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

作者： Li, Shaokai Song, Peng Ji, Liang Jin, Yun Zheng, Wenming Yantai University School of Computer and Control Engineering China Jiangsu Normal University School of Physics and Electronic Engineering China Research Center for Learning Science Southeast University Key Laboratory of Child Development and Learning Science Southeast University Ministry of Education China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9781728163277

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches. © 2023 IEEE.

关键词： Speech recognition

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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IoT Service Description and Composition Method with Knowledge Graph

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 1047-1065页

作者： Tang, Zheng-Yi Liu, Chuan Lian, Zhi-Zhu Wang, Jin-Shui Hossain, Md. Alamgir School of Computer Science and Mathematics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Xiyuangong Road Fujian Province Fuzhou City350121 China Key Laboratory of Hunan Province for Mobile Business Intelligence Hunan University of Technology and Business Yuelu Road Hunan Province Changsha City410205 China Department of Management Hajee Mohammad Danesh Science and Technology University Dinajpur5200 Bangladesh

Service computing techniques have achieved remarkable results in solving the cohesiveness problem of Web applications and are therefore introduced into the IoT do-main to break the trend of inlining IoT systems. However, compared with the traditional Internet, IoT has richer elements and semantic connotations, so the existing service description framework needs to be improved and extended. In this paper, we propose an ontology description framework for IoT services, design corresponding inference rules, and give a storage method for IoT services by integrating the knowledge graph technology for the strongly associated characteristics of IoT services. On this basis, an algorithm for combining IoT services based on graph planning is proposed. The experimental results show that the proposed method in this paper can provide richer entity semantics and ef-fectively improve the efficiency and success rate of IoT service composition. © The Authors.

关键词： Ontology

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Infrastructure-Based Vehicle Localization System for Indoor Parking Lots Using RGB-D Cameras

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 61-69页

作者：曹炳全贺越生庄瀚洋杨明 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China

Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet ***,infrastructure-based cooperative driving systems have become a means to realizing intelligent *** this paper,we propose a novel and practical vehicle localization system using infrastructure-based RGB-D cameras for indoor parking *** the proposed system,we design a depth data preprocessing method with both simplicity and efficiency to reduce the computational burden resulting from a large amount of ***,the hardware synchronization for all cameras in the sensor network is not implemented owing to the disadvantage that it is extremely cumbersome and would significantly reduce the scalability of our system in mass ***,to address the problem of data distortion accompanying vehicle motion,we propose a vehicle localization method by performing template point cloud registration in distributed depth ***,a complete hardware system was built to verify the feasibility of our solution in a real-world *** in an indoor parking lot demonstrated the effectiveness and accuracy of the proposed vehicle localization system,with a maximum root mean squared error of 5 cm at 15Hz compared with the ground truth.

关键词： infrastructure-based RGB-D camera vehicle localization point cloud registration

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION WITH CO-DISTRIBUTION ALIGNMENT

arXiv

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arXiv 2023年

作者： Wang, Tao Huang, Zhongzheng Wu, Jiawei Cai, Yuanzheng Li, Zuoyong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University China College of Computer and Data Science Fuzhou University China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China School of Electrical and Mechanical Engineering Fujian Agriculture and Forestry University China

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Multi-Objective Team Personality Tourism Route Design Based on Fuzzy APACA

Multi-Objective Team Personality Tourism Route Design Based ...

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Emerging Technology (INCET), International Conference for

作者： Xiaoling Song Huanhuan Peng Key Laboratory of Intelligent Information Processing Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In order to solve the challenge of multi-objective team personalized tourism route design, in view of the shortcomings of the existing divide and conquer algorithms, this study proposes an innovative personalized tourism route design method based on fuzzy APACA. This new scheme uses the principles of gradient descent theory to accurately identify and locate key influencing factors, and accordingly carries out a sensible classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of fuzzy APACA, this scheme cleverly constructs the design strategy of tourism routes. The empirical results show that this scheme shows a significant improvement compared with the traditional divide and conquer algorithm in terms of key performance indicators such as the accuracy of personalized tourism route design and the processing efficiency of key factors, showing its obvious strong advantages. In the multi-objective team, the personalized tourism route design plays a crucial role, which can accurately predict and optimize the growth trend and output results of the multi-objective team's personalized tourism route design. However, in the face of complex simulation tasks, traditional divide-and-conquer algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of fuzzy APACA optimization for the design of personalized tourism routes, and accurately controls the influencing parameters through gradient descent theory, which is used as the road map for index allocation, and then uses fuzzy APACA to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the design of multi-

关键词： Accuracy Roads key performance indicator Interference Prediction algorithms Market research Classification algorithms

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Fractional-Order Sliding Mode Finite Time control for the Speed and Tension System of the Reversible Cold Strip Rolling Mill

Fractional-Order Sliding Mode Finite Time Control for the Sp...

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第43届中国控制会议

作者： Zhipeng Lin Le Liu Ranyang Deng Yiming Fang Miao Xu Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Yanshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To enhance the tracking control performance of the speed and tension system of the reversible cold strip rolling mill and reduce the impact of uncertainties,a fractional-order sliding mode finite time control strategy is proposed based on adaptive sliding mode disturbance ***,novel fractional-order adaptive sliding mode disturbance observers(NFOASMDOs)are employed to dynamically observe the unmatched uncertainties in the system,thereby effectively enhancing the tracking control ***,the dynamic surface backstepping control is combined with the fractional-order sliding mode control to complete the designs of the finite time fractional-order sliding mode controllers(FTFOSMCs),which suppress the system's chattering phenomenon effectively,and avoid the "differential explosion" ***,the feasibility of the presented control method is illustrated by the simulation comparison study.

关键词： Reversible cold strip rolling mill Adaptive sliding mode disturbance observer Fractional-order sliding mode control Dynamic surface backstepping control

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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tian, Zhuojun Zhang, Zhaoyang Li, Yiwei Bennis, Mehdi Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Oulu Center for Wireless Communications Oulu90014 Finland State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing Hangzhou310027 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China

To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the ‌Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is learned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents. To further reduce the complexity of hardware implementation, we incorporate a group sparse regularization term in the loss function, enabling the learned local model to achieve structured sparsity. Then, a binary mask aggregation algorithm is designed by introducing an intermediate aggregation tensor and adding a personalized fine-tuning step in each iteration, which constrains model updates towards the local data distribution. The proposed method effectively leverages the relativity among agents while meeting personalized requirements in heterogeneous node conditions. We also provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method. Numerical simulations confirm the validity of the DSLTH and demonstrate the effectiveness of the proposed algorithm. © 2015 IEEE.

关键词： Iterative methods

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