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第42届中国控制会议

作者： Faxiang Zhang Guanbin Gao Jing Na Jiande Wu Faculty of Mechanical and Electrical Engineering and Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University

This paper solves the neural network(NN) tracking control problem for uncertain nonlinear servo system with timevarying parameters and *** address the uncertain nonlinear function with unknown time-varying parameters(i.e.,unknown nonlinear spatiotemporal function),the time-varying parameter extraction method is used to separate the time-varying puameters from uncertain nonlinear spatiotemporal function,which yields an unknown state-based boundary *** the tools of NN and adaptive technology,an adaptive neural tracking controller is designed,which guarantees the uniformly ultimately bounded(UUB) performance of the resulting closed-loop *** effectiveness of the designed method is verified by simulations.

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Noise Detection and Removal for Underground Communication Systems Based on L2 N onnal Inner Product

Noise Detection and Removal for Underground Communication Sy...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Bangwei Yu Yong Wang Liangang Qi Yang Liu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Chengdu China

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate(BER). In this paper, a Normal Inner Product Orthogonal Matching Pursuit(NIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, a cyclic prefix(CP) is added in front of the sent data. Then the observation vector is obtained by making a difference between the CP of the latter set of data and the CP of the former set of data. Then, the columns of the sensing matrix that are most correlated with the observation vectors are selected as candidate support sets by calculating the L2 norm. The least squares method was used to solve for the previous set of pump noise estimates. Finally, the pump noise is reconstructed using the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We discuss the noise characteristics of pump noise and analyze the denoising performance of the NIPOMP algorithm in depth. Simulation results show that the algorithm significantly reduces the system BER in complex channel environments with more drastic fading, including gas-phase, liquid-phase, gas-liquid two-phase, and liquid-solid two-channel environments.

关键词： Liquids Communication systems Simulation Oils Noise Noise reduction Matching pursuit algorithms Vectors Sensors Signal to noise ratio

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Generative artificial intelligence and its applications in materials science:Current situation and future perspectives

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Journal of Materiomics 2023年第4期9卷 798-816页

作者： Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear Science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia

Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity ***,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of *** applications of task-specific generative models involving materials inverse design and data augmentation are also *** ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials ***,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding *** work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

关键词： Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning

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A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks 13

A Heuristic Sparsification Method to Improve the Performance...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Zhang, Fangyu Wei, Yanjie Lin, Zhenli Pan, Yi Liu, Jin Centre for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350313925

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced. © 2023 IEEE.

关键词： Topology

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Impulsive Control of Heterogeneous Multi-agent Systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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Chinese Automation Congress (CAC)

作者： Ranran Jiao Bo Li Xiangyong Chen Xiaowei Jiang Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol.

关键词： Energy consumption Protocols Automation Event detection Delay effects Control systems Delays Multi-agent systems

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A Weighting Possibilistic Fuzzy C-Means Algorithm for Interval Granularity 17th

A Weighting Possibilistic Fuzzy C-Means Algorithm for Interv...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Tang, Yiming Xi, Lei Wu, Wenbin Wu, Xi Li, Shujie Chen, Rui Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819923847

Granular clustering is an emerging branch in the field of clustering. However, the existing granular clustering algorithms are still immature in terms of weight setting of granular data and noise resistance. In this study, a weighting possibilistic fuzzy c-means algorithm for interval granularity (WPFCM-IG) is proposed. To begin with, a new weight setting method for interval granular data is given. The principle of justifiable granularity is used as the evaluation criterion of granular data, and a weight is assigned to each granular data from two perspectives of coverage and specificity to measure the quality of the granular data. In addition, the idea of possibilistic clustering is introduced, which is helpful to improve the noise resistance. And, with the proposed weights of interval granular data, the influence of data with smaller weights on the clustering results can be reduced during the clustering process. Based upon these ideas, the WPFCM-IG algorithm is put forward, and its core idea, formula derivation and implementation process are described. Finally, the performance of the proposed algorithm is verified by comparison experiments on the artificial and UCI datasets. The experimental results show that the WPFCM-IG algorithm is better than other advanced algorithms in this field in terms of reconstruction error. Next, the WPFCM-IG algorithm is smoother than other algorithms on the collaborative relationship curve between the fuzzy coefficient and the reconstruction error, so WPFCM-IG can better optimize the fuzzy coefficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Ultra-high extinction ratio optical pulse generation with a thin film lithium niobate modulator for distributed acoustic sensing

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Photonics Research 2024年第1期12卷 40-50页

