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3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Yin, Xincheng Zhou, Yuhang Liu, Haiwang Wang, Youliang Li, Shujuan Key Lab. of NC Machine Tools and Integrated Manufacturing Equipment of the Education Ministry Xi'an710048 China School of Mechanical and Electrical Engineering Lanzhou University of Technology Lanzhou730050 China

ISBN: (纸本)9781510681804

SiC single crystal is a typical difficult-to-process material with high brittleness and high hardness, SiO2 oxide layer with lower hardness is formed on SiC surface by plasma electrochemical reaction, and then it is mechanically removed by abrasive with lower hardness to obtain lower surface roughness. Through the molecular dynamics simulation of mechanochemical action and microscopic removal of SiC oxide layer by mechanical polishing, the microscopic processes such as contact deformation, atomic scraping, friction change and temperature change between a single CeO2 abrasive particle and SiO2 oxide layer are studied. The indentation and scratching effects of abrasive particles on the oxide layer at different times are revealed from the atomic scale, and the microscopic removal mechanism of SiC oxide layer during mechanical polishing is explained. The research results enhance the understanding of microscopic removal of the SiO2 oxide layer by CeO2 abrasive particles, which provides a theoretical basis for the optimization of processing parameters for SiC oxide layer removal. © 2024 SPIE.

关键词： Single crystals

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Preclinical Assessment of Upper Limb Tremor in Parkinson's Disease with Deep Learning and Wearable Technology

Preclinical Assessment of Upper Limb Tremor in Parkinson's D...

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

作者： Lin, Fang Wang, Zhelong Qiu, Sen Zhao, Hongyu School of Control Science and Engineering Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Liaoning Dalian China

ISBN: (纸本)9798350337020

Tremors are typically experienced by patients at the beginning of Parkinson's disease (PD). Clinicians evaluate clinical symptoms based on scale and experience, but mild tremors do not have significant characteristics and are difficult to observe with the naked eye. Implementing an intelligent and objective method to identify PD patients with early tremor symptoms and healthy controls (HC) is necessary. This study used wearable sensors to collect 9-axis inertial signals and 2-channel sEMG signals at the wrists of 13 HC and 24 PD patients from Dalian Municipal Central Hospital. Based on a Long Short-Term Memory Network (LSTM), an attention mechanism, and a Fully Convolutional Network (FCN), we develop a model to classify and recognize data from PD patients. Compared the proposed method with several classification methods, the results showed that the proposed method achieved higher classification accuracy (91.78%), precision (100%), recall (87.50%), and F1-score (93.33%) of PD class than Support Vector Machine, FCN, and LSTM. The computing time of the proposed method is approximately 1 second. The proposed method identifies early PD patients by pre-clinical assessment of mild upper limb tremors, which is valuable for early treatment and rehabilitation. © 2023 IEEE.

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Mechanistic data-driven combined modelling and predictive control method for coal-fired power unit 5

Mechanistic data-driven combined modelling and predictive co...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Hu, Jingyu Han, Zhongyang Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Liaoning 116024 China

ISBN: (纸本)9798350325294

With the increase of the proportion of new energy sources connected to the grid, challenges are also emerged for modeling and control of the conventional coal-fired power units to increase their operational flexibility of deep peak-regulation. Traditional data-driven modelling (DDM) requires a large amount of training data containing a variety of operating conditions, which are hard to be fully satisfied in engineering practice. This paper investigates the mechanics and dynamic characteristics of boiler-turbine systems in the coal-fired power unit, and proposes a mechanistic data-driven combined modelling (M-DDM) method. It not only requires less amount of data comparing with the DDM based method, but also covers a wide range of operating conditions by considering the dynamic characteristics of the coal-fired power unit. A model predictive control (MPC) based method was subsequently designed as the control vehicle. In order to improve the computing efficiency and reduce the probability of model mismatch considering the involved ordinary differential equations (ODEs) and nonlinearities. reinforcement learning is embedded into the MPC. Finally, simulations are conducted for demonstrating the superior performance of the proposed methods comparing with other commonly deployed modeling and control methods. © 2023 IEEE.

