Parameters related with the number of scenarios.
\r\n\tThe development of the interpersonal model and the Kleinian school in the second half of the last century allowed the emergence of an original understanding of the unconscious mind. Within the intersubjective paradigm, the psychoanalytic situation is conceptualized as an interpersonal field to which both the analyst and the patient contribute substantially. We have shown elsewhere how the failure to give a full account of such an intersubjective dimension in both psychoanalytic theory and practice amounts to a core liability in contemporary psychoanalytic discourse.
\r\n\r\n\tThe present book will focus on a few areas where the insufficient development of our discipline is currently apparent: five wounds that mark the body of the psychoanalytic enterprise.
\r\n\r\n\tNew contributions are particularly needed in the following areas: Current conceptualization of the unconscious mind is mechanistic and not suited to incorporate the full network of interpersonal exchanges which unfolds in the analytic room; Furthermore, the development of interpersonal psychoanalysis and the theory of the object relations warrants a greater appreciation of the impact of extratranference relations (e.g., couple, family, peers) on the patient's inner life both within and without the psychoanalytic situation.
\r\n\r\n\tAn integration of theories and models from other psychological paradigms is clearly in order here; the book will also focus on Barangers’ theory of the bi-personal field that makes traditional unipersonal models of the psychoanalytic process untenable. Also, it will help in the understanding of the reciprocal interactions of the two partners in the psychoanalytic dyad in most psychoanalytic institutes the training format relies naively on models from the academic or the professional domains. This fosters rigidity, conformism, and a hierarchical organizational style in the institutional life; e) all over the long span of his creative life Freud showed consistent interest in the application of psychoanalysis to literature, the arts, religion, and politics. Contemporary psychoanalysis is getting more and shyer and is pressed at the margins of social and political debate. The psychoanalytic theory includes unique lore of knowledge about the conscious and unconscious mind. Without it, a comprehensive understanding of human reality will stay out of the reach of contemporary culture.
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Gric",authors:[{id:"19853",title:"Prof.",name:"Liudmila",middleName:null,surname:"Nickelson",fullName:"Liudmila Nickelson",slug:"liudmila-nickelson"},{id:"24827",title:"Prof.",name:"Steponas",middleName:null,surname:"Asmontas",fullName:"Steponas Asmontas",slug:"steponas-asmontas"},{id:"24828",title:"Mr.",name:"Tatjana",middleName:null,surname:"Gric",fullName:"Tatjana Gric",slug:"tatjana-gric"}]},{id:"15089",title:"Silicon Carbide Based Transit Time Devices: The New Frontier in High-power THz Electronics",slug:"silicon-carbide-based-transit-time-devices-the-new-frontier-in-high-power-thz-electronics",signatures:"Moumita Mukherjee",authors:[{id:"21168",title:"Dr.",name:"Moumita",middleName:null,surname:"Mukherjee",fullName:"Moumita Mukherjee",slug:"moumita-mukherjee"}]},{id:"15090",title:"Contact Formation on Silicon Carbide by Use of Nickel and Tantalum in a Materials Science Point of View",slug:"contact-formation-on-silicon-carbide-by-use-of-nickel-and-tantalum-in-a-materials-science-point-of-v",signatures:"Yu Cao and Lars Nyborg",authors:[{id:"18297",title:"Dr.",name:"Yu",middleName:null,surname:"Cao",fullName:"Yu Cao",slug:"yu-cao"},{id:"21819",title:"Prof.",name:"Lars",middleName:null,surname:"Nyborg",fullName:"Lars Nyborg",slug:"lars-nyborg"}]},{id:"15091",title:"Properties and Applications of Ceramic Composites Containing Silicon Carbide Whiskers",slug:"properties-and-applications-of-ceramic-composites-containing-silicon-carbide-whiskers",signatures:"Brian Bertram and Rosario Gerhardt",authors:[{id:"19005",title:"Prof.",name:"Rosario",middleName:null,surname:"Gerhardt",fullName:"Rosario Gerhardt",slug:"rosario-gerhardt"},{id:"21440",title:"Mr.",name:"Brian D.",middleName:null,surname:"Bertram",fullName:"Brian D. Bertram",slug:"brian-d.-bertram"}]},{id:"15092",title:"Spectroscopic Properties of Carbon Fibre Reinforced Silicon Carbide Composites for Aerospace Applications",slug:"spectroscopic-properties-of-carbon-fibre-reinforced-silicon-carbide-composites-for-aerospace-applica",signatures:"Davide Alfano",authors:[{id:"19679",title:"Dr.",name:"Davide",middleName:null,surname:"Alfano",fullName:"Davide Alfano",slug:"davide-alfano"}]},{id:"15093",title:"Effect of Self-Healing on Fatigue Behavior of Structural Ceramics and Influence Factors on Fatigue Strength of Healed Ceramics",slug:"effect-of-self-healing-on-fatigue-behavior-of-structural-ceramics-and-influence-factors-on-fatigue-s",signatures:"Wataru Nakao",authors:[{id:"21462",title:"Dr.",name:"Wataru",middleName:null,surname:"Nakao",fullName:"Wataru Nakao",slug:"wataru-nakao"}]},{id:"15094",title:"Contribution to the Evaluation of Silicon Carbide Surge Arresters",slug:"contribution-to-the-evaluation-of-silicon-carbide-surge-arresters",signatures:"Arnaldo Gakiya Kanashiro and Milton Zanotti Jr.",authors:[{id:"18365",title:"Prof.",name:"Arnaldo",middleName:"Gakiya",surname:"Kanashiro",fullName:"Arnaldo Kanashiro",slug:"arnaldo-kanashiro"},{id:"21617",title:"MSc.",name:"Milton",middleName:null,surname:"Zanotti, Jr.",fullName:"Milton Zanotti, Jr.",slug:"milton-zanotti-jr."