作者： YUAN SHEN XIAOQIAN SHU LINGMEI MA SHAOLIANG YU GENGXIN CHEN LIU LIU RENYOU GE BIGENG CHEN YUNJIANG RAO Research Center for Optical Fiber Sensing Zhejiang LaboratoryHangzhou 311100China Research Center for Intelligent Optoelectronic Computing Zhejiang LaboratoryHangzhou 311100China International Research Center for Advanced Photonics College of Optical Science and EngineeringZhejiang UniversityHangzhou 310058China Fiber Optics Research Center(FORC) Key Laboratory of Optical Fiber Sensing and CommunicationsUniversity of Electronic Science and Technology of ChinaChengdu 611731China

We experimentally demonstrate ultra-high extinction ratio(ER)optical pulse modulation with an electro-optical modulator(EOM)on thin film lithium niobate(TFLN)and its application for fiber optic distributed acoustic sensing(DAS).An interface carrier effect leading to a relaxation-tail response of TFLN EOM is discovered,which can be well addressed by a small compensation component following the main driving *** ultrahigh ER>50 dB is achieved by canceling out the tailed response during pulse modulation using the EOM based on a cascaded Mach–Zehnder interferometer(MZI)*** modulated optical_(√)pulses are then utilized as a probe light for a DAS system,showing a sensitivity up to-62.9 dB·rad∕Hz~2(7 pε/Hz)for 2-km single-mode sensing *** crosstalk suppression of 24.9 dB along the fiber is also obtained when the ER is improved from 20 dB to 50 dB,clearly revealing its importance to the sensing performance.

关键词： modulator pulse interferometer

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A Feature Reduction-Induced Subspace Multiple Kernel Fuzzy Clustering Algorithm 17th

A Feature Reduction-Induced Subspace Multiple Kernel Fuzzy C...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Tang, Yiming Li, Bing Pan, Zhifu Sun, Xiao Chen, Renhao Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819923847

High-dimensional data poses a great challenge to clustering, and subspace clustering algorithms have unique advantages when working with high-dimensional data. However, there are still some difficulties in adapting the nonlinear feature dimension, the selection and collaborative settings of kernel functions. Therefore, in this study, we come up with a feature reduction-induced subspace multiple kernel fuzzy clustering. Firstly, in order to solve the information loss caused by the nonlinear characteristics of the data, the multiple kernel clustering method is introduced, and some nonlinear features of the data are collaboratively mapped to the high-dimensional linear space, so as to better understand the characteristic information of the data. Secondly, due to the complexity of subspace and multiple kernel learning, we introduce the idea of feature reduction, and reduce the data dimension according to the importance of information, which can reduce the complexity of the algorithm on the one hand, and improve the role of important feature attributes in clustering on the other hand. Finally, the proposed RS-MKFC algorithm and the related 6 algorithms are compared on 6 ordinary datasets and 4 high-dimensional datasets, and it is found that the proposed algorithms are superior over the other 6 algorithms. At the same time, we discover the ability of RS-MKFC algorithm to screen important features as well as the function of feature reduction, and good results are achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Efficient Finite-Difference Time-Domain Method for Solving Electromagnetic Scattering in Layered Anisotropies

Efficient Finite-Difference Time-Domain Method for Solving E...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yilin Kang Yuxian Zhang Xiaoli Feng Lixia Yang Zhixiang Huang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education and with the Information Materials and Intelligent Sensing Laboratory of Anhui Province Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits and with School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, we propose an efficient numerical method for layered models based on the conventional finite difference time-domain (FDTD) method. The three-dimensional (3-D) layered model is compressed into one-dimensional (1-D) calculation, which improves the problem of large space memory and long running time for 3-D modeling while ensuring the accuracy and feasibility of the algorithm. On the one hand, by employing the transformation relationship between the transverse wave vector and the field in space, the expanded Maxwell's rotation equations are simplified, so that the equation system only retains the spatial transformation in the propagation direction. This processing allows for the calculation of the propagation characteristics of layered media without requiring more computational resources. On the other hand, to ensure the universality of the algorithm, a fully tensor anisotropic medium is selected for detecting the propagation data. At the same time, we design the layered structure and conduct the consistent 3-D modeling in the simulation software COMSOL. The results showed that our proposed efficient numerical algorithm greatly reduced the loss of computing resources while ensuring accuracy.

关键词： Solid modeling Three-dimensional displays Accuracy Tensors Software algorithms Vectors Mathematical models Numerical models Time-domain analysis Finite difference methods

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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