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Design of a new kind of chemical experiment container with virtual reality fusion

Virtual Reality & Intelligent Hardware 2023年第4期5卷 317-337页

作者： Lurong YANG Zhiquan FENG Junhong MENG Department of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan 250022China

Background At present,the teaching of experiments in primary and secondary schools is affected by cost and security *** research on virtual experiment platforms has alleviated these ***,the lack of real experimental equipment and use of a single channel to understand user intentions weaken these platforms operationally and degrade the naturalness of *** To solve these problems,we propose an intelligent experimental container structure and a situational awareness algorithm,both of which are verified and applied to a chemical experiment involving virtual-real ***,the acquired images are denoised in the visual channel using the maximum diffuse reflection chroma to remove ***,container situational awareness is realized by segmenting the image liquid level and establishing a relation-fitting ***,strategies for constructing complete behaviors and making priority comparisons among behaviors are adopted for information complementarity and independence,respectively.A multichannel intentional understanding model and an inter-active paradigm that integrates vision,hearing,and touch are *** The experimental results show that the accuracy of the intelligent container situation awareness proposed in this paper reaches 99%,and the accuracy of the proposed intention understanding algorithm reaches 94.7%.The test shows that the intelligent experimental system based on the new interaction paradigm also has better performance and a more realistic sense of operation experience in terms of experimental *** The results indicate that the proposed experimental container and algorithm can achieve a natural level of human-computer interaction in a virtual chemical experiment platform,enhance the user′s sense of operation,and achieve high levels of user satisfaction.

关键词： Multichannel intent fusion understanding Virtual-real fusion Natural interaction Remove image highlights Nonlinear curve fitting

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Neural network-based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2182-2184页

作者： Xueli Wang Shangwei Zhao Ming Yang Xin Wang Xiaoming Wu the Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan 250014 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)*** specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement *** addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

关键词： Service DoS service

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network link weight optimization based on antisymmetric deep graph networks and reinforcement learning 6

Network link weight optimization based on antisymmetric deep...

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： Chen, Yuhan Guo, Yingya Department of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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PESTA: An Elastic Motion Capture Data Retrieval Method

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Journal of computer science & Technology 2023年第4期38卷 867-884页

作者：蒋子飞李伟黄艳尹义龙郭宗杰彭京亮 School of Software Shandong UniversityJinan 250101China Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles 90089U.S.A Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan 250022China School of Information Science and Engineering University of JinanJinan 250022China

Prevalent use of motion capture(MoCap)produces large volumes of data and MoCap data retrieval becomes crucial for efficient data *** clips may not be neatly segmented and labeled,increasing the difficulty of *** order to effectively retrieve such data,we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment(PESTA)in this *** retrieves similarities at the sub-sequence level,achieves robustness against singular frames and enables control of tradeoff between precision and *** firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and,based on which,compactly symbolizes any MoCap ***,it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the ***,it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed *** performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.

关键词： motion capture(MoCap) content-based retrieval adversarial autoencoder temporal alignment

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Multiple Random Feature Superpixel-based Fuzzy Clustering for Image Segmentation 9

Multiple Random Feature Superpixel-Based Fuzzy Clustering fo...

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9th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Zhang, Chuanbin Chen, Long Tian, Yifei Shi, Zhaoyin Wang, Yingxu University of Macau Department of Computer and Information Science China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9798350342239

Random feature maps attempt to approximate the kernel method with low computational complexity, and they are efficient and effective algorithms for dealing with the non-linear structure of data. Nevertheless, the existing random feature-based methods are sensitive to hyper-parameters, and their performances are affected by the randomness in feature maps. To improve the robustness of random feature maps and further speed up kernel-based image segmentation algorithms, a multiple random feature superpixel-based fuzzy clustering is proposed in this paper. First, we generate a group of random features from the original image via a novel multiple random feature scheme. Then, we use the superpixel segmentation to reduce the scale of multiple random features. The final segmentation results are obtained by performing the multi-view fuzzy clustering on the multiple random feature superpixels. Experiments on two shared benchmark data sets verify the superiority of our approach. © 2023 IEEE.

关键词： Image segmentation

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MDGCN-Lt:Fair Web API Classification with Sparse and Heterogeneous Data based on Deep GCN

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1294-1314页

作者： Boyuan Yan Yankun Zhang Wenwen Gong Haoyang Wan Wenwei Wang Weiyi Zhong Caixia Bu Department of School of Computer Science Qufu Normal UniversityRizhao 276500China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China Department of Univalsoft Joint Stock Co. Ltd.Weifang 261000China

Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that *** develop edge applications,developers search API categories to select APIs that meet specific ***,the accurate classification of APIs becomes critically ***,existing approaches,as evident on platforms like ***,face significant ***,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API ***,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data ***,the long-tailed distribution of API data introduces biases,compromising the fairness of classification *** these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous *** tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API ***-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data *** results affirm our approach's superiority over existing methods.

关键词： bidirectional encoder representations from transformers deep graph convolutional networks logit adjustment web application programming interface classification application programming interface correlation graph

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Lithological Facies Classification Using Attention-based Gated Recurrent Unit

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Tsinghua science and Technology 2024年第4期29卷 1206-1218页

作者： Yuwen Liu Yulan Zhang Xingyuan Mao Xucheng Zhou Jingwen Chang Wenwei Wang Pan Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Modern Posts Nanjing University of Posts and TelecommunicationsNanjing 210023China

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

关键词： attention mechanism facies classification GRU MLP(multilayer perceptron)

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural networks

A New Method for Predefined-Time Synchronization Control of ...

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2023 IEEE International Conference on Memristive computing and Applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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