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Chinese Control and Decision Conference, CCDC

作者： Yuxin Shen Rong Guo Hongyan Ma 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 of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Permanent Magnet Synchronous Linear Motors (PMSLM) has the advantages of high thrust force density, high acceleration, high speed, high precision, high efficiency etc. However, the detent force of PMSLM is an inevitable problem, caused by their inherent end-disconnection structure. Excessive detent force will cause thrust fluctuations and reduce the system’s stability. To resolve the above problems, an improved PMSLM structure based on arc-teeth is proposed to reduce detent force. Firstly, the motor permeability distribution is analyzed at different moments based on the flux-tube method, and the expression of detent force is derived. Second, the finite element model of PMSLM is established to optimize the height of arc-teeth, and the optimal structural parameters of the motor is obtained. Finally, the performance advantages of the proposed PMSLM are verified by comparing with traditional linear motor.

关键词： Fluctuations Shape Force Stators Motors Harmonic analysis Permanent magnets

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Improved Fall-down Detection Algorithm: FE-YOLO

Improved Fall-down Detection Algorithm: FE-YOLO

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Chinese Control and Decision Conference, CCDC

作者： Yuchen Zhang Hongyan Ma Zongyuan Xie 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 of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To address the concerns regarding dataset completeness and detection speed for common fall injuries in the elderly, a comprehensive dataset has been established. Additionally, a lightweight fall detection model named FE-YOLO, which is based on YOLOv8, has been developed. This model replaces the network’s internal feature extraction module with a more efficient Fasternet Block module and introduces an enhanced detection head that utilizes data sharing and grouped convolution techniques. When compared to the YOLOv8n model, the FE-YOLO model demonstrates a reduction in parameters by 43.1%, a decrease in GFLOPs by 51.9%, an improvement in model inference speed by 9.70%, and an increase in FPS by 10.5%. Notably, these enhancements do not significantly impact the average accuracy of the model. The efficiency of the FE-YOLO model renders it suitable for deployment on devices with limited computing power.

关键词： YOLO Convolution Computational modeling Feature extraction Inference algorithms data models Fall detection

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Research on Optimization Algorithm for Fire Emergency Evacuation in Experimental Buildings of Universities

Research on Optimization Algorithm for Fire Emergency Evacua...

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Chinese Control and Decision Conference, CCDC

作者： Xincheng Han Hongyan Ma Wei He Shun Wang 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

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

College laboratory buildings have dense population, numerous electrical devices, and high fire risk. However, the existing evacuation A* algorithm has the disadvantages of long time and poor adaptability. In order to effectively solve the problems of long evacuation simulation time, low efficiency, and inability to change evacuation directions according to fire conditions in college laboratory buildings, this paper proposes an improved A* algorithm. This paper improves the heuristic function in the A* algorithm by introducing a logarithmic decay factor. By changing the weight of the estimated cost, it reduces the number of unnecessary search nodes and steers the search algorithm in the correct direction, thereby improving search efficiency. Secondly, it introduces a bidirectional A* search strategy to reduce search nodes and improve the efficiency of the algorithm. Simulation results show that the improved A* algorithm has fewer traversed nodes, shorter search paths, and higher search efficiency, which verifies the effectiveness and feasibility of the improved A* algorithm in experimental building fire evacuation. This study provides a solution for fire evacuation in college laboratory buildings.

关键词： Heuristic algorithms Simulation Buildings Sociology Laboratories Search problems Path planning

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Air quality prediction based on ICEEMDAN-SSA-LSTM

Air quality prediction based on ICEEMDAN-SSA-LSTM

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Chinese Control and Decision Conference, CCDC

作者： Yanling Qin Hongyan Ma 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

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To address the problem that the Air Quality Index (AQI) is difficult to predict, a prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) is proposed. First, the AQI sequence was decomposed by ICEEMDAN. Second, the initial learning rate, max training epoch and the number of hidden layers of the LSTM model are optimized by the SSA algorithm to establish the SSA-LSTM model. Last, the results decomposed were imported into the SSA-LSTM model to predict AQI. Compared with the prediction results of the LSTM, CEEMD-LSTM, and CEEMDAN-SSA-LSTM model, the accuracy (measured by correlation coefficients) of the CEEMDAN-SSA-LSTM is 98%. The result confirmed that the ICEEMDAN-SSA-LSTM can obtaining an improvement of the correlation coefficients and reduce the mean square error, which is valuable for engineering applications.

