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Chinese Control and Decision Conference, CCDC

作者： Zongyuan Xie Hongyan Ma Yongxue Ye Jiaming Dou Shengyan Li Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China National Virtual Simulation Experimental Center for Smart City Education Beijing China

ISBN: (纸本)9781665478977

Cracks are common and dangerous hazards in buildings. Aiming at the defects of low accuracy and poor feasibility in traditional crack width detection, proposing a crack width measurement method based on an orthogonal skeleton line. Firstly, the crack image is processed by grayscale. Secondly, the processed image is median filtering and threshold segmentation. Finally, the skeleton and outline of the crack image were extracted by this method, and the measurement and comparison of simulated cracks and actual cracks are completed. The simulation results show that this method has high accuracy and feasibility and can meet the accuracy of engineering practice.

关键词： Image segmentation Simulation Buildings Process control Position measurement Gray-scale Skeleton

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach

The State of Charge Estimation of Lithium-ion Batteries Usin...

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Chinese Control and Decision Conference, CCDC

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China National Virtual Simulation Experimental Center for Smart City Education Beijing China

ISBN: (纸本)9781665478977

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. In order to improve the estimation accuracy of SOC, this paper develops a SOC estimation model for a lithium-ion battery using a Particle Swarm Optimization-Extreme Learning Machine(PSO-ELM) algorithm. The PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM model. The inputs to the PSO-ELM model are the battery voltage, current, and temperature, and the output is the actual SOC values. The performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion batteries Temperature measurement Temperature distribution Neurons Estimation Particle measurements Battery charge measurement

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Design of a clever carbon monoxide monitoring system

Design of a clever carbon monoxide monitoring system

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第35届中国控制与决策会议

作者： Yanxi Cao Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big DataBeijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

Carbon monoxide is a colorless and odorless gas that is extremely *** monoxide cannot be accurately detected or eliminated by the senses in high concentrations in the body under relatively airtight *** a result,the human body can only inhale it *** monoxide inhalation can deplete the body's oxygen supply,causing symptoms such as nausea,dizziness,headaches,and,in severe cases,*** response to the event's insidiousness,urgency,and ***,because the current carbon monoxide monitoring system is not portable,it is proposed to design a microcontroller as the core,with a concentration collection module,a wireless transmission module,an LCD module,and so *** with information between intelligent monitoring systems via wireless transmission module and mobile phone *** system consists of a microcontroller,concentration acquisition module,wireless transmission module,and LCD display *** the performance of the system is tested by changing the environmental conditions,while a control test is *** results show that the system has a fast and stable transmission rate,small size,and wireless monitoring *** can be informed of environmental conditions via their mobile phones,providing early warning and quick alarms.

关键词： Wi-Fi transmission carbon monoxide monitoring microcontroller unit indoor real-time monitoring

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Research on energy consumption prediction of public buildings based on improved support vector machine

Research on energy consumption prediction of public building...

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第35届中国控制与决策会议

作者： Liyao Yang Hongyan Ma Yingda Zhang Shengyan Li School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big DataBeijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

An improved support vector machine model for energy consumption prediction is proposed to achieve efficient energy saving in *** this paper,firstly,a gray correlation analysis model is used to measure the correlation between temperature,humidity,sunlight and other factors and building energy ***,the sparrow search algorithm(SSA) is introduced to optimize the penalty coefficient c and kernel function parameter g of the support vector machine,and then the SSA-SVM energy consumption prediction model is ***,the experimental results are analyzed by comparing with the data derived from the support vector machine prediction(SVM) model and BP neural network energy consumption *** experimental results show that compared with SVM and BP neural network,the prediction results of SSA-SVM model perform better in error index,indicating that the energy consumption prediction of SSA-SVM model has higher prediction accuracy;the maximum relative error of SSA-SVM prediction model is 0.0514,and the maximum relative errors of the other two models are greater than 0.55,indicating that SSA-SVM model has a higher degree of reliability.

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Application of Computer Software Simulation in Building Fire Evacuation Engineering:A Case Study of a University Library in beijing

Application of Computer Software Simulation in Building Fire...

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第35届中国控制与决策会议

作者： Shun Wang Hongyan Ma Shengyan Li Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big DataBeijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

University library is an important building for teaching and *** atrium library has the characteristics of good lighting and flexible use,and the atrium structure was extensively ***,the fire in the atrium library will cause more serious *** this paper,we build a fire and evacuation model of a university atrium library and conduct a simulation *** with BIM technology,we restored the physical information of a university library and establish a fire *** was used to simulate the fire somke diffusion,and the smoke factors were analyzed to derive the available safe evacuation time(ASET).Pathfinder was used to simulate personnel evacuation,which determines the required safe evacuation time(REST).The simulation results show that the visibility of the smoke is the most important factor affecting personnel evacuation,and the personnel needs 108 s to evacuate the fire area and 291 s to enter the stairwell to *** the personnel in the library by area can shorten the evacuation time by about 74 *** study is expected to provide support for the fire simulation and personnel evacuation in atrium building.

