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Reliability Measurement Of Vehicle sensor Data Based On Historical Perception

Reliability Measurement Of Vehicle Sensor Data Based On Hist...

引用

ieee international conference on Data Science in Cyberspace (DSC)

作者： Chenqing Guo Jinxin Zuo Yueming Lu Weixuan Xie School of Cyberspace Security Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

On-board sensors are utilized for monitoring and collecting a diverse range of data pertaining to the vehicle and its surroundings. However, in the field of Internet of Vehicles, security threats encountered by vehicle sensors often result in compromised accuracy of collected data. therefore, it is crucial to investigate trusted measurement methods for vehicle sensor data. this paper proposes a reliable measurement approach for vehicle sensor data based on historical perception. the method initially achieves historical feature extraction through time correlation of single sensor sequence data and spatial correlation of multisensor data. Subsequently, a reliability measurement method is devised to quantify real-time data reliability by assessing the deviation from the theoretical expected value. Finally, historical data reliability is integrated with real-time data reliability to enhance accuracy of the overall reliability measurement process. this approach takes into consideration spatio-temporal correlation of vehicle sensor data and resolves issues stemming from unbalanced datasets that lead to inaccurate evaluation results. Experimental findings demonstrate an accuracy rate of 98.91% for the proposed method.

关键词： Correlation Accuracy Reliability theory Feature extraction Real-time systems Spatial databases sensors Security Monitoring Internet of Vehicles

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Simulation Verification Method for Image sensor Interface Processing Unit Based on UVM

Simulation Verification Method for Image Sensor Interface Pr...

引用

Power Electronics, Computer Applications (ICPECA), ieee international conference on

作者： Wenji Ma Department of FPGA Verification Organization Beijing Sunwise Information Technology Ltd. Beijing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Image sensors are widely employed across various industries, including smart mobile devices, autonomous vehicles, and surveillance systems. the Image sensor Interface Processing Unit is typically characterized by a high degree of complexity to support diverse image sensor interfaces. Consequently, the effective and efficient verification of this unit is essential for upholding project quality and adhering to timelines. this article presents a simulation verification methodology for the Image sensor Interface Processing Unit utilizing the Universal Verification Methodology (UVM). It explores the features of different sensor interfaces, the architectural framework, core base classes, runtime mechanisms, and reusable components within the UVM verification platform. the study identifies functional verification points based on the specific characteristics and application contexts of the Image sensor Interface Processing Unit and formulates testing incentives. By analyzing the simulation results, the approach ensures comprehensive coverage of all functional points, achieving a 100% functional coverage rate. the testing platform is systematically designed to provide robust portability while meeting fundamental functional verification requirements, thereby serving as a reference for the development of future module verification environments.

关键词： Image sensors Industries Runtime Simulation Surveillance Computer architecture Power electronics Mobile handsets Next generation networking Testing

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Photovoltaic Array Fault Detection and Classification based on T-distributed Stochastic Neighbor Embedding and Robust Soft Learning Vector Quantization 21

Photovoltaic Array Fault Detection and Classification based ...

引用

21st ieee international conference on Environment and Electrical Engineering / 5th ieee Industrial and Commercial Power systems Europe (EEEIC/I and CPS Europe)

作者： Afrasiabi, Shahabodin Afrasiabi, Mousa Behdani, Behzad Mohammadi, Mohammad Javadi, Mohammad S. Osorio, Gerardo J. Catalao, Joao P. S. Shiraz Univ Shiraz Iran INESC TEC Porto Portugal REMIT UPT Covilha Portugal C MAST UBI Covilha Portugal FEUP Porto Portugal

ISBN: (纸本)9781665436137

Photovoltaic (PV) as one of the most promising energy alternatives brings a set of serious challenges in the operation of the power systems including PV system protection. Accordingly, it has become even more vital to provide reliable protection for the PV generations. To this end, this paper proposes two-stage data-driven methods. In the first stage, a feature selection method, namely t-distributed stochastic neighbor embedding (t-SNE) is implemented to select the optimal features. then, the output of t-SNE is directly fed into the strong data-driven classification algorithm, namely robust soft learning vector quantization (RSLVQ) to detect PV array fault and identify the fault types in the second stage. the proposed method is able to detect the two different line-to-line faults (in strings and out of strings) and open circuit fault and fault type considering partial shedding effects. the results have been discussed based on simulation results and have been demonstrated the high accuracy and reliability of the proposed two-stage method in detection and fault type identification based on confusion matrix values.

关键词： fault detection and classification photovoltaic robust soft learning vector quantization (RSLVQ) t-distributed stochastic neighbor embedding (t-SNE)

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Evaluation of a Low-cost Capacitive Non-intrusive sensor for the Detection, Localization and Identification of Defects in MVAC Cables

Evaluation of a Low-cost Capacitive Non-intrusive Sensor for...

