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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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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

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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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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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Bearing Remaining Life Prediction Method Based on Wavelet Transform Denoising and Feature Preservation CycleGAN under Unbalanced Samples

Bearing Remaining Life Prediction Method Based on Wavelet Tr...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Songyang Wang Jun Yu Kun Zhao Zhenyu Guo School of Automation Harbin University of Science and Technology Harbin China State Key Laboratory of Process Automation in Mining & Metallurgy School of Automation Harbin University of Science and Technology Harbin China Cixing Group Co Ltd Beijing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

With the rapid development of industrialization today, mechanical equipment has undergone significant development and progress in terms of type and quality. Rolling bearings are the key components of mechanical transmission and the most vulnerable mechanical components. Only 10% of rolling bearings can operate as designed. Exploring more scientific and reasonable test methods to predict the remaining service life of rolling bearings can not only reduce operating costs, but also prevent accidents. Life prediction models can be divided into three categories: statistical models, dynamic models, and data-driven prediction models. The existing life prediction models based on statistics, dynamics, etc. continue to increase the workload and difficulty of life prediction in the case of increasingly complex, time-consuming and labor-intensive mechanical equipment. With the rapid development of information technology, industrial process data collection has become efficient and convenient, providing sufficient data resources for data-driven intelligent life prediction technology, and gradually showing great development potential. The remaining life prediction mainly includes two key parts: the feature extraction of bearing vibration data and the construction of life prediction model. Therefore, this paper takes the rolling bearing as the research object, and carries out the remaining life prediction research from the two directions of feature extraction and prediction model construction. In the bearing operation monitoring data, the proportion of the fault data relative to the normal operation data is very small, forming a typical data imbalance phenomenon, and the traditional life prediction is not ideal when dealing with such data. In this paper, a secondary filtering method combining wavelet transform and parameter adaptive variational modal decomposition is used to preprocess the data and enhance the features; and fully consider the influence of unbalanced cluster data, a

关键词： Vibrations Wavelet transforms Training Noise reduction Neural networks Rolling bearings Predictive models

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Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid Transformer

arXiv

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arXiv 2024年

作者： Sun, Guodong Liu, Junjie Liu, Mingxuan Liu, Moyun Zhang, Yang School of Mechanical Engineering Hubei University of Technology Wuhan430068 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102628 China Hubei Key Laboratory of Modern Manufacturing Quality Engineering Hubei University of Technology Wuhan430068 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Self-supervised monocular depth estimation aims to infer depth information without relying on labeled data. However, the lack of labeled information poses a significant challenge to the model's representation, limiting its ability to capture the intricate details of the scene accurately. Prior information can potentially mitigate this issue, enhancing the model's understanding of scene structure and texture. Nevertheless, solely relying on a single type of prior information often falls short when dealing with complex scenes, necessitating improvements in generalization performance. To address these challenges, we introduce a novel self-supervised monocular depth estimation model that leverages multiple priors to bolster representation capabilities across spatial, context, and semantic dimensions. Specifically, we employ a hybrid transformer and a lightweight pose network to obtain long-range spatial priors in the spatial dimension. Then, the context prior attention is designed to improve generalization, particularly in complex structures or untextured areas. In addition, semantic priors are introduced by leveraging semantic boundary loss, and semantic prior attention is supplemented, further refining the semantic features extracted by the decoder. Experiments on three diverse datasets demonstrate the effectiveness of the proposed model. It integrates multiple priors to comprehensively enhance the representation ability, improving the accuracy and reliability of depth estimation. Codes are available at: https://***/MVME-HBUT/MPRLNet. © 2024, CC BY-NC-SA.

关键词： Textures

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Thermally Stable Piezoelectric Sensors for Quantitative Pressure Sensing Based on Linear Piezoelectric Zinc Oxide Thin Films

SSRN

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SSRN 2023年

作者： Li, Yongkuan Lin, Hongsen Lin, Jianrong Zhou, Cheng Luo, Aixin Yang, Jiachen Zhang, Jieyu Hong, Zhixiang Hou, Xueyan Xiao, Peng Fan, Bingfeng Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Physics and Optoelectronic Engineering Foshan University 528225 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Process Automation in Mining & Metallurgy Beijing General Research Institute of Mining & Metallurgy Beijing100160 China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Institute of Microscale Optoelectronics Shenzhen University Shenzhen518060 China

