Summary of the evaluation metrics used in the methodology, grouped by concept similarity.
\\n\\n
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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It is a conventional renewable energy source for generating electricity in small- and large-scale production. Due to its important utilization and future prospects, various interesting topics of research related to hydroelectric power generation are covered in this book. This book is the result of significant contributions from several researchers and experts worldwide. It is hoped that the book will become a useful source of information and basis for extended research for researchers, academics, policy makers, and practitioners in the area of renewable hydropower technologies.",isbn:"978-953-51-3382-7",printIsbn:"978-953-51-3381-0",pdfIsbn:"978-953-51-4723-7",doi:"10.5772/63684",price:119,priceEur:129,priceUsd:155,slug:"renewable-hydropower-technologies",numberOfPages:108,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"15ea891d96b6c9f2d3f28d5a21c09203",bookSignature:"Basel I. 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Dr. Ismail was the leading research investigator in a collaborative project with Goldcorp-Musselwhite Canada Ltd. and Engineering, Lakehead University from 2007 to 2010. This innovative project was state-of-the-art in geothermal heat pump technology applied in Northwestern Ontario, Canada. 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It was observed that the turbine is safe in its entire operating range as far as phenomenon of resonance is concerned. Additionally, it was observed that maximum harmonic response of the turbine on the application of dynamic loading is far lesser than its failure limit within the specified operating range.",signatures:"Edwin Chica and Ainhoa Rubio-Clemente",downloadPdfUrl:"/chapter/pdf-download/53621",previewPdfUrl:"/chapter/pdf-preview/53621",authors:[{id:"189040",title:"Prof.",name:"Ainhoa",surname:"Rubio Clemente",slug:"ainhoa-rubio-clemente",fullName:"Ainhoa Rubio Clemente"},{id:"193744",title:"Prof.",name:"Edwin",surname:"Chica",slug:"edwin-chica",fullName:"Edwin Chica"}],corrections:null},{id:"53620",title:"Planning Hydropower Production of Small Reservoirs Under Resources and System Knowledge Uncertainty",doi:"10.5772/66912",slug:"planning-hydropower-production-of-small-reservoirs-under-resources-and-system-knowledge-uncertainty",totalDownloads:1546,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Available energy from water varies widely from season to season, depending on precipitation and streamflows, especially in small catchments. In addition, the reservoir operation problem is associated with the inability of operators to formulate crisp boundary conditions, due to uncertainty in knowledge. In this chapter, an approach for planning the operation of small multipurpose reservoir systems for hydropower generation and flood control under consideration of the stochastic nature of inflows and initial storage levels and allowed formulation of constraints with some range of uncertainty will be presented. The approach is based on joint chance constrained and fuzzy programming, which addresses the problem of including risk directly in the optimization. Therefore, the stochastic nature of inputs is incorporated directly in the model through the use of convolution of random variables. Furthermore, probabilistic/vague constraints and preassigned tolerance levels are used to transform the stochastic optimization problem into its deterministic equivalent. 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Since 1911, 500 kW power generation at Pharping, now reached 782.45 MW production in 2016. Nepal government has planned to increase its current 67.3% access in electricity to 1426 MW (87%), by 2022. Globally, 16.6% generation of hydroelectricity, 1,079 GW production, in 2015 will be increased to 1,473 GW by 2040 as projected. Although, hydropower is considered as a renewable clean energy, dam closure, influence within the downstream river and connected ecosystems have consequent impacts on hydropower production. Nepal’s topography offered more RoR types of hydropower and has more risk of landslide, flooding, GLOFs, LDOFs, and flash floods. Despite, Nepal contributes 0.027% of total global Green House Gas (GHG) emissions; Nepal has focused on renewable energy, hydropower production, targeting 12000 MW by 2030 to fulfill its growing demand of 11,500 MW. Consequent development of clean energy, GHG reduction, single Bhotekoshi hydropower can reduce 160092 tons CO2/year. 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In other words, a premature birth is one that occurs before the start of the 37th week of pregnancy. Premature babies, especially those born very early, often have complicated medical problems. Preterm birth occurs for a variety of reasons. Most preterm births happen spontaneously, but some are due to early induction of labour or caesarean birth, whether for medical or non-medical reasons. Common causes of preterm birth include multiple pregnancies, infections and chronic conditions such as diabetes and high blood pressure; however, often no cause is identified. There could also be a genetic influence. Better understanding of the causes and mechanisms will advance the development of solutions to prevent preterm birth. WHO has developed new guidelines with recommendations for improving outcomes of preterm births. This set of key interventions can improve the chances of survival and health outcomes for preterm infants. The guidelines include interventions provided to the mother – for example steroid injections before birth, antibiotics when her water breaks before the onset of labour, and magnesium sulfate to prevent future neurological impairment of the child. This book aims to provide readers with comprehensive information on preterm birth, current causes of preterm birth, possible consequences, and updated information on precautions to prevent premature birth.
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You likely also know that it is used for producing spatial sound over headphones in most of today’s personal mixed reality experiences. While conceptually sound, binaural rendering is subject to several limitations in practice, some of them leading users to perceive distorted versions of the encoded 3D scene. Those distortions range from slight localisation blur to critical scenarios where auditory events are perceived on the opposite hemisphere from their actual position. Researchers have been working on techniques to address this problem of binaural localisation accuracy for some time now. To establish the benefit of these techniques, they predominantly, and quite naturally, rely on localisation performance evaluations.
The problem that concerns us here is that there is no standard for said evaluation. As a consequence, fully appreciating the value of a technique often requires careful reading and interpretation of both protocols and associated results. This becomes truly problematic when comparing the results of several studies, where differences in protocol and evaluation metrics make for complicated analysis at best, simply impossible in some cases. Without inter-study comparison, it becomes hard to reach any conclusion on the overall and added value of an HRTF selection, synthesis, or learning method. The objective of this chapter is to lay the foundations of such a standard.
