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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 136-146页

作者： Wang, Xin Zhou, Yuhao Luo, Biao Jiang, Yuming Huang, Tingwen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Automation Central South University Changsha410083 China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7491 Norway T&M University at Qatar Doha23874 Qatar

This article is concerned with the event-triggered adaptive finite-time control problem for a class of switched cyber-physical systems (CPSs) subject to uncertain deception attacks. We deploy the finite time command filter to alleviate the possible explosion of complexity rendered by the virtual control signal’s duplicative differentiation. To stabilize the target system with a guaranteed disturbance rejection performance index, a disturbance observer is constructed by using fuzzy logic systems (FLS) with the hope of estimating the unknown external disturbance. Furthermore, the Nussbaum gain is utilized to resist the unknown control efficiency generated by uncertain deception attacks. Based on the common Lyapunov function (CLF) method, sufficient conditions are derived to ensure that all signals in the closed-loop system are uniformly bounded at a finite time. Finally, the simulation example shows the validity and efficacy of the developed control method. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Closed loop systems

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RESEARCH ON THE REWARDS AND PUNISHMENTS OF URBAN SEWAGE TREATMENT UNDER THE UNCERTAINTY THEORY

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第3期86卷 111-124页

作者： Ruixi, Yuan Lin, Zhang Li, Cun-Lin Masron, Tajul Ariffin Yang, Bao-Jun School of management North Minzu University Yinchuan750021 China Universiti Sains Malaysia Pinang 13500 Malaysia School of Mathematics and Information Science Guangzhou University Guangzhou510006 China School of Management The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Management Universiti Sains Malaysia Pinang 13500 Malaysia School of Business Northinzu University Yinchuan750021 China

This paper fills a gap in urban sewage treatment decision-making by exploring reward and punishment mechanisms within the uncertainty theory framework. Addressing challenges in characterizing sewage treatment capacity due to incomplete information, the study integrates incentive and punishment mechanisms in a decision-making model. Results indicate that government rewards encourage companies to treat more sewage, but a nuanced relationship exists between rewards, treatment capacity, and expected returns. The study emphasizes the need for a balanced approach, suggesting a focus on technology enhancement over sole reliance on rewards. Examining a double-headed sewage market, the research establishes an uncertain Cournot game model, providing insights into sewage treatment dynamics under asymmetric information. In conclusion, the paper contributes to practical reward and punishment strategies in sewage treatment policies, offering theoretical support for effective decision-making in urban sewage management within a concise framework.. © 2024, Politechnica university of Bucharest. All rights reserved.

关键词： Sewage treatment

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Hybrid of Spans and Table-Filling for Aspect-Level Sentiment Triplet Extraction 30

Hybrid of Spans and Table-Filling for Aspect-Level Sentiment...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Nuo, Minghua Guo, Chaofan College of Computer Science Inner Mongolia University Inner Mongolia Hohhot010021 China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Hohhot010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Inner Mongolia Hohhot010021 China

ISBN: (纸本)9782493814104

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research. Recently, researchers have proposed different tagging schemes, containing tagging of words, tagging of word-pairs, and tagging of spans. However, the first two of these methods are often insufficient for the identification of multi-word terms, while the span tagging can label the entire phrase span, but it lacks the interactive information between words. In this paper, we propose Hybrid of Spans and Table-filling (S&T) model which combining span with table-filling. Specifically, S&T model achieve full fusion of syntactic and contextual features through cross-attention and generate the structures of word-pair table through Biaffine. Then, our model converts it to a span table by computing semantic distance based on syntactic dependency tree, which can enrich each unit of span table with semantic and interactive information. Meanwhile, the initial sentence features are constructed as simple phrase tables to enhance textual information of the phrase itself. In decoding, we define 8 types of labels for identifying three dimensions including aspect, opinion, and sentiment. Finally, the extensive experiments on widely-used dataset show S&T model achieves competitive results in ASTE task, the results certify the effectiveness and robustness of our S&T model. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Filling

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer 29

Improving Fake News Detection of Influential Domain via Doma...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Fake detection

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Vehicle Detection and Classification Algorithm Based on Improved Mask R-CNN 12th

Vehicle Detection and Classification Algorithm Based on Impr...

