Optimal Detection of Faulty Traffic Sensors Used in Route Planning

In a smart city, real-time traffic sensors may be deployed for various applications, such as route planning. Unfortunately, sensors are prone to failures, which result in erroneous traffic data. Erroneous data can adversely affect applications such as route planning, and can cause increased travel time. To minimize the impact of sensor failures, we must detect them promptly and accurately. However, typical detection algorithms may lead to a large number of false positives (i.e., false alarms) and false negatives (i.e., missed detections), which can result in suboptimal route planning. In this paper, we devise an effective detector for identifying faulty traffic sensors using a prediction model based on Gaussian Processes. Further, we present an approach for computing the optimal parameters of the detector which minimize losses due to false-positive and false-negative errors. We also characterize critical sensors, whose failure can have high impact on the route planning application. Finally, we implement our method and evaluate it numerically using a real-world dataset and the route planning platform OpenTripPlanner.Comment: Proceedings of The 2nd Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE 2017), Pittsburgh, PA USA, April 2017, 6 page

The Development of Audit Detection Risk Assessment System: Using the Fuzzy Theory and Audit Risk Model

The result of audit designation is significantly influenced by the audit evidence collected when planning the audit and the degree of detection risk is further depends on the amount of audit evidence. Therefore, when the assessment factors of detection risk are more objective and correct, audit costs and the risk of audit failure can be reduced. Thus, the aim of this paper is to design an audit detection risk assessment system that could more precisely assess detection risk, comparing with the traditional determination method of detection risk in order to increase the audit quality and reduce the possibility of audit failure. First, the grounded theory is used to reorganize 53 factors affecting detection risk mentioned in literatures and then employed the Delphi method to screen the 43 critical risk factors agreed upon by empirical audit experts. In addition, using the fuzzy theory and audit risk model to calculate the degree of detection risk allow the audit staff to further determine the amount of audit evidence collected and set up initial audit strategies and construct the audit detection risk assessment system. Finally, we considered a case study to evaluate the system in terms of its feasibility and validity

Safety and Inspection Planning of Older Installations

Abstract: A basic assumption often made in risk- and reliability-based inspection planning is that a Bayesian approach can be used. This implies that probabilities of failure can be updated in a consistent way when new information (from inspections and repairs) becomes available. The Bayesian approach and a no-crack detection assumption imply that the inspection time inter-vals usually become longer and longer with time. For ageing platforms several small cracks should be expected to be observed according to the bath-tub curve development often assumed – implying an increased risk for crack initiation (and coalescence of small cracks) and increased crack growth. This should imply shorter inspection time intervals for ageing structures. Different approaches for updating inspection plans for older installations are proposed. The most promis-ing method consists of increasing the rate of crack initiations at the end of the expected lifetime – corresponding to a bath-tub hazard rate effect. The approach illustrated is for welded steel details in platforms. Systems effects are considered, including the use of dependence between inspection and failure events in different components for inspection planning

Battery Insulation Performance Analysis in Electric Vehicles for the Improvement of Battery Lifetime

Battery is the main component both as an energy provider and as an interface for several systems in an electric vehicle. It has three important parameters: current, voltage and temperature that must be maintained as the battery can have a harmful reaction that can lead to overcurrent. The battery must also not overcharging or discharging for too long because it can cause a damage and affect its lifetime. Another error that can arise is sensor failure due to the interference or noise that can cause error in data reading. To prevent this problems, it needs a protection by means of isolation in operating the battery. In this research, planning in optimizing battery work was conducted by designing the process of detection and isolation of faults occurred in batteries, particularly lithium polymer battery to reach its more optimal and good performance. Battery modelling was needed as the parameter identification and Kalman Filter algorithm was applied to help to reduce the detection rate and fault isolation. The results of detection and isolation of overcurrent and sensor failure using Kalman Filter were found quite accurate. In overcurrent isolation, a discharge current of 6A was obtained from the maximum current limit of 10 A, and for sensor failure isolation, the Kalman Filter algorithm succeeded in improving the results of the previous reading

Detection and Operation of Unintentional Islands in the Presence of Distributed Generation Units

The complexities and challenges for reliable operation of power system have increased due to various types of Distributed Generators (DG) in the Distribution Network (DN) to supply the increasing load demand. It necessitates a comprehensive approach in planning the system towards effective and reliable operation of the system. During the operation of the system, detection of unintentional islanding is critical as non-detection of islanding event could lead to cascaded failure of the system due to active or reactive power imbalance leading to frequency, angle or voltage instability. If undetected, the instability in the islanded part can cascade into the stable part of the system resulting in complete failure of the system. A robust Modified Islanding Detection Technique (MIDT) has been proposed for identifying the islanding event early and accurately in the distribution networks with DGs installed for multiple objectives and is compared with existing passive Islanding Detection Techniques (IDT). A rank-based load shedding scheme is proposed for stable and reliable operation of the identified island, which sheds only the most vulnerable loads in the island for regaining the frequency and voltage stabilities. The proposed MIDT and rank based load shedding schemes were tested on 11kV IEEE 118 Bus Test system

