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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wu, Xiangxi Wang, Ziqi Li, Yibo Bi, Jing Yuan, Haitao College of Computer Science Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350365221

The global water environment confronts numerous challenges, e.g., water pollution, overexploitation, and ecological degradation. Comprehensive protection and management are imperative for sustainable water resource utilization. Water quality predictions provide timely warning of future water quality problems and enable early action to avoid deterioration. As science and technology are increasingly applied in comprehensive water environment management, a diverse array of multimodal data is gathered from various sources, including remote sensing images and hydrological time series. However, current water quality prediction methods, e.g., statistical, machine learning, and deep learning methods fail to utilize multimodal data to enhance their accuracy of water quality prediction. To solve the above problem, this work proposes a multi-factor and long-term water quality prediction model based on multimodal data fusion named Low-rank Multimodal Fusion TimesNet (LMF-TimesN et), It first extracts features from hydrological time series and remote sensing images, respectively. Then, they are fused with the low-rank multimodal fusion network to extract diverse information. Finally, TimesNet is adopted to integrate fused multimodal water environment information for water quality prediction. Experimental results on a real-world dataset show that LMF - TimesNet achieves higher prediction accuracy and generalization ability than its state-of-the-art peers. © 2024 IEEE.

关键词： Data quality

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in Engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

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computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib Ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood Alam Fuad AAwwad Emad A.A.Ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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Digital twin-based Intrusion Detection in Smart Grid : A Multi-kernel Knowledge Replay Approach

Digital twin-based Intrusion Detection in Smart Grid : A Mul...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Qihao Li, Mingyan Kang, Jiawen Hu, Fengye Jilin Univeristy College of Communication Engineering Changchun China Chongqing University College of Computer Science Chongqing China Guangdong University of Technology School of Automation Guangdong China

ISBN: (纸本)9798350351255

In this paper, we propose a novel intrusion detection scheme within smart grid networks, which is named digital twin-based multi-kernel knowledge replay (DtMKR) scheme. The scheme is designed to improve resilience against the noise and interference, and efficiently address the clustering of diverse and multi-sourced phasor measurement units (PMUs) packets. Specifically, we create channel-vectors to represent the packet features using the power gain and delay spread properties of the channel impulse response derived from the received packets. Then, we investigate the DtMKR scheme to alleviate the impact of noise and interference effects and enhance the precision of the malicious PMU packets detection from benign ones without the need for a comprehensive pre-established database of channel features for all PMUs in the network. In the scheme, the designed multi-kernel enabled Markov decision process (MDP) clustering functions are implemented to map the channel-vectors into a new feature space, thereby the dispersive effects on the channel-vectors are minimized. In addition, we consider both the realtime packet clustering and the paired learning scheme on the digital twin side, and utilize memory recall to mitigate the model overestimation problem and enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate the efficacy of the proposed DtMKR scheme in accurately identifying malicious packets originating from PMUs attackers, distinguishing them from benign traffic, and mitigating the impact of transmission impairments typical in environment. © 2024 IEEE.

关键词： Markov processes

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Exact satisfiability and phase transition analysis of the regular(k,d)-CNF formula

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Frontiers of computer Science 2024年第1期18卷 263-265页

作者： Guoxia NIE Daoyun XU Xi WANG Zaijun ZHANG College of Computer Science and Technology Guizhou UniversityGuiyang 550025China School of Mathematics and Statistics Qiannan Normal University for NationalitiesDuyun 558000China Key Laboratory of Industrial Automation and Machine Vision of Qiannan Duyun 558000China

1Introduction The satisfiability(SAT)problem has been considered the"seed"of other NP-complete *** regular partial exact(k,d)-SAT problem is an important extension of the SAT *** any(k,d)-CNF formula with a variable set V,V'is a proper subset of V,the problem involves determining whether a truth assignment set on V'exists such that only a literal in each clause is *** V'=V,it is a regular exact(k,d)-SAT ***,both experimental verifications and theoretical analyses of k-SAT problem have shown that the ratioα(clause constraint density)of the number of clauses m to the number of variables n is an important parameter affecting the satisfiability of the formula[1].However,the regular(k,d)-SAT problem has the same clause constraint density d/k.

