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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Cao, Yong Zhou, Yinian Zhang, Zhao Wu, Wenjun Chen, Xihai Yang, Sha Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Smart Grid Protection and Control Nanjing China Nari Group Corporation Nanjing China

ISBN: (纸本)9783030786083

Transformers are prone to metal corrosion due to long-term exposure in the external environment. It is necessary to detect corrosion in time to prolong their service life-time. Detecting metal corrosion based on deep learning is becoming a feasible method at present. However, because of the irregularity and detachability of metal corrosion, traditional annotation approach results in such problems as ambiguity and uncertainty in the annotating process. Thus, a novel hierarchical annotation approach is proposed in this paper. The rationale for this initiative is as follows: traditional annotation approach has been used to annotate a large area covering the range of corrosion, as long as the area is visually continuous and adjacent to the corrosion that cannot be clearly divided. The annotation result is recorded as training set A;secondly, in the annotating boxes from the first step, the areas with obvious and relatively independent features are re-annotated to form the second level of nested annotation. The annotation result is recorded as training set B. Finally, Faster R-CNN and YOLOv5 models were trained by A, B training sets respectively in the experiment. It can be concluded that the detection performance with hierarchical annotation approach proved better than traditional annotation approach for Faster R-CNN and YOLOv5 models. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Detecting Anomalous Behaviours Based on system Logs: A Dynamic Graph Perspective

Detecting Anomalous Behaviours Based on System Logs: A Dynam...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Ming Wang Yuman Wang Tianqi Wu Cen Chen Kexiang Qian State Grid Corporation of China co. Ltd Beijing China State Grid Smart Grid Research Institute co. Ltd SGRI Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Henan Electric Power Company State Grid Henan Electric Power Research Institute State Grid Laboratory of Power Monitoring System Cyber Security Technical Standard Verification School of Information and Communication Engineering Beijing University of Posts and Telecommunications

system logs provide rich information about the current system’s state. To make use of system logs, various existing work extracts a causal graph from the log events, and then applies graph-based machine learning algorithms to detect the anomalous behaviors. One limitation of these work is that they treat the graph in a static way, neglecting the dynamic nature of anomalous behaviors. In this paper, we propose to detect anomalous behaviors based on system logs using dynamic graph modeling. Our method consists of several key steps, including negative sampling which helps to balance the dataset, edge encoding which encodes the spatial information for each behavior, and sequence modeling which further models the temporal information. Experimental evaluations on real datasets demonstrate the effectiveness of the proposed approach.

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Research and Application of Simplified Service Bus in New Generation Dispatching Technical Support system and Heterogeneous system

Research and Application of Simplified Service Bus in New Ge...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Yi Qiang Wan Shupeng Wang Zhemin He Weihua Zhang Ping Zhang Feng System R&D Center NARI Group Corporation(State Grid Electric Power Research Institute) NARI Technology Co. Ltd Nanjing China State Grid Shanxi Electric Power Co. Ltd Taiyuan China System R&D Center State Key Laboratory of Smart Grid Protection and Control Nanjing China

With the rapid development of UHV AC / DC hybrid power grid, in order to cope with the large-scale access of new energy, the power market reform has been continuously promoted. Therefore, it is proposed to build a new generation of dispatching technical support system to meet the needs of future power system development and safe, stable and high-quality operation. This paper proposes a simplified service bus that supports a new generation of service-oriented architecture, and meets the deployment characteristics of cloud terminals that are cross platform, lightweight, easy to deploy, open and efficient. The simplified service bus can well meet the location independent characteristics and be easily deployed in and out of the system. The flexible protocol processing mode cooperating with the gateway can meet the needs of accessing the services of the system and external systems. At the same time, it supports multiple man-machine network and automatic switching of service network shutdown under N-1 mode, which greatly improves the availability and stability of the system, Which Provide a strong guarantee for the safe, stable and efficient operation of the new generation system.

关键词： Cloud computing Protocols Switches system integration Power system stability Logic gates Dispatching

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A PSO-RBF neural network for BOD multi-step prediction in wastewater treatment process

A PSO-RBF neural network for BOD multi-step prediction in wa...

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2021 China Automation Congress, CAC 2021

作者： Liu, Yonglei Li, Wenjing Faculty of Information Technology Beijing University of Technology Beijing Institute of Artificial Intelligence Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9781665426473

The single-step prediction of the effluent biochemical oxygen demand (BOD) in wastewater treatment process (WWTP) cannot provide the information for future trends, which will affect the decision-making for subsequent control strategies in WWTP. To solve this problem, a radial basis function (RBF) neural network soft-sensing model based on PSO algorithm (PSO-RBF) is proposed for multi-step prediction of the biochemical oxygen demand (BOD) in WWTP. First, we propose an RBF model for multi-step prediction based on MIMO strategy. Then, the PSO algorithm is used to optimize the RBF parameters. Finally, the proposed model is applied to a four-step BOD prediction in WWTP. Experimental results show that this model can better predict BOD in multiple steps compared to traditional models. © 2021 IEEE

关键词： Forecasting

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A New Image Quality Database for Multiple Industrial Processes

arXiv

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arXiv 2024年

作者： Ma, Xuanchao Jiang, Yanlin Liu, Hongyan Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection China Beijing Key Laboratory of Computational Intelligence and Intelligent System China Beijing Artificial Intelligence Institute China School of Electronic & Information Engineering Liaoning University of Technology Liaoning China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology China

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

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Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains

Evaluating Image Quality of DIBR-synthesized Views in Textur...

