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检索条件"机构=State and Beijing Key Laboratory of Process Automation in Mining and Metallurgy"

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RFID-Based Indoor Human Behavior Recognition Using SATCN: A Self-Attention Enhanced Temporal Convolutional Network

RFID-Based Indoor Human Behavior Recognition Using SATCN: A ...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Zhenchao Chen Mulan Yang Xuehan Hou Lvqing Yang Wensheng Dong Bo Yu Qingkai Wang School of Informatics Xiamen University Xiamen China School of Economics and Finance Xi’an Jiaotong University Xian China School of Software Xi’an Jiaotong University Xian China Zijin Zhixin(Xiamen)Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the field of indoor human behavior recognition, traditional deep learning methods often underperform in adequately capturing temporal features and core information in RFID behavior data, leading to suboptimal predictive performance. We note that human behavior is primarily composed of complex action sequences, where certain sequences formed by Received Signal Strength Indicator (RSSI) values may exhibit more representative and information-rich patterns. Therefore, we propose SATCN: an innovative model that leverages Temporal Convolutional Networks (TCN) and self-attention mechanisms for advanced RFID-based human behavior recognition. The SATCN model employs TCN to adeptly capture the inherent temporal dependencies within behavior data, while integrating self-attention mechanisms to focus on high-value features in the data. Experimental validation on RFID indoor human behavior recognition dataset reveals that SATCN achieves an accuracy of 94.51%, a 10.83 percentage point increase over existing models, highlighting SATCN’s potential in enhancing the accuracy of RFID-based indoor human behavior recognition.

关键词： Measurement Deep learning Accuracy Federated learning Convolution Data models Behavioral sciences

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RFRN: Cross-domain RFID Activity Recognition Using a Few Samples

RFRN: Cross-domain RFID Activity Recognition Using a Few Sam...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qianwen Mao Mulan Yang Xuehan Hou Lvqing Yang Wensheng Dong Bo Yu Qingkai Wang School of Informatics Xiamen University Xiamen China School of Economics and Finance Xi’an Jiaotong University Xian China School of Software Xi’an Jiaotong University Xian China Zijin Zhixin(Xiamen)Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Performing cross-domain activity recognition using a few samples is a challenge in the field of radio frequency identification (RFID) sensing. Due to multipath effect, RF signals collected from different domains exhibit unbalanced label distribution and heterogeneous signal distribution, which leads to domain shift problem. Moreover, constrained by cost, the labeled data available for training in each domain is limited. In this paper, we propose RFRN, an RFID activity recognition system based on Relation Network, and improve the recognition accuracy through several designs. First, the structure of Relation Network is improved to extract activity-related features, filter domain-related interference and then match different activities without additional fine-tuning. Second, a new task generation strategy is proposed to make full use of source domains and enable the model to experience more tasks. Experiments demonstrate that RFRN can effectively handle the domain shift problem and adapt well to new domains with a few samples. With one and five samples of each activity, RFRN outperforms the baselines by at least 7.3% and 4.0% on a real-world dataset with balanced label distribution and around 22.2% and 7.7% on a dataset with unbalanced label distribution, respectively.

关键词： Training Matched filters RF signals Interference Activity recognition Feature extraction Real-time systems

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Nonlinear Successive Compensation Method for Plant-Wide Production Index Based on Industrial Data

Nonlinear Successive Compensation Method for Plant-Wide Prod...

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第34届中国控制与决策会议

作者： Kang Li Guobin Zou Yinlian Duan Fuli Wang BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining and Metallurgy Research College of Information Science and Engineering Northeastern University

Due to the complexities of high-dimensionality,nonlinearity,many constraints,and process model mismatch,the plant-wide production index needs to be compensated when the production index is applied to the actual production *** paper proposes a nonlinear successive compensation method for the plant-wide production index based on industrial *** on the set point of the production index obtained from the plant-wide optimization,the method uses the large amount of industrial data stored in the industrial process to establish the relationship between production index compensation and the increase of economic benefit,optimizing the solution value of the production index compensation that maximizes the economic benefit *** iterative compensation,the method gradually improved the economic benefit of the production *** proposed method was applied to the hydrometallurgical production process of a *** results verified the effectiveness of the proposed method.