}]},{id:"15095",title:"Silicon Carbide Neutron Detectors",slug:"silicon-carbide-neutron-detectors",signatures:"Fausto Franceschini and Frank H. Ruddy",authors:[{id:"21548",title:"Dr.",name:"Fausto",middleName:null,surname:"Franceschini",fullName:"Fausto Franceschini",slug:"fausto-franceschini"},{id:"21551",title:"Dr.",name:"Frank",middleName:null,surname:"Ruddy",fullName:"Frank Ruddy",slug:"frank-ruddy"}]},{id:"15096",title:"Fundamentals of Biomedical Applications of Biomorphic SiC",slug:"fundamentals-of-biomedical-applications-of-biomorphic-sic",signatures:"Mahboobeh Mahmoodi and Lida Ghazanfari",authors:[{id:"17813",title:"Dr.",name:"Mahboobeh",middleName:null,surname:"Mahmoodi",fullName:"Mahboobeh Mahmoodi",slug:"mahboobeh-mahmoodi"},{id:"21336",title:"Prof.",name:"Lida",middleName:null,surname:"Ghazanfari",fullName:"Lida Ghazanfari",slug:"lida-ghazanfari"}]},{id:"15097",title:"Silicon Carbide Whisker-mediated Plant Transformation",slug:"silicon-carbide-whisker-mediated-plant-transformation",signatures:"Shaheen Asad and Muhammad Arshad",authors:[{id:"18190",title:"Dr.",name:"Shaheen",middleName:null,surname:"Asad",fullName:"Shaheen Asad",slug:"shaheen-asad"},{id:"21743",title:"Dr.",name:"Muhammad",middleName:null,surname:"Arshad",fullName:"Muhammad Arshad",slug:"muhammad-arshad"}]},{id:"15098",title:"Silicon Carbide: Synthesis and Properties",slug:"silicon-carbide-synthesis-and-properties",signatures:"Houyem Abderrazak and Emna Selmane Bel Hadj Hmida",authors:[{id:"18643",title:"Dr.",name:"Houyem",middleName:null,surname:"Abderrazak",fullName:"Houyem Abderrazak",slug:"houyem-abderrazak"},{id:"23082",title:"Dr.",name:"Emna Selmane",middleName:null,surname:"Bel Hadj Hmida",fullName:"Emna Selmane Bel Hadj Hmida",slug:"emna-selmane-bel-hadj-hmida"}]},{id:"15099",title:"Combustion Synthesis of Silicon Carbide",slug:"combustion-synthesis-of-silicon-carbide",signatures:"Alexander S. Mukasyan",authors:[{id:"21434",title:"Dr.",name:"Alexander S.",middleName:null,surname:"Mukasyan",fullName:"Alexander S. Mukasyan",slug:"alexander-s.-mukasyan"}]},{id:"15100",title:"In Situ Synthesis of Silicon-Silicon Carbide Composites from SiO2-C-Mg System via Self-Propagating High-Temperature Synthesis",slug:"in-situ-synthesis-of-silicon-silicon-carbide-composites-from-sio2-c-mg-system-via-self-propagating-h",signatures:"Sutham Niyomwas",authors:[{id:"17615",title:"Prof.",name:"Sutham",middleName:null,surname:"Niyomwas",fullName:"Sutham Niyomwas",slug:"sutham-niyomwas"}]},{id:"15101",title:"High Reliability Alumina-Silicon Carbide Laminated Composites by Spark Plasma Sintering",slug:"high-reliability-alumina-silicon-carbide-laminated-composites-by-spark-plasma-sintering",signatures:"Vincenzo M. Sglavo and Francesca De Genua",authors:[{id:"17425",title:"Dr.",name:"Francesca",middleName:null,surname:"De Genua",fullName:"Francesca De Genua",slug:"francesca-de-genua"},{id:"17426",title:"Prof.",name:"Vincenzo Maria",middleName:null,surname:"Sglavo",fullName:"Vincenzo Maria Sglavo",slug:"vincenzo-maria-sglavo"}]},{id:"15102",title:"High Temperature Phase Equilibrium of SiC-Based Materials",slug:"high-temperature-phase-equilibrium-of-sic-based-materials",signatures:"Yuhong Chen, Laner Wu ,Wenzhou Sun, Youjun Lu and Zhenkun Huang",authors:[{id:"21059",title:"Dr.",name:"Laner",middleName:null,surname:"Wu",fullName:"Laner Wu",slug:"laner-wu"}]},{id:"15103",title:"Liquid Phase Sintering of Silicon Carbide with AlN-Re2O3 Additives",slug:"liquid-phase-sintering-of-silicon-carbide-with-aln-re2o3-additives",signatures:"Laner Wu, Yuhong Chen ,Yong Jiang, Youjun Lu and Zhenkun Huang",authors:[{id:"21059",title:"Dr.",name:"Laner",middleName:null,surname:"Wu",fullName:"Laner Wu",slug:"laner-wu"},{id:"135946",title:"Prof.",name:"Yuhong",middleName:null,surname:"Chen",fullName:"Yuhong Chen",slug:"yuhong-chen"},{id:"135947",title:"Prof.",name:"Yong",middleName:null,surname:"Jiang",fullName:"Yong Jiang",slug:"yong-jiang"},{id:"135948",title:"Prof.",name:"Youjun",middleName:null,surname:"Lu",fullName:"Youjun Lu",slug:"youjun-lu"},{id:"135949",title:"Prof.",name:"Zhenkun",middleName:null,surname:"Huang",fullName:"Zhenkun Huang",slug:"zhenkun-huang"}]},{id:"15104",title:"Investigations on Jet Footprint Geometry and its Characteristics for Complex Shape Machining With Abrasive Waterjets in Silicon Carbide Ceramic Material",slug:"investigations-on-jet-footprint-geometry-and-its-characteristics-for-complex-shape-machining-with-ab",signatures:"S. Srinivasu D. and A. Axinte D.",authors:[{id:"23402",title:"Dr.",name:"Srinivas",middleName:null,surname:"Devadula",fullName:"Srinivas Devadula",slug:"srinivas-devadula"},{id:"23403",title:"Dr.",name:"Dragos",middleName:null,surname:"Axinte",fullName:"Dragos Axinte",slug:"dragos-axinte"}]},{id:"15105",title:"Ductile Mode Micro Laser Assisted Machining of Silicon Carbide",slug:"ductile-mode-micro-laser-assisted-machining-of-silicon-carbide",signatures:"Deepak Ravindra, Saurabh Virkar and John Patten",authors:[{id:"21605",title:"Dr.",name:"Deepak",middleName:null,surname:"Ravindra",fullName:"Deepak