One of the most frequent causes of auditory space distortion in binaural rendering is related to the use of
In practice, most users will end up experiencing binaural rendering using an HRTF that is not their own, as in the case of a non-individual HRTF, generally taken from an existing database. Presently, measuring an individual’s HRTF most often requires specific equipment and access to an anechoic room. Methods exist to simulate an HRTF from geometrical head scans or morphological data, but they suffer the same drawbacks: the techniques are either too costly or burdensome to implement in practical scenarios, or they produce HRTFs that do not exactly match the individual users. As mentioned, using a non-individual HRTF, which the brain has not trained with, often results in distortions of the perceived auditory space. Researchers have been working on this issue, proposing new simulation methods, HRTF selection processes, and even HRTF training programs focused on the reduction of these distortions.
Naturally, all these lines of research end up using a localisation evaluation task to assess the benefit of new techniques. As mentioned above, there exists no standard method for this evaluation, hindering results appraisal and inter-study comparisons.
The objective of this chapter is to outline a set of metrics and propose a methodology to assess localisation performance in the context of HRTF selection and training programs. While the tools proposed can be applied to other contexts, they were designed with HRTF training in mind as not only do they assess instantaneous performance but also performance
Section 2 presents a state of the art of evaluation metrics used to assess localisation accuracy in previous studies. Section 3 introduces the proposed methodology and the set of metrics on which it is built. Section 4 is a case-study, using the methodology to re-analyse and compare the results of five contemporary experiments on HRTF learning. Section 4 concludes this chapter.
This section presents and discusses a variety of metrics and methods of analysis introduced in previous studies for the evaluation of auditory localisation performance, in the context of HRTF selection and learning. Further, it discusses what aspect of the data or human behaviour is highlighted by each metric.
The majority of the metrics used in the literature to assess localisation performance are derived from the angular distance from the source position to the participant’s response. This section discusses the most common of these metrics, their interpretation, and limitations. It builds upon the work presented in Letowski and Letowski [1].
Many auditory localisation tasks have participants indicating perceived target locations
(a) Spherical, and (b) interaural coordinate systems used in the methodology, for a source positioned at angles (55°, 46°) as defined in each coordinate system. Spherical azimuth angle
Alternatively, the
Other conventions have been proposed, such as the
Regardless of the coordinate system used, angular errors can be calculated using either the
Care must be taken in calculating signed and absolute errors because of the discontinuities in the azimuth and polar angles of the spherical and interaural coordinate systems. If a source is close to the discontinuity and the response crosses it (
Both the spherical and interaural coordinate systems introduce spatial compression at their poles. In the interaural coordinate system for example, the circumference of the cone-of-confusion at 80° lateral angle is much smaller than that of a cone at 0° lateral angle. Therefore, polar angle errors at the poles (near ±90° lateral angle) are more exaggerated than near the median plane. The same problem impacts azimuth errors near the poles (near ±90° elevation angle) for the spherical coordinate system.
Previous studies have sought to avoid the spatial compression problem altogether by limiting the analysis to targets away from the poles [12]. The downside of this method is that it limits the scope of the study’s conclusions because a large region of space cannot be studied. Still others have proposed compensation schemes, using for example the lateral angle to weight the response contribution to the average polar error [13, 14, 15]. Carlile et al. [13] for example weighted polar response errors using the cosine of the target lateral angle, decreasing response contributions as targets moved towards the interaural axis. This method more accurately reflects the arc length between the target and response locations on the circle, keeping in mind that this weighting does not take the lateral angle of the response into account.
Due to the discontinuities and spatial compression in the angular metrics of the typical coordinate systems, some work has simply examined the distance between the participant responses and the true target positions to assess the extent of localisation error. The most basic method, the
Great-circle error on its own does not provide information about the direction of the response. Paired with the
To perform statistical analyses of the localisation accuracy, the variance in the response locations must be quantified [19, 20] . Given the two-dimensionality of the data, previous work has used Kent distributions on a sphere [17, 21] to determine ellipses that portray the variance of the data along major and minor axes of the spread of the responses. With Kent distributions, circular statistical tests may be conducted to evaluate the significance of the distance between the centroid of the responses and the target location (such as the Rayleigh
The
Rather than looking at single or mean error values to assess localisation accuracy, Hofman et al. [23] and Trapeau et al. [24] studied the linear regression between targets and responses elevation angles. Termed “elevation gain”, the slope of this regression provides a higher level metric that can be used to detect compression or dilation effects in participant responses. Van Wanrooij and Van Opstal [25] extended this technique, applying the regression on target versus response azimuth as well as elevation angles. To account for azimuthal dependence of the elevation gain, they also introduced the notion of “local elevation gain”, averaging elevation gain values based on a sliding azimuthal window. This metric allows the assessment of how elevation compression and dilation effects impact different regions of the sphere.
An analysis based on angular distances alone would fail to distinguish local accuracy misinterpretations from critical space confusions, where responses are often on the opposite hemisphere from target positions. These kinds of errors are very common in studies using non-individualised HRTFs [8, 10, 26, 27], though they also occur when listening with one’s own ears or HRTF [5].
One of the simplest techniques is that used by Honda et al. [28], which defines a hit-miss criterion based on a threshold great-circle error value. Though intuitive, the method does not provide much information on the nature or potential origin of the confusions.
A slightly more elaborate form of confusion classification was used by Middlebrooks [12], which flags responses as confusions when they are in a different hemisphere than that of the target. To avoid reporting small local accuracy errors as confusions for targets near the hemispheres limits, only those responses with polar angle errors greater than 90° were considered when searching for confusions. The classification thus resulted in three types of “quadrant confusions”: front-back, up-down, and left-right. Majdak et al. [14] further improved the definition, introducing a weighting factor to compensate for polar angle compression near the interaural axis. A comparable strategy was adopted by Carlile et al. [13], excluding from confusion checks those targets too close to the interaural axis.
A parallel classification was proposed by Martin et al. [29], determining confusion types based on cone-of-confusion angle values rather than sphere quadrants. The classification was further refined by Yamagishi and Ozawa [30], Parseihian and Katz [8] and Zagala et al. [16], adding “precision” and “combined” confusions to the already existing confusion types. This classification is discussed in more detail in Section 3.1.4.
Given the relatively high incidence of front-back confusions in non-individual HRTF localisation tasks, results often exhibit a bi-modal distribution [10]. Analyses applied to data that contain a large portion of front-back confusions will have large variance and potentially inaccurate averages. The other confusion types also have a similar, if somewhat less characteristic, impact on the data, artificially inflating localisation errors. As such, it is common practice to split the data to analyse confusions separately from
Another approach that preserves the sample size of the data consists of ‘folding’ the responses into the same subspace as that of the target prior to the analysis. This technique has only ever been applied to mirror front-back confusions [18], as it may only apply to very specific circumstances and tends to inflate the power of the resulting conclusions [1].