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12th International Conference on Computer Engineering and Networks, CENet 2022

作者： Deng, Hongwei Fu, Wenli Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811969003

Vehicle detection and classification technology is an important part of ITS system. It is often used in traffic flow statistics, ETC non-parking charging system and other scenarios, which solves a series of problems effectively in traffic control. In this paper, we design ResNet-HDC feature extraction network and New-FPN feature fusion network to address coarse-grained vehicle classification and recognition problem. We improve Mask R-CNN algorithm and propose a vehicle detection and classification algorithm based on improved Mask R-CNN to complete accurate recognition and classification of vehicles. To further optimize the training results, vehicle images are preprocessed before the algorithm model training and high-performance Tesla T4-GPU processor is used to enhance image recognition during the training process. After training and testing, the results show that the method enhance the precision of vehicle detection effectively, and the average accuracy is increased by about 10%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Two-dimensional Eletrostatically Actuated MEMS Micromirror 5

Two-dimensional Eletrostatically Actuated MEMS Micromirror

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5th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2022

作者： Tao, Wei Ren, Xingang He, Shuping Wu, Xianliang Cai, Xueyuan Huang, Zhixiang Key Laboratory of Target Recognition and Feature Extraction of Anhui Province Lu'an China Ministry of Education Anhui University Key Laboratory of Intelligent Computing & Signal Processing Hefei China Anhui University Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Hefei China School of Electronic Engineering and Intelligent Manufacturing Anqing Normal University Anqing China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Recently, the development of LiDAR in the field of vehicle has made the core micromirror with essential characteristics of large tilted angle and small size. We proposed a two-dimensional electrostatically actuated MEMS micromirror with a slender torsion bar. With the modified theoretical formula, two different torsional modes and corresponding torsional frequencies of the micromirror are obtained, which are then compared to the results by finite element simulation, and revealed a good agreement. The rotation angle of the micromirror is improved through optimizing the micromirror structure. Meanwhile, it is found that the driving voltage and the moment of inertia introduce the softening behavior of the spring, which will then affect the torsion frequency. To prevent the torsion bar stress is too large, with the sinusoidal driving voltage of 75V and 100V, the corresponding mechanical rotation angles are 14.8° and 15.3° at a driving frequency of 340.5Hz and 1500Hz, respectively. © 2022 IEEE.

关键词： Torsional stress

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Research on financial social public opinion communication model based on variation mechanism 22

Research on financial social public opinion communication mo...

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Proceedings of the 2022 6th International Conference on Electronic information Technology and Computer Engineering

作者： Maojun Huang Mei Hong Lin Dong Da Yuan Key Laboratory of Intelligent Information Processing Shandong Technology and Business University China

ISBN: (纸本)9781450397148

In the context of rapid development of the Internet, the place of spreading financial public opinion is converted from traditional offline places to major online social platforms. Mastering the development mechanism of financial social opinion dissemination on online social media can effectively estimate the length of influence of public opinion and the scope of affected people, and provide effective guidance to relevant staff. Based on the Susceptible Infected Recovered Model, this paper divides the people involved in opinion diffusion into commenters and discussers, and introduces the variation mechanism to design the Susceptible-comment-discussion-removal model, then simulates the model to study the effects of different initial states and parameters on the model, and finally verifies the validity of the model by combining the real data of stock bars. The simulation experiments and validation show that the model can effectively describe the spread of public opinion among the user groups of financial social platforms when it occurs, and provide a valid reference for related workers. However, the content of public opinion, the personal influence of communicators, and the lag effect of communication all have an impact on communication, and these issues need to be addressed in future research.

关键词： variation mechanism financial social opinion SIR model public opinion dissemination SCDR model

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Group-Oriented Multi-Signature supporting Monotonic Endorse Policies in Hyperledger Fabric 6

Group-Oriented Multi-Signature supporting Monotonic Endorse ...