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Research on the Assistant Decision-making of Gas Regulator Based on Case-based Reasoning

Research on the Assistant Decision-making of Gas Regulator B...

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第32届中国控制与决策会议

作者： Sijie Li Yahui Wang Yinxing Dong School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing North Hua chuang Vacuum Technology Co Ltd

In order to improve the efficiency of fault decision-making of gas surge tank, this paper proposes a casebased reasoning(CBR) for assistant decision-making of gas surge tank. On the basis of the real case of surge tank fault, the characteristic attributes of the case are extracted, and the case is represented by the frame representation method. Through analytic hierarchy process, local similarity calculation model and global similarity calculation model, the assistant decision-making method of gas surge tank based on case-based reasoning is established. Finally, the source case and target case are analyzed, and the results are proved. The application of this method in assistant decision-making of gas surge tank fault has practical significance.

关键词： gas surge tank assistant decision-making case-based reasoning similarity calculation analytic hierarchy process frame representation

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Technical Function-effect Based Patent Multi-to-One Negation Game Model 25

Technical Function-effect Based Patent Multi-to-One Negation...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Du, Lu Liu, Weidong Xiao, Kaiyang Gao, Shixuan Han, Yong College of Computer Science Inner Mongolia University Hohhot China Institute of Scientific and Technical Information of China Beijing China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China Inner Mongolia Intellectual Property Service Center Hohhot China

ISBN: (纸本)9781665405270

As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefore, how to automatically construct negation relation between patents has become the hot research. Constructing negation relation is the process of comparing technical features between patents, which depends on the technical features in the patent claims and the technical function-effect. Although there are many researches on patent one-to-one negation, few researches on patent multi-to-one negation. The research on negation relation of multi-to-one patents, has the following challenges: (1) the definition of patent features is ambiguous in comparison of negation relation;(2) the role of patent function-effect is often ignored in judgement of negation relation. To solve the above issues, we propose a technical function-effect based patent multi-to-one negation game model. In the model, we decompose patent technical features into technical utility and technical function-effect. Patent technical utility is mainly realized by using the valence theory in linguistics. After obtaining technical utility, we choose to have the same technical effect, express the technical features of different technical functions, and obtain the patent technical function-effect. Finally, we combine the technical utility and function-effect to construct a technical utility/function-effect game tree to judge the patent negation relation. According to the two-tailed test in the A/B-test, our model outperforms the baseline model in the evaluation measurement. This model can subsequently be used to calculate the portfolio value and the strategic value of the patents. © 2022 IEEE.

关键词： Patents and inventions

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Design of a clever carbon monoxide monitoring system

Design of a clever carbon monoxide monitoring system

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Chinese Control and Decision Conference, CCDC

作者： Yanxi Cao Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Carbon monoxide is a colorless and odorless gas that is extremely dangerous. Carbon monoxide cannot be accurately detected or eliminated by the senses in high concentrations in the body under relatively airtight conditions. As a result, the human body can only inhale it passively. Carbon monoxide inhalation can deplete the body's oxygen supply, causing symptoms such as nausea, dizziness, headaches, and, in severe cases, death. In response to the event's insidiousness, urgency, and gravity. Simultaneously, because the current carbon monoxide monitoring system is not portable, it is proposed to design a microcontroller as the core, with a concentration collection module, a wireless transmission module, an LCD module, and so on. Interact with information between intelligent monitoring systems via wireless transmission module and mobile phone APP. The system consists of a microcontroller, concentration acquisition module, wireless transmission module, and LCD display module. And the performance of the system is tested by changing the environmental conditions, while a control test is conducted. The results show that the system has a fast and stable transmission rate, small size, and wireless monitoring function. Users can be informed of environmental conditions via their mobile phones, providing early warning and quick alarms.

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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Research on energy consumption prediction of public buildings based on improved support vector machine

Research on energy consumption prediction of public building...

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Chinese Control and Decision Conference, CCDC

作者： Liyao Yang Hongyan Ma Yingda Zhang Shengyan Li School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

An improved support vector machine model for energy consumption prediction is proposed to achieve efficient energy saving in buildings. In this paper, firstly, a gray correlation analysis model is used to measure the correlation between temperature, humidity, sunlight and other factors and building energy consumption. Secondly, the sparrow search algorithm (SSA) is introduced to optimize the penalty coefficient c and kernel function parameter g of the support vector machine, and then the SSA-SVM energy consumption prediction model is established. Finally, the experimental results are analyzed by comparing with the data derived from the support vector machine prediction (SVM) model and BP neural network energy consumption prediction. The experimental results show that compared with SVM and BP neural network, the prediction results of SSA-SVM model perform better in error index, indicating that the energy consumption prediction of SSA-SVM model has higher prediction accuracy; the maximum relative error of SSA-SVM prediction model is 0.0514, and the maximum relative errors of the other two models are greater than 0.55, indicating that SSA-SVM model has a higher degree of reliability.

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