Several studies have shown variations in localisation accuracy as a function of region on the sphere due to, amongst other things, cue interpretation [3] or reporting method [32]. In these cases, decomposition schemes were used to better characterise those variations and understand their origins. As mentioned in Section 2.1.5, Van Wanrooij and Van Opstal [25] for example decomposed the analysis of elevation gain across azimuthal regions. Later, Majdak et al. [14] proposed an analysis split into hemi-fields to detect higher accuracy variations for targets in the rear region. Middlebrooks [12] applied a similar spatial decomposition to detect high variability for responses in the upper-rear quadrant, temporarily excluding them from the analysis to better assess variations in remaining regions. The principal drawback of decomposition is that it reduces the statistical power of the analysis, and can result in unbalanced data sets if responses are not evenly spread across the regions under consideration.
For the evaluation of HRTF learning, it is essential to assess the progression of participant performance over multiple sessions. On the assumption that any adaptation to an HRTF is a process with diminishing returns with repeated training sessions, localisation performances may be modelled as an exponential decay
Analysis of performance evolution can be performed per condition (grouping participants) [8, 10] or per participant [23]. Participant performance evaluation makes it harder to draw general conclusions, but potentially provides deeper insight into performance as not all participants exhibit the same ability to adapt to a new HRTF [24]. This adaptation capacity appears to be a function of initial HRTF affinity or “perceptual quality” [10]. For inter-study comparisons, some form of performance scaling or normalisation may first be required to compensate for such affinities, highlighting performance improvement rather than absolute value [10].
From the literature review in the previous section, a methodology is derived for assessing binaural localisation accuracy. Though it was designed with a focus on HRTF training programs, it should be applicable to any HRTF-related study interested in localisation performance assessment. Section 3.1 introduces the conventions and metrics used in the methodology, itself detailed in Section 3.2. The metrics proposed along with the notions they examine are summarised in Table 1 at the end of this section. A MATLAB toolbox for the evaluation of all the metrics discussed here is available online3.
Name | Notion examined |
---|---|
Space coverage statistic | Density and homogeneity of the evaluation grid |
Confusion rates | Percentage of errors resulting from cone-of-confusion or quadrant ambiguities |
Great-circle error | Overall localisation accuracy |
Local great-circle error | overall localisation accuracy, excluding confusions |
Local lateral error | Localisation accuracy in the horizontal plane, excluding confusions |
Local polar error | Localisation accuracy in the vertical plane, excluding confusions |
Local azimuth error | Localisation accuracy in the horizontal plane, excluding confusions |
Local elevation error | Localisation accuracy in the vertical plane, excluding confusions |
Local lateral compression | Whether localisation errors are distorted systematically towards the median plane |
Local elevation compression | Whether localisation errors are distorted systematically towards the horizontal plane |
Local lateral bias | Whether there is a systematic rotational offset on responses around the |
Local elevation bias | Whether there is a systematic upward offset on responses, towards positive |
Per-region metrics | Decomposition of the analysis across target regions |
Local responses distribution | Whether two sets of responses, excluding confusions, belong to different spherical distributions (using Kent distribution and circular statistics) |
Summary of the evaluation metrics used in the methodology, grouped by concept similarity.
The methodology makes use of both spherical and interaural coordinate systems, illustrated in Figure 1. While the spherical coordinate system provides an intuitive perspective on the results, the interaural system has been especially designed to separate the analysis of binaural and monaural cues, as discussed in Section 2.1.1, making it a natural choice for the analysis of HRTF-related localisation performance.
Space coverage is a set of metrics,
Various test grids and associated space coverage statistics. (a) Homogeneous grid with large number of points, (b) homogeneous grid with small number of points, (c) non-homogeneous grid with small number of points, and (d) horizontal grid with small number of points.
where the perimeter is computed as the sum of the great-circle values between the cell vertices, expressed in radians. The squared value of the perimeter, as well as a
The great-circle error is defined as the minimum arc between the response and the true target position. This metric provides an intuitive way to assess the local localisation accuracy as the spherical distance between the responses and the target. Given
where smaller values correspond to better localisation performances.
The angular direction is coupled to the great circle to enable vector summation of target to response arcs on the sphere. The direction towards the right ear constitutes the positive pole in the interaural coordinate system. The angular direction may then be calculated from the interaural coordinates as:
where
As discussed in Section 2.2, confusion classification schemes are primarily designed to separate small localisation errors from larger errors caused by erroneous localisation behaviours typically observed in binaural localisation tasks. The scheme used in the methodology is designed around notions borrowed from both cone-of-confusion [8, 10, 16, 29] and sphere quadrant [12, 14] classifications. It separates responses into 4 categories: those near the target (
The classification is illustrated in Figure 3a. Responses within a 45° radius cone around the target are defined as precision errors. Responses within a 45° cone around the symmetrical of the target position regarding the
Confusion type as a function of response position on the sphere, for a target at spherical coordinates (35°, 10°) and a listener facing
The proposed 45° threshold value is somewhat arbitrary, based on a segmentation of localisation error distributions of responses from previous studies [8, 9, 10]. This value can be adapted depending on the context of the study and the nominal localisation accuracy expected. To improve understanding, the evolution of confusion zones for a 20° threshold and various target position is illustrated in Figure 4. The sum of the four confusion category rates always sums to 100%.
Confusion type as a function of response position on the sphere for the proposed classification scheme with an angle threshold of 20 and a listener facing
The distinction between in- and off-cone confusions is inspired from the duplex theory [36, 37], separating responses based on whether they are caused by misinterpreting monaural cues (in-cone confusions) or binaural cues (off-cone confusions). The commonly cited front-back confusion category has been maintained, despite not having a clearly identified origin in signal symmetry, as it represents a behaviour frequently observed in localisation studies [38]. Other confusion categories have been considered for this scheme, such as up-down or combined up-down-front-back confusions. They have been discarded however, as their representative patterns were not prevalent in the
Compared to traditional cone-of-confusion classifications defined using only polar angle [8, 10, 16, 29], the main drawback of the proposed scheme is that it is susceptible to ITD mismatch. By only looking at the difference in
An attempt was made to propose a new scheme, inspired by the one used in Stitt et al. [10], alleviating the pole issue by increasing the (polar) spread of the precision zone as targets near the poles, constraining said spread to always span 45 of great-circle angle when projected on the sphere. As illustrated in Figure 3c, this constraint results in a undesirable warping of the precision error zone for targets within a certain lateral distance from the poles.