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2021 China Automation Congress, CAC 2021

作者： Wang, Guangcheng Guo, Nan Liu, Hongyan Zhang, Yonghui Zhou, Yazhen Gu, Ke Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China

ISBN: (纸本)9781665426473

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, Many DIBR-synthesized image quality assessment (IQA) algorithms based on quantifying the distortion existed in texture images have been proposed. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to evaluate the visual quality of DIBR-synthesized images accurately. To this end, we present a DIBR-synthesized image quality assessment metric with multi-modal information (i.e. integrating texture and depth information), dubbed as MMI. MMI predicts the quality of DIBR-synthesized images by jointly measuring the texture structure and depth structure of the synthesized image. The design principle of our MMI is that the local geometric distortion, introduced by DIBR techniques in the hole-filling process, destroys the texture structure and the depth structure of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality by a joint representation of texture structure and depth structure. Experiments show that our MMI is better than the competing state-of-the-art IQA algorithms in predicting DIBR-synthesized image quality. © 2021 IEEE

关键词： Textures

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Remote Sensing Inversion for River Turbidity Estimation Based on Noise Injection and Ensemble Learning

Remote Sensing Inversion for River Turbidity Estimation Base...

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2021 China Automation Congress, CAC 2021

作者： Zhou, Yazhen Shi, Shuang Liu, Hongyan Zhang, Yonghui Gu, Ke Qiao, Junfei Faculty of Information Technology Beijing University of Technology Beijing 100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute

ISBN: (纸本)9781665426473

Turbidity is one main indicator of quality typically used for monitoring water pollution and water eutrophication. The monitoring technology of river water quality can be divided into contact monitoring technology based on physical sensors and non-contact monitoring technology based on remote sensing satellites. The traditional contact monitoring technology has limited monitoring spatial resolution. As a new technology, the remote sensing monitoring technology of the river can conveniently solve the above-mentioned shortcomings and realize the coverage of the whole river basin. However, due to the scarcity of remote sensing data and the measurement errors of remote sensing satellites themselves, learning-based turbidity measurement methods have weak generalization ability and thus are easily prone to produce large deviations. To effectively address such difficulty, a noise injection method is introduced into a river turbidity measurement model based on ensemble learning. To be specific, we randomly generate many noise sets and add them to the remote sensing data to make it closer to the real application scene, and then adopt the ensemble learning model to estimate river turbidity. Experiments validate that the random noise injection strategy can effectively improve the generalization performance of river turbidity measurement. © 2021 IEEE

关键词： Rivers

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A Hierarchy Early Warning Method based on RBF Neural Network for BOD in WWTP

A Hierarchy Early Warning Method based on RBF Neural Network...

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第34届中国控制与决策会议

作者： Junkai Zhang Wenjing Li Faculty of Information Technology Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education

As an indicator of the organic pollution of waste water,the early warning for the Biochemical Oxygen Demand(BOD) is vital and could prevent substandard waste water from environment *** paper presents a BOD early warning method combining multi-step time series prediction and hierarchy ***,an MIMO strategy based on an RBF neural network is designed to predict BOD concentration with multiple steps ***,a hierarchy alarm method based on a double-check from limit-value and tendency is proposed to recognize alarms from the predicted BOD time series ***,the BOD data from the Benchmark Simulation Model 1(BSM1) platform is used to test the effectiveness of the proposed *** experimental results show that the proposed method has a reliable early warning performance in different alarming level.

关键词： Early warning method Multi-step Prediction RBF neural network Biochemical Oxygen Demand(BOD) Waste Water Treatment Process

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An Intelligent Indoor Localization system in the NLOS Environment Based on Deep Learning and CSI Images

An Intelligent Indoor Localization System in the NLOS Enviro...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Lu Sun Liang Wu Zaichen Zhang Jian Dang Yulong Shen Gefeng Huang Liliang Ding National Mobile Communications Research Laboratory Southeast University Nanjing P.R.China Purple Mountain Laboratory Nanjing P.R.China State Key Laboratory of Smart Grid Protection and Control NARI Group Co. Ltd Nanjing China

ISBN: (纸本)9781665450867

With the rapid development of the smart service industry, indoor high-precision localization technology in the non-line-of-sight (NLOS) environment has attracted great interest. Fingerprint-based indoor localization has been widely employed due to its low hardware cost and high localization accuracy. In this paper, we propose an indoor intelligent localization system based on a 2-dimensional deep convolutional neural network (2D DCNN) and image fingerprints extracted from the channel state information (CSI). Simulation results show that the proposed localization system can achieve a high localization accuracy in the NLOS environment.

关键词： Location awareness Deep learning Wireless communication Simulation Signal processing algorithms Fingerprint recognition Signal processing

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Research on External Characteristics for PMSG Wind Turbine systems under Symmetrical Fault

Research on External Characteristics for PMSG Wind Turbine S...

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2020 IEEE Sustainable Power and Energy Conference, iSPEC 2020

作者： Xie, Hua Zhao, Qingchun Liu, Peilin Gao, Xuesong NARI Group Corporation State Key Laboratory of Smart Grid Protection and Control Nanjing China School of Electrical Engineering Shandong University Jinan China

ISBN: (纸本)9781728191645

Wind turbines (WTs) are based on power electronic converters. Due to different control schemes of converters, WTs exhibit diverse external characteristics when grid symmetrical fault occurs. Thus, the output current and output power of WTs cannot be forecasted, which is a challenge for the power system stability. In this regard, this paper proposes a novel strategy for Permanent Magnetic Synchronous Generator (PMSG) wind turbine systems. The strategy aims to force the PMSG exhibit a stable EFC during grid fault and provide an output current reference to the power system. Besides, the proposed strategy aims at enhancing the low-voltage ride-through capability of the PMSG. The effectiveness and robustness of the proposed strategy is verified in DIgSILENT PowerFactory. © 2020 IEEE.

关键词： Synchronous generators

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