Ravindra",slug:"deepak-ravindra"},{id:"21773",title:"Dr.",name:"John",middleName:null,surname:"Patten",fullName:"John Patten",slug:"john-patten"},{id:"21774",title:"Mr.",name:"Saurabh",middleName:null,surname:"Virkar",fullName:"Saurabh Virkar",slug:"saurabh-virkar"},{id:"22753",title:"Mr.",name:"Bogac",middleName:null,surname:"Poyraz",fullName:"Bogac Poyraz",slug:"bogac-poyraz"}]}]}],publishedBooks:[{type:"book",id:"166",title:"Electromagnetic Waves",subtitle:null,isOpenForSubmission:!1,hash:"6561a39a2e8aaffc6cde23ecd65cdfde",slug:"electromagnetic-waves",bookSignature:"Vitaliy Zhurbenko",coverURL:"https://cdn.intechopen.com/books/images_new/166.jpg",editedByType:"Edited by",editors:[{id:"3721",title:"Prof.",name:"Vitaliy",surname:"Zhurbenko",slug:"vitaliy-zhurbenko",fullName:"Vitaliy Zhurbenko"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"415",title:"Behaviour of Electromagnetic Waves in Different Media and Structures",subtitle:null,isOpenForSubmission:!1,hash:"8496cd6f3c63a2b4d0b69076ec095343",slug:"behavior-of-electromagnetic-waves-in-different-media-and-structures",bookSignature:"Ali Akdagli",coverURL:"https://cdn.intechopen.com/books/images_new/415.jpg",editedByType:"Edited by",editors:[{id:"76005",title:"Prof.",name:"Ali",surname:"Akdagli",slug:"ali-akdagli",fullName:"Ali Akdagli"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2431",title:"Dielectric Material",subtitle:null,isOpenForSubmission:!1,hash:"70942e6b7ab8fb1bfa75537709d3910d",slug:"dielectric-material",bookSignature:"Marius Alexandru Silaghi",coverURL:"https://cdn.intechopen.com/books/images_new/2431.jpg",editedByType:"Edited by",editors:[{id:"128198",title:"Dr.",name:"Marius Alexandru",surname:"Silaghi",slug:"marius-alexandru-silaghi",fullName:"Marius Alexandru Silaghi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3706",title:"Wave Propagation in Materials for Modern Applications",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"wave-propagation-in-materials-for-modern-applications",bookSignature:"Andrey Petrin",coverURL:"https://cdn.intechopen.com/books/images_new/3706.jpg",editedByType:"Edited by",editors:[{id:"7760",title:"Dr.",name:"Andrey",surname:"Petrin",slug:"andrey-petrin",fullName:"Andrey Petrin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7617",title:"Electromagnetic Fields and Waves",subtitle:null,isOpenForSubmission:!1,hash:"d87c09ddaa95c04479ffa2579e9f16d2",slug:"electromagnetic-fields-and-waves",bookSignature:"Kim Ho Yeap and Kazuhiro Hirasawa",coverURL:"https://cdn.intechopen.com/books/images_new/7617.jpg",editedByType:"Edited by",editors:[{id:"126825",title:"Dr.",name:"Kim Ho",surname:"Yeap",slug:"kim-ho-yeap",fullName:"Kim Ho Yeap"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[{type:"book",id:"1897",title:"Metamaterial",subtitle:null,isOpenForSubmission:!1,hash:"56517158cb186183585408e26e16cf8f",slug:"metamaterial",bookSignature:"Xun-Ya Jiang",coverURL:"https://cdn.intechopen.com/books/images_new/1897.jpg",editedByType:"Edited by",editors:[{id:"103012",title:"Dr.",name:"Xun-Ya",surname:"Jiang",slug:"xun-ya-jiang",fullName:"Xun-Ya Jiang"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},onlineFirst:{chapter:{type:"chapter",id:"74416",title:"Energy Management and Optimal Power Scheduling in a Smart Building under Uncertainty",doi:"10.5772/intechopen.94989",slug:"energy-management-and-optimal-power-scheduling-in-a-smart-building-under-uncertainty",body:'Buildings have become the major energy consumers over the world as they consume around 40% of total end-use energy [1]. In Europe, the Directive on Energy Performance of Buildings establishes a “nearly Net Zero Energy buildings” (NZEBs) as the aim for all new buildings from 2020 [2]. In recent literature, more and more studies consider nZEBs as part of a smart grid or a micro-grid (MG) and identify trends on energy management techniques and technological solutions for electric power system management. The main advantages of nZEBs have been identified to be the integration of renewable energy sources; the integration of energy storage mechanisms such as plug-in electric vehicles and the implementation of zero-energy concepts such as net zero source energy, net zero energy costs and net zero emissions.
The renewable energy exploitation is one of the most important aspects of NZEBs. Renewable Energy Sources (RES) are those sources of energy that can be derived from natural processes and thus can be replenished continuously such as solar energy, wind energy, biomass, hydropower etc. The wind and solar energies are mostly used in green buildings modeling and design [3] but they come with a number of issues that have to be taken into consideration. The wind energy systems may not be technically feasible at all sites due to the low wind speeds and/or to high unpredictability with respect to solar energy. In addition, the availability of a specific resource depends each time on the corresponding season and may also vary during the day [4]. NZEBs, either as standalone or as parts of a Net Zero Energy District, could help improving the energy performance of an electrical grid by shifting loads and reducing peak demands. Buildings, as one of the most important contributors involved in a smart grid, can deliver useful information such as energy behaviors, power demand and the corresponding load shifting potentials for grid control and optimization [5].
