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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Xia, Changhai Peng, Zhiping Cui, Delong Li, Qirui Sun, Lihui School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China Jiangmen Polytechnic College Jiangmen City529090 China School of Computer Science Guangdong University of Petrochemical Technology Maoming City525000 China

ISBN: (纸本)9781643684703

This study presents a cutting-edge soft sensing approach for coke-making diagnostics, aimed at tackling the challenges posed by multifaceted, nonlinear, non-Gaussian, and noisy operational data prevalent in coke-making ovens. Our proposed method leverages a Bayesian t-distributed mixed regression model, effectively capturing the intricate nature of multivariate, nonlinear, and non-Gaussian data. The utilization of the t-distribution ensures the model's resilience to interference, with model parameter estimation achieved within a Bayesian framework. Conducting simulation experiments and real industrial experiments, as well as comparative analysis with PLSR, GMR, and GPR models, we demonstrate the model's good robustness, excellent prediction accuracy, and robustness, further confirming its potential application in coking diagnosis. © 2023 The authors and IOS Press.

关键词： Coke

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Liufu, Ying Liu, Mei Jin, Long Wang, Feiyue School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems. © 2023 IEEE.

关键词： ACP constraints differential neural solution numerical simulation Quadratic optimization with quadratic

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Image Visibility Graph Analysis of the Eye Disease Images 16

Image Visibility Graph Analysis of the Eye Disease Images

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Wu, Dandan Ma, Zhiyi Yang, Xiaodong Jiang, Guangkang Yan, Chen Ma, Lu China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Suzhou Vocational and Technical College Suzhou234000 China

ISBN: (纸本)9798350330755

Visibility graph algorithm is a widely used time series complex network transformation algorithm. It has been mainly applied to one-dimensional or multidimensional time series, while many real-life objects, such as images, cannot be represented by sequences. In this paper, we focus on two-dimensional images and propose a novel method of image visibility graph network (IVGN) with redefined node-degree features. We use the staining images of corneal ulcer of eye as the research object and demonstrate the application of IVGN to medical images. Our method consists of the following steps: (1) We construct a novel IVGN by treating each pixel as a node and connecting the visible pixels. We compute the average node degree of the IVGN for ocular pathology image analysis. (2) Based on the IVGN, we extract the node-degree features of the visible images and map them to a new degree topology, which is used for image enhancement. (3) By doing so, we build a gray co-occurrence matrix based on the degree topology and extract its four parameters, showing the role of the degree topology in image texture extraction. (4) As a result, we use the proposed contrast-based descriptors to achieve effective detection of eye staining images with different categories and severity levels. © 2023 IEEE.

关键词： Pixels

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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On-line detection model of total alkalinity of water based on improved genetic algorithm

On-line detection model of total alkalinity of water based o...

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2021 International Conference of Social Computing and Digital Economy, ICSCDE 2021

作者： Jin, Tao Wang, Zhen Cai, Shao-Bin Jiang, Yong-Zeng Wang, Yi-Ping College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Computer and Control Engineering Qiqihar University Qiqihar161006 China College of Computer Science and Technology Huaqiao University Xiamen361021 China

ISBN: (纸本)9781665401425

In order to quantitatively analyze the total alkalinity of water in eco-industrial park, conduct on-line detection of total alkalinity of water in eco-industrial park, and improve the monitoring and dredging ability of total alkalinity of water and related pollutants in eco-industrial park, an on-line detection model of total alkalinity of water in eco-industrial park based on improved genetic algorithm was proposed. The statistical information mining model of total alkalinity and concentration characteristic distribution of related pollutants in eco-industrial park is constructed, the hydrogeological conditions are identified, the characteristic structural parameters of total alkalinity in eco-industrial park are determined, the improved genetic evolutionary algorithm method is adopted to manage the distribution characteristic of total alkalinity and concentration of related pollutants in eco-industrial park, and the aquifer parameters in the evaluation area are simulated. A transfer conduction control model of total alkalinity and related pollutants concentration characteristic distribution in eco-industrial park was established. The diversion rate and throughput of total alkalinity and related pollutants concentration characteristic distribution in eco-industrial park were taken as constraint indicators, and the transfer control of total alkalinity and related pollutants concentration characteristic distribution in eco-industrial park was carried out, so as to realize numerical quantitative detection and analysis of total alkalinity in eco-industrial park. The simulation results show that this method has good performance in online detection of total alkalinity of water quality in eco-industrial park, and has strong dredging ability, which improves the treatment level of total alkalinity of water quality and related pollutants in eco-industrial park. © 2021 IEEE.

