Cooperative Cognitive Automobiles

Safety requirements are among the most ambitious challenges for autonomous guidance and control of automobiles. A human-like understanding of the surrounding traffic scene is a key element to fulfill these requirements, but is a still missing capability of today's intelligent vehicles. Few recent proposals for driver assistance systems approach this issue with methods from the AI research to allow for a reasonable situation evaluation and behavior generation. While the methods proposed in this contribution are lend from cognition in order to mimic human capabilities, we argue that in the long term automated cooperation among traffic participants bears the potential to improve traffic efficiency and safety beyond the level attainable by human drivers. Both issues are major objectives of the Transregional Collaborative Research Centre 28 'cognitive automobiles,' TCRC28 that is outlined in the paper. Within this project the partners focus on systematic and interdisciplinary research on machine cognition of mobile systems as the basis for a scientific theory of automated machine behavior

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals

A simulation study of predicting real-time conflict-prone traffic conditions

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative to pre-collision traffic dynamics. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e. Support Vector Machines, k-Nearest Neighbours and Random Forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of Random Forests with 5-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective prediction of conflicts

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

High Accuracy Distributed Target Detection and Classification in Sensor Networks Based on Mobile Agent Framework

High-accuracy distributed information exploitation plays an important role in sensor networks. This dissertation describes a mobile-agent-based framework for target detection and classification in sensor networks. Specifically, we tackle the challenging problems of multiple- target detection, high-fidelity target classification, and unknown-target identification. In this dissertation, we present a progressive multiple-target detection approach to estimate the number of targets sequentially and implement it using a mobile-agent framework. To further improve the performance, we present a cluster-based distributed approach where the estimated results from different clusters are fused. Experimental results show that the distributed scheme with the Bayesian fusion method have better performance in the sense that they have the highest detection probability and the most stable performance. In addition, the progressive intra-cluster estimation can reduce data transmission by 83.22% and conserve energy by 81.64% compared to the centralized scheme. For collaborative target classification, we develop a general purpose multi-modality, multi-sensor fusion hierarchy for information integration in sensor networks. The hierarchy is com- posed of four levels of enabling algorithms: local signal processing, temporal fusion, multi-modality fusion, and multi-sensor fusion using a mobile-agent-based framework. The fusion hierarchy ensures fault tolerance and thus generates robust results. In the meanwhile, it also takes into account energy efficiency. Experimental results based on two field demos show constant improvement of classification accuracy over different levels of the hierarchy. Unknown target identification in sensor networks corresponds to the capability of detecting targets without any a priori information, and of modifying the knowledge base dynamically. In this dissertation, we present a collaborative method to solve this problem among multiple sensors. When applied to the military vehicles data set collected in a field demo, about 80% unknown target samples can be recognized correctly, while the known target classification ac- curacy stays above 95%

Pcie Ip Validation Process Across Process Corner, Voltage And Temperature Conditions

IP validation has become more challenging for FPGA device as it supports high operating speed. The Peripheral Component Interconnect Express (PCIe) is an IP used for high speed data transfer that supported by Intel FPGAs. The base specifications of PCIe 3.0 supports 8.0 GT/s, 5.0 GT/s and 2.5 GT/s. The link training and Initialization takes place at physical layer to initialize the link width and link data rate. The physical layer is getting more complex when it supports higher speed. The operational state only happens when Link Training and Status State Machine (LTSSM) reaches L0 state after device being configured. The stability of link training is improved by optimizing the soft logic design in application layer. Two protocol tests usually validated in industry are link up testing and link & higher layer testing. Debugging tools supported by Quartus are fully utilized to detect any failure during link training. The characterization of link performance covers process corners, voltage and temperature conditions are hard to analyze. By using hypothesis testing method, data collected gives a clear trend on the PCIe link performance. The H0 statement shows a significant difference for passing and failing case. In this research, the worst case happened at low voltage and low temperature regardless of any process corners. The p-value is greater than 0.05 proved H0 statement is accepted. The difference on passing and failing percentage is insignificantly impacting overall link performance of PCIe. It concludes that the bug is random and not caused by any defects on the silicon layout of FPGA device. Thus, IP validation shows the robustness of the device and able to comply with base specification of PCIe