A microgrid is an electric system of limited extent, typically the suburban/district level, that includes distributed generation (i.e., solar, wind, cogeneration, electric vehicles, etc.), consumers and storage facilities, and operates by intelligently managing its own costs and production capacity to ensure a level of quality service. It is connected to the global grid but is designed to operate independently if necessary (islanded mode). Microgrid can be understood as a case of a more general concept called ‘Smart grid’, collecting a set of technological solutions for electric power system management. Its localized nature allows responding efficiently and accurately the energy needs and ensuring adequate levels of quality, safety, security, reliability, and availability. It is able of being disconnected from the global network for several hours without loss of service while ensuring voltage and frequency stability. In addition, the proximity of the sources of production to the consumption allows reducing energy transmission losses. Thus, the use of such a system (mainly decentralized) has as an aim to gain flexibility and adaptability with respect to the classical centralized power system model.
The development and the extensive utilization of building automation systems, Information and Communication Technologies (ICT) and grid energy management system facilitates the bidirectional communication between buildings and a grid which can be widely established and therefore be used for interacting and optimizing the power supply and the demand. This chapter attempts to address the major issues that are related to the design and optimization of grid-connected nearly and/or net zero energy buildings as parts of a smart grid and on which several scholars/researchers have been working the last years.
In this work, a microgrid with a certain number of DER components connected to an office building (in a university campus) provided with electricity by a utility company is considered. These components include a PV installation, a Storage Energy System (ESS), a small Combined Heat and Power (CHP) unit, and a fleet of electric vehicles (EVs) used for work-related trips. The mobility behavior of the EVs fleet is modeled considering deterministic realizations of the probabilistic distributions used for the arrival/departure and the time EVs remain parked. PV production and electric load are modeled under uncertainty. We use actual data from smart meters to formulate the scenarios. We also assume that each DER element can, through an EMS controller, to communicate and control the power exchange from and towards this component. We also consider that two-way communication with the utility company can be achieved via aggregators using advanced metering infrastructure. The energy generated by the DERs can be sold to the grid by the microgrid building-manager, and/or it can be stored for future utilization. The recommended EMS configuration is shown in Figure 1.
Energy management and system configuration.
To classify PV and electric load production, yearly data-measurements from smart meters installed in Walloon region, Belgium, have been used. The smart meters communicate with the utility company server every 15-min providing the updated PV and load measurements. The 15-min datasets were merged to formulate 8760 hourly readings (365 24-hour PV generation and load profiles). The total PV capacity is 50 kVA. The original datasets are shown in Figure 2.
The 365 original profiles for (a) PV production, and (b) electric load demand.
We use the scenario reduction technique introduced in [6] to construct the scenarios. A script developed in Matlab based on [6] is utilized to aggregate the two sources of uncertainty into one. That is, a discrete probability has been assigned to each one of the generated scenarios. Every scenario comprises two 24-hour vectors where each vector corresponds to a specific profile (one vector for PV production and one for load demand). Moreover, this scenario construction technique considers the potential correlation within the data. The latter is very important as, for example, a sunny day with increased PV production is expected to affect the load demand downwards and vice-versa. Moreover, one may notice that the PV profiles of Figure 2a look asymmetric and seem to have been shifted towards the left side of the time axis. This is due to the minimum cut-in voltage level required from the power electronics of the inverter to start being operational.
It is important that the final number of generated scenarios retain most of the relevant information on the stochastic process contained in the original scenario sets, while significantly reducing its cardinality. A very large number of scenarios may result in a computationally intractable associated stochastic programming problem which would require both increased time and computational resources to be solved. On the other hand, a small number of scenarios might not be representative of the original data sets. Thus, in order to decide the appropriate number of scenarios we take into consideration the total expected system cost (TESC), its standard deviation (SD), and the total computational time, as shown in Table 1. Simulations take place on an Intel Core i7-5500U CPU @ 2.4 GHz with 16 GB memory.
Numb of scenarios | 6 scen. | 12 scen. | 24 scen. | 48 scen. |
---|---|---|---|---|
TESC ($) | 26.06 | 20.07 | 16.68 | 15.64 |
SD ($) | 23.22 | 24.33 | 24.35 | 24.45 |
Elapsed time (s) | 0.09 | 0.11 | 0.23 | 0.42 |
Parameters related with the number of scenarios.
We can see in Table 1 that the TESC decreases considerably from the 6 to 12 scenarios, and from 12 to 24. On the other hand, the cost reduction from the 24 to 48 scenarios is smaller. The standard deviation of the TESC increases somehow from the 6 to 12 scenarios, but it remains relatively constant in the rest scenario cases. Finally, one may notice that the computational time needed to obtain the optimal solution is increased around 100% in both cases, from the 12 to 24 and from 24 to 48 scenarios. Considering all the information above, the case of 24 scenarios provides a favorable trade-off between a satisfactory scenario representation and a computationally tractable problem. One should also note that the constructed scenarios are not equiprobable, but probability weighted. The 24 scenarios for PV generation and load demand are illustrated in Figure 3.
The 24 representative scenarios for (a) PV production, and (b) electric load demand.
For the deterministic approach, we used the average yearly profiles (obtained from the original datasets in Figure 2) for both PV production and the electric load demand. These profiles are illustrated in Figure 4.
The average yearly profiles for (a) PV production, and (b) electric load demand.
When connected to the microgrid, the charging and discharging behaviors of the EVs make them considered as either power supplies (when discharging) or power loads (when charging). Here, the EVs selected for the fleet are used for work-related trips and it is also assumed that the mobility behavior with the EVs remains similar as with conventional vehicles.
In this work, the mobility behavior profiles for a fleet of 30 EVs are generated. In Belgium, 82% of the population has fixed working hours and shifts [7]. Usual working hours are considered from 8 am to 6 pm but they are not binding. The arrival time distribution is fitted in the form of chi-square distribution [8] with its probability density function given by:
To simulate the thermal performance of a building, engineers developed, among other tools, the thermal network method. Thermal networks have been used to study the internal mass effects [10], appliances, indoor air temperature and heating load [11] for different buildings. In addition, they represent a comprehensible idea about the heat transfer phenomena in buildings with a simple systematic formulation of the problem. In the thermal network method, the whole mass of the system is accumulated in finite number of nodes, which are connected to thermal capacitances. The heat transfer between two nodes occurs through thermal resistances. It has been shown, that the functionality of control systems can be improved by the implementation of the thermal network method and the system identification approach [12].
