检索结果-内蒙古大学图书馆

Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Ahmed N. Nusari Abdulfattah E. Ba Alawi Ferhat Bozkurt İbrahim Yücel Özbek Electrical and Electronics Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Computer and Control Engineering Department Faculty of Engineering Sana’a University Sana’a Yemen Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Electrical and Electronics Department Faculty of Engineering Ataturk University Erzurum Turkey

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Early detection of vehicle accidents is essential for immediate response and to decrease the severity of injuries while vehicle accidents occur. Among the advanced algorithms YOLOv6, YOLOv7, YOLOv8, and YOLOv9 are compared in this article to determine their performance in detecting vehicle accidents that are categorized as "severity" or "moderate." The study emphasizes how important deep learning is to enhancing accident detection systems. Out of all the models evaluated, YOLOv8m has the highest precision (0.979), and the highest recall (0.927) for YOLOv8n. Furthermore, YOLOv9c performs admirably in mAP50, receiving a score of 0.977, indicating its resilience in identifying accidents under a variety of circumstances. YOLOv9e is noteworthy for its better mAP50-95, which reaches a high of 0.941, demonstrating its ability to localize accident incidents accurately. These results provide valuable insight into how well particular algorithms perform in real-world situations. Through a close examination of their performance measures, this research aids in the advancement of more effective accident detection systems, which in turn improves emergency response operations and lessens the effects of accidents on people and communities. This comparison research emphasizes how crucial it is to use state-of-the-art technology to increase accident detection accuracy and enable timely and efficient response.

关键词： YOLO Deep learning Technological innovation Accuracy Computational modeling Scalability Emergency services

来源：评论

学校读者我要写书评

暂无评论

Parameter Estimation of Transient Voltage Signals with an Unscented Kalman Filter

Parameter Estimation of Transient Voltage Signals with an Un...

引用

2022 North American Power Symposium, NAPS 2022

作者： Sharifi, Fatemeh Mehrizi-Sani, Ali Tehrani, Kambiz The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States Normandy University-ESIGELEC-IRSEEM The Department of Energy and Control Rouen France

ISBN: (纸本)9781665499217

Online parameter estimation of ringdown signals is crucial for analyzing the stability of the power system. This paper aims to estimate the frequency and damping ratio of a transient voltage signal using an unscented Kalman filter (UKF). Traditionally, transient signals in the power system are modeled as the sum of exponentially damped sinusoidal signals, and previous work has been performed to estimate parameters by linearizing the model. However, the proposed algorithm in this paper can approximate parameters without assuming linearity. The performance of this method is evaluated on the IEEE 39-bus system simulated on the PSCAD software connected to the UKF algorithm written in the Python programming language. Simulation results indicate the proposed method detects any fluctuation in parameters accurately. © 2022 IEEE.

关键词： Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Random Forest Regression for Battery State-of-Health Estimation Based on Unsupervised Transfer Component Analysis Domain Adaptation

Random Forest Regression for Battery State-of-Health Estimat...

引用

2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Tian, Jilun Zhang, Jiusi Huangy, Congsheng Chowz, Mo-Yuen Luo, Hao Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States

ISBN: (纸本)9781728119489

Machine learning is an useful approach estimating the state-of-health (SOH) of the lithium-ion batteries and had been successfully implemented on battery management systems (BMS). However, the SOH estimation accuracy of the machine learning relies highly on the similarity of training dataset and battery data of estimation. In other words, the machine learning approach may fail if the battery of estimation is operated in a different condition from the condition set for collecting the training dataset. This paper aims on solving such a problem by proposing a transfer component analysis-random forest regression (TCA-RF) approach. The robustness of this proposed approach is ensured by the random forest regression, which is an ensemble learning approach. The generalization of this proposed approach is enhanced by transfer component analysis since it achieves the domain adaption with well transformation of features between two datasets. Two battery ageing datasets collected in different operating conditions are selected in this paper to validate the success of the proposed work, where these two datasets are collected by the Center for Advanced Life Cycle engineering (CALCE) and the National Aeronautics and Space Administration (NASA) Prognostics Center of Excellence (PCoE). The proposed work is further compared with multiple commonly adopted intelligent algorithms to showcase the high performance of the proposed work. The experimental results shows that the proposed TCA-RF approach has a better accurate SOH estimation comparing to other estimation algorithms by 2% to more 50%. © 2022 IEEE.