The solution proposed for studies needing a classification based on monaural cues interpretation alone is to extend the proposed scheme, artificially adjusting the lateral position of targets prior to the classification to discard errors related to ITD mismatch. This adjustment can be made on a per-participant/target basis, replacing the lateral angle of targets by the mean lateral angle of their associated responses prior to the classification. It can also be performed on a per-response basis by simply assuming that targets and responses always have the same lateral position. The case study of Section 4 uses the second, simple, non-adaptive form of the classification scheme.
Lateral and polar errors are defined as the absolute difference between target and response positions in interaural coordinates. They are used to project localisation errors onto spatial dimensions associated with separate cues in the HRTF, allowing for an analysis of their independent contribution to the overall performance. Both are defined in [0°:180°], where smaller values correspond to better localisation performances. In the methodology, lateral and polar errors will be evaluated only on responses classified as
As mentioned in Section 2.1.3, compression at the poles will lead to artificially inflated polar errors for targets near the interaural axis. A weight, proportional to the target lateral position, can be applied to the polar error to compensate for the compression, defining the
This weight is designed so that, for a target and a response that share the same lateral angle, the polar error weighted is equal to the arc length (great-circle) that separates them, regardless of said lateral angle. Note that while lateral error is not impacted by pole compression, it ‘folds’ near the interaural axis: random responses will overall have a lower local lateral error for targets in this region. This is a valuable feature of the interaural system when assessing the symmetric contribution of binaural cues (ITD/ILD) to localisation error. It can nonetheless lead to artificially deflated lateral errors when used in a different context.
Azimuth and elevation errors are defined as the absolute difference between target and response positions in spherical coordinates. They correspond to a more traditional projection of spherical coordinates, more intuitive yet no longer guided by auditory cue separation. Like interaural errors, azimuth and elevation errors are defined [0°:180°] and will be used only for local precision evaluation. As for polar error, azimuth error compression near the poles can be compensated for, defining the
In addition to absolute errors,
Finally, lateral and elevation
The decomposition of the analysis in sphere regions depends on the context. As such, there exists no one ideal decomposition scheme. To support the case study presented in the next section, the sphere will be split into 6 regions:
The methodology is proposed as a set of analysis steps, each building on the previous one to provide a comprehensive assessment of participants localisation performance.
The first step of the analysis is to assess how much of the space,
Atypical evaluation grids and their potential impact on participant results should also be discussed here. An evaluation on frontal field positions alone is likely to result in better overall performance compared to one encompassing the whole sphere, due to known variations of perceptual accuracy across sphere regions [5]. When using such grids, reporting metrics chance rates,
Finally, the stimulus characteristics (type, duration,
The objective here is to get a rough overview of participant performance during the localisation task, simply answering the question “how far were responses from the true target position?”. The assessment is based on the great-circle error as defined in Section 3.1.3.
The next step consists in separating small precision errors from critical confusions. The nature and types of confusions is characterised early on as they can have a critical impact on localisation performance, often far more detrimental than local localisation accuracy issues. This characterisation is performed using one of the classification methods defined in Section 3.1.3.
This next step takes a closer look at responses classified as precision errors,
Whether or not this step should be included in the analysis, and which metrics it should make use of, depends on the context of the study. An experiment focusing on perceptual ITD adjustment for example would likely make use of both local lateral error as well as lateral compression. A training program attempting to fine tune participant interpretation of monaural cues would on the other hand base its evaluation on the local polar error. For some studies, this decomposition will not make sense and should be avoided to limit Type I error inflation.
This final step consists in repeating all of the above, decomposing the analysis based on target positions to assess how participants fared in specific regions of the sphere. Given the loss of statistical power and the additional clutter that this analysis represents, it only needs to apply to those studies interested in characterising spatial imbalances in performance. The decomposition can then be performed using either a sphere splitting scheme as the one described in Section 3.1.6, or on a per-target position basis. For example, this approach can be used to support the design of HRTF learning programs that would focus dynamically on those regions/confusions that are the most problematic [9].
To further characterise local localisation behaviours, the analysis can be completed by evaluating average response positions and spherical response distributions. The former, computed by summing local great-circle error
The methodology defined in the previous section is applied here to build a comparative analysis on a selection of studies, focusing on the use of, and adaptation to, binaural cues for auditory localisation. The objective of this case study collection is not so much to present a thorough comparison of these studies as to illustrate how the methodology can be applied to a practical use case, and how its constituting metrics react to concrete scenarios. To further focus the case-study on these points, significance assessment is based on the overlapping of estimated distributions Confidence Intervals (CIs) rather than on null-hypothesis tests [39].
Several studies of the impact of HRTF training on localisation accuracy have been selected from existing literature, for which authors graciously provided raw participant data used in the comparative analysis. A short description of each study is provided in the next section, reporting only those elements that concern the present analysis.
