Noise Impact Assessment and Prediction in Mines Using Soft Computing Techniques

Mining of minerals necessitates use of heavy energy intensive machineries and equipment leading to miners to be exposed to high noise levels. Prolonged exposure of miners to the high levels of noise can cause noise induced hearing loss besides several non-auditory health effects. Hence, in order to improve the environmental condition in work place, it is of utmost importance to develop appropriate noise prediction model for ensuring the accurate status of noise levels from various surface mining machineries. The measurement of sound pressure level (SPL) using sound measuring devices is not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular frequency dependent noise prediction models e.g. ISO 9613- 2, ENM, CONCAWE and non-frequency based noise prediction model e.g. VDI-2714 have been applied in mining and allied industries. These models are used to predict the machineries noise by considering all the attenuation factors. Amongst above mathematical models, VDI-2714 is simplest noise prediction model as it is independent from frequency domain. From literature review, it was found that VDI-2714 gives noise prediction in dB (A) not in 1/1 or 1/3 octave bands as compared to other prediction models e.g. ISO-9613-2, CONCAWE, OCMA, and ENM etc. Compared to VDI-2714 noise prediction model, frequency dependent models are mathematically complex to use. All the noise prediction models treat noise as a function of distance, sound power level (SWL), different forms of attenuations such as geometrical absorptions, barrier effects, ground topography, etc. Generally, these parameters are measured in the mines and best fitting models are applied to predict noise. Mathematical models are generally complex and cannot be implemented in real time systems. Additionally, they fail to predict the future parameters from current and past measurements. To overcome these limitations, in this work, soft-computing models have been used. It has been seen that noise prediction is a non-stationary process and soft-computing techniques have been tested for non-stationary time-series prediction for nearly two decades. Considering successful application of soft-computing models in complex engineering problems, in this thesis work, soft-computing system based noise prediction models were developed for predicting far field noise levels due to operation of specific set of mining machinery. Soft Computing models: Fuzzy Inference System (Mamdani and Takagi Sugeno Kang (T-S-K) fuzzy inference systems), MLP (multi layer perceptron or back propagation neural network), RBF (radial basis function) and Adaptive network-based fuzzy inference systems (ANFIS) were used to predict the machinery noise in two opencast mines. The proposed soft-computing based noise prediction models were designed for both frequency and non-frequency based noise prediction models. After successful application of all proposed soft-computing models, comparitive studies were made considering Root Mean Square Error (RMSE) as the performance parameter. It was observed that proposed soft-computing models give good prediction results with accuracy. However, ANFIS model gives better noise prediction with better accuracy than other proposed soft-computing models

Using Georeferenced Data in Social Science Survey Research: The Method of Spatial Linking and Its Application with the German General Social Survey and the GESIS Panel

This book demonstrates the use of georeferenced data for social science survey research which builds upon survey data enriched with geo-coordinates. It reviews the prerequisites and challenges of applying these data to different social science research questions, highlighting the different branches of an interdisciplinary effort. At the center of this presentation is the method of spatial linking: the combination of georeferenced survey data with information from auxiliary geospatial data sources. A collection of spatial linking methods is applied in this book’s empirical applications which underline these methods’ flexibility in different social science sub-disciplines, such as health and family, political attitudes, and environmental inequalities. For this purpose, georeferenced survey data from the German General Social Survey (GGSS) 2014 and the GESIS Panel are used. These empirical applications are part of an emerging field of research for social scientists, requiring new analytic skills from diverse and foreign disciplines, like ecology and engineering. Navigating the organizational and technical requirements for the analysis of georeferenced survey data enables researchers to answer new and innovative research questions.Dieses Buch beschäftigt sich mit der Nutzung georeferenzierter Daten in der sozialwissenschaftlichen Umfrageforschung, deren Ausgangspunkt Umfragedaten sind, die mit Geokoordinaten angereichert wurden. Es widmet sich den Voraussetzungen und Herausforderungen, solche Daten für verschiedene sozialwissenschaftliche Fragestellungen nutzbar zu machen und betont dabei die verschiedenen interdisziplinären Verzweigungen dieses Unterfangens. Im Mittelpunkt der Präsentation steht die Methode der räumlichen Verknüpfung: die Kombination georeferenzierter Umfragedaten mit Informationen aus externen Geodatenquellen. Anhand mehrerer, aus unterschiedlichen Subdisziplinen der Sozialwissenschaften stammender empirischer Anwendungen im Bereich Familie und Gesundheit, politische Einstellungen sowie Umwelt und Ungleichheit wird die Flexibilität der Methode in Form verschiedener räumlicher Verknüpfungen betont. Dazu werden georeferenzierte Umfragedaten der Allgemeinen Bevölkerungsumfrage Sozialwissenschaften (ALLBUS) 2014 und dem GESIS Panel 2014 verwendet. Diese empirischen Anwendungen sind Teil eines aufstrebenden Forschungsfelds für Sozialforschende, welches neue analytische Fertigkeiten aus verschiedenen anderen Fachbereichen wie der Ökologie oder des Ingenieurswesen erfordert. Werden die organisatorischen und technischen Anforderungen zur Analyse georeferenzierter Umfragedaten gemeistert, eröffnet sich Forschenden die Möglichkeit, neue und innovative Fragestellungen zu beantworten

