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Research on named entity recognition method for high-speed railway technology transformation project text 4

Research on named entity recognition method for high-speed r...

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2024 4th International Conference on Artificial intelligence and industrial technology Applications, AIITA 2024

作者： Lian, Xiaoqin Bao, Kexin Gao, Chao Gong, Yonggang Wu, Yanhua Cheng, Zhibo School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China

High-speed railway technology transformation projects are crucial to the layout of high-speed rail transportation production, high-speed rail safety, and high-speed rail operation and management. However, the data of high-speed railway technology transformation projects is mainly stored in the form of unstructured text, and the relevant personnel of high-speed railroad often need to manually go through the form to extract the key information of the project data and enter it into the system, with a low degree of automation and intelligence. Therefore, to improve the efficiency and quality of processing the data of high-speed railway technology transformation projects, and help the intelligent development of high-speed railways, this paper constructs the RoBERTa-BiLSTM-CRF fusion model to automate the identification of key information entities in the data of high-speed railway technology transformation project. The experimental data were selected as technology transformation project documents of a railway bureau from 2013 to 2022, and six defined entity types were identified. Experimental results show that the constructed model can effectively identify six types of defined entities, and the F1 value of each type of entity is above 90%. In addition, compared with other commonly used models, the F1 value has been significantly improved. The fusion model proposed in this paper can be used as a method to automatically extract key information for high-speed rail technology transformation projects. © Published under licence by IOP Publishing Ltd.

关键词： Railroads

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Water Quality Evolution Mechanism Modeling and Estimation Based on Fruit Fly Optimization Algorithm and Particle Filter

Water Quality Evolution Mechanism Modeling and Estimation Ba...

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第三十九届中国控制会议

作者： Yuqin Zhou Zhiyao Zhao Xiaoyi Wang Qian Sun School of Artificial Intelligence Beijing Technology and Business University China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University

Water Quality Evolution Mechanism（WQEM） modeling and Water quality estimation are important technical means for water pollution prevention and control of lakes and reservoirs. However, existing classical WQEM models usually contain unknown parameters with empirical values range, which brings difficulty of estimation water quality changes of specific lakes and reservoirs to meet the accuracy requirements. Furthermore, water quality indicator is susceptible to natural factors and human factors, which makes the water system complex and nonliner, and enhances the difficulty of water quality estimation. Therefore,combining water quality mechanism, this paper proposes a Fruit Fly Optimization Algorithm（FFOA） based WQEM modeling method and studies a method of water quality estimation based on Particle Filter（PF） algorithm. First, a more comprehensive WQEM model is established to characterize the water quality mechanism of lakes and reservoirs. Then, combining observed data of water quality indicator and WQEM, the unknown parameters of a WQEM model are estimated by using FFOA. Finally,PF algorithm is used to estimate the water quality status. Simulation results show that the method can effectively estimate the unknown parameters of the WQEM model and estimate the water quality status.

关键词： Water quality evolution mechanism fruit fly optimization algorithm particle filter modeling estimation

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IMM Based Sequential Fault-tolerant Fusion Estimation with Heavy-tailed Noises

IMM Based Sequential Fault-tolerant Fusion Estimation with H...

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第34届中国控制与决策会议

作者： Di Jia Lu Jiang Tianhua Chen Jiping Xu Xiaoyi Wang School of Artificial Intelligence Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data ChinaNational Light IndustryBeijing Technology and Business University

ISBN: (纸本)9781665478977

In this paper,the problem of interactive multi-model(IMM) based sequential fusion estimation with heavytailed noise in an unreliable transmission environment is *** measurement values are affected by random packet loss during *** non-Gaussian noise,the Student's t-distribution is used to model the heavy-tailed noise that appears in some practical *** IMM estimation method is effectively combined with the Student's t-distribution to investigate a multi-model filtering *** this basis,a corresponding sequential fusion algorithm is ***,the effectiveness of the algorithm is verified by a target tracking example simulation.

关键词： Student's t-distribution interactive multi-model random packet loss state estimation data fusion

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Sensor placement optimization and rapid deployment of a class of visual sensor networks

Sensor placement optimization and rapid deployment of a clas...

