Drivers are expected to traverse through an intersection in malfunctioning flash mode in the same manner as a stop-sign controlled intersection. Red/red flash corresponds to four-way stop control and yellow/red flash corresponds to two-way stop control. However, at a red flashing signal there is no assurance that a driver can see the cross street indication (i.e., yellow or red flash) and therefore does not know if the intersection is operating as a two-way or four-way flash. In addition, some drivers appear unclear on the rules at a flashing signal. This confusion makes the intersection more accident prone. This study builds upon several previous studies, using data extracted from existing files. The objective of this study is to determine the level of drivers' understanding of the flash control through an analysis of violation rates and types at recorded intersection in malfunction flash. Comparing these violation rates to those at comparable stop-control intersections will help illustrate the difference in drivers' understanding of these similar intersection control devices.

This thesis project investigates approaches for malfunction prediction using unsupervised, self-organized models, with an orientation on bus fleets. Certain bus malfunctions are not predictable with conventional methods and preventive replacements are too costly and time consuming. Malfunctions that could result in interruption of service or on degradation of safety  are of high priority to predict.The settings of the desired application define the following constraints: definition of a model by an expert is not desirable as it is not a scalable solution, ambient conditions or usage schedule must not affect the prediction, data communication between the systems is limited so data must be compressed with relevant to the problem features. In this work, definition of normal or faulty operation is not handled by an expert, but using the Wisdom of the crowd idea and Consensus Self-organized models for fault detection (COSMO), or by the system's past state by monitoring an autoencoder's reconstruction error. In COSMO each system constructs a model describing its condition and then all distances between models are estimated to find the Most Central Pattern (MCP), which is considered the normal state of the system. The measure of deviation is the tendency of a system's model to be farther from the MCP after a sequence of observations, expressed as a probability that the deviation is incidental.  Factors that apply to the total of systems, such as the weather conditions are thus minimized.The algorithms approach the problem from the scopes of: linear and non linear relations between signals, distribution of values of a single signal, spectrum information of a single signal. This is achieved by constructing relevant models of each observed system (bus). The performance of the implemented algorithms is investigated using ROC curves and real bus fleet data, targeting at predicting a set of malfunctions of the air pressure system.More tests are performed using artificial data with injected malfunctions, to evaluate the performance of the methods. By applying the method on artificial data, the ability of different methods to detect different malfunctions is exhibited.

This thesis deals with the issue of determining the causes of defects and failures, focusing on a specific property and their significance in determining the price of the property. The first part focuses on the theory explaining the basic concepts used in the valuation of real estate, basic methods of property valuation and classification of construction defects and failures. This chapter includes an analysis of the real estate market in the years 2008 - 2013. The second part is devoted to determining the amount of property damage at a specific property, the calculation of this particular property which is based on technical surveys and subsequent valuation of the property with regard to the results of the survey. The aim is to create a design procedure for reducing the price of the property with the defect.

Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained. However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations. In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants. The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components. The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts. The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose. The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.

This thesis presents an analysis of the characteristics of malfunction flash incidents based on the Georgia Department of Transportation crash reporting database. Malfunction flash is an unintentional state of flash mode in intersection signal hardware. The flash mode is a signal indication of yellow/red or red/red flash. The flash mode can be due to many issues, such as hardware failure, damage, or storms. Crash reports are completed by police officers at the scene. After processing by the local jurisdiction reports are sent to GDOT for archiving and analysis. GDOT archives the reports in a PDF image format without editable text. This research will develop a procedure to convert the archived PDF reports to text files using optical character recognition (OCR) software. The developed procedure will extract the description paragraph of the incident from the PDF. The extracted descriptions may then be searched for useful information about the incident. The text files will be run through a filter for keywords, such as; "malfunction flash," "red/red flash," "yellow/red flash," and others. Incidents flagged by the keywords will be reexamined to determine if they are malfunction flash incidents. The 2006 GDOT incident data base will be used for this effort. From an original possible candidate list of 70,000 signalized intersection incidents malfunction incidents will be identified using this method. A statistical analysis will be completed seeking trends and important characteristics of malfunction incidents.

