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Journal of Network Intelligence 2021年第1期6卷 16-28页

作者： Xue, Xingsi Zhang, Jie Yang, Chaofan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou Fujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin City Guanxi Province537000 China School of Computer Science and Mathematics Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guilin Guangxi541004 China

Although Inquiry-based Learning (IBL) offers compelling opportunities for the engineering education, one of the challenges to the implementation of it is that the learner always lacks of the background knowledge. To provide him with the necessary knowledge when making the inquiry, this paper proposes an Ontology Supported Inquiry Learning technique (OSIL), which works on the basis of two ontologies, i.e. curriculum ontology and the learner’s ontology. The experimental results show that the constructed curriculum ontology and learner ontology is of high-quality, and the answers recommended by OSIL almost perfectly match with the true answers. From the experimental results, we can see that it is a feasible way of using the software engineering and prototype evolution method to construct the curriculum and learner ontology, and the individual inquiry based on two ontologies is able to provide the learners with high-quality personalized services. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Curricula

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Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers

arXiv

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arXiv 2023年

作者： Cao, John Niazi, M. Umar B. Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information & Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis- Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor meassurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators. © 2023, CC BY.

关键词： Nonlinear systems

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Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification 16th

Multi-feature Fusion Based Deep Forest for Hyperspectral Ima...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Liu, Xiaobo Mohammad, Mostofa Zaman Zhang, Chaochao Gong, Xin Cai, Zhihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789811912528

Multi-feature fusion is a useful way to improve the classification of hyperspectral image (HSI). But the multi-feature fusion is usually at the decision level of classifier, which causes less link between features or poor extensibility of feature. In this paper, we propose a multi-feature fusion based deep forest method for HSI classification, named mfdForest. In mfdForest, the morphological features, saliency features, and edge features are extracted, then the three deep multi-grained scanning branches in dgcForest (one of improved deep forest) are used to extract and fuse the extracted features deeply, and the fused features are sent into cascade forest in dgcForest for classification. Experimental results indicate that the proposed framework consumes less training time and has better performance on two HSI data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Spectroscopy

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A Distributed control framework for the optimal operation of DC microgrids

arXiv

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arXiv 2022年

作者： Fu, Zao Cucuzzella, Michele Cenedese, Carlo Yu, Wenwu Scherpen, Jacquelien M.A. School of Cyber Science and Engineering Southeast University Nanjing211189 China Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Physikstrasse 3 Zürich8092 Switzerland

In this paper we propose an original distributed control framework for DC mcirogrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on the dual theory, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC mcirogrid and the designed distributed control system in a power preserving way, we steer the DC microgrid's state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on the singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework. © 2022, CC BY-NC-SA.

关键词： Perturbation techniques

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants

Ethical Considerations in Explainable AI: Balancing Transpar...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Franciskus Antonius Alijoyo S. Sandya Sneha Sri Purnachandra Rao Alapati Dilyorjon Yuldashev Ponni Valavan M Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Krishna University Andhra Pradesh Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy Tamil Nadu India

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1 % • On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology.

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Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1029-1042页

作者： Di Cao Weihao Hu Junbo Zhao Guozhou Zhang Bin Zhang Zhou Liu Zhe Chen Frede Blaabjerg the Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina the Department of Electrical and Computer Engineering Mississippi State UniversityStarkvilleMississippiUSA the Department of Energy Technology Aalborg UniversityAalborgDenmark

With the growing integration of distributed energy resources(DERs),flexible loads,and other emerging technologies,there are increasing complexities and uncertainties for modern power and energy *** brings great challenges to the operation and ***,with the deployment of advanced sensor and smart meters,a large number of data are generated,which brings opportunities for novel data-driven methods to deal with complicated operation and control *** them,reinforcement learning(RL)is one of the most widely promoted methods for control and optimization *** paper provides a comprehensive literature review of RL in terms of basic ideas,various types of algorithms,and their applications in power and energy *** challenges and further works are also discussed.

关键词： Reinforcement learning deep reinforcement learning power system operation and control optimization

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Deep Learning-Based Dimension Measurement System for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

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Chinese Automation Congress (CAC)

作者： Xiao-Di Li Xing-Rong Fan Xian-Ming Zhang Han-Zhi Zhang Chun-Jie Huang Cheng Xu Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) - yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm.

关键词： Shafts Image segmentation Accuracy Measurement uncertainty Noise reduction Manuals Size measurement Stability analysis Robustness Standards

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TEMPORAL-SPATIAL REPRESENTATION LEARNING TRANSFORMER FOR EEG-BASED EMOTION RECOGNITION

arXiv

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arXiv 2022年

作者： Wang, Zhe Wang, Yongxiong Hu, Chuanfei Yin, Zhong Song, Yu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology China

Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition. However, some redundant information within the EEG signals would degrade the performance. Specifically, the subjects reach prospective intense emotions for only a fraction of the stimulus duration. Besides, it is a challenge to extract discriminative features from the complex spatial correlations among a number of electrodes. To deal with the problems, we propose a transformer-based model to robustly capture temporal dynamics and spatial correlations of EEG. Especially, temporal feature extractors which share the weight among all the EEG channels are designed to adaptively extract dynamic context information from raw signals. Furthermore, multi-head self-attention mechanism within the transformers could adaptively localize the vital EEG fragments and emphasize the essential brain regions which contribute to the performance. To verify the effectiveness of the proposed method, we conduct the experiments on two public datasets, DEAP and MAHNOB-HCI. The results demonstrate that the proposed method achieves outstanding performance on arousal and valence classification. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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Point Cloud Completion by Larger Receptive Fields for Intelligent Vehicles

Point Cloud Completion by Larger Receptive Fields for Intell...

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International Conference on Intelligent Transportation

作者： Yunqing Zhang Xiaotong Zhang Kunhua Liu Yisheng Lv Wei He Long Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Waytous Inc. Beijing China

Point cloud completion is a critical task for intelligent vehicles in the Intelligent Transportation Systems (ITS). Due to the disordered nature of point clouds, current point-based point completion methods utilize max pooling operations and Convolutional Neural Networks (CNNs) with a convolution kernel size of 1 to extract point features. However, a network with a kernel size of 1 has a small receptive field and can only extract one point feature at a time; as a result, such a network struggles to precisely extract highly abstracted features. To address this issue, we propose a point cloud completion network with larger receptive fields, termed PLRF-Net, which is composed of two parts: a reconstruction network and a completion network. The reconstruction network focuses on repairing the structures of partial points and includes an offset-attention embedding module, a rearrangement module, several convolution layers with varying kernel sizes, a pooling and repeating layer, and transposed convolution layers with different kernel sizes. Meanwhile, the completion network strives to improve the precision of the completed point cloud and consists of several convolution layers, along with a downsampling and upsampling module. Experimental results indicate that our approach achieves superior quantitative and qualitative outcomes on both the Multiview Partial Point Cloud (MVP) and KITTI datasets.

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuquan Zhang Yu Wu Xinxin Xu Chuansheng Wang Minjia Ma Antoni Grau Jiayan Huang School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices.

关键词： Accuracy Computational modeling Image edge detection Frequency-domain analysis Fatigue Brain modeling Feature extraction Electroencephalography Real-time systems Time-domain analysis

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