Linear piezoelectric materials offer new opportunities for next-generation multifunctional micro/nano systems on chip aside from ferroelectrics. However, the advantage of quantitative pressure sensing of linear piezoelectric materials over ferroelectrics has not been demonstrated, while the thermal stability of linear piezoelectric zinc oxide (ZnO) is yet to be evaluated. In this work, pressure sensors based on linear piezoelectric undoped ZnO and ferroelectric vanadium doped ZnO (V-ZnO) films are studied side by side. Both films possess similar fiber textures oriented along the polar ZnO [002] direction, yet each of the V-ZnO grains consists of strip-like domains observed by SEM. Applying triangular-wave pressures, the linear piezoelectric ZnO sensor generates triangular-wave voltage signals that are one-to-one linearly correlated to the applied pressure, showing decisive advantages for quantitative pressure sensing compared to the ‘sail-like’ voltage signals of the ferroelectric V-ZnO sensor that is many-to-one correlated to the applied pressure. Superior thermal stability is demonstrated in the linear piezoelectric ZnO sensor showing stable pressure sensing properties up to 450°C compared to the degraded sensing properties of the ferroelectric V-ZnO sensor at 150°C. The thermally stable piezoelectric sensors for quantitative pressure sensing based on linear piezoelectric ZnO films serve as candidates for high-performance multifunctional micro/nano systems. © 2023, The Authors. All rights reserved.

关键词： Zinc oxide

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Grade Monitoring using Semantic Features of Flotation Froth Image

Grade Monitoring using Semantic Features of Flotation Froth ...

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第34届中国控制与决策会议

作者： Xu Wang Jingmin Lian Daoxi Liu Yutian Lei School of Information Science and Engineering Northeastern University BGRIMM Technology Group State Key Laboratory of Process Automation in Mining & Metallurgy

In the industrial flotation circuits,grades are the key performance indicators for flotation condition recognition and *** based on machine vision is an effective tool for grade monitoring,and froth image feature extraction as a model input plays a crucial *** the low efficiency of hand-crafted features and the poor interpretability of high-dimensional features extracted by convolutional neural networks,a grade monitoring method with human-understandable semantic features as input is proposed in this ***,based on the combination of generative adversarial networks and encoder,we design a new network structure that maps the froth images data space to the latent semantic *** the semantic features of froth images can be automatically extracted by decomposing the constructed matrix with rich latent ***,as demonstrated in the industrial experiment,the extracted semantic features can not only be visually interpreted but also can be effectively used in grade monitoring.

关键词： Grade Monitoring Generative Adversarial Networks Semantic Feature Extraction Flotation Froth Image

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Abnormal Detection Model of Energy Consumption Data in Beneficiation and metallurgy Enterprises based on Transfer Learning

Abnormal Detection Model of Energy Consumption Data in Benef...

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2021 International Conference on Smart Energy and Energy Internet of Things, SEEIoT 2021

作者： Yu, Zhuchao Chu, Gaixia Zhang, Yuanzheng Zhou, Siyuan He, Shaowei Li, Jing School of Business Administration Northeastern University Shenyang China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China College of Medicine and Biological Information Engineering Northeastern University Shenyang China Qinghai XKXX Information Technology Co. LTD Xining China

The energy consumption data of beneficiation and metallurgy enterprises has the characteristics of multi-dimensional and timing. An energy consumption data anomaly detection model based on transfer learning is proposed to help enterprises monitor the abnormal energy use. The combination of transfer learning and DTW algorithm is used to eliminate the adverse impact of timing on the model, and the ensemble learning is used to realize data detection. Finally, an efficient and accurate energy consumption data anomaly detection model is formed. The detection accuracy of the model is 91.6%, which can effectively meet the requirements of enterprise energy management. © Published under licence by IOP Publishing Ltd.

关键词： Concentration (process)

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Hybrid mean impact value with nonnegative garrote for input variable selection of multi-layer perceptron 20

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

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Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Hongxun Wang Fengying Ma Kai Sun School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy ganization Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy ganization Beijing China Beijing Key Laboratory of Process Automation in 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.

关键词： Variable selection Multi-layer perceptron Mean impact value Nonnegative garrote

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