Investigating Tafheet as a Unique Driving Style Behaviour

Road safety has become a major concern due to the increased rate of deaths caused by road accidents. For this purpose, intelligent transportation systems are being developed to reduce the number of fatalities on the road. A plethora of work has been undertaken on the detection of different styles of behaviour such as fatigue and drunken behaviour of the drivers; however, owing to complexity of human behaviour, a lot has yet to be explored in this field to assess different styles of the abnormal behaviour to make roads safer for travelling. This research focuses on detection of a very complex driver’s behaviours: ‘tafheet’, reckless and aggressive by proposing and building a driver’s behaviour detection model in the context-aware system in the VANET environment. Tafheet behaviour is very complex behaviour shown by young drivers in the Middle East, Japan and the USA. It is characterised by driving at dangerously high speeds (beyond those commonly known in aggressive behaviour) coupled with the drifting and angular movements of the wheels of the vehicle, which is similarly aggressive and reckless driving behaviour. Thus, the dynamic Bayesian Network (DBN) framework was applied to perform reasoning relating to the uncertainty associated with driver’s behaviour and to deduce the possible combinations of the driver’s behaviour based on the information gathered by the system about the foregoing factors. Based on the concept of context-awareness, a novel Tafheet driver’s behaviour detection architecture had been built in this thesis, which had been separated into three phases: sensing phase, processing and thinking phase and the acting phase. The proposed system elaborated the interactions of various components of the architecture with each other in order to detect the required outcomes from it. The implementation of this proposed system was executed using GeNIe 2.0 software, resulting in the construction of DBN model. The DBN model was evaluated by using experimental set of data in order to substantiate its functionality and accuracy in terms of detection of tafheet, reckless and aggressive behaviours in the real time manner. It was shown that the proposed system was able to detect the selected abnormal behaviours of the driver based on the contextual data collected. The novelty of this system was that it could detect the reckless, aggressive and tafheet behaviour in sequential manner, based on the intensity of the driver’s behaviour itself. In contrast to previous detection model, this research work suggested the On Board Unit architecture for the arrangement of sensors and data processing and decision making of the proposed system, which can be used to pre-infer the complex behaviour like tafheet. Thus it has the potential to prevent the road accidents from happening due to tafheet behaviour