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第42届中国控制会议

作者： Heng Deng Jiahui Zhou Zhiyao Zhao Liguo Zhang Key Laboratory of Computational Intelligence and Intelligent Systems Faculty of Information TechnologyBeijing University of Technology Key Laboratory of Industrial Internet and Big Data China National Light IndustryBeijing Technology and Business University

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks(VSNs),this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as *** the proposed method,some necessary models are established as the mathematical basis of the optimization ***,a multi-objective optimization function is designed to balance the coverage and reconstruction error,followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization(PSO) and Genetic Algorithm(GA).Additionally,a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance,which makes the search process simpler and increases execution ***,comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms.

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Research on Forecasting Method of Meteorological Drought Index Based on Time Series Analysis 23

Research on Forecasting Method of Meteorological Drought Ind...

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Proceedings of the 4th International Conference on Artificial intelligence and Computer Engineering

作者： Chao Gao Xiangyu Hu Mengting Bai Jiachen Mi Xiaoqin Lian Jin Li School of Computer and Artificial Intelligence Beijing Technology and Business University China Key Laboratory of Industrial Internet and Big data China National Light Industry Beijing Technology and Business University China

ISBN: (纸本)9798400708831

Drought is a kind of natural disaster, which has adverse effects on agricultural production, water supply and environmental ecosystem. Thus it is of great practical significance to accurately forecast meteorological drought index. In this study, the scPDSI was selected as the evaluation indicator of meteorological drought, and the scPDSI of the Greater Khingan Mountains forest area in China was predicted based on ARIMA model. The experimental results show that the ARIMA model with the optimal order is ARIMA (3,1,3). The correlation coefficient of each ARIMA (3,1,3) model is greater than 0.910, the MAE is less than 0.470, and the MSE is less than 0.520 both on the training set and the test set, indicating that there is a strong correlation between the predicted value and the actual value and each model has high prediction accuracy. Therefore, the ARIMA model can be used for accurate prediction of scPDSI, and then provide technical support for drought early warning.

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Coal Mine Image Dust and Fog Clearing Algorithm Based on Deep Learning Network 2022

Coal Mine Image Dust and Fog Clearing Algorithm Based on Dee...

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4th Asia Pacific Information technology Conference, APIT 2022

作者： Feng, Wenbin Tong, Xin Yang, Xin Chen, Xiuxin Yu, Chongchong Shenyang Research Institute Co. Ltd. China Science and Industry Group Shenfu Demonstration Area of Liaoning 113122 China State Key Laboratory of Coal Mine Safety Technology Shenfu Demonstration Area of Liaoning 113122 China School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9781450395571

The purpose of coal mine dust fog image sharpening is to recover clear content from low visibility images. In response to the problems of over-enhancement and insufficient applicability of traditional image defogging methods based on prior knowledge, this paper proposed an end-to-end parallel high-resolution deep learning network to directly restore the final clear images. In this network, we used a parallel high-resolution deep network structure to deeply integrate multi-scale features between branches to help the network obtain contextual information. We also proposed a lightweight attention module, which reduced the parameter burden of the model and paid more attention to the target area when extracting feature information. Finally, in order to evaluate the effectiveness and versatility of the clarification algorithm proposed in this paper, experiments were conducted on the public data set RESIDE and the coal mine dust and fog image data set produced by myself, and compared with the existing classic clarification algorithm GCANet. The results show that the algorithm proposed in this paper can effectively solve the over-enhancement phenomenon, and improve the clarity and visualization of coal mine images. © 2022 ACM.

关键词： Image enhancement

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A Robot Control Method based on Motor Imagery EEG Signals

A Robot Control Method based on Motor Imagery EEG Signals

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Robotics & Intelligent Manufacturing technology (ISRIMT), International Symposium on

作者： Xiaoqin Lian Mohao Cai Chao Gao Zitong Wangl Yelan Wu Wenyang Guan Yonggang Gong School of Artificial Intelligence Beijing Technology and Business University Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

In recent years, brain-computer interface (BCI)-based research has become a research direction that has attracted much attention in the field of artificial intelligence, especially the brain-computer interface-based human-computer interaction system has a wide range of development forward. In this paper, a new recognition method is proposed for the problem of low accuracy of multi-classification recognition of EEG signals based on motor imagery, and the method is combined with Nao robot control to realize a human-computer interaction system based on brain-computer interface. In this study, the EEG data collected by EMOTIV EPOC FlEX was used as the experimental basis, and firstly, the wavelet packet decomposition was used to process the EEG signals, extract the four important frequency bands of EEG signals, namely, Alpha, Beta, Theta, and Gamma, and compute the differential entropy (DE) features of each band respectively, and reconstructed the three-dimensional according to the spatial characteristics of the brain electrodes EEG feature cube, and input the features into the convolutional neural network of the visual geometry group network for four classifications. Finally, the robot is controlled to realize different limb movements according to different classification results, thus realizing the function of human-machine interaction system and laying a technical foundation for intelligent control based on EEG signals.

