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SRDA: A secure routing and data aggregation approach for wireless smart meter

Journal of Communications 2016年第1期11卷 108-117页

作者： Wan, Ming Shang, Wenli Zeng, Peng Zhao, Jianming Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang110016 China

For the secure two-way communication and forwarding, this paper proposes SRDA, a secure routing and data aggregation approach for wireless smart meter. This approach introduces the unique wireless smart meter identifier to represent its public identity information, and gives a secure routing architecture based on control and forwarding separation. In this architecture, SRDA uses the proxy re-encryption data aggregation algorithm to protect security of data transmission. By using the routing algorithm according to distance and link quality, SRDA achieves the best routing path and provides several alternative paths which have different priorities. Besides, the identifier-based cryptography mechanism is used to issue and update the routing information in security, and the proxy re-encryption mechanism is designed to preserve the data privacy. In conclusion, this paper gives an analysis for the relatively excellent security, scalability and energy efficiency. © 2016 Journal of Communications.

关键词： Cryptography

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Ddos attack detection based on one-class svm in sdn 6th

Ddos attack detection based on one-class svm in sdn

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhao, Jianming Zeng, Peng Shang, Wenli Tong, Guoyu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811581007

Software Defined Networking (SDN) is a new type of network architecture, which provides an important way to implement automated network deployment and flexible management. However, security problems in SDN are also inevitable. DDoS attack belongs to one of the most serious attack types, which is fairly common for today’s Internet. In SDN security fields, DDoS attack detection research has been received more and more attention. In this paper, a DDoS attack detection method based on one-class SVM in SDN is proposed, which provides a better detection accuracy. Furthermore, two new feature vectors, including middle value of flow table item’s duration and protocol data traffic percentage, are extracted to integrate into the item of 11 feature vectors. Additionally, basing on selection and construction method of the 11 feature vectors, a DDoS attack behavior model is established by using one-class SVM algorithm, and the self-adaptation genetic algorithm is designed to optimize the corresponding parameters of the Gaussian kernel of one-class SVM. The experimental results in SDN show that, the proposed new feature vectors are shown to more better detection accuracy, and the proposed method is more feasible by comparing with the BP neural network and RBF neural network algorithms under the same 11 features vectors. © Springer Nature Singapore Pte Ltd 2020.

关键词： Genetic algorithms

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Satellite formation control and navigation experiment platform based on UAVs

Satellite formation control and navigation experiment platfo...

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Chinese control Conference (CCC)

作者： Zhaoqing Xu Hailong Pei College of Automation Science and Engineering Key Laboratory of Autonomous System and Networked Control South China University of Technology Guangzhou Guangdong CN

ISBN: (纸本)9781509009107

In order to simulate the in-orbit movement of formation satellites, the unmanned helicopter is utilized to set up the microgravity environment relative navigation ground simulation platform. The platform can provide the movement of six degrees of freedom and can simulate the formation satellite flying in three-dimensional space. The test platform includes host computer system, communication system, UAVs formation control system, satellite attitude simulation system, and so on. Sensors are equipped with laser radar and visible light cameras. Formation control and navigation of multi-body can be achieved under complex network environment. The platform can be used for the test of satellite autonomous formation simulation, joint fleet navigation, and so on. The algorithm of multi-body formation, multi-source and multi-target tracking based on sensor network, multi-body navigation and remote control can be verified on this platform.

关键词： Satellites Computers Attitude control Satellite navigation systems Satellite broadcasting Helicopters

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The Application of Digital Factory in domestic chemical industry

The Application of Digital Factory in domestic chemical indu...

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第27届中国控制与决策会议

作者： Hejian Ou Tao Zou The High-tech R&D Center The Ministry of Science & Technology Key Laboratory of Networked Control System Chinese Academy of Sciences

ISBN: (纸本)9781479970186

The business competition among chemical companies is more and more fiercely with the development of the *** output,cost,quality of the industrial products are attracting more *** factory(DF),based on the date of the whole product life cycle,is a new production organization which simulate the production cycle,evaluating and optimizing the whole production process in the computer virtual *** is also a combination of the modem digital manufacturing technology and the computer simulation technology that bridges the product design and *** a rising star of the Global *** this paper we introduced the features and key technologies of Digital Factory and established a Digital Factory plan according to domestic chemical *** also analyzed the problems that the Digital Factory confronted in chemical industry spreading.

关键词： Digital Factory chemical industry production and management methods key technology

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NONLINEAR STEAM GENERATOR FEEDWATER system IDENTIFICATION BASED ON WAVELET NEURAL NETWORK 10

NONLINEAR STEAM GENERATOR FEEDWATER SYSTEM IDENTIFICATION BA...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Wang, Hongrui Yuan, Mingzhe Zhang, Pengbin Xing, Kunpeng Guangzhou Institute of Industrial Intelligence Guangzhou China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (纸本)9781837242672

Steam generator is the key equipment in the large and complex nuclear power plant, and its water supply system has the characteristics of time-varying, large delay, nonlinear. In this paper, a segmented linearized model based on wavelet neural network is developed for a steam generator feedwater system of a nuclear power plant. All the data of the automation stages of the steam generator, which are steam flow rate, feedwater flow rate and liquid level from 20% load to 100% load, are collected and divided into six stages based on the characteristics of the data. For the first four stages of data, the feedwater system was modelled using the Hammerstein-Wiener method based on wavelet neural network, which was used as the nonlinear input and nonlinear output module of Hammerstein-Wiener. And for the remaining two stages of data, the nonlinear autoregressive models based on with exogenous (ARX) inputs model approach was modelled using nonlinear ARX as the underlying framework of wavelet neural network. The proposed model can be used for model predictive control to switch between different models based on load changes. Through the verification of simulation experiments, the built model can accurately reflect the trend of actual data, and the predicted values have high accuracy and reliability. © The Institution of Engineering & Technology 2024.