关键词： Model predictive control

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Partial lab.l Feature Selection: An Adaptive Approach

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IEEE Transactions on knowledge and data engineering 2024年第8期36卷 4178-4191页

作者： Zan Zhang Jialu Yao Lin Liu Jiuyong Li Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei Anhui China UniSA STEM University of South Australia Adelaide SA Australia Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei Anhui China

As an emerging weakly supervised learning framework, partial lab.l learning aims to induce a multi-class classifier from ambiguous supervision information where each training example is associated with a set of candidate lab.ls, among which only one is the true lab.l. Traditional feature selection methods, either for single lab.l and multiple lab.l problems, are not applicable to partial lab.l learning as the ambiguous information contained in the lab.l space obfuscates the importance of features and misleads the selection process. This makes the selection of a proper feature subset from partial lab.l examples particularly challenging, and therefore has rarely been investigated. In this paper, we propose a novel feature selection algorithm for partial lab.l learning, named PLFS, which considers not only the relationships between features and lab.ls, but also exploits the relationships between instances to select the most informative and important features to enhance the performance of partial lab.l learning. PLFS constructs an adaptive weighted graph to exploit the similarity information among instances, differentiate the lab.l space and weight the feature space, which leads to the selection of a proper feature subset. Extensive experiments over a broad range of benchmark data sets clearly validate the effectiveness of our proposed feature selection approach.

关键词： Feature extraction Supervised learning Training knowledge engineering lab.ling Classification algorithms Big data

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A Novel Point Cloud Deep Learning Method for Predicting Variable Airfoil Flow Fields 43

A Novel Point Cloud Deep Learning Method for Predicting Vari...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Xinyao Cao, Junzhe Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Airfoil flow field is of great significance to the design optimization of aircraft shape. Convolutional Neural Networks (CNNs) have been applied in flow field prediction studies, which greatly reduce the cost of computation time in traditional Computational Fluid Dynamics (CFD) methods. However, such CNN-based approaches normally bring extra errors due to the procedure of interpolating irregular grid data into Cartesian grids, which may lead to underperforming prediction accuracy. In this paper, an improved PointNet++ point cloud deep learning method was proposed for the airfoil flow field prediction. The unstructured grid vertices in the CFD domain were treated as point cloud as well as the input of neural network, and only the original information of the data was adopted to avoid interpolation. Neighborhood relationship features were defined and spliced with neighborhood point features, and the features were integrated by simultaneously solving for the maximum, minimum, sum, and average values to extract the local or global features of the system to enhance the performance of deep network for extracting the information. Finally, the proposed network was trained and tested by using 549 sets of flow field data formed by 61 airfoils under 9 angle-of-attack conditions, and further predicted for an additional 32 datasets with different airfoils and conditions. The simulation results show that the method proposed in this study gets lower prediction errors in terms of pressure, Mach number, x-direction velocity, and y-direction velocity than the two other current point cloud deep networks, PointNet and PointNet++. At the same time, the computation speed of this method is about 14 times faster than the traditional CFD method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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Fault identification of rotating machinery based on multiscale convolution and deep residual networks 9

Fault identification of rotating machinery based on multisca...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Wang, Huihui Fan, Tianqi Qiu, Sen Liu, Long School of Intelligence and Electronic Engineering Dalian Neusoft University of Information Dalian116023 China Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781510672802

Rotating machinery and equipment have a wide range of industrial applications, and it is important to perform intelligent fault detection during their operation. In order to fully capture the multi-scale features of mechanical fault data while solving the degradation problem of deep networks, we propose an intelligent fault detection model combining multi-scale convolution and deep residual networks. We validate it on four publicly availab.e datasets, and the results demonstrate the excellent performance of the proposed method. © 2024 SPIE.

关键词： Convolution Feature extraction Deep learning Machine learning Vibration data modeling Lithium Convolutional neural networks data centers Signal processing

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is availab.e in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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A Low-Rank Matching Attention Based Cross-Modal Feature Fusion Method for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 1177-1189页

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo Xi'An Jiaotong School of Computer Science and Technology Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi'an710049 China Xi'An Jiaotong School of Computer Science and Technology Shaanxi Xi'an710049 China BigKEJoint Innovation Center SHMEEA Shanghai China Xi'An Jiaotong University School of Mathematics and Statistics Xi'an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. © 2024 IEEE.

关键词： Semantics

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Learning Inter-Entity Interaction for Few-Shot knowledge Graph Completion

Learning Inter-Entity Interaction for Few-Shot Knowledge Gra...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Li, Yuling Yu, Kui Huang, Xiaoling Zhang, Yuhong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Enginerring Hefei University of Technology China

Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is availab.e at https://***/cjlyl/FKGC-CIAN. © 2022 Association for Computational Linguistics.

关键词： knowledge graph

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