Les anomalies sensorielles jouent un rôle central dans les Troubles du Spectre Autistique comme en témoigne leur apparition récente dans les critères diagnostiques de l'autisme (DSM-5, APA, 2013). Il existe toutefois très peu d'outils permettant de les évaluer et qui soient validés en français et adaptés aux adultes avec autisme. Cette recherche vise à (1) créer un nouvel outil, l'ESAA : Evaluation Sensorielle de l'Adulte avec Autisme, (2) réaliser les études de validation psychométriques de celui-ci auprès d'un groupe clinique d'adultes avec autisme, (3) étudier de façon différentielle les profils sensoriels des adultes avec autisme et dégager d'éventuelles dominantes pathologiques de la sensorialité, (4) étudier le lien entre l'intensité des troubles sensoriels et les troubles du comportement. L'échantillon de l'étude est constitué de 118 adultes présentant un autisme modéré à sévère avec retard mental associé, accueillies en institutions médico-sociales ou en structures sanitaires, qui sont partenaires cliniciens de la recherche. La mesure de la réactivité sensorielle de l'adulte avec autisme est effectuée à l'aide de l'ESAA. L'intensité de la réactivité sensorielle est évaluée pour chacune des modalités sensorielles. Les troubles du comportement sont évalués à l'aide de l'EPOCAA : Echelle d'Evaluation pour l'Observation des Comportements-problèmes d'Adultes avec Autisme (Recordon-Gaboriaud & Granier-Deferre, ECPA, 2012). L'échelle permet de déterminer un degré de sévérité des troubles et une typologie comportementale. Les résultats mettent en évidence les qualités métrologiques de l'échelle ESAA, des profils sensoriels différenciés et des réactivités sensorielles dominantes chez les participants. Des corrélations sont observées entre les caractéristiques des sujets et la sévérité des perturbations sensorielles de l'échantillon. Enfin, pour un sous-échantillon de l'étude (n=27), les résultats montrent également des corrélations entre la sévérité des anomalies sensorielles et la sévérité des troubles du comportement ainsi que des liens spécifiques entre les modalités sensorielles affectées et les typologies comportementales des sujets. Ces résultats permettent d'affirmer que ce nouvel outil est pertinent pour identifier les troubles de la réactivité sensorielle des personnes adultes avec autisme et retard mental et qu'il pourrait contribuer (1) à améliorer les connaissances cliniques des adultes avec autisme (2) à mieux analyser et prendre en compte les spécificités sensorielles dans l'accompagnement de ces personnes (3) à aménager un environnement adapté à leurs réactivités sensorielles. / Sensorial anomalies is a major cause of disorders in the autistic spectrum as shown by their importance in diagnostic criteria of autism (DSM-5, 5, APA, 2013). There exist, however, only few validated tools that can evaluate them. This thesis aims at (1) creating a new tool, ESAA: sensorial evaluation of the adult with autism, (2) performing psychometric experiments on adult with autism that validate it, (3) classifying sensorial profiles of adults with autism and identifying potential pathological predominances of sensory, (4) evaluating the relation between the sensorial anomaly intensity and the behavior disorder. The sample of this investigation comprises 118 adults with moderate to severe autism and backwardness who were in medico-social or sanitary partner institutes. ESAA measures the sensorial reactivity of adult with autism. The intensity of sensorial reactivity is captured for each of the sensorial modalities. Behavior disorders are evaluated using EPOCAA: an evaluation scale for behavior disorders observation of adults with autism (Recordon-Gaboriaud & Granier-Deferre, ECPA, 2012). This scale allows to identify a severity level of the disorders and a behavioral typology. Results show the accuracy of the ESAA scale, distinct sensorial profiles and predominant sensorial reactivity of the considered sample. We observe correlations between subject characteristics and the severity of sensorial anomalies of the individuals. Finally, for a subset of the sample (n=27), results also indicate correlations between the severity of sensorial anomaly and the severity of behavior disorder, and between the affected sensorial modalities and the behavioral typology of the individuals. These results allow us to conclude that this new tool is useful to capture the sensorial reactivity disorders of adults with autism and backwardness and that it could contribute to (1) improve clinical knowledge of adults with autism, (2) better analyze and take into account sensorial characteristics of these individuals and (3) setup an environment tuned to their sensorial reactivity.

No description available.

This thesis takes place in the field of Ambient Intelligence (AmI). AmI Systems are interactive systems composed of many heterogeneous components. From a hardware perspective these components can be divided into two main classes: sensors, using which the system observes its surroundings, and actuators, through which the system acts upon its surroundings in order to execute specific tasks.From a functional point of view, the goal of AmI Systems is to activate some actuators, based on data provided by some sensors. However, sensors and actuators may suffer failures. Our motivation in this thesis is to equip ambient systems with self fault detection capabilities. One of the particularities of AmI systems is that instances of physical resources (mainly sensors and actuators) are not necessarily known at design time; instead they are dynamically discovered at run-time. In consequence, one could not apply classical control theory to pre-determine closed control loops using the available sensors. We propose an approach in which the fault detection and diagnosis in AmI systems is dynamically done at run-time, while decoupling actuators and sensors at design time. We introduce a Fault Detection and Diagnosis framework modeling the generic characteristics of actuators and sensors, and the physical effects that are expected on the physical environment when a given action is performed by the system's actuators. These effects are then used at run-time to link actuators (that produce them) with the corresponding sensors (that detect them). Most importantly the mathematical model describing each effect allows the calculation of the expected readings of sensors. Comparing the predicted values with the actual values provided by sensors allows us to achieve fault-detection.

Diploma work includes summary of aeroplanes accidents, incurred by human factor malfunction in general aviation and in ultralight aeroplanes category on Czech Republic territory since year 2000. It analyses accidents enclosures with a view to discover similarities near particular accidents. From ascertained lockworks ared proposal created for possibly steps, how prevent rise of these accidents. Part of this work are also attachments (formularies, etc.).

This master’s thesis provides us into the issues of reduction of house prices due to roof structure defect. First charter takes interest in theorethical solutions for residential houses valuation with defects.Real estate survey is introduced in chapter two. The third part of this master’s thesis is focused on possible roof structure defects and malfunctions. In last charter we can find the valuation of house with defect. The main goal of this thesis is to create a procedure for the reduction of property price with roof structure defect.