of the sintering kinetics for the initial stage. The geometric model for the development of this mathematical relationship is illustrated in Figure 9:
Frenkel’s model for early-stage sintering viscous flow [
The two spheres of the Frenkel’s model use the concept of viscous flow of atoms that relates the vacancy diffusion coefficient
Thus, the transported mass volume as a function of time can be given by [41]
were
Assuming that the decrease in surface energy of the system is equivalent to the energy dissipated through the material flow, then it is possible to derive several equations relating the radius of the neck and ball as a function of sintering time [38,42]:
where
Many aspects can be studied from the kinetic equations, as densification rate, determination of sintering mechanisms, and activation energy. The equation developed by Coble allows to estimate the sintering mechanisms for the initial stage, based on the two spheres Frenkel’s model, as indicated in the Eq. 15 [43]:
where
The intermediate stage initiates densifying mechanisms as volumetric diffusion by crystal lattice in which there is rapid grain growth, shrinkage pore and increased in the density of the material up to ~90% of the theoretical density. Whereas there is grain growth, the model for the initial stage does not fit this stage. The final stage is characterized by the elimination of residual pores with little or no densification, but grain growth is observed. For the determination of sintering mechanisms, intermediate and final stages are used in the model-based grain growth [44]:
where
Most of the kinetic studies of SnO2-based ceramic are developed to oxide mixed synthesis compressed into pellets, where significant amounts of mass are used. However, the appearance of thick and thin films makes possible the integration of smaller electric devices, and thus new techniques for the synthesis and deposition of powders on conductive and insulating rigid substrates have been studied.
The sintering of films has been increasingly used for applications in sensors, fuel cells, or photo catalysis that requires porous films [45,46]. This application is based on the fact that sintering occurs on rigid substrates such as viscous flow, wherein the voltage-limiting densification of the material is the force of attraction between the substrate and the deposited material particles [47,48]. The model used for understanding the sintering of thin films is based on Scherer and Garino’s studies where the rate of densification of the film is delayed by the substrate, as in Eq. 17 [38,41]:
The sintering mechanisms remain the same; however, the densification rate is retarded by tension caused by the substrate, like as the system would be sintered followed viscous sintering mechanism, as with glass.
One of the ceramic materials that have been very exploited for its great technological and industrial interest is the SnO2. Its applications are widely focused on sensors, solar cells, and catalysts, i.e., requiring high porosity, since its sintering process is limited to nondensifying mechanisms such as surface diffusion at low temperatures and evaporation–condensation at high temperatures [49–51]. Accordingly, what has been done to induce densifying sintering mechanisms is to cause solid substitution reactions that decrease the free energy by the formation of substitutional defects and vacancies that facilitate material transport during sintering [52].
It is possible to increase the densification of SnO2 by the addition of small amounts of lower valence densifying agents that generate substitutional defects and oxygen vacancies, such as ZnO, CoO, and MnO2, that promote the mass diffusion by solid solution, according to Eqs. 18, 19, and 20 [52,53]:
Also, there is the densification by CuO, Fe2O3, and MnO doping that promotes liquid solution formation [51]. Another way to improve the densification of SnO2-based varistors is to use the microwave as a source of power in the sintering process. According to Hao et al. [53], while conventional sintering occurs as a consequence of surface energy reduction, microwave sintering not only reduces the surface energy but also creates vacancies in the neck [53]. As a consequence of the increase in vacancies in grain necks, the mass flow also enhances in this region, promoting densification. In the case of dielectric materials, the oscillation of the electric field is the only external factor that will cause the internal heating of the material. Thus, the response of the oscillating electric field to the dielectric is determined by
Sintering mechanisms at Coble initial stage were adjusted to SnO2-based ceramic inserts with 0.95 mol% of ZnO sintered in a microwave oven and compared with results obtained in a conventional oven. The results showed that samples were sintered in a microwave oven to reach 87% after 30 min of sintering at 1050°C and grain size, while in a conventional oven, the density is 67%. It can be seen in Figure 10 by which the sample (a) is in the initial stage of sintering grain size, while in (b) indicating the morphology of the grains is already in intermediate sintering mechanism.
SEM of sintered samples in (a) conventional oven and (b) microwave oven, at 1050°C/30 min (by authors).
The sample sintered in a conventional oven showed a linear shrinkage of 5% and had an activation energy of 325 kJ/mol with predominant mechanisms at this early stage: structural rearrangement of particles, diffusion via crystal lattice, and surface diffusion, while samples sintered in microwave oven showed an activation energy of 111 kJ/mol and mechanisms as broadcast via crystalline reticulum. Figure 11 shows that there was a change sintering mechanisms for conventional sintering since there is a rate change in linear shrinkage rate of the material, whereas for microwave sintering the heating rate was rapid and lower temperature which does not inhibit sintering mechanisms densifying.
Curves of Ln(Y)
The direct relationship between the grain growth and the increasing density for the samples subjected to microwave and conventional heating are shown in Figure 12. With their respective error bars, it may be said that for about the same density of 88% of the samples, the mean grain size for the sintered sample in a microwave oven at 1050°C for 30 min is 1.2 μm, while that for the samples sintered in a conventional oven at 1300°C/30 min is 1.8 μm, and this difference increases even more because it enters the final sintering stage, which is when the grains grow more sharply, so the grain size is increased to about 3 μm. The reduced grain samples sintered in a microwave oven results in more grain boundaries to increase the mechanical strength and modifying the electrical properties of the material.
Evolution of grain size as a function of the calculated density of the samples sintered in a conventional oven and a microwave oven at a temperature of 800 °C to 1050 °C [by authors].
Lustosa et al. [55] conducted a study on thick films of SnO2-based nanoparticles and their electrical properties. The ceramic powder with composition 98.95 mol% SnO2 + 1 mol% ZnO + 0.05 mol% Nb2O5 was synthesized by Pechini method, calcined in a muffle furnace, submitted to milling in the Attritor mill and to the separation of particles by gravimetry. After separation for use of the smaller particles, one ethylic aliquot containing SnO2 powder was taken to an electrophoretic deposition system (Figure 13) for obtain the films. In sequence, the films were submitted to sintering in a microwave oven at 1000°C/40 min. In order to improve the varistor property, a Cr3+ ion deposition was carried out (also by electrophoresis) on films surface, and then the samples were submitted to different heat treatment for the diffusion of cations in grain boundary region. Figure 14 shows the sintered film, which had a low porosity, homogeneous thickness to the full extent of the film. The chromium addition is known to improve the properties of a varistor system by acting on defect formation at grain boundary region and increase the potential barrier parameter.
Electrophoretic system for deposition of SnO2-based particles (by authors).
SEM of the film deposited by electrophoresis and sintered at 1000 °C/40 min: (a) top vision; (b) and (c) different magnifications of cross-sectional vision [
After the heat treatment for Cr3+ diffusion, the films were taken to the electrical characterization. From the varistor responses, shown in Figure 15, it was observed that the heat treatment used after the chromium deposition influenced the improvement of the nonlinear coefficient of the samples. All films had lower rupture voltage less than 65 V and a low leakage current.
Graphs of current density
The addition of crystal lattice modifiers to SnO2 matrix is required because in the SnO2 sintering process, there is a predominance of mass transport mechanisms (evaporation and condensation), which leads to coalescence and grain growth, which hinder densification. Densification is a precondition to obtain the varistor properties since the phenomena involved in the formulation of non-ohmic properties occur in the grain boundary region. Thus, the studies are carried out to understand the doping effect on the sintering and densification, electrical conductivity, and non-ohmic properties of SnO2-varistor. The defects generated by modifying agents are of Frenkel type (generators of interstitial ion) and Schottky type (generators of vacancies) and are responsible for the formation and modification of the potential barrier in the grain boundaries [1,56,57].
The addition of bivalent metals such as CoO [58], ZnO [59], and CuO [60,61] is made to enhance the densification because these cations act as acceptors of electrons and replace the tin ions in crystal lattice, creating oxygen vacancy defects that promote mass diffusion in the network and promoting densification, according to Eq. 21 [58]:
The
The electrical conductivity of the varistor system can be improved with the addition of pentavalent ions as Sb2O5 [62], Nb2O5 [1], and V2O5 [63], which act as electron donors to the crystal lattice, resulting in electron concentration and tin vacancies, as demonstrated in Eq. 22 [1,62]:
Trivalent cations that act as acceptors of electrons are added to SnO2 crystal lattice, such as chromium [63–65], ytterbium [67], and scandium [68], which were used to improve the varistor properties of the system. The segregation of these ions in the grain boundary potential barrier increases the resistivity values and causes the improvement of nonlinear coefficient due to the higher adsorption of electron acceptor species on the grain boundary surface, increasing the barrier height potential and decreasing the conductivity, as demonstrated in Eq. 23 [57,58,67]:
There are many papers available in the literature [56–58,61–63,66–70], which studied the influence of doping agent into the tin oxide matrix ceramic. The possible microstructural, morphological, and varistor property changes that may occur with the addition of certain elements are searched.
Aguilar-Martínez et al. [69] investigated the effect of calcium (sample named SCa), barium (sample named SBa), and strontium (sample named Sr) additions on the microstructure and electrical properties of SnO2-Co3O4-Sb2O5 ceramic varistors.
By XRD analysis, it should be noted that the concentrations of dopants added (SbO, CaO, Ba, and SrO) were too small to be detected by the X-ray equipment. The microstructure of the samples was characterized by scanning electron microscopy. As shown Figure 16, it was found that the addition of strontium and calcium promotes densification and grain growth. The addition of BaO leads to a significant alteration of microstructure, changing the grain size and the morphology of grains from a nearly round shape to smaller and elongated grains. Barium addition causes increase of porosity, reduction of grain size, and changes in the grain morphology (from approximately equiaxed to elongated grains) [69].
SEM images of the as-sintered surfaces of SnO2-based varistors: (a) S, (b) SCa, (c) SSr, and (d) SBa [
Since electrical conduction in SnO2-based varistor ceramics is controlled by the grain-boundary barriers, the observed fact (the significant grain growth in a SnO2-system with SrO and CaO added) suggests that Sr and Ca materials are more suitable for low-voltage varistor preparation. The current–voltage curves of all prepared ceramic samples are nonlinear behavior. Figure 17 shows graphs of current density versus electric field for ceramics with and without additions sintered at 1350 °C [69].
J
Ceramics with calcium addition exhibit the lowest electric field at a fixed current density (10–3 A cm–2). The addition of strontium shows a similar effect on microstructure and current–voltage characteristics. However, the BaO addition showed that low-field conductivity is slightly lower with respect to the reference material, but the high-field part remains unchanged. This behavior may be attributed to the resulting microstructure. Despite the grain morphology and porosity, the samples S (only Co an Sb as dopants), SCa, and SBa showed nonlinear coefficients of 5.7, 5.0, and 4.9, respectively, higher than the value for sample SSr (nonlinear coefficient of 2.7) [69].
The addition of Er2O3(Co, Nb)-doped SnO2 was studied by Qi et al. [70] at different concentrations (0.1, 0.5, 1, and 2 mol%) and different temperatures of sintering (1250 °C, 1300 °C, and 1350 °C for 1 h).The XRD analysis carried out by the authors did not show evidence of the second phase formation into the SnO2-rutile crystalline phase. The SEM micrographs of the varistors prepared are in Figure 18, showing the decreases of grain size associated with the increase of Er2O3 concentration into ceramic matrix. Also, the decreases of grain size occur with lower temperature of sintering. With the addition of 2.0 mol% of Er2O3 modifier agent, the SnO2 grain size was reduced from 12.9 μm to 6.5 μm when the sample sintered at 1350°C for1 h, from 9.7 μm to 3.7 μm when sample was sintered at 1300°C for 1 h, and from 6.8 μm to 2.4 μm when samples were sintered at 1250°C for 1 h.
Microstructure variation of the SnO2-based varistor system sintered at 1350 °C, 1300 °C, and 1250 °C during 1 h with the composition (all in mol%): 100SnO2 + 0.75Co2O3 + 0.1Nb2O5 +
Figure 19 shows the plots of applied electric field versus current density for different concentrations of Er2O3 sintered at 1350 °C, 1300 °C, and 1250 °C during 1 h. It was observed from Figure 19 that the threshold voltage of the SnO2-based varistors increased significantly from 305 V mm−1 to 1083 V mm−1 with increasing Er2O3 concentrations over the range of 0–2.0 mol% sintered at 1350°C during 1 h and from 1083 V mm−1 to 2270 V mm−1 with decreasing sintered temperatures from 1350°C to 1250°C during1 h. Only the samples sintered at 1300 °C have decrease on nonlinear coefficient with Er2O3 addition. There is no observed significant change on height of the potential barrier for samples sintered ate 1250°C and 1300 °C.
E versus J curves for the SnO2-based varistor system sintered at different temperatures during 1 h with the composition (all in mol%) 100SnO2 + 0.75Co2O3 + 0.1Nb2O5 +
The study of SnO2-based varistor systems is recent, so a huge amount of published papers do not exist. Research involving the material is mostly related to the understanding of the influence of dopants on densification materials prepared by mixing oxide and the change in the parameters of the potential barrier formed at grain boundary region, which is directly related to the nonlinear coefficient and determines the quality of varistor ceramics. The bivalent metals (Ba2+, Ca2+, Co2+, Zn2+, and others) have proven action as a densifying agent since the defects generated by their addition to the ceramic matrix assist in mass diffusion. The addition of trivalent ions (Cr3+, Er3+, and others) causes the increase of nonlinearity coefficient due to the higher adsorption electron acceptor species on the surface of the grain boundary and thus causing a reduction in conductivity of the material. The new methodologies for the chemical synthesis of ceramic powder promote the homogeneous distribution of dopants into the ceramic matrix and reduce segregation and the formation of secondary phases, confirmed by XRD analysis, which are harmful factors on the electrical properties of the varistor and facilitate the integration of the material in today’s electronic devices electrical protection. The use of microwave oven is a new processing step aimed to reduce the time and temperature of sintering step and can be considered a promising procedure for the varistors production. The preparation of varistors as film emerges as a new possibility in order to facilitate integration of this material in electronic circuits.
The authors thank the LMA-IQ and the financial support by the Brazilian research funding agencies CNPq and FAPESP (CEPID/CDMF- 2013/07296-2and process n°.
As one of the most significant topics in computer vision and pattern recognition, face recognition attains much attention from both academic and industries over recent decades [1, 2]. With the evolution of neural networks, general face recognition technology emerged as a noteworthy area among researchers [3, 4, 5]. However, identifying face images across widespread range of ages is shortcoming due to human face appearance changes affected by aging process [6, 7]. In order to achieve human face recognition under difference ages, Age-Invariant Face Recognition (AIFR) approach is progressed [8]. AIFR recognizes faces using facial features extracted from human images. AIFR method uses three different models such as generative, discriminative [9], and deep learning methods [10]. Generative approaches are based on the age progression methods in regard to converting the probe image into the same age as that of gallery image [11]. However, generative schemes have several shortcomings [12]. Optimizing the recognition performance in generative model is not easier task. Estimating the accurate results in generative model is highly difficult since it cannot handle aging impact. Discriminative approaches [13] are introduced to resolve discrepancy of generative scheme [14]. It develops feature matching using local descriptors [15] in AIFR. Multiple descriptors-based AIFR is introduced to extract features from periocular region [16]. In this, two descriptors are used to extract features that are Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF).
In order to achieve better result in AIFR, deep learning method is integrated with discriminative approach [17]. In deep learning, Convolutional Neural Network (CNN) algorithm plays vital role in recognizing face with different aging images [18]. Large age gap verification is performed by injecting features in deep networks [19]. Here, deep CNN is used to recognize face where texture features are considered. Aging model-based face recognition with different aging images is introduced under deep learning method [20]. Here, CNN descriptor is utilized to match image with different aging images.
From the aforesaid studies, we determine that there are still many issues present in recognizing face with aging progress. The issues are discussed as follows:
Preprocessing is not effective in most of the chapter that reduces performance of the system.
Pose normalization is not considered in existing AIFR, which is highly significant. Since, AIFR datasets such as MORPH, FG-NET, etc., contain different pose images.
Existing feature extraction procedures lack in extracting features from important regions that tend to reduce recognition rate.
Face recognition algorithms are not up to the level to handle large dataset and thus reduce the result of accuracy.
These problems impose confines on the present AIFR systems and also complicate the recognition and retrieval task especially under different aging images.
In order to tackle abovementioned issues, our work contributes the following processes:
In order to reduce time wastages in preprocessing, we initially execute novel Image Quality Evaluation (IQE) method, which estimates Image Quality Metric (IQM) for each image. If IQM value is below Image Quality Threshold (IQT), then only preprocessing is performed for that image or else directly gone into the pose normalization process.