System identification is an approach to construct mathematical models of dynamic systems by means of measurements of the system’s input and output signals. The system identification needs the measured input and output signals from the system, a model structure, and an estimation method to estimate values for the adjustable parameters in the selected model structure. In a dynamic system, the output signal depends on both the instantaneous values of its input signals and on the initial conditions. In fact, a model is a mathematical relationship between a system’s input and output variables. Differential or difference equations, transfer functions, and state-space equations are common methods to describe a dynamic system. The RC model method describes the system with ordinary differential equations that can be easily represented with the state space method.
Obtaining a good model of the system depends on how well the measured data reflects the behavior of the system. For this purpose, the measured data must capture the dynamics of the system. It is necessary to measure the right variables with enough accuracy and duration to capture the dynamics of interest. In general, to supply an appropriate dataset, the following inputs that excite the system dynamics are important: data duration to capture the important time constants, a detailed analysis of signal-to-noise ratio, and finally measuring the outputs at appropriate sampling intervals [13].
The use of the RC model method provides the structure of the model, but not the numerical values of its parameters. Afterwards, it is possible to represent the system with a state-space model and estimate the values of its parameters from the data. This approach is known as gray-box modeling. The system identification approach refers to methods and algorithms that estimate the model parameters by minimizing the error function (cost function -- the mean square error), as shown below between the model output and the measured data.
where
The MATLAB® system identification toolbox is used in this work to minimize the cost function of Eq. 2 and to estimate the model parameters. MATLAB uses various minimization algorithms to perform the optimization. In our case, the ‘auto’ algorithm is used for the search method to minimize the cost function and to estimate model parameters, as it determines the optimized trajectory among different techniques at each iteration.
The simplified thermal model presented in [14] is used in this study to obtain the thermal load for the university building. The building is simulated using TRNSYS software utilizing weather data from the Uccle meteonorm file (Belgium). It has a heavy structured envelope and the buildings material properties are presented in [14]. Here a 4R2C model is proposed and used to simulate the thermal performance of the building. The corresponding proposed thermal network is represented in Figure 5.
The proposed thermal network.
To determine the parameters in the thermal network, the system identification approach has been used. Data from TRNSYS have been used as the information matrix for the model to be trained. To identify the model’s parameters, the Matlab system identification toolbox is utilized. The information matrix contains one-month data. The model identification determines the values of each resistance and capacitance to achieve the highest fitness between the thermal network and the information matrix. Then, the identified model can predict the thermal performance of the building for a predetermined period of days.
To formulate a daily thermal load profile, so as it can be used by the EMS for its 24-hours scheduling horizon, the average heating load of the predicted working days is calculated. The calculated thermal load offers a temperature approximately around 22°C during working hours (from 9 am to 6 pm). The daily thermal load prediction is illustrated in Figure 6.
Daily thermal load prediction.
The thermal load is low during the night and the early morning hours and starts increasing around 8 am. This is necessary, so as the targeted thermal comfort level to be achieved in the office building during the working hours. The thermal load is covered by CHP’s thermal production.
The mathematical formulation of the EMS is presented in this Section. The objective function which minimizes the total expected system cost is given by Eq. (3) below:
where
The expected cost function (3) is a probability-weighted mean of all the scenarios considered. It minimizes the power requested from the grid
Finally, the third term of Eq. (3) introduces a prioritization mechanism in the form of a penalty factor. Parameters
Eq. (4) enables the actual power generated by the PV to be utilized in three different directions. A portion can be sold directly to the grid
The ESS operation is characterized by Eq. (5)–(10). The actual power provided by the ESS when discharges can be either sold back to the grid
The EVs operation is described in Eq. (12)–(18). Eq. (12) ensures that the discharge power of the EVs is either injected back to the grid
The utilization of small-sized CHP turbines is typical for covering thermal load demand and has been often proposed in literature as a distributed energy resource [16]. The equations that describe the operation of the CHP microturbine are presented in Eqs. (19)–(24) below.
Constraint (19) states that the total power
The total power injected to the grid is described in Eq. (25). The total power injected to the grid at time
The power balance equation is defined in Eq. (26) below.
Constraint (26) forces the balance between the input and the output electric power of the EMS in each time interval. More specifically, it is stated in Eq. (26) that the total load consisting of the office-building electric load demand, the charging needs of the ESS and the sum of the charging needs for the EVs is covered by the power requested from the grid and/or by the combined procurement of power provided by the PV, the ESS, the sum of discharging power of the EVs, and the CHP.
Finally, Eq. (27) and Eq. (28) realize the logic of power exchange.
When the EMS needs to draw power from the grid, power is not allowed to be injected into the grid at the same time, and vice versa. The limitations in power exchange are imposed by parameter
To examine the effectiveness of the proposed EMS algorithm, the impact of different case studies on total system cost is evaluated. The proposed EMS framework is a mixed integer linear problem modeled in GAMS v.24.7.1 and solved by the IBM CPLEX Optimizer v.12.6. The time required to find the optimal solution varies from a few seconds to several minutes, depending on the model. The optimality gas has been set at 1.0E-04.
The electric load demand and PV scenarios are given in
The bidirectional energy flows between the utility company and the end-user (the building-microgrid manager in this case) assume the utilization of smart-metering approach. The day-ahead time-varying price signal which represents the electricity cost at each time interval
Day-ahead electricity price forecast.
The ESS consists of a battery group with a total capacity of 80 kWh. The maximum charging/discharging rate is 40 kW with corresponding power electronics efficiency of 0.88. The minimum allowed state-of-energy of the ESS has been set to 10 kWh (12.5% of max ESS capacity) to prevent deep battery discharging. The initial state-of-energy of the ESS is 40kWh.