关键词： formula constraint exact

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Graph Attention Transformer with Dilated Causal Convolution and Laplacian Eigenvectors for Long-term Water Quality Prediction

Graph Attention Transformer with Dilated Causal Convolution ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Chen, Danqing Yuan, Haitao College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

Water quality prediction has emerged as a prominent research problem in recent years, which entails a spatiotemporal prediction task. However, several challenges are associated with water quality prediction: 1) water quality time series exhibit complex nonlinear relationships, making prediction challenging;2) water quality sensors are distributed across river networks, leading to strong spatial dependencies in water quality prediction;3) current methods like traditional machine learning methods have poor accuracy in long-term water quality prediction. To solve these problems, this work proposes a spatiotemporal prediction model named Graph Attention Transformer with Dilated Causal Convolution and Laplacian Eigenvectors (GTDL). First, dilated causal convolution is adopted to extract temporal features of input sequences. Second, a transformer with Laplacian eigenvectors is designed to extract spatial dependencies of river networks. Third, residual connections are utilized at the output stage to enhance the accuracy of the long-term prediction. Finally, GTDL is evaluated with two real-world water quality datasets and experimental results prove that GTDL outperforms other baseline methods regarding the prediction accuracy. Specifically, compared with five state-of-the-art prediction models, GTDL improves the prediction accuracy by 11.3%-63.5% and 6%-52.4% on two datasets, respectively. © 2024 IEEE.

关键词： Prediction models

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Water Quality Anomaly Detection with Dual Sliding Windows and Convolutional LSTM

Water Quality Anomaly Detection with Dual Sliding Windows an...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Yuan, Ming Yuan, Haitao Wang, Ziqi Qiao, Junfei College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

Water pollution is continuously increasing in water ecosystems across all continents. Surface water sensors can record data on water quality indicators at regular intervals, and the associated water quality sequences show abnormal trends when extreme weather or unusual industrial discharges occur. Therefore, governments can take timely actions to minimize damage and protect the water environment by detecting these abnormal trends promptly. However, current methods make it difficult to interpret different correlations among water quality parameters effectively. To solve this problem, this work proposes a parameter correlation-aware anomaly detection model, which integrates Dual sliding windows, Convolutional LSTM, and a Deep neural network with dropout, called for DCLD short. First, DCLD designs dual sliding windows to capture local and global patterns within the sequence of water quality. Second, DCLD adopts a stacked long short-term memory with a convolutional neural network to capture complex features and long-term dependencies in the time series. Third, DCLD uses a deep neural network incorporating the dropout algorithm to extract abstract features. DCLD mitigates overfitting risks and enhances the model's generalization capacity. Finally, DCLD is evaluated with two real-world water quality datasets, and its anomaly detection accuracy is improved by 5.41% and 0.79% on average over its peers. © 2024 IEEE.

关键词： Convolutional neural networks

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Resource Allocation and Trajectory Optimization in Unmanned Aerial Vehicle-assisted Mobile Edge Computing

Resource Allocation and Trajectory Optimization in Unmanned ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Cheng, Xiangshuai Yuan, Haitao Niu, Siyu Zhai, Jiahui College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

Edge computing offers a groundbreaking architecture for supplying computing, storage, and networking resources to propel the Internet of Things forward. By situating them at the network's edge, this model makes computational power more accessible to users. If tasks are executed entirely at the edge, energy and resource constraints of edge nodes may lead to poor performance. Therefore, it is widely recognized that offloading certain tasks to cloud data centers (CDCs), which possess abundant execution resources, is advantageous. However, implementing CDCs is not widespread and lacks flexibility in isolated regions. This presents challenges and high costs for reliably completing tasks quickly. Consequently, employing more adaptable unmanned aerial vehicles (UAVs) as CDCs in specific scenarios is crucial. The work presents the idea of mobile edge computing supported by the UAV. By considering the needs of user services, we enhance the energy efficiency of the UAV by optimizing their trajectories, transmission power, and computational load distribution. Furthermore, the work introduces an improved algorithm called GeneticSimulated-annealing-based Particle Swarm Optimizer (GSPSO) to optimize the energy efficiency of the UAV. Experimental simulations show that regarding the energy efficiency of the UAV, GSPSO exhibits superior search efficiency, surpassing genetic algorithm, simulated annealing, and particle swarm optimization by 7.39%, 15.03%, and 27.93%, respectively. © 2024 IEEE.

关键词： Simulated annealing

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