Common to most of the presented studies is the notion of HRTF
Majdak et al. [14], a 2010 study on the impact of various reporting methods during training with their individual HRTF. 10 participants trained on auditory localisation: 5 reporting perceived localisation positions with their hand, 5 with their head. Each participant completed 600–2200 localisation trials over a span of 2–32 d. Training and evaluation were performed within each trial: a session was composed of 50 trials, completed in 20–30 min. Each trial consisted of a localisation task with feedback, testing participants on 1380 positions overall, distributed on a sphere, using a 500 ms burst of white noise as stimulus. As the reporting method proved to have only a small impact on training efficiency, the 10 participants have hereafter been aggregated in a single group (
Parseihian and Katz [8], a 2012 study on accommodation to non-individual HRTF. 12 participants trained on auditory localisation, each completing 3 sessions of 12 min each on 3 consecutive days. Each session consisted of an interactive audio localisation game followed by a localisation task evaluation testing participants on 25 positions distributed on a sphere, using a 180 ms sequence of white noise bursts as stimulus. Before training, each participant ranked a set of 7
Stitt et al. [10], a 2019 study on accommodation to non-individual HRTF. 16 participants trained on auditory localisation, each completing 10 sessions of 12 min each over a span of 10–20 weeks. The worst-match HRTF selection, training game, stimulus, and tested audio source positions during the localisation task evaluation at the end of each training session were the same as those of
Steadman et al. [15], a 2019 study on accommodation to non-individual HRTF. 27 participants trained on auditory localisation, each completing 9 sessions of 12 min each over a span of 3 d. A localisation task evaluation was conducted at the beginning and end of each day as well as between each training session the first day, testing participants on 12 positions distributed on a sphere using a 1.6 s stimulus merging bursts of white noise and speech signal. All participants trained with the same randomly-matched HRTF selected from the 7 LISTEN database of
Poirier-Quinot and Katz [9], a 2021 study on accommodation to non-individual HRTF. 12 participants trained on auditory localisation (
In all these experiments, the training sessions lasted for 12 min, except for
In the analysis, evaluation sessions are numbered from 1 to 11, each separated by a 12 min training.
The space coverage of target positions evaluated during the localisation task of each study are reported in Figure 5. The high density of the grid of
Space coverage statistics of the evaluation task in the selected studies (a) majdak, (b) parseihian/stitt, (c) poirier and (d) steadman.
As could be expected, all the grids present high
Two different reporting methods were used in the five studies: head pointing (
The evolution of great-circle angle error across studies and training sessions is reported in Figure 6. Besides the clear benefit of training observed in all studies, the metric also highlights the overall positive impact of HRTF quality on initial performance. Interestingly, while the results from
Great-circle error mean and CI evolution across sessions and experiments. The great-circle error value for random responses is of ≈90° for all experiments.
Much like the great-circle error, precision confusion rates can be used to assess performance evolution during training, as illustrated in Figure 7. Trends observed on initial precision rates and their evolution reflect the observation made on the great-circle error analysis. Precision rates and great-circle angle values are indeed highly correlated across training sessions, with correlation coefficients in [
Precision confusion rates mean and CI evolution across sessions and experiments.
This widening of the CIs is particularly apparent in the comparison of the other confusion rates, reported in Figure 8 for the evaluation that took place after the first training session. While a trend indeed suggests that the amount of confusions increases with decreasing HRTF quality, overlapping CIs often prevent any definite conclusion. Observing these rates can still help inform the analysis, as the poor performance of
Confusion rates after the first training session across experiments.
Maybe the most interesting use of confusion rates is to decompose the overall performance evolution. As illustrated by its confusion rate evolution in Figure 9,
Confusion rates mean and CI evolution across sessions for
Results of the confusion classification indicate that roughly 50% of responses were within the vicinity of the target (precision errors) after the first training session across experiments. The analysis here focuses on these responses, assessing local accuracy issues to complete that on localisation confusions.
Figure 10 reports local great-circle errors across training sessions and experiments. Looking once more at
Local great-circle error mean and CI evolution across sessions and experiments.
Local lateral error evolution across sessions for all experiments is reported in Figure 11a. As expected, initial performances indicate that participants using individual HRTF were quite apt at lateral localisation, accustomed as they were to the presented ITD and ILD cues.
(a) local lateral error, and (b) local lateral compression evolution across sessions and experiments.
Participants trained with individual HRTF did not improve much on local lateral error overall, starting at
Focusing on local lateral compression evolution, Figure 11b reveals a systematic over-estimation of the lateral angle across experiments,
Local polar error evolution across sessions for all experiments is reported in Figure 12a. Overall performance was still a function of HRTF quality, but for
Participants (a) local polar error, and (b) local elevation compression across training sessions and experiments.
The analysis of local elevation compression also reveals a stronger tendency to under-estimate target elevation,
This section illustrates how splitting results analysis across sphere regions might highlight spatial imbalances in performance. To avoid further cluttering the chapter, only two example decompositions will be presented: confusion rates based on sphere regions, and local great-circle error based on individual target locations.
Decomposition of confusion rates based on the regions defined in Section 3.1.6 is illustrated Figure 13. Results displayed are aggregated over all five studies, to focus the analysis on general binaural localisation behaviours. The first noticeable result is that targets in the front-down region were the most susceptible to front-back and in-cone confusions initially, resulting in a very low precision rate (30% vs. 47% and more for the other regions) prior to the first training session. Interestingly, confusion rates in the front-down region were systematically higher than those in the front-up region, for all but off-cone confusions. The initial rate of front-back confusions of targets in front of participants, more than twice that of targets behind them, is likely due to the absence of visual feedback during the localisation task, increasing likelihood of perceiving a sound as behind if they cannot see its source, regardless of HRTF cues.
Evolution of confusion rates across sessions, decomposed based on sphere regions, aggregated over all experiments.
A second interesting result is the negligible evolution of front-back confusions for targets in the back regions throughout training (
These observations suggest that future training programs could be improved by focusing slightly more on reducing front-back and in-cone confusions in the front-down region. Stagnating rates, such as that of front-back confusions in the back-up region, around 15% across sessions, would also suggest that there is room for improvement in the design of didactic training programs that would aid participants towards reaching 0% confusion rates.
Further refining the analysis, Figure 14 focuses on the assessment of mean response locations for each target presented in
Evolution of mean response locations across targets and sessions in
This additional step in the analysis can be seen as an extension of the evaluation task characterisation proposed in Section 4.2.2 specific to the assessment of localisation performance
Techniques have been proposed to conduct training efficiency analysis on unbalanced initial conditions. Stitt et al. [10] for example applied per-participant arithmetic normalisation, based on group baseline performances. Realigning initial conditions, this technique allows to focus the analysis on relative improvement, as illustrated in Figure 15.
Great-circle error evolution across sessions and experiments. Data normalised (subtraction) with group mean results of session 2 as reference.
Another technique for relative improvement comparison, used for example by Majdak et al. [31] and Poirier-Quinot and Katz [9], is to compare the coefficients of a regression applied on performance evolution. As mentioned in Section 2.3.2, two main regression models have been adopted to fit said evolution depending on the training stages represented in the data. Figure 16 illustrates how both can be fitted to local great-circle error evolution across experiments. Groups performance evolution was first fitted to the exponential form in Figure 16a, resorting to the linear form in (b) when the evolution did not follow an exponential form, resulting in regression parameters CIs so wide as to prevent any meaningful interpretation. The use of a regression is particularly attractive, as it reduces the performance evolution analysis to a simple high level coefficient comparison, coefficients that can usually be interpreted in simple terms such as initial performance or improvement rate.