关键词： Water quality

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A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles

A Learning-based Incentive Mechanism for Mobile AIGC Service...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jiani Fan Minrui Xu Ziyao Liu Huanyi Ye Chaojie Gu Dusit Niyato Kwok-Yan Lam School of Computer Science and Engineering Nanyang Technological University Singapore College of Control Science and Engineering Zhejiang University People’s Republic of China

Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV) network have numerous advantages over traditional cloud-based AIGC services, including enhanced network efficiency, better reconfigurability, and stronger data security and privacy. Nonetheless, AIGC service provisioning frequently demands significant resources. Consequently, resource-constrained roadside units (RSUs) face challenges in maintaining a heterogeneous pool of AIGC services and addressing all user service requests without degrading overall performance. Therefore, in this paper, we propose a decentralized incentive mechanism for mobile AIGC service allocation, employing multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context, optimizing user experience and minimizing transmission latency. Experimental results demonstrate that our approach achieves superior performance compared to other baseline models.

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Discrete memristive neuron model and its interspike interval-encoded application in image encryption

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science China(Technological sciences) 2021年第10期64卷 2281-2291页

作者： BAO Han HUA ZhongYun LIU WenBo BAO BoCheng College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China

Bursting is a diverse and common phenomenon in neuronal activation patterns and it indicates that fast action voltage spiking periods are followed by resting *** interspike interval(ISI)is the time between successive action voltage spikes of neuron and it is a key indicator used to characterize the ***,a three-dimensional memristive Hindmarsh-Rose(mHR)neuron model was constructed to generate hidden chaotic ***,the properties of the discrete mHR neuron model have not been investigated,*** this article,we first construct a discrete mHR neuron model and then acquire different hidden chaotic bursting sequences under four typical sets of *** make these sequences more suitable for the application,we further encode these hidden chaotic sequences using their ISIs and the performance comparative results show that the ISI-encoded chaotic sequences have much more complex chaos properties than the original *** addition,we apply these ISI-encoded chaotic sequences to the application of image *** image encryption scheme has a symmetric key structure and contains plain-text permutation and bidirectional diffusion *** results and security analyses prove that it has excellent robustness against various possible attacks.

关键词： chaos complexity chaotic bursting sequence memristive neuron model interspike interval(ISI) image encryption

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Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Ma Yuanrong Xu Suyu Dong Wei Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Weihai China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system’s effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time.

关键词： Accuracy Image recognition Attention mechanisms Image matching Fingerprint recognition Feature extraction Robustness Computational efficiency Bioinformatics

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An Erasure-Coded Data Update Method for Distributed Storage Clusters

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2022年第11期59卷 2451-2466页

作者： Zhang, Zilin Liu, Duo Tan, Yujuan Wu, Yu Luo, Longpan Wang, Weilüe Qiao, Lei College of Computer Science Chongqing University Chongqing400044 China Beijing Institute of Control Engineering Beijing100080 China

Erasure coding is widely deployed in distributed storage clusters to provide data reliability, but the disk I/O overhead becomes a performance bottleneck when data updates are intensive. On the one hand, traditional data update strategies need to read the original data chunk, and then write new data when updating the data chunk. In the case of intensive updates, frequent write-after-read seriously affects the write performance of the storage clusters. On the other hand, the operations of updating the parity chunk include reading the increments randomly distributed in the log file and merging them with the data file, which also introduces additional disk seek overhead. In this paper, a data updating method, named PARD (parity logging with reserved space and data delta), is proposed to solve these problems. The main idea of PARD is to use the linear calculations of erasure coding to reduce write-after-read, and take advantage of the disk characteristics to reduce the disk seek overhead. PARD comprises three key design features: 1) Adopting in-place data updates and log-based parity updates. 2) Taking advantage of the linear calculations of erasure coding to construct the log based on data increments. For a series of write requests to the same data chunk, only the first update needs to read the original data chunk, and the subsequent update executes the pure write, which remarkably reduces the write-after-read. 3) According to the characteristics of disk, reserving space for the log at the end of data file to reduce the disk seek overhead of reading and writing log. Experiments show that when the chunk size is 4 MB, PARD gains at least, 30.4%, 47.0% and 82.0% improvements in update throughput compared with PLR, PARIX, and FO, respectively. © 2022, science Press. All right reserved.

关键词： Digital storage

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Semantic Correspondence with Peripheral Position Coding

Semantic Correspondence with Peripheral Position Coding

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Jinjian Chen Zuoyong Li Taotao Lai Huosheng Xie College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM.

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