Stories within Immersive Virtual Environments

[eng] How can we use immersive and interactive technologies to portray stories?How can we take advantage of the fact that within immersive virtual en-vironments people tend to respond realistically to virtual situations andevents to develop narrative content? Stories in such a media would allowthe participant to contribute to the story and interact with the virtualcharacters while the narrative plot would not change, or change only upto how it was decided a priori. Participants in such a narrative would beable to freely interact within the virtual environments and yet still beaware of the main trust of the stories presented. How can we preserve the‘respond as if it is real’ phenomenon induced by these technologies, butalso develop an unfolding plot in this environment? In other words, canwe develop a story, conserving the structure, its psychological and cul-tural richness and the emotional and cognitive involvement it supposes,in an interactive and immersive audiovisual space?In recent years Virtual Reality therapy has shown that an Immersive Vir-tual Environment (IVE) with a predetermined plot can be experienced asan interactive narrative. For example, in the context of Post TraumaticStress Disorder treatment, the reactions of the participants and the thera-peutic impact suggest that an IVE is a qualitatively different experiencethan classical audiovisual content. However, the methods to develop suchkind of content are not systematic, and the consistency of the experienceis only granted by a therapist or operator controlling in real time theunfolding narrative. Can a story with a strong classical plot be renderedin an automated and interactive immersive virtual environment?..[cat] Podem emprar la realitat virtual immersiva per contar històries? Com po-dem aprofitar el fet que dins dels entorns virtuals immersius les personestendeixen a respondre de manera realista a les situacions i esdevenimentsvirtuals per desenvolupar històries? Els participants en aquest tipus denarrativa podrien interactuar lliurement amb els entorns virtuals i noobstant això experimentarien les històries presentades com a plausibles iconsistents. Una història en aquest medi audiovisual permetria als parti-cipants interactuar amb els personatges virtuals i contribuir activamentals esdeveniments escenificats en l’entorn virtual. Malgrat això, la tramaestablerta a priori no canviaria, o canviaria només dins els marges es-tablerts per l’autor. Com podem preservar el fet que hom tendeix a "re-spondre com si fos real" induït per aquestes tecnologies mentre desenvolu-pem una trama en aquests entorns? En altres paraules, podem desenvolu-par una història conservant-ne l’estructura, la riquesa cultural i psicolò-gica i la implicació emocional i cognitiva que suposa, en una realitatvirtual immersiva i interactiva?Recentment la teràpia de realitat virtual ha mostrat que un entorn vir-tual amb un guió preestablert pot ser percebut com una narració inter-activa. Per exemple, en el context del tractament de Trastorns per EstrèsPostraumàtic, les reaccions i impactes terapèutics suggereixen que pro-voca una sensació de realitat que en fa una experiència qualitativamentdiferent als continguts audiovisuals clàssics. No obstant això, la consistèn-cia de l’experiència tan sols pot ser garantida si un un terapeuta o op-erador controla en temps real el flux dels esdeveniments constituint elguió narratiu. Podem representar un guió clàssic en un entorn virtualautomatitzat?..

A new methodology for modelling urban soundscapes: a psychometric revisitation of the current standard and a Bayesian approach for individual response prediction

Measuring how the urban sound environment is perceived by public space users, which is usually referred as urban soundscape, is a research field of particular in terest for a broad and multidisciplinary scientific community besides private and public agencies. The need for a tool to quantify soundscapes would provide much support to urban planning and design, so to public healthcare. Soundscape liter ature still does not show a unique strategy for addressing this topic. Soundscape definition, data collection, and analysis tools have been recently standardised and published in three respective ISO (International Organisation for Standardization) items. In particular, the third item of the ISO series defines the calculation of the soundscape experience of public space users by means of multiple Likert scales. In this thesis, with regards to the third item of the soundscape ISO series, the soundscape data analysis standard method is questioned and a correction paradigm is proposed. This thesis questiones the assumption of a point-wise superimposition match across the Likert scales used during the soundscape assessment task. In order to do that, the thesis presents a new method which introduces correction values, or metric, for adjusting the scales in accordance to the results of common scaling behaviours found across the investigated locations. In order to validate the results, the outcome of the new metric is used as tar get to predict the individual experience of soundscapes from the participants. In comparison to the current ISO output, the new correction values reveal to achievea better predictability in both linear and non-linear modelling by increasing the ac-curacy of prediction of individual responses up to 52.6% (8.3% higher than theaccuracy obtained with the standard method).Finally, the new metric is used to validate the collection of data samples acrossseveral locations on individual questionnaires responses. Models are trained, in aiterative way, on all the locations except the one used during the validation. Thisprocedure provides a strong validating framework for predicting individual subjectassessments belonging to locations totally unseen during the model training. The results show that the combination of the new metrics with the proposed modelling structure achieves good performance on individual responses across the dataset withan average accuracy above 54%. A new index for measuring the soundscape is fi-nally introduced based on the percentage of people agreeing on soundscape pleas-antness calculated from the new proposed metric and performing a r-squared valueequals to 0.87.The framework introduced is limited by cultural and linguistic factors. Indeed,different corrected metric space are expected to be found when data is collected from different countries or urban context. The current values found in this thesis areso expected to be valid in large British cities and eventually in international hub andcapital cities. In these scenarios the corrected metric would provide a more realisticand direction-invariant representation of how the urban soundscape is perceived compared to the current ISO tool, showing that some components in the circumplex model are perceived softer or stronger according to the dimension. Future research will need to understand better the limitations of this new ramework and to extendand compare it towards different urban, cultural, and linguistic contexts

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing