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Advanced Devices and Instrumentation 2024年 5卷

作者： Shang, Ziqin Chen, Shaohua Li, Guannan Zheng, Yi Dai, Jianping Qiu, Jinbo Hu, Huizhu State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Hangzhou Applied Acoustics Research Institute Hangzhou China Navigation Research Center College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Research Center for Novel Computing Sensing and Intelligent Processing Zhejiang Lab Hangzhou China State Key Laboratory of Intelligent Coal Mining and Strata Control Shanghai Institute Co. Ltd China Coal Technology & Engineering Group Shanghai China

Straightness measurements of fully mechanized coal faces are a core requirement for intelligent, comprehensive coal mining. In coal mining, using an inertial measurement unit (IMU) assisted by an odometer for straightness measurement is vital for automating longwall mining. IMUs are less vulnerable to dim and harsh underground mining conditions. However, IMU divergence cannot be controlled solely by the forward velocity obtained from the odometer. To address this problem without external sensors, this paper proposes a straightness measurement method based on a fiber optic gyroscope-IMU/odometer and shearer motion constraints. The motion of the shearer is constrained to correct the pitch and roll attitude angle errors in strapdown inertial navigation solution (SINS) attitude solutions. An extended Kalman filter was established to combine the SINS and dead reckoning using the output from the odometer. Finally, the position estimation obtained by Kalman filter fusion was converted into straightness. An experiment based on a custom-built test rail demonstrated that the proposed algorithm effectively improved the accuracy of straightness measurements in multiple cutting cycles. The error of the results in the horizontal plane was reduced by 45% compared to the traditional IMU/odometer method. This study is important in promoting the unmanned and intelligent development of China’s coal industry. © 2024 Ziqin Shang et al.

关键词： Coal industry

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The role of visual angle in pattern phase transition of collective motions

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EPL 2019年第5期128卷 50003-50003页

作者： Li, Jiawei Chen, Guanrong Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

Abundant collective motion patterns of animal groups have different kinds of functions like migration, predator avoidance and foraging. To explore the phase transition mechanism behind such charming collective behaviors, some self-propelled particle models have been proposed, most of which however have isotropic inter-particle interactions and hence could not reproduce sophisticated natural collective patterns. As a remedy, this letter develops an anisotropic selfpropelled particle model. By slightly tweaking the vision range and inter- particle attraction, the proposed model demonstrate transitions between four distinct collective motion patterns, i.e., torus, dumbbell, twist, and worm. To investigate more insightfully into the phase transition nature, quantitative analysis is carried out, revealing the relationship of visual angle-based interagent interactions and abundant pattern transitions existing in large numbers of natural, social and artificial grouping behaviors. From the industrial application point of view, the present study can help adjust the formation of multiple unmanned systems by simply tweaking a couple of vision-related parameters in their models. Copyright (C) EPLA, 2020

关键词： Self-organized systems Pattern formation General studies of phase transitions

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Remaining useful life prediction for rotating machinery based on dynamic graph and spatial-temporal network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第3期34卷 035102-035102页

作者： Zeng, Xiangyu Yang, Chaoying Liu, Jie Zhou, Kaibo Li, Di Wei, Shangwan Liu, Yujie Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Image Informat Proc & Intelligent Cont Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Belt & Rd Joint Lab Measurement & Control Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China China Construct Third Engn Bur Grp Co Ltd Wuhan 430000 Peoples R China

Deep learning-based rotating machinery remaining useful life (RUL) prediction approaches rarely consider spatial dependencies and global temporal correlation of monitoring signals simultaneously. Superiorly, graph convolutional networks (GCNs) learn relationship information among graph nodes, considering the spatial dependencies of signals. It is beneficial for constructing high-quality graphs to improve the prediction performance in single-sensor monitoring scenarios. In this paper, an RUL prediction approach for rotating machinery based on a dynamic graph and a spatial-temporal network (STNet) is proposed. Short-time Fourier transform is introduced to obtain node features, and dynamic edge connections are established through node importance weights. Furthermore, an STNet is constructed to learn graph features, in which the GCN is used to mine spatial dependencies of input graphs, and a bi-directional long short-term memory network is applied to capture global temporal correlations. Finally, an autoencoder-based graph readout layer is designed to pass learned graph features. Case studies are conducted to demonstrate its effectiveness.

关键词： rotating machinery remaining useful life prediction graph convolutional network bi-directional long short-term memory network dynamic graph

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Highly Biocompatible Drug-Delivery Systems Based on DNA Nanotechnology

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JOURNAL OF BIOMEDICAL NANOTECHNOLOGY 2017年第7期13卷 747-757页

作者： Li, Xin Hong, Li Song, Tao Rodriguez-Paton, Alfonso Chen, Congzhou Zhao, Haiyan Shi, Xiaolong Wuhan Univ Renmin Hosp Dept Gynecol 2 Wuhan 430060 Hubei Peoples R China China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Shandong Peoples R China Univ Politecn Madrid Dept Inteligencia Artificial E-28660 Madrid Spain Huazhong Univ Sci & Technol Sch Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Nanotechnology has been widely used in pharmacology and medicine, particularly for developing methods to enhance drug bioavailability and reduce the toxic side effects of drugs, and has given rise to the "nano drug-delivery system." Recent work has demonstrated that DNA nanostructures, which can efficiently penetrate cells and are highly biocompatible, hold considerable potential for use in designing effective nano drug-delivery systems. Here, we briefly review the advantages offered by DNA nanostructures for drug delivery, the various types of currently available drug-delivery systems based on DNA nanostructures, and the cellular uptake of DNA nanostructures.

关键词： DNA Nanostructure DNA Origami Drug-Delivery System Nanotechnology

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Short-term hybrid prognostics of fuel cells: A comparative and improvement study

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RENEWABLE ENERGY 2024年 237卷

作者： Sheng, Chuang Fu, Jun Qin, Hongchuan Zu, Yanmin Liang, Yezhe Deng, Zhonghua Wang, Zhuo Li, Xi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Accelink Technol Co Ltd Wuhan 430000 Peoples R China

Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the stack's service life, leading to a novel hybrid forecasting approach. The first grey model prediction method based on residual exponential smoothing optimization (ES-R-GM) can capture the voltage deterioration trend. We explore the integration of two different ES techniques, specifically the double ES (ES2) and cubic ES (ES3), to investigate their effect on enhancing the predictive accuracy of the GM model. The second method of the adaptive network fuzzy inference system (ANFIS) can characterize local nonlinear behavior. We utilize the simulated annealing (SA) algorithm to optimize ANFIS results under different fuzzy rule selection strategies. The outcomes of the two prediction methods mentioned above are combined to create a hybrid prediction using the data fusion method and the moving window technique. Various hybrid methods are evaluated under general conditions and further detailed optimization. The data collected from the experimental platform confirms the suggested hybrid framework. The results show that the hybrid ES3-R-GM + ANFIS-SC method outperformed the single models in final prediction accuracy and can effectively track both global trends and local changes. Simultaneously, it takes less time to calculate than the literature. Moreover, when applied to consistent public datasets, the hybrid approach maintains its robustness and accuracy compared with other hybrid prognostic methods.

关键词： Solid oxide fuel cell Hybrid prediction Exponential smoothing Moving window method

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Zhou, Jian-Lan Chen, Xue-Yan Wang, Hong -Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci Technol Res Inst Shenzhen 518057 Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China

This study proposes a similarity -conformity -based clustering and decentralized feedback mechanism to manage incompatible decision -makers (DMs) in the large-scale group construction decision -making problem. Firstly, conformity is proposed to measure the consistency of the most preferred alternative of any two DMs and then extends this concept to define the similarity -conformity level of DMs. The average conformity between DMs in a subgroup represents the cohesion degree of this subgroup. Based on these concepts, a new clustering method considers the conflicts of interest among DMs and introduces the similarity -conformity to divide them into several subgroups. Then, the number of DMs in a subgroup and the cohesion degree are combined to formulate the subgroups' weights. A decentralized feedback mechanism is proposed to manage two categories of incompatible DMs in the consensus -reaching process. The proposed consensus model adjusts the incompatible DMs' opinions or subgroups' weights under four scenarios. In the proposed model, the four adjustment methods prove to be convergent, and they improve the group consensus level by ensuring the DMs' conformity and the logical consistency of preference relation. Finally, it provides an example of the alternative selection for transportation infrastructure construction. The comparative study and the sensitivity analysis are performed to verify the effectiveness of the proposed method.

关键词： Large-scale group decision -making Probabilistic linguistic preference relation Similarity -conformity level Clustering Decentralized feedback mechanism Construction decision -making

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Channel Reflection: Knowledge-Driven Data Augmentation for Eeg-Based Bcis

SSRN

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SSRN 2024年

作者： Wang, Ziwei Li, Siyang Luo, Jingwei Liu, Jiajing Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China China Electronic System Technology Co. Ltd. Beijing100089 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: 1) CR is effective, i.e., it can noticeably improve the classification accuracy;2) CR is robust, i.e., it consistently outperforms other data augmentation approaches in the literature;and, 3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further increase the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Effective usage of credit records promotes cooperation on weighted networks

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Physical Review E 2010年第3期81卷 036112-036112页

作者： Chao Zhai Hai-Tao Zhang Yang Zhao Michael Z. Q. Chen Zhi-Hai Rong Bing-Hong Wang Key Laboratory of Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan 430074 People’s Republic of China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan 430074 People’s Republic of China Department of Modern Physics University of Science and Technology of China Hefei 230026 People’s Republic of China School of Automation Nanjing University of Science and Technology Nanjing 210094 People’s Republic of China Department of Mechanical Engineering The University of Hong Kong Pok Fu Lam Road Hong Kong Department of Automation Donghua University Shanghai 200051 People’s Republic of China

The cooperative behaviors of players on weighted networks are investigated by incorporation of trust mechanisms into a well-accepted game model, i.e., the networked prisoner’s dilemma game, afterwards some weight-updating schemes are designed according to the credit records. Despite the differences in network topologies and strategy updating protocols, a simple yet significant principle surfaces that, to promote the emergence of cooperation over abundant weighted networks, only the latest credit record of partners is required to be taken into consideration, whereas incorporating more previous records may even deteriorate the cooperation performance. To support such an appealing principle, we have investigated more deeply into the role of credit records so as to give a detailed explanation underlying it. The virtue of this work lies in providing insights into the effective usage of the currently available credit records.

关键词： Network topology

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Named Entity Recognition Based on Anchor Span for Manufacturing Text Knowledge Extraction

SSRN

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SSRN 2024年

作者： Li, Yahui Sun, Qi Zhou, Chunjie Liu, Lu Tian, Yu-Chu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Hubei Wuhan430074 China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China School of Computer Science Queensland University of Technology BrisbaneQLD4001 Australia

intelligent industrial manufacturing heavily relies on structured knowledge. Named Entity Recognition (NER), an essential technique for extracting structured knowledge from text, has garnered significant research interest. However, current NER approaches face difficulties in handling multiple levels of entity nesting in knowledge extraction, especially within the manufacturing context. To address this issue, we present an Anchor Span-based NER (ASNER) approach for accurately and efficiently extracting manufacturing text knowledge. This method combines token and span classification. Initially, the head and tail features of the entity are extracted, with the corresponding anchor span of potential entities generated based on a boundary match neural network to localize the entities. Subsequently, token spatial contextual features are extracted using biaffine attention and convolutional neural networks, and classification categories are assigned to the anchor span-filtered entity features. Experimental studies demonstrate the effectiveness of the proposed ASNER approach. © 2024, The Authors. All rights reserved.

关键词： Character recognition

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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