The thermal efficiency
As mentioned earlier, a bidirectional energy flow concept for EVs and their potential V2B and V2G capabilities could significantly reshape the current perception of power systems. The first step is their integration into the smart grid (or microgrid). The EVs are equipped with constantly bigger battery capacities increasing thus their potential contribution as DERs. The EVs could either be granted to (University’s or a company’s) personnel for commuting purposes under the form of a third-party contract and/or they could be privately owned. In both cases, it would make sense to assume that the EV users would be willing to allow the building-microgrid operators to use their batteries’ capacity but they would not prefer to have a lower state-of-energy upon departure compared to their arrival. In addition, in the case of self-owned EVs, possible monetary benefits for the EV owners may be needed for motivating them to opt-in the EMS scheme.
In our base case study, the first business model is considered, namely the EVs are provided to the personnel and, in exchange, the EVs’ users have to participate in the EMS framework. It is considered here that the final state-of-energy of the EVs should be at least equal to their initial one. We have also considered
First, we consider the total system cost (TSC), as shown in Table 2.
Case | Description | Total system cost |
---|---|---|
1 | No EMS in operation (average of all historical data) | 59.47 |
2 | With EMS in operation (average of all historical data) | 13.51 |
3 | Expected mean of all 24 scenarios | 16.68 |
4 | Most probable scenario of the 24 (prob. 9.3%) | 58.11 |
Total system cost across all case [$].
The first case corresponds to an operation of the microgrid without the presence of an EMS and thus, no optimization takes place. That is, the loads cannot be shifted and are always met. In addition, as the EVs should depart at least having the same battery state of energy as the one they had when arrived, charging/discharging of the EVs are not activated. The ESS operation is also omitted, as its charging /discharging cannot be coordinated due to the absence of an EMS. Finally, when there is a net energy consumption at time
Total system cost distribution for the 24 scenarios.
The importance of considering an EMS in microgrid’s operation is depicted in the TSC results across all cases, as shown in Table 2. First, the total system cost of case 1, where no EMS is assumed, is 340% higher compared to case 2, where an EMS is present coordinating the microgrid operation (from $13.51 to $59.47). The expected TSC for case 3 is 23% higher compared to case 2 due to the impact of some extreme scenarios on the final result. Moreover, the total cost distribution across all the different scenarios (Figure 8) implies that the final total system payoff for the majority of the scenarios is positive in terms of cost (a positive value declares a cost, while a negative one declares a profit). Finally, one may notice that the TSC for the most probable scenario, as seen in case 4, is much higher compared to the other two cases (case 2 and 3) in which an EMS is also present on microgrid’s operation. The reason is that for this particular scenario, the PV generation and the building load demand are very different compared to the corresponding annual average values, as these are considered for case 2 (Figure 4). More specifically, the projected PV generation in the most probable scenario is much lower than the yearly average, as presented in case 2. On the contrary, the building load demand is higher than the average. Therefore, the results presented in this Section should be interpreted taking this context into account.
To analyze a few more aspects of the optimization results and examine the individual scheduling of each DER, as it is decided by the EMS, we compare the microgrid’s operation under two different case studies: case 2, which from now on will be referred to as simply the
Most probable scenario for (a) PV production, and (b) electric load demand.
Figure 10 presents the total power requested by the EMS from the grid and injected back to it for the deterministic approach and the most probable scenario.
Power requested from and injected to the grid for the most probable scenario and the deterministic approach.
There are many observations one might make regarding Figure 10. First, notice that the power requested from the grid is zero during the whole 24-hour time horizon for the deterministic approach. This implies that the microgrid can fully cover its electric load demand using its own distributed energy resources. In addition, it is able to inject a great portion of its produced energy back to the grid. From 1 pm to 4 pm though, the microgrid neither requests nor injects power back to the grid. This means that the produced energy is entirely used to cover the local microgrid load demand.
On the other hand, we can see that during the most probable scenario, the microgrid draws power from the grid from around 9 am to 5 pm which indicates that the microgrid’s distributed energy resources cannot fully cover the load demand during that period. This is mostly due to the limited daily PV production assumed in this scenario in combination with a higher than average electric load demand. In addition, one may notice that the total power injected back to the grid is much lower in the most probable scenario.
To better understand how EMS coordinates the operation of the microgrid’s components, Figure 11 presents the decomposition of the total power injected to the grid for the involved DERs (PV, ESS, CHP, and EVs).
Decomposition of power injected to the grid for the (a) deterministic approach, and (b) the most probable scenario.
In both the deterministic and the most probable scenario, CHP is the DER that injects most of the power back to the grid. We can see that in the deterministic case PV also contributes, especially during the noon hours. The ESS is more active in the case of the most probable scenario, while one might notice that the EVs are not used at all as a potential source for energy to be injected to the grid. This happens mainly due to the lowest prioritization factor EVs have for selling energy back to the grid as described earlier, but also due to the penalty that has been set to prevent EVs battery degradation. Finally, we can observe that in both the deterministic and the most probable scenario, the EMS tries to inject most of the power back to the grid during the peaks of electricity price (around 7 am and 6 pm as shown in Figure 7) to maximize the reward.
Figure 12 shows the decomposition of the projected PV generation for the deterministic approach and the most probable scenario.
Decomposition of PV production for the (a) deterministic approach, and (b) the most probable scenario.
In the deterministic case study, PV production is mostly sold to the grid (early and noon hours) or stored in the ESS for future exploitation (afternoon hours). Only a small portion at 1 pm is used to cover the building’s load demand. On the contrary, in the most probable scenario, all the produced PV energy is used to meet the building’s load demand.
Figure 13 shows how the electric power produced by the CHP is divided among the grid, the ESS, and the local building load. Like the PV, most of the CHP electric production in the most probable scenario is used to cover the building’s load. Moreover, we can see that the EMS tries to inject most of the CHP’s produced energy back to the grid, during the electricity price peak hours. Finally, in both cases a smaller amount of the CHP’s produced energy is stored in the ESS for future implementation. The thermal load demand parameter
Decomposition of CHP electric produced power for the (a) deterministic approach, and (b) the most probable scenario.
Storage is an important distributed energy resource for the system. As stated in Eqs. (9)-(10), the ESS can either be charged from the grid, the PV, and the CHP. When discharging, its energy can be either injected into the grid and/or cover a portion in building’s load demand. Figure 14 shows the decomposition of the ESS available energy for the deterministic approach, as well as for the most probable scenario.