UAV Simulation Environment for Autonomous Flight Control Algorithms

This thesis presents the development of a UAV simulation environment for the design, analysis, and comparison of autonomous flight control laws. The simulation environment was developed in MATLAB/Simulink, with custom map generation software and FlightGear 3-D visualization. Graphical user interface of the simulation environment is user-friendly and all available options are discussed in detail. Aircraft dynamic models are presented, with emphasis on newly designed UAV models. Five different aircraft models are available, with several path planning and trajectory tracking algorithms implemented. Emphasis is given to simulation of failures and other abnormal conditions, so that appropriate tools for failure detection, evaluation, and accommodation can be designed. The development of new path planning methodologies, such as optimized point of interest or automatic landing algorithms, is introduced. New developments in trajectory tracking algorithms, including adaptive controllers are discussed. An example simulation study is presented to investigate obstacle avoidance path planning algorithms, as well as the performance of trajectory tracking algorithms under both nominal and failure conditions. The results of this study are discussed with respect to optimum algorithm choice, as well as the user-friendliness of the UAV simulation environment as a whole. Finally, possible strategies for future improvements and expansion of the UAV simulation environment and its components are introduced

Failure Detection in Deep Neural Networks for Medical Imaging

Deep neural networks (DNNs) have started to find their role in the modern healthcare system. DNNs are being developed for diagnosis, prognosis, treatment planning, and outcome prediction for various diseases. With the increasing number of applications of DNNs in modern healthcare, their trustworthiness and reliability are becoming increasingly important. An essential aspect of trustworthiness is detecting the performance degradation and failure of deployed DNNs in medical settings. The softmax output values produced by DNNs are not a calibrated measure of model confidence. Softmax probability numbers are generally higher than the actual model confidence. The model confidence-accuracy gap further increases for wrong predictions and noisy inputs. We employ recently proposed Bayesian deep neural networks (BDNNs) to learn uncertainty in the model parameters. These models simultaneously output the predictions and a measure of confidence in the predictions. By testing these models under various noisy conditions, we show that the (learned) predictive confidence is well calibrated. We use these reliable confidence values for monitoring performance degradation and failure detection in DNNs. We propose two different failure detection methods. In the first method, we define a fixed threshold value based on the behavior of the predictive confidence with changing signal-to-noise ratio (SNR) of the test dataset. The second method learns the threshold value with a neural network. The proposed failure detection mechanisms seamlessly abstain from making decisions when the confidence of the BDNN is below the defined threshold and hold the decision for manual review. Resultantly, the accuracy of the models improves on the unseen test samples. We tested our proposed approach on three medical imaging datasets: PathMNIST, DermaMNIST, and OrganAMNIST, under different levels and types of noise. An increase in the noise of the test images increases the number of abstained samples. BDNNs are inherently robust and show more than 10% accuracy improvement with the proposed failure detection methods. The increased number of abstained samples or an abrupt increase in the predictive variance indicates model performance degradation or possible failure. Our work has the potential to improve the trustworthiness of DNNs and enhance user confidence in the model predictions

Imaging Biomarkers in Prostate Stereotactic Body Radiotherapy: A Review and Clinical Trial Protocol

Advances in imaging have changed prostate radiotherapy through improved biochemical control from focal boost and improved detection of recurrence. These advances are reviewed in the context of prostate stereotactic body radiation therapy (SBRT) and the ARGOS/CLIMBER trial protocol. ARGOS/CLIMBER will evaluate 1) the safety and feasibility of SBRT with focal boost guided by multiparametric MRI (mpMRI) and 18F-PSMA-1007 PET and 2) imaging and laboratory biomarkers for response to SBRT. To date, response to prostate SBRT is most commonly evaluated using the Phoenix Criteria for biochemical failure. The drawbacks of this approach include lack of lesion identification, a high false-positive rate, and delay in identifying treatment failure. Patients in ARGOS/CLIMBER will receive dynamic 18F-PSMA-1007 PET and mpMRI prior to SBRT for treatment planning and at 6 and 24 months after SBRT to assess response. Imaging findings will be correlated with prostate-specific antigen (PSA) and biopsy results, with the goal of early, non-invasive, and accurate identification of treatment failure