Preprocessing is performed to reduce uncertainties in upcoming face recognition processes such as feature extraction, recognition, and retrieval. For this purpose, we implement two processes such as illumination normalization and noise removal. Illumination normalization adopts DGC-CLAHE and noise removal adopts ASBF algorithm.
Pose normalization is significant to diminish difficulties present in feature extraction and thus enhances the recognition and retrieval performance.
Our work extracts feature from three regions that are periocular, nose, and mouth in order to increase recognition rate. Here, two descriptors are utilized that are CNN and SIHKS, which perform better than other existing descriptors such as LBP, SIFT, etc.
In order to reduce recognition and retrieval time, we fuse features after extraction using CCA.
Recognition and retrieval are performed through SVM algorithm, which performs well even with unstructured, semistructured data such as text, images, and trees.
Outline of this chapter is summarized as follows: Section 2 deliberates state-of-the-art works existing in AIFR with their limitations. Section 3 exemplifies problems occurring in previous works related to AIFR. Section 4 explains brief study of our proposed work with our proposed algorithms. Section 5 illustrates numerical results obtain from our simulation environment and also compares it with existing methods. Finally, section 6 concludes our contribution and also provides comment on our future work.
This section discusses the state-of-the-art work related to AIFR along with their limitations. In this, we discussed works that comprise preprocessing, feature extraction, recognition, and retrieval processes.
Kishore et al. [21] have suggested Periocular Region-Based AIFR Using Local Binary Pattern. In this, three sequential processes are executed to recognize faces that are preprocessing, feature extraction, and classification. In preprocessing, enhancement and denoising processes are employed in each facial image. Local Binary Pattern (LBP) descriptor [22] was used to extract features from the periocular region [23] of the given face image. Periocular region contains eyes, eye lashes, and eye brow parts of the face. Chi-square distance was used as classifier to recognize face after feature extraction. Chi-square distance doesn’t recognize face accurately since it is highly sensitive to the sample size.
Nanni et al. [24] have introduced ensemble of texture descriptor and preprocessing techniques to recognize image effectually. Four face recognition processes are performed that are preprocessing, feature extraction, feature transform, and classification. Preprocessing executes three techniques that are adaptive single index retinex (AR) in order to enhance scene detail and color enhancement in darker area. Anisotropic smoothing and different of Gaussian (DoG) are algorithms executed to normalize the illumination field. Features are extracted using two descriptors that are Patterns of the Oriented Edge Magnitudes (POEM) and the Monogenic Binary Coding (MBC). At last, different distance functions are used to recognize face. Accuracy of face recognition was very less due to poor feature extraction mechanism. Chi et al. [25] have offered temporal nonvolume preserving approach to facial age progression and AIFR. In preprocessing, face region was detected and aligned based on the fixed position of the eyes and mouth corners. And then it maps the texture features of the test image with the trained image in order to verify images. Here, deep CNN algorithm was utilized to map features. In this, preprocessing step doesn’t perform effective processes such as normalization, noise removal that tend to reduce system performance.
Bor et al. [26] have introduced Cross Age Reference Coding (CARC) for AIFR. Initially, it executes face detection algorithm in order to detect face region in image. And it extracts features from the detected region for which it utilizes high-dimensional LBP algorithm. LBP extracts 59 local features from the detection regions. In this, Principal Component Analysis (PCA) algorithm was used to reduce dimensionality of extracted feature. After that, CARC recognizes face using local features transformation. More analysis is required on feature extraction since it plays vital role in AIFR. Yali et al. [27] have pointed out distance metric optimization driven CNN for AIFR. Here, two models are integrated that are feature learning and distance metric learning. This integration is achieved through CNN algorithm with parameters optimized using network propagation algorithm. CNN learns features using the convolution layer and recognizes face using the distance metric. Finally, recognized images are retrieved effectually. Herein, recognition rate was very less due to ineffective feature extraction.
Pournami et al. [28] have offered deep learning and multiclass SVM algorithm to recognize face. Here, preprocessing was performed to increase the accuracy of the face recognition where image resizing was performed. CNN feature descriptor was used to extract features from the given image. Here, fully connected layer extracts features from the image and then features are given as input to the multiclass SVM classifier. Resizing only performed in preprocessing thus introduced more noise in extracted feature. Garima et al. [29] have suggested techniques for face verification across different age progression with large age gap. Initially, image normalization was performed where RGB image was converted into the grayscale image and the image is rotate as the eyes are aligned horizontally. In this, face features are extracted using Center Symmetric Local binary Pattern (CSLBP) algorithm. And also weighted K-nearest Neighbor (K-NN) algorithm was used to recognize face from extracted features. K-NN doesn’t perform well for large dataset and thus reduces the accuracy of face recognition. Saroj et al. [30] have pointed out pyramid binary pattern for age-invariant face verification. In this, pyramid binary pattern was used to extract texture feature. Texture features are given as input to the PCA in order to reduce dimensionality of the extracted features. And then, classification was performed through SVM algorithm. Here, texture feature was only extracted to classify the face with age invariant. Thus it reduces accuracy in face recognition since dataset contains different images with large age gap.
Mrudula et al. [31] have offered face recognition across aging using GLBP features. Preprocessing performs three sequential processes that are image resizing, RGB to gray, and illumination normalization. Here, combined feature descriptor was used to extract features from the given image. LBP and Gabor descriptors are combined, which was known as GLBP descriptor. During classification, PCA was used to reduce feature dimensionality and K-NN algorithm was used to recognize face across aging. Herein, GLBP descriptor introduces high false-positive rate in age-invariant face recognition. Zhen et al. [32] have pointed out local polynomial contrast binary patterns for face recognition. Polynomial filters are used to extract the attributes from the given image. In this, LBP descriptor was used to extract texture from the given image. Fisher Linear Discriminant (FLD) algorithm is used to reduce dimension of extracted features. Here, extracted features are classified using nearest neighbor classifier to recognize given image in training set. Nearest neighbor classifier consumes more time to classify image since all the work is performed in testing stages only.
Mohanraj et al. [33] have suggested ensemble of CNN for face recognition in order to resolve aging, pose variation, and low-resolution problem. Preprocessing was established to resize the given image. After that, features are extracted using three different CNN algorithms. Features are concatenated and given to the classifier in order to predict the person. Here, random forest classifier is used to recognize the face. Noise removal was not performed in preprocessing and thus reduces the accuracy of face recognition. Rupali et al. [34] have introduced component-based face recognition. Here, three face components are considered that are nose, lips, and ears. Preprocessing is performed to resize the image and features are extracted using CNN algorithm. Features are extracted from nose and face regions that are given to the FLD algorithm to reduce the dimensions. These features are given to KNN classifier in order to predict the image. In KNN, initial K value prediction is complex that leads to ineffective results. Venkata et al. [35] have pointed out real-time face recognition using deep learning and LBP. During preprocessing, it resizes the given image. In this, LBP was used to extract features from the given images. Extracted features are given to the CNN in order to provide weight to each feature. CNN provides weight in order to estimate the matched face with the training images. Here, texture feature only extracted to recognize face across aging that tends to reduce recognition rate.
Mohsen et al. [36] have offered age-based human face image retrieval using zernike moments. In this, Zernike moment was used to extract features from the images. Here, Zernike moment utilizes Zernike Basis Function (ZBF), which captures both local and global featured fro face image. And, Multi-Layer Perceptron (MLP) algorithm was used to recognize age in training image. Accurate result was not obtained in MLP classifier, thus reducing the recognition rate. Danbei et al. [37] have offered face aging synthesis application based on feature fusion. Initially, face detection was performed and feature points are positioned. For this purpose, triangulation and affine transformations are used, which position the feature points. Here, facial texture features are extracted to recognize face across aging. Extracted features are fused in order to recognize face with the training images effectually. More analysis is required on facial recognition since it describes up to feature fusion process.