引用

ieee international conference on Dielectrics (ICD)

作者： Jérémie Cicéron Pascal Rain Cornel Ioana CNRS Grenoble INP G2Elab Univ. Grenoble Alpes Grenoble France CNRS Grenoble INP GIPSA-lab Univ. Grenoble Alpes Grenoble France Altrans Energies Grenoble France

ISBN: (数字)9798350308976

ISBN: (纸本)9798350308983

Partial discharges (PDs) monitoring is a common tool for diagnosing electrical systems. An innovative, low-cost sensor for distance detection and positioning of partial PDs has been developed. An ambitious challenge for this PD monitoring system is to predict dielectric breakdowns on a grid and particularly in MVAC cables. the first part of the article demonstrates the proper functioning of this sensor on a test bench. In a second part, monitoring measurements of PD activity issued from artificial defects are presented. PD initiated in submillimetric cavities were collected. Parameters and PD patterns have been interpreted with the aim to correlate with the electrical treeing propagation until breakdown. the final section is dedicated to a concise review of electrical treeing monitoring. Analysis of existing approaches are presented, as well as the interesting opportunities offered by the sensor.

关键词： Partial discharges Location awareness Reviews Data acquisition Dielectric measurement Dielectrics Object recognition

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An Optimal Multilayer Perceptron with Dragonfly Algorithm for Intrusion Detection in Wireless sensor Networks 5

An Optimal Multilayer Perceptron with Dragonfly Algorithm fo...

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5th international conference on computing Methodologies and Communication, ICCMC 2021

作者： Amaran, Sibi Madhan Mohan, R. Annamalai University Department of CSE

ISBN: (纸本)9781665403603

Wireless sensor network (WSN) comprises several sensor nodes which are randomly deployed in the interesting region for data gathering operations. Since the sensor nodes are majorly deployed in harsh and accessible open regions, it is highly vulnerable to attacks and thereby necessitates an effective intrusion detection system (IDS). the rise of machine learning (ML) algorithms finds useful in the design of IDS, particularly for energy constrained WSN. In this view, this study introduces Optimal Multilayer Perceptron (OMLP) with Dragonfly Algorithm (DA) for intrusion detection in WSN. the major intention of OSVM is to determine the possible intrusions and identifies the class type effectually. the OMLP technique is mainly used to determine the weights and bias of the MLP by the DA. the utilization of DA results in the effective choice of weight and bias values and thereby led to improved detection performance. the experimental validation of the OMLP model takes place on the benchmark NSL KDDCup 99 dataset. the obtained experimental outcome reported the proficient performance of the OMLP model with higher accuracy and detection rate of 94.21% and 95.18% respectively. © 2021 ieee.

关键词： Intrusion detection

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An ML-Accelerated Framework for Large-Scale Constrained Traffic Engineering

An ML-Accelerated Framework for Large-Scale Constrained Traf...

引用

international conference on distributed computing systems

作者： Cheng Gu Xin Song Ben Hok Ng Qiao Xiang Zehua Guo Geng Li Huawei Technologies University of Toronto Xiamen University Zhejiang Lab Beijing Institute of Technology Zhengzhou Research Institute BIT

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Traffic engineering (TE) mechanisms are crucial for achieving optimal levels of network performance over wide-area networks across geographically distributed datacenters. Existing work on traffic engineering formulated the challenges at hand as combinatorial optimization problems, which could take hours to compute on modern wide-area network topologies at the scale of thousands of nodes. To improve the performance of TE mechanisms, we introduce DeepTE, a new TE framework based on machine learning (ML) that is designed for the best possible scalability and performance, capable of completing the computation within milliseconds with networks involving thousands of nodes, and of generating near-optimal TE policies while guaranteeing that all constraints are satisfied. DeepTE is also designed with a distributed ML model architecture, which can be horizontally scaled up to multiple GPUs for even better performance. With real-world traffic matrices, our extensive array of performance evaluations of DeepTE on various network topologies and TE problems show that DeepTE is capable of producing policies within 5% of the optimal results while offering up to 100x performance improvements over state-of-the-art traffic engineering mechanisms.

关键词： Performance evaluation Network topology Scalability Computational modeling Machine learning Computer architecture distributed computing

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Evaluation of Security Aspects of Wireless sensor Networks 5

Evaluation of Security Aspects of Wireless Sensor Networks

引用

5th ieee international conference on Computational systems and Information Technology for Sustainable Solutions, CSITSS 2021

作者： Minnoor, Niveditha Pannaga Shree, B.S. Sahana, B. R v College of Engineering Department of Electronics and Communication Bangalore India

ISBN: (纸本)9781665406109

Wireless sensor Networks require multiple robust security policy implementations in place to effectively secure any data that is being transmitted from or received by it. this in turn, requires efficient security policies and methods designed such that in the case of any attack, data is always protected. A single policy, while being efficient by itself, will not secure the network to the required extent against all types of attacks. Multiple security methods must be implemented in a layered manner in order to provide robust security. In this paper, broad categories of applications of Wireless sensor Networks, and a brief description of these applications with a focus on security critical use cases such as the military are studied. Security goals and types of attacks with the corresponding countermeasures are evaluated. Various existing implementations of Wireless sensor Network security protocols are reviewed, and the drawbacks of these methods are discussed. Proposals to overcome these drawbacks are suggested in the final sections of the paper. © 2021 ieee.