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6th IEEE International Conference on Blockchain, Blockchain 2023

作者： Zhang, Peng Huang, Yongwen Ge, Fa Liu, Yuhong Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Santa Clara University Department of Computer Science and Engineering Santa Clara United States

ISBN: (纸本)9798350319293

As multi-signature can produce one joint signature on the same message by an interactive protocol among a group of signers, it is widely used in all kinds of blockchain platforms (e.g., Bitcoin, Hypherledger Fabric, Algorand) to reduce the proportion of signatures in blocks, so that the platform scalability is improved. However, as existing multi-signature schemes mainly focus on 'AND' relationship among multiple signers, they cannot adequately support the Fabric endorsement process that generally requires monotonic endorse policy with both 'AND' and 'OR' operations. Therefore, a Group-oriented Multi-Signature scheme (GMS) is proposed, which can aggregate the signatures of multiple signers from any group into one. Furthermore, GMS is used in the process of Fabric endorsement instead of ECDSA signature, so that the transaction protocol is improved. Analysis shows that, by aggregating multiple endorsement signatures according to the monotonic endorse policies, the proposed GMS has the security features of unforgeability, anonymity, revocability and traceability, and the improved transaction protocol has advantages in terms of smaller block size and faster verification time. In addition, it can protect the endorsement policies and the identities of the endorsement nodes anonymity. © 2023 IEEE.

关键词： Authentication

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DEHRFormer: Real-Time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes 48

DEHRFormer: Real-Time Transformer for Depth Estimation and H...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Chen, Sixiang Ye, Tian Shi, Jun Liu, Yun Jiang, Jingxia Chen, Erkang Chen, Peng Xinjiang University School of Information Science and Engineering Uruqmi China Southwest University College of Artificial Intelligence Chongqing China Jimei University School of Ocean Information Engineering Xiamen China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China

ISBN: (纸本)9781728163277

Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene depth are lost. In this paper, we propose a real-Time transformer for simultaneous single image Depth Estimation and Haze Removal (DEHRFormer). DEHRFormer consists of a single encoder and two task-specific decoders. The transformer decoders with learnable queries are designed to decode coupling features from the task-Agnostic encoder and project them into clean image and depth map, respectively. In addition, we introduce a novel learning paradigm that utilizes contrastive learning and domain consistency learning to tackle weak-generalization problem for real-world dehazing, while predicting the same depth map from the same scene with varicolored haze. Experiments demonstrate that DEHRFormer achieves significant performance improvement across diverse varicolored haze scenes over previous depth estimation networks and dehazing approaches. © 2023 IEEE.

关键词： Signal encoding

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Dual-branch Transformer with Series Decomposition for Long-term Orbit Correction

Dual-branch Transformer with Series Decomposition for Long-t...

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2024 International Technical Meeting of The Institute of Navigation, ITM 2024

作者： Liu, Ziyu Wang, Jingran Zheng, Shaolong Wei, Shiyi Li, Zhenni Xie, Shengli Wang, Mingwei School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education China

ISBN: (纸本)9780936406367

Time to First Fix (TTFF) is one of the most important measures of GNSS receiver performance. Under the hot-start mode, the receiver needs only 1 to 2 seconds for positioning. However, under the cold start mode, it takes more than 20 seconds to receive the ephemeris to obtain the accurate satellite position. This is due to the fact that current satellite orbit prediction method cannot provide position information accurately based on the old ephemeris, which is detrimental to fast positioning. To address the above challenge, we propose a novel Transformer-based model for long-term orbit corrections. The model makes full use of the time-series information of historical orbit errors and is able to capture long-term dependencies from orbit error series in different lengths. The predicted orbits can be corrected according to the predicted error components, thereby achieving accurate satellite orbit prediction. Due to the temporal patterns in the orbit error series, the features and information hidden in their complex temporal patterns are often difficult to be extracted and utilised. To tackle the intricate temporal patterns, we introduce an inner series decomposition block to deconstruct the seasonal part and trend-cyclical part from orbit errors series, which also empowers the model with immanent decomposition capacity. In addition, in order to extract the cross-time dependency within the curves as well as the cross-dimension dependency between the curves, we propose the Dual-Branch Attention (DBA) layer to capture the cross-time and cross-dimension dependency. Integrating the proposed series decomposition block and the Dual-Branch Attention(DBA) layer into Transformer, we build a Dual-branch Transformer with series decomposition (DBSD-Transformer) for long-term orbit corrections. Experimental results on satellite orbit error data in 2022 from the BeiDou MEO-10 satellite show the effectiveness of our method. Compared to the prediction method based on the Kepler model, t

关键词： Orbits

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