Kishore et al. [38] have offered Hybrid Local Descriptor (HLD) and LDA-assisted K-Nearest Neighbor classification in AIFR. Here, Gaussian filter was used to reduce noise that results in information degradation, since it removes fine details of the image and resultant image is blurred. WLD-based feature extraction loses more information due to lack of pixel consideration. K-NN-based classification requires more time due to absence of training phase and finding good similarity measure is also difficult. Muhammad et al. [39] have introduced Demographic Features (DF)-assisted AIFR and retrieval. In this, feature extraction takes more time, since each feature was extracted in three individual CNNs. Position and orientation of the object were ignored in hidden layer of CNN that result in less accuracy in feature extraction and recognition. Chenfei et al. [40] have pointed out Coupled Auto Encoder (CAN) algorithm based feature extraction in AIFR. Herein, feature extraction was not effective due to lack of texture and shape-oriented features. In CAN, data relationships are not considered that affect classification results and weight computation is also very difficult. Huiling et al. [41] have introduced Identity Inference Model (IIM)-based age subspace learning to recognize image in AIFR. Herein, wLBP-based feature extraction was used that results in less accuracy, since it contains more noise in extracted features due to absence of noise removal process. Fahad et al. [42] have introduced Composite Temporal Spatio (CTS) modeling in order to recognize image in AIFR. Here, preprocessing was required to improve the accuracy in age-invariant face recognition, since image database contains illumination, pose variation, etc. Naïve Bayes–based classification results are always biased one, since it doesn’t rely on class conditional dependency.
This section briefly describes our proposed method in detail along with the description of utilized algorithm.
Our Multi-Feature-assisted AIFR (MF-AIFR) method tackles problems that are present in the previous AIFR works. For this purpose, MF-AIFR establishes the five consecutive processes that are IQE, Preprocessing, Pose Normalization, Feature Extraction and Fusion, Feature Recognition and Retrieval as depicted in Figure 1. Our work novelty is present in the IQE method, since previous AIFR method doesn’t concentrate on the quality evaluation. In order to save time, MF-AIFR performs IQE where images that are not satisfied IQT only given to the preprocessing step or else it is directly given to the pose normalization process. During preprocessing, MF-AIFR performs two processes that are illumination normalization using DGC-CLAHE and noise removal using ASBF algorithm. Pose normalization is executed to enhance feature extraction performance where EA-AT algorithm is utilized. Multiple features are extracted from the three different regions of face image that are periocular, mouth, and nose in regard to enhancing accuracy result. Here, two descriptors are executed that are CNN for texture feature and SIHKS for demographic and shape features extraction. Here, demographic features comprise age, gender, and race. Extracted features are fused using CCA in accord to reduce the complex recognition process. For recognition and retrieval, MF-AIFR pursues SVM algorithm, which has high scalability compared with other machine learning algorithm.
Architecture for proposed work.
Figure 1 illustrates the architecture for our proposed work. The process depicted in architecture is described briefly in upcoming sections.
Reducing computation time in AIFR and retrieval is noteworthy in order to achieve efficient performance. For this purpose, MF-AIFR performs novel IQE, which estimates IQM for each image. IQM comprises subsequent metrics that are Brightness
These metrics are designated as follows:
Where
Where
Where
Where
Where
Where
Using above parameters, we estimate IQM for each image. It can be measured as follows:
After computing IQM, this value is compared with the IQT in order to select whether next process is preprocessing or pose normalization for given image.
Where
MF-AIFR performs preprocessing in order to enhance the recognition rate in simulation results. For this purpose, we perform two processes in preprocessing that are illumination normalization and Noise removal.
Illumination normalization is performed in order to enhance the image quality and also avoid negative effects of the image. MF-AIFR adopts DGC-CLAHE algorithm for illumination normalization. Proposed DGC-CLAHE performs better than existing CLAHE method. It enhances both luminance and contrast of the image adaptively. Our DGC-CLAHE algorithm performs dual gamma correction, which enhances the dark areas of the image. This algorithm adaptively sets the clip points of each image, which depends on the dynamic range of each block of the image. In this, first gamma correction is executed to boost the entire luminance present in the image block. Second gamma correction is executed to adjust the contrast in very dark region in order to avoid overenhancement in bright regions.
Initially, DGC-CLAHE sets clip point adaptively based on the dynamic range, which can be expressed as follows:
Where
DGC-CLAHE defines enhancement weight for the global gray levels of the blocks by first gamma correction (
Where
Where
Noise removal is substantial process in face recognition in regard to enhancing recognition accuracy. For this purpose, our MF-AIFR utilizes ASBF algorithm to remove noise from given image. Proposed ASBF algorithm preserves fine details of the image while removing noise and also sharpens the image. ASBF algorithm is used to remove universal noises such as impulse and Gaussian.
In ASBF algorithm, noisy pixel is detected using Sorted Quadrant Median Vector (SQMV), which incorporates significant features such as edge or texture information. Our ASBF algorithm executes three sequential processes as depicted in Figure 2. Initially, Adaptive Median Filter (AMF) is used to identify the corrupted pixels in the image. Secondly, the edge of the image is preserved using edge detector, which accurately predicts the edge existence in the current window. Noise detector is used to classify the noise into impulse and Gaussian. Switching Bilateral Filter (SBF) contains ranging filter, which switches the modes between impulse and Gaussian based on noise detector result.
ASBF function blocks.
Existing noise filtering algorithm utilizes constant window size such as 3*3, which may fail to distinguish noisy and noise-free pixel accurately and thus results in blur output image. In order to avoid this drawback, our AMF adaptively changes the window size based on the number of noisy pixels present in given image.
Noise detector is used to predict whether pixel is filtered by SBF Gaussian (
At last, pixel with Gaussian and impulse noises are classified based on the above discussed conditions. These outputs are given as input to the SBF with SQMV.
The output from the SBF filter is expressed as follows:
Where
From the above discussions, we conclude that our proposed ASBF removes not only Gaussian noise but also impulse noise while keeping the image fine details and images. This way of performing preprocessing increases the accuracy in AIFR.
Pose normalization is substantial process to increase accuracy in face recognition. Since, our database FG-NET contains different pose images and thus requires pose normalization before entering into feature extraction and retrieval. Our MF-AIFR carried out EA-AT algorithm in order to correct the different poses into the frontal view and thus increases the feature extraction efficiency. EA-AT algorithm initially estimates pose angle of given image using Euler Angle. Then, estimated angle is provided to the Affine Transformation to get frontal view of the given image. Euler angles are three angles in order to describe the orientation of the face with respect to the fixed coordinate.
Figure 3 illustrates the Euler angle with their coordinates in Z vector. Three angles are describes as follows: Yaw, Pitch, and Roll. In this, yaw angle (
Euler angles representation.
Where
Roll angle (
Pitch angle (
These three angles are given as input to the affine transformation algorithm in order to rotate into the correct view. There exist four basic affine transformations that are illustrated as follows:
Translate—It moves a set of point in fixed distance in x and y.
Scale—It scales the set of points in up or down directions.
Rotate—It rotates the set of points about the origin.
Shear—It offsets a set of points in distance proportional to their x and y coordinates.
In mathematical form, an affine transformation of
Where,
Where
Feature extraction and fusion are a major part of this work in order to produce optimum results in AIFR. Our MF-AIFR extracts multiple features from three set of regions. We extract images from three regions that are periocular, nose, and mouth. Since, these three regions are significant to recognize the image across aging. From these regions, we extract three type of features that are texture, shape, and demographic, which are briefed in Table 1. Here, texture feature is extracted using the CNN descriptor, and SIHKS descriptor is used to extract the shape and demographic-related features.
Features | Feature description | Types of features |
---|---|---|
Texture | Texture feature represents the surface characteristics of the image | Contrast, Dissimilarity, Entropy, Homogeneity, Correlation, and Angular Second Moment |
Shape | Shape features represents the physiological identity of given image | Boundary of the periocular, nose, and mouth regions, Convexity, and Solidity |
Demographic | Demographic features represent the individual uniqueness of the given image. | Race, Age, and gender |
Features description.
Our MF-AIFR utilizes CNN descriptor for texture feature extraction since it provides robust performance in learning features layer by layer. CNN applies multiple filters on the raw input image in order to extract high-level features. Here, we extract six texture features in given image such as contrast, dissimilarity, entropy, homogeneity, correlation, and angular second moment. These features are described as follows: In CNN, three different types of layers are present that are Convolutional layer, Polling layer, and Fully connected layer.
It gathers image from the input layer, which is made up of a set of learnable filters. In our work, convolutional layer comprises six filters in order to generate feature map. Six filters in the convolutional layer generate six feature maps. The feature map is the consequence of the every filter that convolved through whole image. Convolution operation can be described as follows:
Where
It is used to perform downsampling operation in order to reduce the spatial size of the convolutional layers. Polling operation is implemented on the pixel values captured by the pooling mask. The pooling operation is described as follows:
Where
Fully connected layer is used to extract the features that are obtained in the preceding layers. The results obtained in the last convolutional and pooling layer are given as input to the fully connected layer in order extract features.