Regressions on local great-circle error evolution across training and experiments, (a) exponential regression “
As mentioned, these techniques are generally applied to compensate for unbalanced initial performance. Although they are perfectly valid to assess the impact of HRTF quality or training efficiency on
As illustrated throughout Section 4.2, drawing clear cut conclusions from the comparison of results from several studies is difficult at best. Most of the time, it is simply impossible, generally because of uncontrolled variations across test conditions. These variations, limiting both intra- and inter-study analysis, are discussed in this section.
Variations in the evaluation protocols and procedures between studies in the literature present a challenge for comparing the multiple experiments. Different experimental design choices, such as reporting method, spectral content and duration of the stimulus, and evaluation grid, have a direct impact on the baseline performance of participants [32]. For example, given the choice by
The use of a unique grid for localisation tasks across studies would assuredly simplify results comparisons. Said grid could, for example, be designed to be homogeneously distributed on the sphere [35]. For more flexible test conditions, a series of test grids of increasing point densities could be defined, where test positions of any given grid would be present on its higher density neighbours, easing down-sampling for comparison. Regarding the stimulus used or the reporting method, a simple solution would be to settle on those that respectively optimise localisation accuracy [47] and minimise reporting bias [32]. Pending the adoption of common practices, the bias induced by those design choices could technically be assessed from the results of a control group using individual HRTFs.
Another issue when comparing performance evolution across studies is the alignment of the evaluation sessions for fair comparison. As proposed in Section 4.2.1, a simple solution is to align them based on training duration. Time alignment would seem a better option than its alternative, based on the number of positions presented during the training. Time is of direct interest for end-users, and an alignment based on presented positions would bias the analysis in favour of slower exploratory training paradigms.
Finally, the merging of both evaluation and training sessions, as used in
Variations between participants’ performance is an issue common to most psychophysical studies studies. Two aspects of these variations can become critical in the context of HRTF learning studies.
The first aspect concerns imbalances in initial participant performance across tested conditions. As discussed in Section 4.2.8, such imbalance is likely to weaken or void conclusions resulting from the analysis. For within experiment comparisons, a simple solution is to run a pre-training evaluation session, to then create groups of equivalent performance based on the metrics used in the analysis. The problem naturally worsens when dealing with inter-study analysis. The use of a control group using individual HRTF is again advised to serve as a baseline reference for the comparative analysis.
The second aspect concerns the difference in participants’ immediate sensitivity to HRTF quality, and their ability to adapt to a non-individual HRTF. Both have been discussed in previous studies, where some participants were more prone to instantly benefit from a best-match HRTF [49] or to adapt to a poorly matched HRTF [10]. To avoid missing out on interesting behaviours due to the variance introduced by some participants, it is recommended to conduct a second pass of the analysis on sub-groups, for example aggregated based on their improvement rate [10]. Although the conclusions from the sub-group analysis may be weaker compared to an overall analysis, the technique provides readers with a more thorough understanding of the training as well as the potential advantages and limitations of the tested conditions.
In the present context, procedural learning refers to participants becoming familiar with the various aspects of the localisation task, resulting in a performance improvement that is not due to an accommodation to HRTF specific cues (perceptual learning). As of yet, there exists no model for
Results of control groups generally prove extremely valuable during inter-study comparison. Participants only taking part in the evaluation and not the training, as in
Additionally, simple experimental design choices can be applied to avoid having to deal with certain forms of procedural training. The proprioceptive adjustment required for accurately reporting perceived positions [14] can for example be greatly accelerated by using a natural 3D reporting method coupled to a visual pointer [9], as well as providing a reference grid to help orientation in the sphere [31]. Thorough beta testing can further eliminate design flaws that participants can exploit to improve their performance, such as the use of too small a set of test positions, or unconstrained tracking allowing for small head movements during the stimulus presentation phase of the localisation task.
Other aspects of procedural training, such as having participants focus on the listening task, can only be removed by introducing a pre-experimental training session. Such a session was applied in
Overall, it is reasonable to assume that one could design a pre-training session that accommodates procedural learning in roughly 15 min, even taking into account this last point, and relaxing the time constraint imposed in
This chapter presented a methodology for the assessment of auditory localisation accuracy in the context of HRTF selection and learning tasks. Based on existing metrics and decomposition schemes, the methodology consists of a series of steps guiding analysis towards the creation of comprehensive and repeatable performance assessments. A collected case-study was then proposed that compared the results of five contemporary experiments on HRTF learning and illustrates how the methodology can be applied to better understand participant performances and their evolution.
The initial intent of this chapter was to propose a set of metrics and an analysis workflow that would be adopted and adapted by the community to standardise the evaluation of localisation performance. In time, the standardisation would help simplify the comparison of results from different studies, allowing to assess hypotheses and draw conclusions beyond the scope of the constituting studies. While the proposed case-study provides a glimpse at the benefits of such standardisation, it is limited by one of, if not the most, major issue of inter-study comparison: the lack of a reference between tested conditions. Without this reference, conclusions drawn from the analysis can hardly be generalised, much like those that would result from a comparison between language learning techniques without
As of now, the only applicable solution to provide such reference across studies is to systematically add a control group composed of participants using their own HRTF to the experiment. A large enough group composed of experts and novices alike would indeed provide a stable reference that can be used to assert a certain equivalence in
With luck, the issue will solve itself as the next generation of HRTF individualisation techniques render selection and training obsolete. In the meantime, methodologies such as the one proposed here should help improve the rigour of studies and consequently the understanding of the fundamental issues regarding auditory localisation and spatial hearing accommodation to non-individual HRTFs and their applications.