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Research on recognition method of adolescent schizophrenia based on EEG feature fusion

Research on recognition method of adolescent schizophrenia b...

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IEEE/ICME International Conference on Complex Medical Engineering, CME

作者： Xiaoqin Lian Zitong Wang Mohao Cai Chao Gao Wenyang Guan Yonggang Gong School of Artificial Intelligence Beijing Technology and Business University Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

ISBN: (数字)9798350316117

ISBN: (纸本)9798350316124

Schizophrenia is a common mental illness, and adolescent schizophrenia has a higher recurrence rate and disability rate than adult schizophrenia. In order to improve the recognition accuracy of adolescent schizophrenia, this study proposed a classification method of adolescent schizophrenia combined with artificial intelligence technology. In this method, the pre-processed original electroencephalogram (EEG) signals are decomposed by Discrete Wavelet Transform (DWT) and Ensemble Empirical Mode Decomposition (EEMD), respectively, and then the decomposed signals are extracted and the extracted features are fused. Finally, five machine learning models are used for classification. The results show that compared with DWT or EEMD methods alone, the feature fusion method proposed in this paper exhibits a higher accuracy in classification, in which the K-Nearest Neighbours (KNN) model achieves the best classification performance. The identification accuracy, recall, precision, F1 score, and Kappa coefficient of the model were 97.48 % , 95.71%, 99.56%, 97.59%, and 0.9494, respectively, which proved that the proposed method could effectively distinguish adolescent schizophrenia patients from healthy adolescents, and could assist doctors in disease analysis, providing a new method for the diagnosis of adolescent schizophrenia.

关键词： Accuracy Mental disorders Telemedicine Medical services Machine learning Feature extraction Brain modeling

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Sensor Placement Optimization and Rapid Deployment of a Class of Visual Sensor Networks

Sensor Placement Optimization and Rapid Deployment of a Clas...

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Chinese Control Conference (CCC)

作者： Heng Deng Jiahui Zhou Zhiyao Zhao Liguo Zhang Key Laboratory of Computational Intelligence and Intelligent Systems Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks (VSNs), this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as possible. In the proposed method, some necessary models are established as the mathematical basis of the optimization process. Then, a multi-objective optimization function is designed to balance the coverage and reconstruction error, followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Additionally, a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance, which makes the search process simpler and increases execution speed. Finally, comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms.

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AdaBoost-SVM Face Recognition Algorithm Based on LBP and Gabor Feature Fusion

AdaBoost-SVM Face Recognition Algorithm Based on LBP and Gab...

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big data & Artificial intelligence & Software Engineering (ICBASE), International Conference on

作者： Xiaoqin Lian Chunquan Liu Chao Gao Guochun Ma Yonggang Gong Jin Li School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In recent years, facial recognition technology has seen rapid advancements and is now extensively utilized in security surveillance and financial transactions. The face recognition process usually includes image preprocessing, feature extraction, and classification. However, it is often difficult for a single feature to fully capture the complex features of a human face, thus affecting the accuracy of recognition. To address this issue, this study presented an AdaBoost-SVM face recognition algorithm that fuses local binary pattern and Gabor features, which can enable comprehensive extraction of facial features from images and enhance recognition accuracy. This method first performs bilateral filtering on the face image to reduce noise and retain edge information. Then, it extracts the local binary pattern and Gabor features and weights the fusion. Finally, it uses the AdaBoost-SVM classifier for identification. Moreover, the algorithm performance was comprehensively evaluated using the ten-fold cross-validation method. The experimental results demonstrated that this method achieves recognition accuracy of $\mathbf{9 8. 7 5 \%}$ and $96.25 \%$ on ORL and Yale A face databases respectively, and still performs well in complex lighting and expression-changing environments, significantly improving the accuracy of face recognition and its robustness.

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