Shape and demographic features are extracted using SIHKS algorithm. Shape features are boundary of the eye, nose and mouth, Convexity, and Solidity. Demographic features comprise age, race, and gender information. Here, race feature represents the skin tone of the face image. These features plays key role in recognizing face across aging.
Proposed SIHKS descriptor performs better than HKS algorithm since conventional method has drawback such as sensitivity to scale especially to the global scale. Hence, we proposed SIHKS algorithm, which performs better in scale invariance, and it is able perform at any point even at scale selection is impossible. In addition to it, it also performs well extracting shape and demographic-oriented features compared with other shape feature descriptor. SIHKS extracts features using three steps that are listed as follows:
Logarithmical sampling in time t. It can be expressed using below equation.
Where
Taking logarithm of heat signature with time variations. It can be described as the below equation,
Where
Taking discrete time Fourier transform of heat signature. It can be expressed as below equation,
With the above steps, our SIKHS estimates scale-invariant quantity
Figure 4 illustrates the texture feature extraction in CNN with their significant layers such as convolutional layer, pool layer, and fully connected layer.
Feature extraction in CNN.
Feature fusion is estimated to reduce extracted feature dimension of extracted features such as shape, texture, and demographic features. This dimensionality reduction will result in better performance in face recognition, which the process of recognition and retrieval is easier. For this purpose, our MF-AIFR algorithm utilizes CCA algorithm, which performs effectively in feature fusion. Feature fusion is defined as the combination of multiple feature vectors into single feature vector. Proposed CCA is a statistical tool for recognizing linear relationship among sets of features vectors in order to determine the inter subject covariances. Canonical covariates of the given feature vectors are obtained using below expression,
Where
Recognition and retrieval are final process in our MF-AIFR, which is performed by utilizing SVM algorithm. Here, we select SVM algorithm to correctly recognize the face cross aging and also retrieve the recognized image for given input image. Figure 5 illustrates the input and output space models of the SVM algorithm.
SVM input and feature space representation.
Proposed SVM algorithm performs well in even unstructured and semistructured data. It addition to it, SVM also scales relatively well to high dimensionality of database. SVM gets input as fused features from previous process obtained using CCA algorithm. SVM is the binary classification method that discovers the optimal linear decision surface based on the concept of structural risk minimization. The decision surface represents the weighted combination of the elements present in the training set. These elements are illustrated as the support vectors and characterize the boundary between two different classes. The output of the SVM algorithm is a set of support vectors
The linear surface is represented as follows:
Where k represents the weight factor and b represents the bias term and z represents the training or testing data. These two parameters are used separate the hyperplane position and orientation. The weight factor k is calculated using below expression,
Kernel function plays vital role in SVM, which classifies features effectually. In MF-AIFR, we use Radial Basis Function (RBF) kernel. RBF performs well compared with other kernel functions. It doesn’t require any prior knowledge about data. It can be expressed as follows:
Here,
To characterize the performance of the proposed MF-AIFR, this section is divided into four aspects such as dataset description, simulation setup, application scenario, results, and discussion.
This section deliberates dataset information used in this chapter. Here, we utilize FG-NET database to perform face recognition and retrieval. Face and gesture recognition NETwork (FG-NET) aging database was released in the year of 2004 in an attempt to support research activities regarding the changes in the facial appearance caused by aging. FG-NET database comprises 1002 images from 82 different subjects. Each subject comprises 6–18 images with the age ranging between the newborns to the 69-year-old subjects. Our FG-NET database contains considerable variations such as poses and illuminations.
Table 2 illustrates the details of the FG-NET dataset briefly. Dataset contains 34 male subjects and 48 female subjects’ images. Each subject has 1–12 images across their age progression.
Parameters | Values | # Images |
---|---|---|
# subjects | 82 | 1002 |
#Males | 34 | Max (1–12) per subject |
#Females | 48 | Max (1–12) per subject |
Dataset description.
Different age bands present in the FG-NET dataset are represented in Table 3. FG-NET dataset comprises subjects from the age of 0 to 69 years old.
Factors | Ages | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
0–5 | 6–10 | 11–15 | 16–20 | 21–25 | 26–30 | 31–35 | 36–40 | 41–45 | 46–69 | |
#Subjects | 75 | 70 | 71 | 68 | 46 | 38 | 30 | 24 | 19 | 10 |
#Images | 233 | 178 | 164 | 155 | 81 | 62 | 38 | 31 | 26 | 34 |
Different age bands of FG-NET dataset.
Our proposed MF-AIFR is implemented in MATLAB R2017b tool with C programming language. Our MATLABR2017b is executed in windows operating system. MATLAB is a multi-paradigm statistical computing environment developed by MathWorks. MATLAB permits matrix manipulations, implementation of algorithms plotting of functions and data, creation of user interfaces, and interfacing with programs written in other languages, which include C, C++, C#, JAVA, and Python.
To evaluate performance of the MF-AIFR, we consider following metrics that are described as follows:
Where
Where
This compares the simulation results of the MF-AIFR with existing methods such as HLD, DF, and CAN. Here, we compare results using six performance metrics that are Accuracy, Recall, Precision, Recognition Rate, Rank-1 Score, and F-Score. Table 4 illustrates the comparisons of previous methods with their strength, weakness, and research statements.
Accuracy metric is one of the significant metrics to evaluate the performance of the proposed work. This metric defines the how accurate our MF-AIFR in terms of correct classification of images. The performance of this metric is evaluated by alternating the number of images.
Figure 6 demonstrates that comparisons on accuracy of the MF-AIFR with respect to the existing methods such as CAN, DF, and HLD. These comparisons show that our MF-AIFR achieves better performance compared with the existing methods. Since, our method utilizes better feature descriptors such as CNN and SIHKS. Both algorithms extract features effectually from three regions that are periocular, nose, and mouth. This selected region plays a key role in recognizing face across aging. And CNN and SIHKS provide robust performance even in high-dimensional dataset. As a result, our method achieves high accuracy as 95%. By contrast, CAN and DF method attain less accuracy compared with our method due to its poor feature extraction procedures since it doesn’t concentrate on the vital regions such as periocular, nose, and mouth. Meanwhile, HLD obtains high accuracy compared with both CAN and DF method due to its feature extraction from periocular region, which plays significant role in face recognition across aging. Though, it achieves less accuracy compared with our method due to its poor descriptor algorithm since it loses large amount of information during feature extraction.
Comparisons on accuracy.
Table 5 illustrates the average simulation results comparison of accuracy with the existing and proposed methods.
Reference | Key concentration | Strength | Weakness | Research statements | |||||
---|---|---|---|---|---|---|---|---|---|
Accuracy | Recall | Precision | Recognition rate | F-Score | Rank 1-score | ||||
Kishore et al. [41] | HLD-AIFR & Retrieval | Adopts large datasets | It removes fine details of the image and resultant image is blurred. Feature extraction loses more information due to lack of pixel consideration. | Low | Medium | Low | Low | Medium | Low |
Muhammad et al. [42] | DF-AIFR & Retrieval | Better demographic Estimation | Takes more time in feature extraction | Very Low | Low | Low | Very Low | Low | Very Low |
Chenfei et al. [13] | CAN-AIFR | Complexity is less | Data relationships are not considered that affects the recognition results. | Low | Medium | Very Low | Low | Low | Low |
Huiling et al. [15] | IIM-AIFR | Flexible to large dataset | More noise in extracted features due to absence of noise removal | Medium | Low | Very Low | Medium | Very Low | Low |
Fahad et al. [9] | CTS-AIFR | Recognition time is less | Naïve Bayes based recognition results are always biased one, since it doesn’t rely on class conditional dependency. | Very Low | Medium | Very Low | Very Low | Very Low | Low |
Comparisons on previous methods in AIFR.
Methods | Accuracy (%) |
---|---|
HLD | 80.2 |
DF | 73.2 |
CAN | 67.6 |
MF-AIFR | 90.2 |
Accuracy comparisons [average].
From the above comparison, it is noticed that our method achieves better accuracy percentage as 90.2% compared with the existing methods.
Recall is used to evaluate the performance of the MF-AIFR in terms of the correct recognition of face image. Recall performance is evaluated by changing the number of images.