关键词： Training Correlation coefficient Adaptation models Accuracy Empirical mode decomposition Atmospheric modeling Predictive models

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The State of Charge Estimation of Lithium-ion Battery Pack Based on PSO-KELM

The State of Charge Estimation of Lithium-ion Battery Pack B...

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Chinese Control and Decision Conference, CCDC

作者： Mengyuan Chen Hongyan Ma 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 of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Accurately estimating the State of Charge (SOC) is of great significance for ensuring the safe operation of lithium-ion batteries and preventing overcharging or discharging. However, as SOC is the internal state of the battery unit and cannot be directly measured, it is difficult to obtain accurate SOC values. To improve the estimation accuracy of SOC, this paper establishes a prediction model using Kernel Extreme Learning Machine (KELM), and uses Particle Swarm Optimization (PSO) to optimize the kernel function parameter S and regularization coefficient C of KELM and determine the optimal values, a SOC prediction method for lithium-ion batteries based on PSO-KELM is proposed. The input of the PSO-KELM model is battery voltage, current, and temperature, the output is the actual SOC value. The results show that the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE) of SOC prediction are reduced to 2.76%, 2.18%, and 6.89%, increase coefficient of determination (R 2 ) to 0.9919. The PSO-KELM model improves prediction accuracy compared to ELM and KELM models, and has good convergence and generalization properties.

关键词： Lithium-ion batteries Accuracy Extreme learning machines Estimation Predictive models Battery charge measurement data models

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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A new method for predicting plant proteins function based on multi label classification algorithm

A new method for predicting plant proteins function based on...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuai Chen Juan Wang Maozu Guo School of Computer Science Inner Mongolia University Hohhot China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Protein function annotation is an important content of bioinformatics. It is unrealistic to experimentally annotate a large number of protein functions, so automated prediction of the functions of proteins is required. Studying plant proteins can help us cultivate new plant varieties. However, the existing methods mainly aim at predicting the single function of plant proteins. Most proteins only have sequence information. Therefore, the multi-function prediction method of plant protein based on sequence has become the focus of research. In this study, we propose a method based on sequence to predict the function of plant proteins, called PlantGO. We describe the functional annotation problem as a multi-label classification problem using Gene Ontology (GO) terminology, and predict multiple functions for plant proteins. PlantGO extracts features from three aspects and performs feature fusion respectively. To avoid redundant information in features, PlantGO uses the random forest to select features and obtains three groups of optimal features. PlantGO uses a multi-label learning algorithm MLKNN to predict protein function, which proves the effectiveness and accuracy of the algorithm. Finally, PlantGO adopts the ensemble learning method to integrate all models into a unified model, further improving the prediction performance. Therefore, PlantGO has achieved excellent performance. The performance metrics are Accuracy (0.936), F1 Score (0.944), Ranking loss (0.011), which is better than the previous method in the independent test. We have developed an online server to predict the functions of plant proteins.

关键词： Proteins Protein engineering Annotations Terminology Predictive models Prediction algorithms Feature extraction

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EulerNet: Adaptive Feature Interaction Learning via Euler’s Formula for CTR Prediction

arXiv

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arXiv 2023年

作者： Tian, Zhen Bai, Ting Zhao, Wayne Xin Wen, Ji-Rong Cao, Zhao Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia China Poisson Lab Huawei Beijing China

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler’s formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://***/RUCAIBox/EulerNet. © 2023, CC BY.

关键词： Forecasting

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on intelligent Computing and Signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Design of fuzzy control based elevator group control algorithm

Design of fuzzy control based elevator group control algorit...

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第34届中国控制与决策会议

作者： Xu Mengbin Wei Dong Zhao Ke School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Municipal Science and Technology Commission Key Laboratory of Intelligent Processing for Building Big Data Beijing Xicheng District Special Equipment Inspection Institute

In view of the problem that it is dithcult to establish the mathematical model of elevator group control system and the optimization effect of traditional control method is not ideal,a group control model based on fuzzy control is ***,we analyze the basic principle of elevator group control system,then we elaborate the algorithm flow of elevator group control system scheduling optimization,and finally,we verify the algorithm in the simulation platform based on Visual Studio,and the simulation results show that fuzzy control can effectively solve the scheduling optimization problem of elevator group control system.

关键词： Elevator group control system Fuzzy control Scheduling optimization evaluation function

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