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Rethinking development and major research plans of Industrial Internet in China

Fundamental research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural Science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

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Unilateral Alignment: An interpretable machine learning method for geophysical logs calibration

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Artificial Intelligence in Geosciences 2021年第1期2卷 192-201页

作者： Wenting Zhang Jichen Wang Kun Li Haining Liu Yu Kang Yuping Wu Wenjun Lv Department of Automation University of Science and Technology of ChinaHefei230027China College of Control Science and Engineering China University of PetroleumQingdao266580China Institute of Advanced Technology University of Science and Technology of ChinaHefei230031China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei230088China Shengli Geophysical Research Institute SINOPEC GroupDongying257022China

Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue,so the trained model cannot well generalize to the unseen data without calibrating the *** this paper,we formulated the geophysical logs calibration problem and give its statistical explanation,and then exhibited an interpretable machine learning method,i.e.,Unilateral Alignment,which could align the logs from one well to another without losing the physical *** involved UA method is an unsupervised feature domain adaptation method,so it does not rely on any labels from *** experiments in 3 wells and 6 tasks showed the effectiveness and interpretability from multiple views.

关键词： Interpretable machine learning Geophysical logs calibration Data distribution discrepancy

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An analysis of machine learning algorithms in rotating machines maintenance

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IFAC-PapersOnLine 2022年第2期55卷 252-257页

作者： Alexandre S. Roque Vinicius W. Krebs Iuri Castro Figueiro Nasser Jazdi Alexandre S. Roque Vinicius W. Krebs and Iuri C. Figueiro are members of the Research Group on Electronics and Power Systems (GPESP) of the Computer Science and Engineering Department Regional Integrated University of High Uruguay and Missions Brazil Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart Germany

The industrial maintenance activities represent an increase in production costs, mainly caused by unnecessary production stops. Recent technologies approaches are handling the continuous monitoring of industrial machines, storing sensors data, and also maintenance history. More data analysis is necessary specifically for rotating machines presenting methodologies to reduce the maintenance. In order to handle this problem, a comparative analysis of machine learning methods is presented. The strategy aims to predict failures and then indicates the maintenance necessity before a break occurs. Thus, it is applied and analyzed the specific machine learning algorithms, Gradient Boosting and Random Forest, using a dataset of rotation machines. The results show that both methods have an excellent performance (metrics accuracy, precision, and recall), with slightly better results in Gradient Boosting (hit rate of 99.93%) indicating the prominent application of these algorithms in this industrial scenario.

关键词： Maintenance Rotation machines Machine learning Intelligent inspection

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A Precise Carbon Emission Model on Electricity Consumption Side with Carbon Emission Flow Theory 4

A Precise Carbon Emission Model on Electricity Consumption S...

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4th International Conference on Electrical engineering and control Technologies, CEECT 2022

作者： Wang, Daxin Zhang, Qian Cui, Huahu Cheng, Honghu Zhang, Xuemeng Fan, Lei Anhui University School of Electrical Engineering and Automation Engineering Research Center of Power Quality Ministry of Education Hefei China Anhui University Anhui Key Laboratory of Industrial Energy-Saving and Safety Hefei China Anhui Power Exchange Center Co. Ltd. Hefei China Anhui Electric Power Design Institute Co. LTD. China Energy Engineering Group Hefei China Anhui University Anhui Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control Hefei China

ISBN: (纸本)9781665498999

The measurement of carbon emissions on the customer side is a prerequisite for low-carbon dispatch and carbon trading, and the calculation of carbon emissions per kWh is a necessary condition for building a low-carbon power system. In this paper, we propose a refined measurement model for customer-side carbon emissions based on the carbon emission stream theory. Firstly, the tidal flow distribution in the distribution network is calculated by using the forward-back substitution method with distributed power sources. Secondly, the carbon potential vector, carbon potential factor, and correlation matrix of carbon flow at the distribution network side are calculated. According to the obtained calculation results, a fine-grained carbon emission calculation model for distribution network side including PV and energy storage is proposed to be constructed on the bases of carbon emission flow theory. This model can delineate the correlation between the carbon emissions of the generating units and the carbon flows of each load on the user side. Finally, the IEEE33 node and a 10 kV feeder model of a station area in Anhui Province are chosen as simulation objects. The adaptability and calculation speed of the distribution-side carbon flow calculation model is analyzed. The results show that the calculation model in this paper can solve the distribution-side carbon flow with energy storage and distributed power sources quickly and accurately. © 2022 IEEE.