Figure 7 shows that our MF-AIFR achieves less recall percentage compared with other methods.
Comparisons on recall.
Since, our MF-AIFR correctly recognizes the face as per given test image, thus reduces false detection of face images. Reason for this is that our method executes pose normalization before entering into the feature extraction process. Pose normalization enhances the feature extraction efficiency. Thus it leads to correct identification and retrieval of the test image. As a result, our MF-AIFR achieves less recall percentages compared with existing methods. Whereas existing methods such as DF and CAN achieves high recall percentages due to lack of pose normalization and complex feature extraction procedures. In the meantime, HLD method reduces recall percentage compared with DF and CAN methods since it doesn’t follow complex feature extraction procedures. Still, recall of HLD is high compared with MF-AIFR due to lack of pose normalization and information degradation in noise removal process. Table 6 designates the average simulation results comparison of recall with the existing and proposed methods.
Methods | Recall (%) |
---|---|
HLD | 75.6 |
DF | 87 |
CAN | 80 |
MF-AIFR | 70 |
Recall comparisons [average].
From the above comparison results, it is seen that our MF-AIFR method achieves less recall percentage as 70% compared with the existing methods.
Precision is used to measure performance of our work in terms of relevance instances retrieved compared with the total images. Precision performance is measured via altering the number of image.
Figure 8 depicts that MF-AIFR achieves high precision percentages compared with existing methods. MF-AIFR performs preprocessing process before entering into the feature extraction and recognition process. Preprocessing performs illumination normalization and noise filtering since our FG-NET dataset contains illumination and noises in images. These two processes enhance the quality of the image that tends to easy the feature extraction and recognition process. CAN and DF methods achieves less precision due to lack of preprocessing such as noise removal and illumination normalization. Likewise, HLD also obtains less precision owing to fine detail removal in Gaussian-based noise filtering. Since Gaussian filter doesn’t concentrate on fine details of the image, which results in blur image.
Comparisons on precision.
Table 7 designates the average simulation results comparison of precision with the existing and proposed methods. From the above comparison, we conclude that MF-AIFR achieves better precision percentage as 90.6% compared with existing methods.
Methods | Precision (%) |
---|---|
HLD | 81.6 |
DF | 71.6 |
CAN | 65 |
MF-AIFR | 90.6 |
Precision comparisons [average].
F-Score metric considers both false positive and false negative values in account to estimate performance of this work. The performance of this metric is simulated by varying the number of images.
Figure 9 illustrates that comparison on F-Score result of MF-AIFR with existing methods such as DF, CAN, and HLD. From this figure, it is noticed that our method achieves high F-Score compared with existing methods. Our MF-AIFR uses two descriptors such as CNN and SIHKS to extract texture, shape, and demographic features. Here, SIHKS descriptor performs very well in scale invariance and also provides better extraction results even when scale selection is impossible. It extracts shape and demographic features effectually, which plays substantial role in face recognition across aging. At the same time, CAN and DF methods attain less F-Score owing to the absence of significant feature extraction such as texture and shape features. Meanwhile, HLD also attains less F-Score since it doesn’t concentrate on shape features extraction and thus reduces the face recognition and retrieval efficiency.
Comparisons on F-score.
Table 8 describes the average simulation results comparison of F-Score with the existing and proposed methods. From the above comparison, we observed that MF-AIFR method achieves high F-Score percentage as 87.2% compared with existing methods.
Methods | F-Score (%) |
---|---|
HLD | 78.6 |
DF | 71.6 |
CAN | 59.6 |
MF-AIFR | 87.2 |
F-score comparisons [average].
Recognition rate is used to measure the ability of MF-AIFR in terms of the face recognition. It can be measured through changing the number of features.
Figure 10 designates the comparisons on recognition rate of MF-AIFR with respect to the existing methods CAN, DF, and HLD methods. From this figure, it is observed that our MF-AIFR attains high recognition rate compared with existing method. We propose SVM algorithm for recognition and retrieval. It performs well in recognition even in high dimensionality of dataset. In addition to it, we also perform feature fusion before entering into the recognition and retrieval process.
Comparisons on recognition rate.
Feature fusion reduces the dimension of feature vectors and thus tends to enhance the performance of SVM algorithm. Therefore, our method achieves better recognition rate compared with existing method. Meanwhile, DF method has less recognition rate compared with other methods due to lack of effective recognition and retrieval processes since it simply ranks the images. Likewise, CAN also attains less recognition rate compared with our method since it isn’t able to establish data relationship between different features. Meantime, HLD method attains less recognition rate due to usage of KNN for recognition. KNN takes more time, and discovering similarity measure is tedious.
Table 9 defines the average simulation results comparison of recognition rate with the existing and proposed methods. Above comparison illustrates that recognition rate of MF-AIFR is higher than that of other existing methods.
Methods | Recognition rate (%) |
---|---|
HLD | 87 |
DF | 69.2 |
CAN | 79.2 |
MF-AIFR | 92.2 |
Recognition rate comparisons [average].
Rank-1 Score considers the performance of cumulative match for given images in proposed work. It represents the efficacy of our work in terms of recognition and retrieval.
Figure 11 exhibits comparisons on rank-1 score results with respect to the existing methods. From this figure, it is seen that our MF-AIFR attains high rank-1 score compared with the existing methods. Our proposed DGC-CLAHE algorithm based illumination normalization performs well compared with existing CLAHE; it enhances the fine details of the image. ASBF-based noise filtering also provides better performance in noise removal, which sharpens the image. This way of preprocessing results in high matching results in face recognition. At the same time, existing methods such as DF and CAN attain less rank 1 score since it doesn’t use effective algorithm for preprocessing and thus reduce the quality of given image drastically. Likewise, HLD also attains less rank 1 score compared with our method. Since, it doesn’t perform illumination normalization and noise filtering also not effective. From this analysis, we conclude that our MF-AIFR attains better results in rank 1-score compared with other methods.
Comparisons on rank 1-score.
Table 10 signifies average simulation results comparison of rank 1-score with the existing and proposed methods. From the above comparison, we prove that our MF-AIFR method achieves higher rank 1 score percentage as 89.8% compared with existing methods.
Methods | Rank 1 score |
---|---|
HLD | 79.6 |
DF | 73 |
CAN | 65 |
MF-AIFR | 89.8 |
Rank 1 score comparisons [average].
Performance of the computation time is evaluated by varying the number of images. This metric must be low in order to attain better performance in image retrieval across aging.
Figure 12 depicts the comparisons on computation time results with respect to the existing methods. It is noticed that our MF-AIFR method achieves less computation time compared with the existing methods such as CAN, DF, and HLD. MF-AIFR performs IQE process before entering into the preprocessing step. The images that are not satisfying IQT only undergone preprocessing; otherwise it is directly given to the pose normalization step. Thus it reduces the time wastages in performing preprocessing for all input images. In addition to it, our work also reduces time in feature extraction and classification by using effective algorithms such as CNN, SIHKS, and SVM. These algorithms require less time to process the given inputs. As a result, MF-AIFR achieves less computation time. In the meantime, existing methods such as CAN and DF attain high computation time compared with other methods. Since it performs preprocessing for all images and also doesn’t utilize effective algorithm to process the given input image and thus leads to increase in computation time. Likewise, HLD also attains high computation time compared with MF-AIFR since it performs preprocessing for all images regardless of their quality.
Comparisons on computation time.
Table 11 deliberates the comparisons of computation time and thus shows that our method attains less computation time as 12.4ms compared with other methods including HLD, DF, and CAN.
Methods | Computation time (ms) |
---|---|
HLD | 60 |
DF | 63 |
CAN | 73 |
MF-AIFR | 12.4 |
Computation time comparisons [average].
This section signifies highlights of this research regarding face recognition across aging. In order to achieve better performance in AIFR, our work establishes five consequent processes. Table 12 describes the benefits of proposed algorithms along with their functionalities. This table illustrates each algorithm with their benefits in performance metrics such as precision, recall, accuracy, recognition rate, and rank 1 score.
Algorithms | Main functionality | Benefits related to performance |
---|---|---|
DGC-CLAHE | Illumination Normalization | Enhances the recognition rate and accuracy |
ASBF | Noise removal | Enhances recognition rate and feature extraction efficiency |
EA-AT | Pose Normalization | Easier the feature extraction process and Increases the precision level |
CNN | Texture Feature Extraction | Enhances the accuracy in face recognition across aging and perform well in large scale data set |
SIHKS | Shape & Demographic Feature extraction | Increases the rank 1-score and adapts large scale data set. |
SVM | Recognition and Interval | Simple processing, increases the accuracy and reduces the recall |
Benefits of proposed algorithms.