This work was funded in part through a fundamental research collaboration partnership between Sorbonne Université, CNRS, Institut
Intro
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. 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To achieve such properties, these structures are based on techniques that allow detection and diagnosis of failures in real time. Detection signals faults and diagnostics provide the root cause and location. Fault detection is based on signal and process mathematical models, while fault diagnosis is focused on systems theory and process modeling. Monitoring and supervision complement each other in fault management, thus enabling normal and continuous operation. Its application avoids stopping productive processes by early detection of failures and by applying real-time actions to eliminate them, such as predictive and proactive maintenance based on process conditions. The integration of all these methodologies enables intelligent monitoring and supervision systems, enabling real-time fault detection and diagnosis. Their high performance is associated with statistical decision-making techniques, expert systems, artificial neural networks, fuzzy logic and computational procedures, making them efficient and fully autonomous in making decisions in the real-time operation of a production system.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Gustavo Pérez Alvarez",authors:[{id:"216192",title:"Dr.",name:"Gustavo",middleName:"Pérez",surname:"Alvarez",slug:"gustavo-alvarez",fullName:"Gustavo Alvarez"}]},{id:"65244",doi:"10.5772/intechopen.83810",title:"Fault Diagnosis Techniques for a Wind Turbine System",slug:"fault-diagnosis-techniques-for-a-wind-turbine-system",totalDownloads:1283,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Silvio Simani and Paolo Castaldi",authors:[{id:"209626",title:"Prof.",name:"Silvio",middleName:null,surname:"Simani",slug:"silvio-simani",fullName:"Silvio Simani"},{id:"209627",title:"Dr.",name:"Paolo",middleName:null,surname:"Castaldi",slug:"paolo-castaldi",fullName:"Paolo Castaldi"}]},{id:"70067",doi:"10.5772/intechopen.90157",title:"Analytic Prognostic in the Linear Damage Case Applied to Buried Petrochemical Pipelines and the Complex Probability Paradigm",slug:"analytic-prognostic-in-the-linear-damage-case-applied-to-buried-petrochemical-pipelines-and-the-comp",totalDownloads:2880,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"In 1933, Andrey Nikolaevich Kolmogorov established the system of five axioms that define the concept of mathematical probability. This system can be developed to include the set of imaginary numbers by adding a supplementary three original axioms. Therefore, any experiment can be performed in the set \n\nC\n\n of complex probabilities which is the summation of the set \n\nR\n\n of real probabilities and the set \n\nM\n\n of imaginary probabilities. The purpose here is to include additional imaginary dimensions to the experiment taking place in the “real” laboratory in \n\nR\n\n and hence to evaluate all the probabilities. Consequently, the probability in the entire set \n\nC\n=\nR\n+\nM\n\n is permanently equal to one no matter what the stochastic distribution of the input random variable in \n\nR\n\n is; therefore the outcome of the probabilistic experiment in \n\nC\n\n can be determined perfectly. This is due to the fact that the probability in \n\nC\n\n is calculated after subtracting from the degree of our knowledge the chaotic factor of the random experiment. Consequently, the purpose in this chapter is to join my complex probability paradigm to the analytic prognostic of buried petrochemical pipelines in the case of linear damage accumulation. Accordingly, after the calculation of the novel prognostic model parameters, we will be able to evaluate the degree of knowledge, the magnitude of the chaotic factor, the complex probability, the probabilities of the system failure and survival, and the probability of the remaining useful lifetime; after that a pressure time t has been applied to the pipeline, which are all functions of the system degradation subject to random and stochastic influences.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Abdo Abou Jaoude",authors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé"}]},{id:"65747",doi:"10.5772/intechopen.82781",title:"Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB",slug:"prognostics-102-efficient-bayesian-based-prognostics-algorithm-in-matlab",totalDownloads:1359,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"An efficient Bayesian-based algorithm is presented for physics-based prognostics, which combines a physical model with observed health monitoring data. Unknown model parameters are estimated using the observed data, from which the remaining useful life (RUL) of the system is predicted. This paper focuses on the Bayesian method for parameter estimation of a damage degradation model where epistemic uncertainty in model parameters is reduced with the observed data. Markov-chain Monte Carlo sampling is used to generate samples from the posterior distribution, which are then propagated through the physical model to estimate the distribution of the RUL. A MATLAB script of 76 lines is included in this paper with detailed explanations. A battery degradation model and crack growth model are used to explain the process of parameter estimation, the evolution of degradation and RUL prediction. The code presented in this paper can easily be altered for different applications. This code may help beginners to understand and use Bayesian method-based prognostics.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Ting Dong, Dawn An and Nam H. Kim",authors:[{id:"278745",title:"Prof.",name:"Nam-Ho",middleName:null,surname:"Kim",slug:"nam-ho-kim",fullName:"Nam-Ho Kim"},{id:"285438",title:"Ms.",name:"Ting",middleName:null,surname:"Dong",slug:"ting-dong",fullName:"Ting Dong"},{id:"285439",title:"Dr.",name:"Dawn",middleName:null,surname:"An",slug:"dawn-an",fullName:"Dawn An"}]},{id:"68233",doi:"10.5772/intechopen.88217",title:"Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques",slug:"fault-detection-of-single-and-interval-valued-data-using-statistical-process-monitoring-techniques",totalDownloads:731,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Principal component analysis (PCA) is a linear data analysis technique widely used for fault detection and isolation, data modeling, and noise filtration. PCA may be combined with statistical hypothesis testing methods, such as the generalized likelihood ratio (GLR) technique in order to detect faults. GLR functions by using the concept of maximum likelihood estimation (MLE) in order to maximize the detection rate for a fixed false alarm rate. The benchmark Tennessee Eastman Process (TEP) is used to examine the performance of the different techniques, and the results show that for processes that experience both shifts in the mean and/or variance, the best performance is achieved by independently monitoring the mean and variance using two separate GLR charts, rather than simultaneously monitoring them using a single chart. Moreover, single-valued data can be aggregated into interval form in order to provide a more robust model with improved fault detection performance using PCA and GLR. The TEP example is used once more in order to demonstrate the effectiveness of using of interval-valued data over single-valued data.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Mohammed Ziyan Sheriff, Nour Basha, Muhammad Nazmul Karim, Hazem Nounou and Mohamed Nounou",authors:[{id:"21281",title:"Prof.",name:"Hazem",middleName:"Numan",surname:"Nounou",slug:"hazem-nounou",fullName:"Hazem Nounou"},{id:"21282",title:"Prof.",name:"Mohamed N.",middleName:null,surname:"Nounou",slug:"mohamed-n.-nounou",fullName:"Mohamed N. Nounou"},{id:"191340",title:"Mr.",name:"M. Ziyan",middleName:null,surname:"Sheriff",slug:"m.