关键词： Electric power distribution

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An enhanced 3DCNN-ConvLSTM for spatiotemporal multimedia data analysis

An enhanced 3DCNN-ConvLSTM for spatiotemporal multimedia dat...

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作者： Wang, Tian Li, Jiakun Zhang, Mengyi Zhu, Aichun Snoussi, Hichem Choi, Chang School of Automation Science and Electrical Engineering Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Computer Science and Technology Nanjing Tech University Nanjing China Institut Charles Delaunay University of Technology of Troyes Troyes France IT Research Institute Chosun University Gwangju Korea Republic of

At present, human action recognition is a challenging and complex task in the field of computer vision. The combination of CNN and RNN is a common and effective network structure for this task. Especially, we use 3DCNN in CNN part and ConvLSTM in RNN part. We divide the video into multiple temporal segments by average and compress each segment into one feature map by pooling layer. Adding the pooling layer, dropout layer, and batch normalization layer into ConvLSTM is our groundbreaking work. We test our model on KTH, UCF-11, and HMDB51 datasets and achieve a high accuracy of action recognition. © 2019 John Wiley & Sons, Ltd.

关键词： 3DCNN action recognition ConvLSTM

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A Review of Intelligent Vision Enhancement Technology for Battlefield

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Wu, Yufeng He, Yanjun Li, Ning Wang, Guowei Zhang, Longfei Zhang, Fuquan Zhu, Yifan The 15th Research Institute China Electronics Technology Group Corporation Beijing100083 China Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Department of Computer Science and Technology Tsinghua University Beijing100081 China

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future;the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars. © 2023 Yufeng Wu et al.

关键词： Augmented reality

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Leveraging protein-protein interactions in phenotype predict...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Riccardo Smeriglio Joana Rosell-Mirmi Petia Radeva Jordi Abante Dpt. of Physics Universitat de Barcelona Spain Control and Computer Engineering Dpt. Politecnico di Torino Turin Italy Headache Research Group Vall d’Hebron Institut de Recerca Barcelona Catalonia Spain Dpt. of Mathematics & Computer Science Institute of Neurosciences Universitat de Barcelona Barcelona Catalonia Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Dpt. of Biomedical Sciences Institute of Neurosciences Universitat de Barcelona Dpt. of Mathematics & Computer Science Universitat de Barcelona August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Catalonia Spain

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

关键词： Proteins Training Phenotypes Limiting Prevention and mitigation Predictive models Graph neural networks Proposals Alzheimer's disease Genotypes

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Defect Detection Method of Railway Fastener Based on SPP-improved ResNet

Defect Detection Method of Railway Fastener Based on SPP-imp...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Wang, Dengfei Su, HongSheng Chen, GuangWu Xu, DeJie Wang, LiFang Zhao, Xiaojuan Lanzhou Jiaotong University School of Automation and Electrical Engineering Lanzhou China Lanzhou Jiaotong University School of Traffic and Transportation Lanzhou China Scientific Research Institute of China Railway Hohhot Bureau Group Co. Ltd Hohhot China Lanzhou Jiaotong University Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province Lanzhou China

ISBN: (纸本)9781665401159

Missing and broken states of railway fasteners are directly related to driving safety. In order to realize the accurate detection of fasteners, virtual image technology is used to simulate defect state of fastener to expand feature information of fasteners, so as to overcome the unbalanced problem of fastener samples. Secondly, a deep network image detection method for railway fasteners based on SPP-improved ResNet (SPP-ResNet) is proposed to improve detection performance. Experimental results show that the accuracy is 95.1% when the network is trained on the virtual image extended dataset with Single size input train method, which is 2.4% higher than the accuracy of the unexpanded dataset. It is proved that virtual image can improve the accuracy of fastener detection. After the SPP-ResNet is trained by using multi size input training, the detection accuracy is 96.6%, which is 2.3% higher than the accuracy of ResNet. Therefore, the proposed method can meet the needs of railway-fastener automatic detection under high-speed railway development. © 2021 IEEE.

关键词： Image recognition

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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