关键词： Nuclear power plants

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Research on the energy efficiency evaluation of aluminum reduction cell based on the fuzzy competition-cooperation DEA model

Research on the energy efficiency evaluation of aluminum red...

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第26届中国过程控制会议（CPCC2015）

作者： Kong Lei Wang Zhuo Li Jilong Zhao Li Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control System Chinese Academy of Sciences University of Chinese Academy of Sciences

As the traditional aluminum reduction cell energy efficiency analysis method is not comprehensive, this paper considers the impact of energy, materials and status of cells on the efficiency, and presents a new energy efficiency evaluation method of aluminum reduction cell based on the fuzzy competition-cooperation Data Envelopment Analysis cross-efficiency evaluation model. The paper first selects Al2O3 mass, Al F3 mass, anode movement, the average cell voltage and the cell age as the input indexes of the model, and the actual amount of molten aluminum as the output index;then in order to further explore the relationship among the decision making units, the fuzzy competition-cooperation DEA cross-efficiency evaluation model is proposed instead of the traditional DEA model;at last, the new model is adopted to evaluate the efficiency of the aluminum reduction cells with the real field data.

关键词： aluminum reduction cell efficiency evaluation Data Envelopment Analysis fuzzy clustering competition-cooperation

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Velocity control system with variable universe adaptive fuzzy-PD method for agricultural vehicles

Velocity control system with variable universe adaptive fuzz...

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World Congress on Intelligent control and Automation (WCICA)

作者： Na Guo Jingtao Hu Shenyang Institute of Automation Chinese Academy of Science Shenyang China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China

The velocity of agricultural vehicle is an important variable for the navigation system and variable rate applicator in precision agriculture, and the stable velocity in the filed improves the control accuracy of those systems. After analyzed the powertrain system of agricultural vehicles, a simple and general velocity control system was designed regardless of the structure of powertrain. Meanwhile, this system can be adapted without damage the vehicle's original mechanical structure, and cooperate with the precision agriculture application via the CAN bus. The variable universe method and incomplete derivation control strategy were introduced in the designed of fuzzy-PD controller to improve the adaptability. The road test was accomplished on LOVOL TA800 tractor and YAMMAR VP6 rice transplanter. The test results show that the mean absolute error is less than 0.045 m/s, and the velocity control system is reliable and effective.

关键词： Vehicles Agricultural machinery Engines Velocity control Gears Niobium Mechanical power transmission

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Improving performance of robots using human-inspired approaches: a survey

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Science China(Information Sciences) 2022年第12期65卷 5-35页

作者： Hong QIAO Shanlin ZHONG Ziyu CHEN Hongze WANG State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artifcial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Realizing high performance of ordinary robots is one of the core problems in robotic research. Improving the performance of ordinary robots usually relies on the collaborative development of multiple research fields, resulting in high costs and difficulty to complete some high-precision tasks. As a comparison,humans can realize extraordinary overall performance under the condition of limited computational-energy consumption and low absolute precision in sensing and controlling each body unit. Therefore, developing human-inspired robotic systems and algorithms is a promising avenue to improve the performance of robotic systems. In this review, the cutting-edge research work on human-inspired intelligent robots in decisionmaking, cognition, motion control, and system design is summarized from behavior-and neural-inspired aspects. This review aims to provide a significant insight into human-inspired intelligent robots, which may be beneficial for promoting the integration of neuroscience, machinery, and control, so as to develop a new generation of robotic systems.

关键词： human-inspired intelligent robots brain-inspired intelligence decision making visual cognition musculoskeletal robots

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Natural Gas Load Forecasting Based on Multi-Stage Model Identification 14

Natural Gas Load Forecasting Based on Multi-Stage Model Iden...

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14th IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems, CYBER 2024

作者： Zhang, Pengbin Wang, Hongrui Xing, Kunpeng Yuan, Mingzhe Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China China Nuclear Power Engineering Co. Ltd. Shenzhen518124 China

ISBN: (纸本)9798331506056

Natural gas load forecasting is crucial for the safe operation of urban natural gas. Based on the system identification method, the short-term natural gas load forecasting is studied in the paper. Employing a multi-stage identification framework, the initial values of the model parameters using the least squares and the instrumental variables method, while the model parameters are further optimized by the prediction error method to improve the accuracy of the model identification. The proposed method is validated by the simulation based on the public natural gas data in the U.S., demonstrating the algorithm's effectiveness in forecasting the daily natural gas load effectively. © 2024 IEEE.

关键词： Prediction models

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