关键词： Wireless sensor networks

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Measuring the Impact of an IoT Temperature sensor Framework for Tracking Contagious Diseases 5

Measuring the Impact of an IoT Temperature Sensor Framework ...

引用

5th ieee international conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2021

作者： Lawson, Victor J. Banerjee, Madhushri Faculty Of Information Technology Georgia Gwinnett College LawrencevilleGa United States

ISBN: (纸本)9781665401968

Due to the COVID-19 pandemic, much computer science research has been dedicated to utilizing sensor readings for medical purposes. throughout this period, the need for virus symptom tracking has become a promising area for remotely deployed sensor networks and platforms. Our research goal is to prove that the temperature readings from these sensor network platforms can be statistically linked to public record, medical case study data. the expected outcome of our project is to prove the correlation between sensor network tracking of remote human temperature data and medical records for COVID cases. the results of this study will prove that tracking human temperature can assist in tracking disease outbreaks in various populations. Our framework platform is comprised of four main modules: (1) Temperature Collection, (2) Internal Data Validation (3) Internal-External data merger, (4) Data Analytics. the temperature data are collected from internal databases, mobile sensing devices and medical health professionals. After collection, the internal data are validated by our software, TAU-FIVE, a multi-tier data quality validation system, then merged with external data sources into a data analytic based data warehouse. the data mart queries are designed to compare the location and date of temperature sensor data with known data sets from government officials. Once blended into a fully operational data warehouse, these data marts produce high quality data analysis linking remotely sensed human temperature readings to sources of disease outbreaks. © 2021 ieee.

关键词： Data Analytics

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Multi-Objective Optimization Scheme for Energy systems in Distribution Networks Based on Cloud-Edge-Device Collaborative Framework

Multi-Objective Optimization Scheme for Energy Systems in Di...

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Power Electronics, Computer Applications (ICPECA), ieee international conference on

作者： Jingzhi Liu Jianming Zhang Hongyi Yang Zhenghong Bao Fei Zhan Zhidong Liu State Grid Qinghai Electric Power Research Institute Xining Qinghai China State Grid Qinghai Electric Power Company Xining Qinghai China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

As energy systems evolve, single-system economic optimization no longer meets the growing multi-energy demands of users. To address this problem, this paper proposes a multi-objective optimization method for distribution network energy systems based on cloud-edge-device collaborative (CEDC) architecture. First, a CEDC system for the energy system of the distribution network is constructed in response to energy scheduling requirements, and edge-device clusters are used to achieve distributed management of energy system units. Second, based on the energy scheduling requirements, corresponding energy scheduling commands are generated for the target energy systems. these energy scheduling commands are decomposed into at least one computational task, then the computational tasks are assigned to the edge cluster control units of the corresponding target energy systems. then, a multi-objective optimization scheme is designed, which jointly minimizes the total power supply cost of a single energy system and the total delay time across multiple energy systems. Finally, the scheme is solved by the particle swarm optimization (PSO) algorithm introducing the simulated annealing. Simulation results show that the proposed algorith can minimize the total energy cost and overall system delay.

关键词： Costs Processor scheduling Computational modeling Collaboration Distribution networks Simulated annealing Computer architecture Delays Particle swarm optimization distributed management

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Design of a Scaled-Vehicle Research Platform for Software-Defined Vehicles 11

Design of a Scaled-Vehicle Research Platform for Software-De...

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11th international conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Oh, Sung Bhin Do, Young Soo Lim, Se Jeong Choi, Hyeok Jun Cha, Sung Keun Hong, Hyeong Keun Lim, Seong Hyeon Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

Modern vehicles are integrated systems of complex software that support advanced features such as autonomous driving and over-the-air updates. Software-defined vehicles (SDVs) are emerging as a future automotive paradigm for managing increasing software complexity by centralizing computing resources and enabling flexible software updates and feature deployment. However, most SDV research is conducted in simulation environments because using real vehicles presents many challenges, including high cost, safety concerns, and implementation difficulties. In this paper, we present the design of an SDV research platform using a 1/5-scale vehicle model to overcome these limitations of SDV research. the platform implements a zone architecture and integrates various embedded systems to replicate the distributed computing environment of a modern vehicle. the platform allows researchers to experiment with different SDV concepts such as distributed computing, OTA updates, middleware, and vehicle software management without the constraints of full-scale vehicle testing. Our design demonstrates the feasibility of using extended vehicles for SDV research and provides insights into platform design for SDV research. © 2025 ieee.

关键词： distributed cloud

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