Decomposition of ESS provided power for the (a) deterministic approach, and (b) the most probable scenario.
Figure 14 can be better analyzed taking into account Figure 15, which demonstrates the evolution of the ESS state of energy for the two aforementioned case studies.
State of energy for the ESS for the (a) deterministic approach, and (b) the most probable scenario.
We can see that in both deterministic and most probable scenario cases, the ESS is mainly active during two distinct period of times, in the morning (between 7 am and 8 am), and in the afternoon (between 5 pm and 7 pm). There are two main observations one may make regarding the ESS operation. First, the ESS uses two discharge cycles in the deterministic approach, while it only discharges once in the most probable scenario. The relatively high PV generation considered in the deterministic scenario is responsible for this second cycle of charge/discharge. Looking at Figure 12, we notice that PV production during the afternoon hours is mostly directed to the ESS. Second, the ESS covers mainly the building’s load demand in the deterministic case, while in the most probable scenario the ESS injects most of its energy back to the grid.
EVs constitute the third available DER in the microgrid but contrary to the rest DERs (PV, ESS, and CHP), they are not actively involved in microgrid’s energy exchange. The EVs battery degradation cost on the one hand, and the lowest energy prioritization factor that has been assigned to them on the other hand, do not make the an attractive alternative power source for the EMS (in terms of cost). Nevertheless, the EVs can always be used as a back-up ancillary power source in case of an emergency situation.
Sensitivity analysis is used to study the robustness of the solution to a linear programming model. If there is cause for concern regarding the accuracy of the data used, sensitivity analysis is undertaken to determine the way the solution might change if the data were different. When the solution does not change (or when the nature of the solution does not change, as when the basis remains optimal), one may assume that the proposed solution is appropriate.
The following example presents how the dual variable of a constraint can be used for the sensitivity analysis. Figure 16 shows the dual prices of constraint Eq. (4) for the 24 hours of the daily time horizon. One should recall that this is a resource constraint, and specifically it bounds the actual power generated by the PV to be less-than-or-equal-to the maximum PV generation, as this is defined by parameter
Value of sensitivity factor: Dual variable corresponding to the upper bound of constraint
This value implies the sensitivity of the system cost with respect to the actual PV power utilized by the system. Note that the positive value for this dual variable means that the total system cost decreases with the additional availability of PV power. More specifically, it indicates the decrease in the total system cost that corresponds to the increase of the available PV generation by 1 kWh. The fact that the value of the dual variable is positive during the whole day implies that additional PV potential has always positive impact on the total system cost, regardless the time of the day. However, one might also notice that there some time periods (7 am, from 5 pm to 9 pm), where the extra PV power would be more beneficial for the system compared to the rest time periods. In a similar way, one could evaluate the impact of the relaxation of the rest important resources to the total system cost.
The transition to the new “smart” era requires the utilization of smart technology through comprehensive and efficient energy management functions. We propose in this study, a two-way communication energy management framework for a microgrid in a university campus including local renewable energy sources, a storage system, a combined heat and power small turbine, and a fleet of EVs used for work-related trips. Two-way energy exchange is allowed using net metering technology. The developed MILP framework incorporates an optimizer which decides the power exchange among the DER components of the microgrid and the grid, exploiting the V2B and V2G capabilities of the distributed energy resources. It also provides a specific level of thermal comfort to the building’s occupants by meeting the predicted heating load. The formulation of an EMS model which takes into account the PV and load variability is very important if we want to consider the impact of planning for one scenario, and having another scenario occurs. To overcome this challenge, actual smart metering data for a period of one year have been used to construct a number of potential scenarios. The PV and load demand data are classified using a scenario construction technique, leading to the formulation of 24 different PV and electric load scenarios, each one represented by a designated probability. The importance of considering an EMS in microgrid’s operation is depicted in the total system cost across all cases. Results confirm that the EMS substantially decreases the total system cost by optimally coordinating and scheduling the microgrid operation. An additional significant remark is that the majority of the total daily system’s cost is due to the natural gas expenses required for the operation of the CHP microturbine. Finally, we compare the optimal scheduling of the microgrid’s DERs under the deterministic case and the most probable scenario. The most probable scenario assumes a lower PV production and a higher building electric load demand than the average values considered in the deterministic case, resulting in a substantially different energy scheduling for the DERs. It is worth noting that under the deterministic approach and the current design, the microgrid seems to be self-sufficient in terms of covering its energy demand. However, this is not the case under the most probable scenario approach, where the microgrid relies also on grid energy to meet its load demand, on top of the energy production of its own DERs. Suggestions for future work include the introduction of additional stochasticity parameters (e.g., electricity price) and the integration of power flow constraints into the optimization problem.