关键词： Hydrometallurgical Industrial data optimal compensate plant-wide production index

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Based on Clustering Algorithm Expert System of Self-learning Rule Base

Based on Clustering Algorithm Expert System of Self-learning...

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第34届中国控制与决策会议

作者： Cai Guoliang Wang Guicheng Guan Changliang Yin Fengfeng Zhu Jiale Zijin ZhiKong (Xiamen) Technology Co. Ltd. State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining& Metallurgy Shanghai Institute of Technology

Through data analysis method to obtain the internal connection of the data set,and extract different characteristics from it,and provide a strategy for the self-learning and acquisition of the expert system rule *** article analyzes the composition and operating principle of the expert system,and selects the clustering algorithm to design the process of self-learning rules,and then obtains the rule base of the expert system,trying to solve the problem of difficulty in obtaining the empirical reasoning rule base.A circuit fault diagnosis example is selected to verify the feasibility of the *** simulation tests,it is proved that the algorithm can realize self-learning rules,and an expert system rule base is initially formed,and fault information can be reasoned and diagnosed to simulate or replace empirical rules to a certain extent.

关键词： Self-learning K-means Clustering Algorithm Expert System Rule Base

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Nonlinear Successive Compensation Method for Plant-Wide Production Index Based on Industrial Data

Nonlinear Successive Compensation Method for Plant-Wide Prod...

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Chinese Control and Decision Conference, CCDC

作者： Kang Li Guobin Zou Yinlian Duan Fuli Wang BGRIMM Technology Group Beijing China State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining and Metallurgy Research Beijing China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665478977

Due to the complexities of high-dimensionality, nonlinearity, many constraints, and process model mismatch, the plant-wide production index needs to be compensated when the production index is applied to the actual production process. This paper proposes a nonlinear successive compensation method for the plant-wide production index based on industrial data. Based on the set point of the production index obtained from the plant-wide optimization, the method uses the large amount of industrial data stored in the industrial process to establish the relationship between production index compensation and the increase of economic benefit, optimizing the solution value of the production index compensation that maximizes the economic benefit increment. Through iterative compensation, the method gradually improved the economic benefit of the production process. The proposed method was applied to the hydrometallurgical production process of a refinery. The results verified the effectiveness of the proposed method.

关键词： Economics Industrial economics Production Big Data Data models Complexity theory Indexes

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PHM of Roller Tyre in Nonferrous Metal Crushing process Based on DUAE

PHM of Roller Tyre in Nonferrous Metal Crushing Process Base...

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第35届中国控制与决策会议

作者： Zhenyu Guo Yu Fu Kang Li Guobin Zou Qingkai Wang BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy College of Information Science and Engineering Northeastern University

The roller mill is a crucial production equipment for the nonferrous metal beneficiation process,responsible for crushing *** time,the roller tyre of the roller mill gradually wears down,ultimately impacting the crushing ***,Prognostics and Health Management(PHM) of the roller tyre is crucial to enhancing the production efficiency of the roller *** this study,a PHM method based on Dual Autoencoder(DUAE) is proposed to predict the grinding roller tyre's service *** effectiveness of the proposed PHM method is validated using actual data from a domestic *** research demonstrates that the PHM of roller tyres can be effectively conducted.

关键词： roller mill PHM DUAE nonferrous metal beneficiation process

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PHM of Roller Tyre in Nonferrous Metal Crushing process Based on DUAE

PHM of Roller Tyre in Nonferrous Metal Crushing Process Base...