Face recognition across aging becomes challenging due to changes in the human faces with age progressions. In order to address this bottleneck, this chapter proposes MF-AIFR method where four successive processes performed that are listed as follows: IQE is performed to reduce time spend in preprocessing and thus enhances performance of our system drastically. An image that doesn’t satisfy the IQT is given as input to the preprocessing step. Here, illumination normalization and noise removal are performed, which enhances the accuracy in face recognition and retrieval. Illumination normalization adopts DGC-CLAHE, and noise removal adopts ASBF algorithm. In order to normalize the pose, we adopt EA-AT algorithm, which is performed to enhance the feature extraction efficacy. Two types of descriptors are utilized for features extractions that are CNN and SIHKS. Here, we extract multiple features such as texture, shape, and demographic features. We extract features from three types of regions that are periocular, nose, and mouth. CNN extracts texture features, and SIHKS extracts shape and demographic features. This way extracting features increases our recognition rate. In recognition and retrieval, we execute SVM algorithm, which follows the simple procedure and provides better results. At last, we evaluate the performance of MF-AIFR system using seven metrics that are Accuracy, Recall, Precision, Rank-1 Score, F-Score, Recognition rate, and Computation time. Thus it shows that our work performs better than existing methods such as HLD, DF, and CAN.
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Dimensional wood lumber is readily available and due to its convenient unit dimension can be packaged neatly and transported to work sites by either commercial transport or personal vehicle. The unit pieces of dimensional lumber are light and easily handled once on the work site. Design of light-framed single-family homes is typically conducted by an architect or construction contractor using prescriptive building codes. A structural engineer can assist, if needed, with design items not within the scope of the building code or if alternative design approaches are required. An owner may choose to involve the engineer to improve quality or economy of the home design. Engineers typically become involved with design items such as foundation design, steel framing design, or engineered product specification. In this chapter, the design of a typical light-framed home is discussed. The main structural assemblies are described and subsequently designed using a combination of prescriptive guidance and engineering design.",book:{id:"8299",slug:"timber-buildings-and-sustainability",title:"Timber Buildings and Sustainability",fullTitle:"Timber Buildings and Sustainability"},signatures:"Anthony C. Jellen and Ali M. Memari",authors:[{id:"252670",title:"Prof.",name:"Ali",middleName:null,surname:"M. Memari",slug:"ali-m.-memari",fullName:"Ali M. Memari"},{id:"276003",title:"Mr.",name:"Anthony",middleName:null,surname:"Jellen",slug:"anthony-jellen",fullName:"Anthony Jellen"}]},{id:"70758",title:"Bridges: Structures and Materials, Ancient and Modern",slug:"bridges-structures-and-materials-ancient-and-modern",totalDownloads:1797,totalCrossrefCites:1,totalDimensionsCites:5,abstract:"Every nation needs the infrastructure to perform all kind of activities related to the improvement and service of the society. Transportation system became part of the infrastructure due its connection between two destinations, using maritime, land, or aerial methods, creating a link for social and economic activity. Bridges are widely used to cross rivers, valleys, and roads, providing a passage with other parts of the land since ancient times to modernity. Each structure has different requirements to cover, such span clearage, traffic flow, geometry and characteristics of the place to build; therefore, a great variety of bridges can be developed. Common materials used on construction are structural steel, reinforced concrete, pre-stressed concrete, or post-tensioned concrete; depending on the structural behavior of each type of bridge, there will be a maximum clear span to cover, which depends directly on the project’s budget. There are a variety of loads and environmental conditions that the new and existing structure needs to support effectively, including dead load, traffic, rain, wind, flood, and seismic events, using effective structural design process and techniques; on the other hand, there are long-term deterioration process, such as corrosion, wear, and fatigue, which should be considered on the maintenance process, avoiding additional costs, several damages, and catastrophic failures. Prevention and control of degradation process is achieved by effective maintenance methods applying protection technology such as paints, coating and cathodic protection. The purpose of this chapter is to show a brief review of ancient and modern bridges, including the process of design, material selection, construction, and maintenance.",book:{id:"8355",slug:"infrastructure-management-and-construction",title:"Infrastructure Management and Construction",fullTitle:"Infrastructure Management and Construction"},signatures:"Arturo Gonzalez, Michael Schorr, Benjamin Valdez and Alejandro Mungaray",authors:[{id:"16436",title:"Dr.",name:"Michael",middleName:null,surname:"Schorr",slug:"michael-schorr",fullName:"Michael Schorr"},{id:"65522",title:"Dr.",name:"Benjamin",middleName:null,surname:"Valdez",slug:"benjamin-valdez",fullName:"Benjamin Valdez"},{id:"311533",title:"MSc.",name:"Arturo",middleName:null,surname:"Gonzalez",slug:"arturo-gonzalez",fullName:"Arturo Gonzalez"},{id:"311534",title:"Dr.",name:"Alejandro",middleName:null,surname:"Mungaray",slug:"alejandro-mungaray",fullName:"Alejandro Mungaray"}]},{id:"60236",title:"The Feasibility of Constructing Super-Long-Span Bridges with New Materials in 2050",slug:"the-feasibility-of-constructing-super-long-span-bridges-with-new-materials-in-2050",totalDownloads:1872,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter explores the possibility of designing and constructing a super-long-span bridge with new materials in 2050. The proposed bridge design has a total span of 4440 m with two 330-m end spans and a central span of 3780 m. The height of the two pylons is 702 m, and the deck width is 40 m. The features of this structure include the combination of a suspension bridge and cable-stayed bridge, application of carbon fibre materials, extension of deck width and pretension techniques. Linear static analysis, dynamic analysis and theoretical analysis are conducted under different loading cases. In linear static analysis, the stresses under critical load combinations are smaller than the ultimate strength of the materials. However, the maximum deflection under the dead and wind load combination exceeds the specified serviceability limit.",book:{id:"6395",slug:"bridge-engineering",title:"Bridge Engineering",fullTitle:"Bridge Engineering"},signatures:"Faham Tahmasebinia, Samad Mohammad Ebrahimzadeh\nSepasgozar, Hannah Blum, Kakarla Raghava Reddy, Fernando\nAlonso-Marroquin, Qile Gao, Yang Hu, Xu Wang and Zhongzheng\nWang",authors:[{id:"211659",title:"Dr.",name:"Faham",middleName:null,surname:"Tahmasebinia",slug:"faham-tahmasebinia",fullName:"Faham Tahmasebinia"},{id:"221172",title:"Dr.",name:"Samad M.E.",middleName:null,surname:"Sepasgozar",slug:"samad-m.e.-sepasgozar",fullName:"Samad M.E. Sepasgozar"}]},{id:"61896",title:"Children’s Playgrounds in Slovak Mass Housing Estates: History and Current Trends",slug:"children-s-playgrounds-in-slovak-mass-housing-estates-history-and-current-trends",totalDownloads:1349,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Children’s playgrounds represent an important amenity in the concepts of mass housing, The study chapter presents the unique concepts of children’s playgrounds that have been applied in the Slovak mass housing estates of the second half of the twentieth century, designed by architects and artist, and inspired by the best European experiences, for example, by the landscape design of the Stockholm School. The early inhabitants of the Slovak mass housing estates were predominantly young families with children. The residential aging of this homogenous social structure caused that during the lifespan of housing estates, the demand for playgrounds decreased, they became underused and fell into decay. Today, the social structure of mass housing estates becomes more heterogeneous, what puts new requirements on the design of open public spaces and, as well as, on the regeneration and design of children’s playgrounds, to serve the rising demands of the inhabitants and to enhance the livability of the housing estates. The study examines the current examples of the children’s playgrounds from Slovak mass housing estates, which show that nowadays the typified design of the standardized catalog type elements is used and preferred.",book:{id:"7205",slug:"housing",title:"Housing",fullTitle:"Housing"},signatures:"Katarína Kristiánová",authors:[{id:"224853",title:"Dr.",name:"Katarina",middleName:null,surname:"Kristianova",slug:"katarina-kristianova",fullName:"Katarina Kristianova"}]},{id:"66232",title:"Geotechnical Engineering Applied on Earth and Rock-Fill Dams",slug:"geotechnical-engineering-applied-on-earth-and-rock-fill-dams",totalDownloads:2359,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter presents the importance of geotechnical engineering on the site selection, design, construction, operation, and maintenance of earth-rock dams and earth structures; it emphasizes the geotechnical engineering work related to dam safety during the operation stage. Preliminary geological