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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fang, Shuyan Liu, Yu Liu, Song Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Prov. Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Wuhan University of Science and Technology Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan China

ISBN: (纸本)9798350337488

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules. © 2023 IEEE.

关键词： Molecular property prediction Molecular representation learning Morgan Fingerprint Self-supervised learning

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An Unsupervised Person Search Method for Video Surveillance 22

An Unsupervised Person Search Method for Video Surveillance

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Feng, Deying Yang, Jie Wei, Yanxia Xiao, Hairong Zhang, Laigang School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Liaocheng University China

ISBN: (纸本)9781450396110

We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. Thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. The spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features. © 2022 ACM.

关键词： Monitoring

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Distributed Model Predictive control for Asynchronous Multi-agent systems with Self-Triggered Coordinator

arXiv

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arXiv 2024年

作者： Chen, Qianqian Li, Shaoyuan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enable the robustness of the system. Then, the trigger interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade-off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the multi-agent system is achieved under asynchronous communication. Finally, the recursive feasibility and stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Robust Remote Sensing Scene Classification with Multi-view Voting and Entropy Ranking 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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Comparison of Class I Flextensional Transducers with Concave Staves Machined by Different Materials

Comparison of Class I Flextensional Transducers with Concave...

引用

OCEANS

作者： Duo Teng Junwei Wang Zhong Luo Guolei Zhu School of Marine Science and Technology National Key Laboratory for Underwater Information Processing and Control Northwestern Polytechnical University Xi’an China

A Class I concave stave flextensional transducer is developed. Aiming at the electroacoustic performance of low frequency, high power, light weight and small size, the effect of the concave staves machined by the different materials on the flextensional transducer is studied. The finite element simulations illustrate the effect of the aluminium alloy, TC4 titanium alloy and 304 steel concave staves. The prototypes of the Class I concave stave flextensional transducer have been designed and fabricated. They have the size of Φ 96 × 160 mm, the weight of below 3 kg. Moreover, their resonance frequency is below 1.6 kHz and their source level is more than 194 dB. The tests verified the changes caused by the concave staves with different materials.

关键词：

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

引用

Chinese Automation Congress (CAC)

作者： Ying Zhang Kang Chen Zhezhuang Xu Ye Lin Qingbo Zeng Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Education College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement.

关键词： Correlation Automation Statistical analysis Education Linear regression Process control Data collection Software Hybrid learning Interviews

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HighlightRemover: Spatially Valid Pixel Learning for Image Specular Highlight Removal 24

HighlightRemover: Spatially Valid Pixel Learning for Image S...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Ling Ma, Yidong Jiang, Zhi He, Weilei Bao, Zhongyun Fu, Gang Xu, Wenju Xiao, Chunxia School of Computer Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial Systems Wuhan University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Hong Kong Amazon Palo Alto United States

ISBN: (纸本)9798400706868

Recently, learning-based methods have made significant progress for image specular highlight removal. However, many of these approaches treat all the image pixels uniformly, overlooking the negative impact of invalid pixels on feature reconstruction. This oversight often leads to undesirable outcomes, such as color distortion or residual highlights. In this paper, we propose a novel image specular highlight removal network called HighlightRNet, which utilizes valid pixels as references to reconstruct the highlight-free image. To achieve this, we introduce a context-aware fusion block (CFBlock) that aggregates information in four directions, effectively capturing global contextual information. Additionally, we introduce a location-aware feature transformation module (LFTModule) to adaptively learn the valid pixels for feature reconstruction, thereby avoiding information errors caused by invalid pixels. With these modules, our method can produce high-quality highlight-free results without color distortion and highlight residual. Furthermore, we develop a multiple light image-capturing system to construct a large-scale highlight dataset called NSH, which exhibits minimal misalignment in image pairs and minimal brightness variation in non-highlight regions. Experimental results on various datasets demonstrate the superiority of our method over state-of-the-art methods, both qualitatively and quantitatively. © 2024 ACM.

关键词： Pixels

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Graphene-Based Terahertz Tunable Double-Broadband Perfect Absorber

SSRN

引用

SSRN 2024年

作者： Fu, Maixia Liu, Zhonghao Duan, Yule Zhou, Fei Li, Yinsheng Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

The rapid advancement of terahertz(THz) technology has sparked significant interest in exploring tunable broadband functionaldevices. In this study, we propose an antisymmetric graphene skeletonized structure to achieve a terahertz double-perfect broadbandabsorber. This absorber consists of a gold (Au) substrate, a TOPAS dielectric layer, and C4 regular antisymmetric-patternedgraphene embedded in the polymer. It exhibits two unpolarized perfect absorbing broadbands through the resonance mechanisms oflocalized surface-isolated exciton resonance and electromagnetic dipole resonance. The results indicate high absorbance exceeding90% across two distinct frequency ranges of 0.93-2.03 THz and 4.16-5.3 THz. The dual-broadband absorption feature stems fromthe pronounced aggregation effect of the "Hollow-out corner" on the electric field, enabling modulation of the second Harmonicwhile leaving the first Harmonic largely unaffected. The absorber’s response can be dynamically tuned through adjusting the Fermienergy level and structural parameters of graphene. Our design opens up possibilities for diverse applications, in photodetectors,THz modulation, and stealth technologies. © 2024, The Authors. All rights reserved.

关键词： Graphene

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Fault Diagnosis for Small Samples Based on VMR

Fault Diagnosis for Small Samples Based on VMR

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Chinese Automation Congress (CAC)

作者： Xuejin Wu Yanghu Hu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis.

关键词： Fault diagnosis Training Neural networks Rolling bearings Feature extraction Data models Signal reconstruction Convolutional neural networks Long short term memory Overfitting

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control of chaos in Frenkel-Kontorova model using reinforcement learning

引用

Chinese Physics B 2021年第5期30卷 247-254页

作者： You-Ming Lei Yan-Yan Han School of Mathematics and Statistics Northwestern Polytechnical UniversityXi'an 710072China Ministry of Industry and Information Technology(MIIT)Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi'an 710072China

It is shown that we can control spatiotemporal chaos in the Frenkel-Kontorova(FK)model by a model-free control method based on reinforcement *** method uses Q-learning to find optimal control strategies based on the reward feedback from the environment that maximizes its *** optimal control strategies are recorded in a Q-table and then employed to implement *** advantage of the method is that it does not require an explicit knowledge of the system,target states,and unstable periodic *** that we need is the parameters that we are trying to control and an unknown simulation model that represents the interactive *** control the FK model,we employ the perturbation policy on two different kinds of parameters,i.e.,the pendulum lengths and the phase *** show that both of the two perturbation techniques,i.e.,changing the lengths and changing their phase angles,can suppress chaos in the system and make it create the periodic *** form of patterns depends on the initial values of the angular displacements and *** particular,we show that the pinning control strategy,which only changes a small number of lengths or phase angles,can be put into effect.

关键词： chaos control Frenkel-Kontorova model reinforcement learning

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