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Chinese Control and Decision Conference, CCDC

作者： Zhenyu Guo Yu Fu Kang Li Guobin Zou Qingkai Wang BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China College of Information Science and Engineering Northeastem University Shenyang China

The roller mill is a crucial production equipment for the nonferrous metal beneficiation process, responsible for crushing ores. Over time, the roller tyre of the roller mill gradually wears down, ultimately impacting the crushing efficiency. Therefore, Prognostics and Health Management (PHM) of the roller tyre is crucial to enhancing the production efficiency of the roller mill. In this study, a PHM method based on Dual Autoencoder (DUAE) is proposed to predict the grinding roller tyre’s service life. The effectiveness of the proposed PHM method is validated using actual data from a domestic concentrator. This research demonstrates that the PHM of roller tyres can be effectively conducted.

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Inspection trolley based on the distance detection obstacle

Inspection trolley based on the distance detection obstacle

引用

Chinese Control and Decision Conference, CCDC

作者： Guicheng Wang Ao Sheng Xiaobin Li Min Zhang Changliang Guan Fengfeng Yin School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China Library Shanghai Institute of Technology Shanghai China Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

In Industry 4.0 background, unmanned full-process automated production has gradually become a hot topic, and the realization of intelligent and information factory requires the joint cooperation of multiple industries, in which the inspection device that replaces traditional manual work is an inescapable topic. At present, domestic and foreign research on intelligent inspection trolley is focused on obstacle avoidance function based on various sensors. In this context, it is introduced an algorithm based on monocular camera as a sensor, using Python language to access OpenCV library, and utilizing Gaussian filter function and Canny operator-based image contour extraction to complete the distance measurement. The algorithm can achieve the distance measurement and obstacle avoidance function at a lower cost. The test results show that the system can automatically identify the distance of obstacles in the inspection process, and according to the visual information captured by the camera in real time, it can independently generate steering action and collision reminder, and can complete the preliminary requirement of intelligent inspection.

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Research on soft sensor of copper concentrate grade based on NNG-LSSVM 4

Research on soft sensor of copper concentrate grade based on...

引用

4th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2020

作者： Wu, Hao Sun, Jianpeng Pan, Bingqing Sun, Kai Jinan China State Key Laboratory of Process Automation Mining and Metallurgy Ganization Beijing China Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing China

ISBN: (纸本)9781450387811

In the ore flotation process, the grade of copper concentrate determines the quality of the final product. Nevertheless, in the actual production, the measurement of this parameter takes a long time, making it difficult to measure in real time online. In this paper, a soft sensor method based on nonnegative garrote (NNG) and least squares support vector machine (LSSVM) is proposed. This paper uses the actual production data provided by the DCS system to predict and model this variable The simulation results show that the researched soft-sensing method can accurately predict the change of copper concentrate grade, and can achieve real-Time estimation during actual production process. In addition, the accuracy of the proposed algorithm is obviously superior to other soft-sensing methods. © 2020 ACM.

关键词： Least squares approximations

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Hybrid mean impact value with nonnegative garrote for input variable selection of multi-layer perceptron 4

Hybrid mean impact value with nonnegative garrote for input ...

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4th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2020

作者： Wang, Hongxun Ma, Fengying Sun, Kai Jinan China State Key Laboratory of Process Automation Mining Metallurgy Ganization Beijing China Beijing Key Laboratory of Process Automation Mining Metallurgy Beijing China

ISBN: (纸本)9781450387811

In the paper, a new adaptive variable selection algorithm for nonlinear regression multi-layer perceptron (MLP) is proposed. The proposed algorithm applies the nonnegative garrote (NNG) to shrink the input weights of the trained MLP. An adaptive indicator is designed by introducing the mean impact value (MIV) algorithm, which can improve the coefficient shrinkage efficiency of the NNG algorithm. The proposed algorithm uses cross-validation and Bayesian information criterion to calculate the optimal shrinkage parameter. The performance of adaptive variable selection algorithm is demonstrated by an example of artificial dataset. Simulation results show that the algorithm has better model accuracy and simplicity than other state-of-Art algorithms. © 2020 ACM.

关键词： Shrinkage

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