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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Jiahui Yang, Lvqing Chen, Sien Dong, Wensheng Yu, Bo Wang, Qingkai Xiamen University School of Informatics Xiamen361005 China Information Technology Co. Ltd. Xiamen361005 China Jimei University School of Navigation Xiamen361021 China Technology Co. Ltd. Xiamen361005 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China

ISBN: (纸本)9798350331684

Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning. © 2023 IEEE.

关键词： Optimization

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Multi-condition flotation process condition recognition method based on deep learning and multivariate data pre-processing

Multi-condition flotation process condition recognition meth...

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Chinese Control and Decision Conference, CCDC

作者： Yicheng Wang Shu Wang Kang Li College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The state and trend of flotation surface foam play a crucial role in flotation operations as they provide important indications of working conditions, index predictions, and operational volume adjustments. While researchers have focused on manual feature design of froth images or automatic extraction of depth features and their relationship to flotation conditions, the information contained in the flotation process variables and the importance of data pre-processing are usually overlooked. To address these shortcomings, this study proposes an integrated framework for flotation process data preprocessing and condition recognition. It includes a fast imputation algorithm based on multi-condition data center of mass (FCKNN) for overcoming the challenges of imputing missing values in on-line industrial processes, as well as a high-precision work condition recognition method that fuses deep visual features with key process variables. Finally, the framework was applied to the gold flotation process. The experimental results show that the proposed framework can perform work condition recognition with high accuracy and solve the challenge of online missing value imputation.

关键词： Employee welfare Visualization Image recognition Accuracy process control Manuals Feature extraction

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Trend-Aware Patch Feature Fusion Transformer for Fault Diagnosis of Vibration Signals

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IEEE Sensors Journal 2025年

作者： Zhang, Ruicong Chu, Fei Qiu, Yazhi He, Dakuo Wang, Fuli China University of Mining and Technology School of Information and Control Engineering Xuzhou221000 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110000 China

The Vision Transformer (ViT) has demonstrated reliable performance in industrial equipment fault diagnosis by capturing global features and long-range dependencies in vibration signals. Nonetheless, when processing vibration signals, ViT primarily focuses on the correlations between patches, while neglecting the trend variation information hidden between signal units. This limitation hinders the model’s ability to extract latent fault information. To address this issue, we propose a trend-aware patch feature fusion Transformer (TAPformer) for fault diagnosis of vibration signals. Specifically, we design both patch-level and signal-level trend-aware self-attention blocks and adopt a weighted approach to integrate coarse-grained patch-level trend features with fine-grained signal-level trend features at the output of each self-attention head. This approach enables the model to capture fault information hidden within trend variations, effectively enhancing its fault detection capabilities. TAPformer establishes optimal weighted fusion hyperparameters through ablation experiments and is tested on four public datasets. Compared to eight advanced models, TAPformer achieves the highest accuracy across all four datasets, demonstrating the effectiveness and generalization capability of the proposed method. © 2001-2012 IEEE.

关键词： Fault detection

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Stochastic Configuration Networks for Data Augmented Seismic Magnitude Prediction

Stochastic Configuration Networks for Data Augmented Seismic...

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2024 China automation Congress, CAC 2024

作者： Qiu, Yuanhang Wang, Dianhui Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (纸本)9798350368604

Geophysical exploration yields a vast amount of monitoring data that accurately captures the geological dynamics of areas under investigation, providing a rich dataset for the analysis of seismic events. Despite a growing interest in utilizing monitoring data for event-centric information learning, challenges such as data imbalance significantly pose formidable obstacle to accurate information learning. In this study, a novel data augmentation method termed Adaptive Feature Expansion (AFE) is proposed to mitigate data imbalance. Additionally, an innovative randomized learning paradigm with incremental model configuration mechanism, Stochastic Configuration Networks (SCNs), is employed to learn seismic information and predict magnitude ranges. The experimental results indicate that AFE can effectively balance seismic data in the feature space and present excellent scalability. SCNs outperform traditional machine learning algorithms in terms of predictive performance. This work makes insightful contributions to geological hazard assessment. © 2024 IEEE.

关键词： Seismic response

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RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLO...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yujia Luo Qingsong Hu Yuanxun Cheng Shuya Yuan Yuansheng Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz are easily obstructed, making traditional LOS-based methods ineffective in complex environments. To overcome this, NLOS localization methods and Reconfigurable Intelligent Surfaces offer promising solutions by dynamically modifying the wireless environment. This paper introduces a Direct Position Determination method that integrates Deep Neural Networks (DNN) with the Sparrow Search Algorithm (SSA) to tackle the challenges of high computational complexity and low localization accuracy in RIS-aided systems. An NLOS scenario with RIS is established, and a far-field channel model is derived. The method uses DNN to model the nonlinear relationship between the likelihood function and user position, and then employs SSA to optimize the DNN-based fitness function, efficiently solving the objective function. Experimental results show that the DNN-SSA method consistently achieves high localization accuracy across different scenarios, even at low SNR levels, demonstrating its effectiveness and potential for 6G high-precision localization.

关键词： Location awareness 6G mobile communication Wireless communication Accuracy Simulation Search methods Artificial neural networks Reconfigurable intelligent surfaces Nonlinear optics Signal to noise ratio

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Inspection trolley based on the distance detection obstacle

Inspection trolley based on the distance detection obstacle

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第35届中国控制与决策会议

作者： Guicheng Wang Ao Sheng Xiaobin Li Min Zhang Changliang Guan Fengfeng Yin School of Electrical and Electronics Engineering Shanghai Institute of Technology Library Shanghai Institute of Technology State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy

In Industry 4.0 background,unmanned fullprocess 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 *** present,domestic and foreign research on intelligent inspection trolley is focused on obstacle avoidance function based on various *** 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 *** algorithm can achieve the distance measurement and obstacle avoidance function at a lower *** 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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A Lagrangian Mesh-Free Multi-Phase Model for Simulating Complex Dynamics of Bubbly Flows

MetaResource

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MetaResource 2025年第1期2卷 11-32页

作者： Ying Tian Xingyan Guo Qiang Zhang Zhengchang Shen Xiangwei Dong Chunzhi Li Kun Cao College of Mechanical and Electronic Engineering Shandong University of Science and Technology Qingdao China BGRIMM TECHNOLOGY GROUP Beijing China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Zhongtianhechuang Energy Co. Ltd. Ordos China

Smoothed particle hydrodynamics (SPH) is a Lagrangian, mesh-free numerical method renowned for its ability to handle fluid dynamics problems with large interface deformations and multiphase flow coupling. This study introduces an SPH-based multiphase flow model for simulating bubbly flows involving various immiscible fluids. The model uses a volume-based density discretization equation, ensuring numerical accuracy near interfaces, independent of density ratios, thus maintaining accuracy and stability even at high density ratios. By integrating the Continuous Surface Force method for surface tension into the multiphase SPH model, the study simulates interfacial behavior between phases. The model accurately predicts Laplace pressure differences across interfaces, maintaining interface stability at density ratios up to 100.0. Simulations of single and double bubble ascents elucidate the influence of the Bond number on bubble shape, rising distance, and velocity. As the Bond number increases, bubbles flatten and develop tails, affecting their integrity. The study also simulates multiple bubbles ascents in water, showcasing the model's ability to capture complex interfacial behaviors in bubbly flows, including deformation, adsorption, coalescence, and tearing.

关键词： Deformable models Accuracy Deformation Force Hydrodynamics Mathematical models Stability analysis Surface tension Numerical models Numerical stability

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Intelligent expert control method and application in SABC grinding circuit based on hybrid model

Intelligent expert control method and application in SABC gr...

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Chinese Control and Decision Conference, CCDC

作者： Guobin Zou Junwu Zhou Daoxi Liu Tao Song Kang Li College of Information Science and Engineering Northeastern University State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China

SABC grinding circuit (Semi Autogenous + Ball mill + Crushing circuit) is an important typical configuration in comminution production, which has control problems such as uncertainty and strong coupling. In this paper, an intelligent expert control method for grinding process based on hybrid model is proposed. First, a hybrid model driven by mechanism and data is proposed to realize online perception of production indicators such as particle size in cyclone overflow; then, the application scope of expert rules is improved by intelligent correction of uncertainties; finally, the process mechanism knowledge and the online perception data of the hybrid model are integrated to realize the coordinated production of each sub process in the target SABC circuit. The method proposed in this paper has been applied in an intelligent expert control system of SABC circuit in a concentrator to meet the production target.

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Intelligent Operational Adjustment of Copper Flotation process Based on CBR-GRNN

Intelligent Operational Adjustment of Copper Flotation Proce...

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Chinese Control and Decision Conference, CCDC

作者： Hao Yan Xinchun Jia Zhenji Feng Tao Song Fuli Wang School of Automation and Software Engineering Shanxi University Taiyuan China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The mechanism of the copper flotation process is complex and highly uncertain, making it difficult to establish a precise mechanism model. For a long time, the operational adjustment of the flotation process has mainly relied on manual labor, which makes it difficult to ensure mineral quality and economic benefits for the enterprise. In order to achieve automation of the flotation process operation, this article proposes an intelligent operational adjustment method based on case-based reasoning and the generalized regression neural network (CBR-GRNN). In the offline stage, the working conditions are divided based on PCA and K-means method, and the case bases are established for each type of different working conditions. In the online stage, we first determine the category of the target case and search in the corresponding case base to obtain the most similar operational adjustment solution. Due to the fact that the most similar case may not fully match the target case, further compensation is necessary. Therefore, this paper proposes a new difference-driven case revision scheme using GRNN. The final solution for reuse is the operational adjustment value compensated by GRNN. Finally, the experimental results in an actual copper flotation process have demonstrated that the proposed method can effectively perform operational adjustment. Furthermore, the superiority of the proposed method is further demonstrated by comparing it with the other two techniques.

关键词： Employee welfare Economics automation Uncertainty Neural networks process control Minerals

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Developing Deep Learning Based Intelligent Perception Algorithm for Thickener Equipment Monitoring 8

Developing Deep Learning Based Intelligent Perception Algori...

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8th International Joint Conference on Rules and Reasoning, RuleML+RR-Companion 2024

作者： Liu, Zuochen Wang, Qingkai Dang, Jiandong Wang, Xiaochun Li, Kang Avogadro, Roberto Roman, Dumitru Ma, Xiang State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing100160 China Beijing100160 China Mining Co. Ltd Aahui Hefei231500 China SINTEF AS POB 124 Blindern Oslo0314 Norway

Dense dehydration by use of thickener equipment is a critical process in mineral processing, directly impacting the quality of concentrate products. However, current industrial practices lack effective methods for monitoring the condition of thickeners. This makes it difficult for operators to accurately perceive the internal state of the thickener, leading to a relatively inefficient production process. This paper addresses the challenge of real-time detection of one of the key process parameters, feed rate, in thickener by employing a data-driven model-ling approach with intelligent perception. The upstream processes are analyzed, and relevant variables are selected for modeling. The Long Short-Term Memory (LSTM) - Recurrent Neural Network (RNN) and the Gated Recurrent Unit (GRU) - RNN with the Particle Swarm Optimization (PSO) algorithm are used to build and train the models. The results show that the PSO-GRU-based intelligent perception model for thickener feed rate estimation achieves higher accuracy and shorter training times compared with the LSTM-RNN model. © 2024 Copyright for this paper by its authors.

关键词： Long short-term memory

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