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Although typical biological treatments of water offer some advantages such as low cost and operability, many investigations referring to the removal of pesticides have suggested that in many cases they have low effectiveness due to the limited biodegradability of many agrochemicals. In recent years, research for new techniques for water detoxification to avoid these disadvantages has led to processes that involve light, which are called advanced oxidation processes (AOPs). Among the different semiconductor (SC) materials tested as potential photocatalysts, titanium dioxide (TiO2) is the most popular because of its photochemical stability, commercial availability, non-toxic nature and low cost, high photoactivity, ease of preparation in the laboratory, possibility of doping with metals and non-metals and coating on solid support. Thus, in the present review, we provide an overview of the recent research being developed to photodegrade pesticide residues in water using TiO2 as photocatalyst.",book:{id:"6407",slug:"application-of-titanium-dioxide",title:"Application of Titanium Dioxide",fullTitle:"Application of Titanium Dioxide"},signatures:"Nuria Vela, Gabriel Pérez-Lucas, José Fenoll and Simón Navarro",authors:[{id:"202983",title:"Dr.",name:"Simón",middleName:null,surname:"Navarro",slug:"simon-navarro",fullName:"Simón Navarro"},{id:"202988",title:"Dr.",name:"Nuria",middleName:null,surname:"Vela",slug:"nuria-vela",fullName:"Nuria Vela"},{id:"202989",title:"Dr.",name:"José",middleName:null,surname:"Fenoll",slug:"jose-fenoll",fullName:"José Fenoll"},{id:"206059",title:"Dr.",name:"Gabriel",middleName:null,surname:"Pérez-Lucas",slug:"gabriel-perez-lucas",fullName:"Gabriel Pérez-Lucas"}]}],mostDownloadedChaptersLast30Days:[{id:"55440",title:"Solubility Products and Solubility Concepts",slug:"solubility-products-and-solubility-concepts",totalDownloads:3090,totalCrossrefCites:6,totalDimensionsCites:7,abstract:"The chapter refers to a general concept of solubility product Ksp of sparingly soluble hydroxides and different salts and calculation of solubility of some hydroxides, oxides, and different salts in aqueous media. A (criticized) conventional approach, based on stoichiometry of a reaction notation and the solubility product of a precipitate, is compared with the unconventional/correct approach based on charge and concentration balances and a detailed physicochemical knowledge on the system considered, and calculations realized according to generalized approach to electrolytic systems (GATES) principles. 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The PA industry is spread out worldwide in Europe, Asia and America, including countries that operate phosphate rock (PR) mines and produce PA, phosphatic fertilizers and phosphate-based products.",book:{id:"5595",slug:"phosphoric-acid-industry-problems-and-solutions",title:"Phosphoric Acid Industry",fullTitle:"Phosphoric Acid Industry - Problems and Solutions"},signatures:"Benjamín Valdez Salas, Michael Schorr Wiener and Juan Ricardo\nSalinas Martinez",authors:[{id:"16436",title:"Dr.",name:"Michael",middleName:null,surname:"Schorr",slug:"michael-schorr",fullName:"Michael Schorr"}]},{id:"62941",title:"Inorganic Coordination Chemistry: Where We Stand in Cancer Treatment?",slug:"inorganic-coordination-chemistry-where-we-stand-in-cancer-treatment-",totalDownloads:2160,totalCrossrefCites:5,totalDimensionsCites:10,abstract:"Metals have unique characteristics such as variable coordination modes, redox activity, and reactivity being indispensable for several biochemical processes in cells. Due to their reactivity, their concentration is tightly regulated inside the cells, and abnormal concentrations are associated with many disorders, such as cancer. As such metal complexes turned out to be very attractive as potential anticancer agents. The discovery of cisplatin was a crucial moment, which prompted the interest in Pt(II) and other metal complexes as potential anticancer agents. This chapter highlights the state of the art on metal complexes in cancer therapy, highlighting their uptake mechanisms, biological targets, toxicity, and drug resistance. Finally, based on the importance of selective target of cancer cells, drug delivery systems will also be discussed.",book:{id:"7549",slug:"basic-concepts-viewed-from-frontier-in-inorganic-coordination-chemistry",title:"Basic Concepts Viewed from Frontier in Inorganic Coordination Chemistry",fullTitle:"Basic Concepts Viewed from Frontier in Inorganic Coordination Chemistry"},signatures:"Pedro Pedrosa, Andreia Carvalho, Pedro V. Baptista and Alexandra R. Fernandes",authors:[{id:"253664",title:"Prof.",name:"Alexandra R",middleName:null,surname:"Fernandes",slug:"alexandra-r-fernandes",fullName:"Alexandra R Fernandes"}]},{id:"57464",title:"General Aspects of the Cobalt Chemistry",slug:"general-aspects-of-the-cobalt-chemistry",totalDownloads:2305,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"This chapter aims to collect and summarize the chemical properties of cobalt and some new cobalt compounds. It deals with the progress of cobalt chemistry. Cobalt has been substantial in both chemical reactions and within many compounds. Some of them are heterocyclic reactions, cobalt-based catalyst and cobalamin. Also, it discusses variety of applications of cobalt in a wide range of areas and toxicity of cobalt. The studies carried out in this area so far have enabled and will be continued to be responsible for producing unknown and difficult reactions. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. 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. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"13633",title:"Prof.",name:"Abdelhamid",middleName:null,surname:"Mellouk",slug:"abdelhamid-mellouk",fullName:"Abdelhamid Mellouk",profilePictureURL:"https://mts.intechopen.com/storage/users/13633/images/1567_n.jpg",institutionString:null,institution:{name:"Paris 12 Val de Marne University",institutionURL:null,country:{name:"France"}}},{id:"109268",title:"Dr.",name:"Ali",middleName:null,surname:"Al-Ataby",slug:"ali-al-ataby",fullName:"Ali Al-Ataby",profilePictureURL:"https://mts.intechopen.com/storage/users/109268/images/7410_n.jpg",institutionString:null,institution:{name:"University of Liverpool",institutionURL:null,country:{name:"United Kingdom"}}},{id:"3807",title:"Dr.",name:"Carmelo",middleName:"Jose Albanez",surname:"Bastos-Filho",slug:"carmelo-bastos-filho",fullName:"Carmelo Bastos-Filho",profilePictureURL:"https://mts.intechopen.com/storage/users/3807/images/624_n.jpg",institutionString:null,institution:{name:"Universidade de Pernambuco",institutionURL:null,country:{name:"Brazil"}}},{id:"38850",title:"Dr.",name:"Efren",middleName:null,surname:"Gorrostieta Hurtado",slug:"efren-gorrostieta-hurtado",fullName:"Efren Gorrostieta Hurtado",profilePictureURL:"https://mts.intechopen.com/storage/users/38850/images/system/38850.jpg",institutionString:null,institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},{id:"239041",title:"Dr.",name:"Yang",middleName:null,surname:"Yi",slug:"yang-yi",fullName:"Yang Yi",profilePictureURL:"https://mts.intechopen.com/storage/users/239041/images/system/239041.jpeg",institutionString:null,institution:{name:"Virginia Tech",institutionURL:null,country:{name:"United States of America"}}}]},onlineFirstChapters:{paginationCount:10,paginationItems:[{id:"82112",title:"Comparative Senescence and Lifespan",doi:"10.5772/intechopen.105137",signatures:"Hassan M. 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