-ziyan-sheriff",fullName:"M. Ziyan Sheriff"},{id:"191345",title:"Prof.",name:"M. Nazmul",middleName:null,surname:"Karim",slug:"m.-nazmul-karim",fullName:"M. 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The purpose here is to include additional imaginary dimensions to the experiment taking place in the “real” laboratory in \n\nR\n\n and hence to evaluate all the probabilities. Consequently, the probability in the entire set \n\nC\n=\nR\n+\nM\n\n is permanently equal to one no matter what the stochastic distribution of the input random variable in \n\nR\n\n is; therefore the outcome of the probabilistic experiment in \n\nC\n\n can be determined perfectly. This is due to the fact that the probability in \n\nC\n\n is calculated after subtracting from the degree of our knowledge the chaotic factor of the random experiment. Consequently, the purpose in this chapter is to join my complex probability paradigm to the analytic prognostic of buried petrochemical pipelines in the case of linear damage accumulation. Accordingly, after the calculation of the novel prognostic model parameters, we will be able to evaluate the degree of knowledge, the magnitude of the chaotic factor, the complex probability, the probabilities of the system failure and survival, and the probability of the remaining useful lifetime; after that a pressure time t has been applied to the pipeline, which are all functions of the system degradation subject to random and stochastic influences.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Abdo Abou Jaoude",authors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé"}]},{id:"65244",title:"Fault Diagnosis Techniques for a Wind Turbine System",slug:"fault-diagnosis-techniques-for-a-wind-turbine-system",totalDownloads:1283,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Silvio Simani and Paolo Castaldi",authors:[{id:"209626",title:"Prof.",name:"Silvio",middleName:null,surname:"Simani",slug:"silvio-simani",fullName:"Silvio Simani"},{id:"209627",title:"Dr.",name:"Paolo",middleName:null,surname:"Castaldi",slug:"paolo-castaldi",fullName:"Paolo Castaldi"}]},{id:"65747",title:"Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB",slug:"prognostics-102-efficient-bayesian-based-prognostics-algorithm-in-matlab",totalDownloads:1359,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"An efficient Bayesian-based algorithm is presented for physics-based prognostics, which combines a physical model with observed health monitoring data. Unknown model parameters are estimated using the observed data, from which the remaining useful life (RUL) of the system is predicted. This paper focuses on the Bayesian method for parameter estimation of a damage degradation model where epistemic uncertainty in model parameters is reduced with the observed data. Markov-chain Monte Carlo sampling is used to generate samples from the posterior distribution, which are then propagated through the physical model to estimate the distribution of the RUL. A MATLAB script of 76 lines is included in this paper with detailed explanations. A battery degradation model and crack growth model are used to explain the process of parameter estimation, the evolution of degradation and RUL prediction. The code presented in this paper can easily be altered for different applications. 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Fault detection is based on signal and process mathematical models, while fault diagnosis is focused on systems theory and process modeling. Monitoring and supervision complement each other in fault management, thus enabling normal and continuous operation. Its application avoids stopping productive processes by early detection of failures and by applying real-time actions to eliminate them, such as predictive and proactive maintenance based on process conditions. The integration of all these methodologies enables intelligent monitoring and supervision systems, enabling real-time fault detection and diagnosis. Their high performance is associated with statistical decision-making techniques, expert systems, artificial neural networks, fuzzy logic and computational procedures, making them efficient and fully autonomous in making decisions in the real-time operation of a production system.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Gustavo Pérez Alvarez",authors:[{id:"216192",title:"Dr.",name:"Gustavo",middleName:"Pérez",surname:"Alvarez",slug:"gustavo-alvarez",fullName:"Gustavo Alvarez"}]},{id:"69286",title:"Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems",slug:"probabilistic-methods-for-cognitive-solving-of-some-problems-in-artificial-intelligence-systems",totalDownloads:809,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"As a result of the analysis of dispatcher intelligence centers and aerial, land, underground, underwater, universal, and functionally focused artificial intelligence robotics systems, the problems of rational control, due to be performed under specific conditions of uncertainties, are chosen for probabilistic study. The choice covers the problems of planning the possibilities of functions performance on the base of monitored information about events and conditions and the problem of robot route optimization under limitations on risk of “failure” in conditions of uncertainties. These problems are resolved with a use of the proposed probabilistic approach. The proposed methods are based on selected probabilistic models (for “black box” and complex systems), which are implemented effectively in wide application areas. The cognitive solving of problems consists in improvements, accumulation, analysis, and use of appearing knowledge. 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He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. 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She holds a degree in Dentistry from the Federal University of Alfenas (UNIFAL), while her specialization and professional improvement in Stomatology took place at Hospital Heliopolis (São Paulo, SP). Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). 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Her Ph.D. research work on the soft tissue-implant interface at the University of Sheffield has yielded several important publications in the key implant journals. She was awarded an Excellent Exchange Award by the University of Sheffield which gave her the opportunity to work at the famous Faculty of Dentistry of the University of Gothenburg, Sweden, under the tutelage of Prof. Peter Thomsen. In 2016, she was appointed as a visiting scholar at UCLA, USA, with attachment in Hospital Dentistry, and involvement in research work related to zirconia implant. In 2016, her contribution to dentistry was recognized by the Royal College of Surgeon of Edinburgh with her being awarded a Fellowship in Dental Surgery. She has authored numerous papers published both in local and international journals. She was the Editor of the Malaysian Dental Journal for several years. 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His passion for teaching then led him to join the faculty of dentistry at University Malaya and he has since became a valuable lecturer and clinical specialist in the Department of Restorative Dentistry. He is currently the removable prosthodontic undergraduate year 3 coordinator, head of the undergraduate module on occlusion and a member of the multidisciplinary team for the TMD clinic. He has previous membership in the British Society for Restorative Dentistry, the Malaysian Association of Aesthetic Dentistry and he is currently a lifetime member of the Malaysian Association for Prosthodontics. Currently, he is also the examiner for the Restorative Specialty Membership Examinations, Royal College of Surgeons, England. He has authored and co-authored handful of both local and international journal articles. 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