关键词： Lithium-ion batteries

来源：评论

学校读者我要写书评

暂无评论

Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

引用

IEEE Transactions on control of Network Systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

来源：评论

学校读者我要写书评

暂无评论

Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

引用

23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 348-361页

作者： Tsai, Bo-Yu Diddi, Sandeep Vara Sankar Ko, Li-Wei Wang, Shuu-Jiun Chang, Chi-Yuan Jung, Tzyy-Ping Institute of Biomedical Engineering and the Brain Research Center National Yang Ming Chiao Tung University Hsinchu300 Taiwan Hsinchu300 Taiwan College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering Institute of Biomedical Engineering Brain Research Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300 Taiwan Kaohsiung Medical University Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung807 Taiwan Taipei Veterans General Hospital Taipei11217 Taiwan National Yang Ming Chiao Tung University Brain Research Center School of Medicine Taipei112 Taiwan University of California San Diego Swartz Center for Computational Neuroscience San DiegoCA92093 United States

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c =1-10 for activity-specific BCI applications and a moderat

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis 21

Personalized Federated Learning with Kolmogorov-Arnold Enhan...

引用

21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Kulevome, Delanyo Kwame Bensah Man, Qiu Mawuli, Ernest Smith Opoku-Mensah, Edward Qilu Institute of Technology Department of Computer and Information Engineering Shandong Jinan250200 China Qilu Institute of Technology Department of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Department of Electrical/Electronic Technology Education Kumasi Ghana HEC Department of Information Technologies Montréal Canada

ISBN: (纸本)9798331519254

Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. The framework is designed to address variations in equipment conditions and data distributions, providing personalized models tailored to specific clients. A comprehensive evaluation is conducted using benchmark datasets, demonstrating significant improvements in fault classification accuracy, model interpretability, and training efficiency under non-IID data settings using the KAN-enhanced CNN model. This study contributes to advancing predictive maintenance practices, paving the way for resilient and intelligent industrial systems. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications

arXiv

引用

arXiv 2024年

作者： Engelaar, Maico H.W. Zhang, Zengjie Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Lazar, Mircea Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Performance of Intelligent Energy Management Systems for Hybrid Electric Vehicles Based on Embedded Raspberry Pi Module 30th

Evaluating the Performance of Intelligent Energy Management ...

引用

Advances in Systems engineering - Proceedings of the 30th International Conference on Systems engineering, ICSEng 2023

作者： Gaber, Mohab Khamis, Ahmed Zydek, Dawid Egyptian Navy R&D Center Alexandria Egypt Guidance and Control Department Military Technical College Cairo Egypt Electrical and Computer Engineering Department University of Nevada Las Vegas United States

ISBN: (纸本)9783031405785

The global development of electrical power and generation systems has been accelerated by regulations to reduce greenhouse gas emissions and improve ecologically friendly energy sources. For the industry, designing and producing these criteria is extremely challenging. Electrification technologies are the best answer that is selected by the designers. They rely on electricity to run all systems;however, the electricity is generated by burning fossil fuels. The method adopted to address it is the diversification of fossil fuels with renewable energy sources. Technologies used to support this approach are utilizing intelligent energy management strategies. This paper proposes a hybrid power system based on the Adaptive Neuro-Fuzzy Interface System (ANFIS) technology and an intelligent Energy Management System (EMS). The performance of ANFIS is simulated by the SIMULINK/MATLAB system. The Raspberry PI embedded system is used as an intelligent energy management control system on a test bed laboratory bench in order to collect data in real-time. To verify the correctness of the EMS, the simulation results are compared with real-time data from the test bed with a Raspberry PI embedded device. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy management systems

来源：评论

学校读者我要写书评

暂无评论

Proposed Intelligent Energy Management Systems for Hybrid Electric Traction System 30th

Proposed Intelligent Energy Management Systems for Hybrid El...

引用

Advances in Systems engineering - Proceedings of the 30th International Conference on Systems engineering, ICSEng 2023

作者： Gaber, Mohab Khamis, Ahmed Zydek, Dawid Egyptian Navy R&D Center Alexandria Egypt Guidance & Control Department Military Technical College Cairo Egypt Electrical and Computer Engineering Department University of Nevada Las Vegas United States

ISBN: (纸本)9783031405785

The global development of electrical power and generation systems has been accelerated by regulations to reduce greenhouse gas emissions and improve ecologically friendly energy sources. For the industry, designing and producing systems that meet these criteria is extremely challenging. The best option for electrification technologies is to rely on electricity to run all systems instead of burning fossil fuels. To cut greenhouse gas emissions, the diversification of renewable energy sources is adopted. These electrification strategies are supported by intelligent Energy Management Systems (EMSes). In this paper, hybrid electric traction system with a fuel cell serving as the primary power source is considered, while a battery bank serves as an auxiliary power source. The EMS between the hybrid system and the system load is challenging to implement. This paper proposes an EMS for hybrid electric power system based on Adaptive Neuro-Fuzzy Interface System (ANFIS) technology. The performance of ANFIS is simulated in the MATLAB Simulink. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuel cells

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：