studies required to select the best dam site are described first. Next, the field and laboratory studies related to the curtain design and dam foundation treatment, as well as geotechnical studies required for the construction, operation, and maintenance of the dam, are discussed. Recent developments in the following three areas are also included: (a) seismic considerations for the design, construction, and maintenance of earth dams; (b) importance of water flow control through the dam embankment and dam foundation, required to avoid internal soil erosion and excessive pore pressure; and (c) dam safety in Mexico and around the world. A case history of a recent failure is used for illustration purposes. In this example, design and construction shortcomings resulted in serious damages on an earth dam. Conclusions and recommendations related to this topic are presented at the end of this chapter.",book:{id:"7587",slug:"hydraulic-structures-theory-and-applications",title:"Hydraulic Structures",fullTitle:"Hydraulic Structures - Theory and Applications"},signatures:"Raúl Flores-Berrones and Norma Patricia López-Acosta",authors:[{id:"58505",title:"Dr.",name:"Raul",middleName:null,surname:"Flores-Berrones",slug:"raul-flores-berrones",fullName:"Raul Flores-Berrones"}]}],onlineFirstChaptersFilter:{topicId:"114",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"1082726",title:"Potential of Nonlinear Dynamics Tools in the Real-Time Monitoring of Large Dams: The Case of High Enguri Arc Dam",slug:null,totalDownloads:7,totalDimensionsCites:0,doi:"10.5992/intechopen.1000204",abstract:'Large dams are grand structures with a complex nonlinear dynamic behavior. These nonlinear effects, though relatively small, can be very important for the analysis of dam mesoscopic damage accumulation using available monitoring data, namely, the time series of strains/tilts of the dam structure in response to the periodic filling/draining process of the reservoir. The authors derive the characteristics of the unknown dynamics using the time series of tilts and strains of a structure by means of recurrence plots (RPs), recurrence quantification analysis (RQA), Lempel-Ziv complexity (LZC), mutual information (MI), detrended fluctuation analysis (DFA), and singular spectrum analysis (SSA) for studying dam dynamics. Anomalies in the nonlinear dynamics characteristics of the measured time series of the tilts/strains of dam during the reservoir regular filling/discharge process may signal the abnormal behavior of the object. These methods were used for the analysis of the monitoring data of the 271-m-high Enguri arch dam, still one of the highest (in its class) dams in the world, which was built in the canyon of Enguri river (West Georgia) in the 1970s. Since 1996, the European Centre “Geodynamical Hazards of High Dams” of the Council of Europe has been operating on Enguri dam. Since 2020, the International Project DAMAST: Dams and Seismicity has been going on in the Enguri dam area.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Tamaz Chelidze, Teimuraz Matcharashvili, Ekaterine Mepharidze, Levan Mebonia, Mirian Kalabegashvili and Nadezhda Dovgal"},{id:"1084630",title:"Analysing the Possibility of Failure of Cascade Dam System and a Case Study from Brazil",slug:null,totalDownloads:13,totalDimensionsCites:0,doi:"10.5992/intechopen.1000202",abstract:'A cascade dam system poses more hazards for downstream life and structures, when compared with a single dam located on a river. Therefore, there is a need to develop differentiated procedures to classify and regulate these dams. In the state of Mato Grosso (MT), Brazil, it is common to find multipurpose dams, which can be considered as a cascade, when a dam failure causes adverse effects in downstream dams. The objective of the study is to analyse the possibility of dam failure located in the cascade system operated by the municipality of Várzea Grande, MT by the Associated Potential Damage (APD) classification used throughout the country. In order to do this, the specification namely “Simplified Methodology to Define the Classification Flood Zone of Associated Potential Damage of a Dam” developed by the National Laboratory for Civil Engineering in Portugal (LNEC in Portuguese) was utilised. This specification was adapted by the National Water and Sanitation Agency (ANA in Portuguese) in Brazil. In the case study, there are three dams (Dam 1, Dam 2 and Dam 3) in the cascade system. Dam 1 can cause overtopping problem for Dam 2 and Dam 3. According to APD classification, dams considered for the study are categorised as “high dam”.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Angélica Luciana Barros de Campos, Ruben Jose Ramos Cardia and Welitom Ttatom Pereira da Silva"},{id:"1081439",title:"Recent Evaluation on Total Risk of Cascade Dams on Murat River of Upper Euphrates Basin, Turkey",slug:null,totalDownloads:25,totalDimensionsCites:0,doi:"10.5992/intechopen.1000206",abstract:'The dams within a cascade system pose a high total risk to the downstream life, even if they provide significant benefits in terms of flood protection, irrigation water, and domestic water supply and energy production; a dam in a cascade system also poses a substantial risk from the point of view of other structures in the basin and causes the danger to grow due to the triggering effect from the point of view of dam failure. In this study, the total hazard of the dams in the Murat River located in the upper part of the Euphrates-Tigris Basin, the largest basin in Turkey, will be evaluated, and calculations made about it will be summarized. The possible hazards in a cascade system will be highlighted. Ten large dams of various types ranging from 36 m to 138 m in height from the river basin have been considered in this context. The analysis results show that six dams are under near-source effect in terms of seismicity, and all of the dams considered have a high total risk, although they have different hazard ratios. In addition, three separate dams located within the cascade structure carry a much greater risk regarding the dangers that other structures may create.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Hasan Tosun"},{id:"1082008",title:"Uncertainty Factors Influencing Hydroelectric LCA Studies: A Review",slug:null,totalDownloads:6,totalDimensionsCites:0,doi:"10.5992/intechopen.1000185",abstract:'Despite the increase in research on Life Cycle Assessment (LCA) of Hydroelectric Power Plants (HPP) there are issues that need to be better discussed. This review aims to discuss factors that influence HPP LCAs such as: indirect emissions, different stages of HPPs (construction, operation, and decommissioning), scale/productivity of HPPs, types of projects (reservoir and run-of-river) and use of the ground. Most of the results obtained by HPP LCAs indicate that the construction phase is the most influential phase for indirect emissions due to the use of steel and concrete. The comparison of the HPP’s LCA results with the LCA of other energy sources indicates that for the analyzed category Global Warming Potential (GWP), the HPPs present a good environmental performance considering the quantified emissions, their productivity and useful life. The present review highlights some uncertainty factors that influence HPP LCA studies and cites the need to carry out future studies on the environmental impacts of HPPs including these factors.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Marla T.B. Geller and Anderson Alvarenga de Moura Meneses"},{id:"1084778",title:"Geomembranes in Dam Engineering",slug:null,totalDownloads:14,totalDimensionsCites:0,doi:"10.5992/intechopen.1000175",abstract:'Geomembrane systems are used to provide, enhance, or restore watertightness in dams since 1959. In new construction, they are installed on embankment dams, RCC dams, and cofferdams, while in rehabilitation they are used on all types of dams. They can be installed as a full-face liner, or to line parts of the dam where a higher risk of infiltration is expected, or as external water stop at peripheral and vertical joints and at contraction joints. They can be exposed to the water of the reservoir or be covered by a ballast layer; a watertight seal at all peripheries prevents water infiltration underneath the geomembrane liner. A geomembrane water barrier is a technically and cost-effective sustainable solution. The chapter discusses the design of the state-of-the-art solutions, the technical and economic advantages, installation aspects, performance, and references, with significant examples of all available options. A recent solution for underwater placement, developed for repair but applicable also in new construction, will be presented.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Gabriella Vaschetti"},{id:"1083097",title:"Managing the Quality of the Impounded Water",slug:"tbc-29",totalDownloads:11,totalDimensionsCites:0,doi:"10.5992/intechopen.1000168",abstract:'Design, construction, and operation of a dam should involve planning and careful consideration not only of the foundation and mass of the dam itself but also of the proper management of the reservoir, and of communities displaced by the reservoir, and impacted in any way upstream or downstream. Many management problems involve a reservoir’s density stratification, resulting in low oxygen, phosphorus release, and hydrogen sulfide (H2S) in the lower layers. Control measures include selective withdrawal and artificial aeration. Case examples are given. Other problems introduced by damming are often best dealt with by measures slow and well-considered, as illustrated by examples. References for further study are provided.
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