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7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Li, Min Li, Meijing Cheng, Yonglong Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Big Data and Artificial Intelligence Laboratory Industrial and Commercial Bank of China Software Development Center Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399883

With the advancement of diverse industries, text clustering has garnered significant attention in fields such as data mining, document retrieval, and pattern classification. The K-Means algorithm, a simple, efficient, and widely adopted clustering approach, has been extensively studied for its applicability in text data analysis. However, the exponential growth of information presents a formidable challenge when clustering large-scale data. The traditional K-Means algorithm proves ineffective in handling high-dimensional, massive text data, and the initial centroid selection significantly impacts the clustering outcomes. To address these limitations, this research introduces a clustering algorithm based on the virtual centroid and nearest centroid strategy. Furthermore, we incorporate the MapReduce parallelization framework to tackle the clustering problem associated with vast text data. Experimental results demonstrate the algorithm's commendable scalability and effective handling of large datasets. © 2023 ACM.

关键词： MapReduce

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Design of Intelligent Flow Control System for Extracorporeal Cardiopulmonary Support Based on Blood Oxygen Feedback 4

Design of Intelligent Flow Control System for Extracorporeal...

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4th International Academic Exchange Conference on Science and technology Innovation, IAECST 2022

作者： Lian, Xiaoqin Luo, Zhihong Gao, Chao Mi, Jiachen 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: (纸本)9798350320008

In view of the problem that the current extracorporeal cardiopulmonary support equipment only has the function of pumping blood, and requires full-time personnel to operate frequently, and the degree of intelligence is low, this paper designs an intelligent flow control system for extracorporeal cardiopulmonary support based on blood oxygen feedback, which realizes the system's independent constant flow blood perfusion. The overall design idea and architecture of the system based on STM32 and TX2 are given. The hardware composition based on STM32, software design idea and implementation method are described in detail. Experiments are carried out based on the human simulation platform. The experimental results show that the system can perform constant flow blood perfusion autonomously and adjust the perfusion volume intelligently to maintain the human oxygen saturation between 65% and 75%. At the same time, the system can be connected with the remote control center through the Internet of Things to achieve remote monitoring of equipment status. The degree of automation and intelligence of extracorporeal cardiopulmonary support equipment has been generally improved. © 2022 IEEE.

关键词： Feedback

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Research on thermodynamic simulation modeling and air temperature control of solar greenhouse

Research on thermodynamic simulation modeling and air temper...

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2022 International Workshop on Automation, Control, and Communication Engineering, IWACCE 2022

作者： Lian, Xiaoqin Yu, Xiaokun Gao, Chao Gong, Yonggang Wu, Yelan Guan, Wenyang Li, Jin School of 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

ISBN: (纸本)9781510660915

The solar greenhouse is an infrastructure widely used in agricultural production, and the precise control of its temperature can stabilize yield and production quality. To improve the air temperature control performance without affecting the actual production, a method for simulation modeling of the greenhouse and its air temperature control was proposed based on the thermodynamic principle. Firstly, by combining the following main influencing factors: the heat gained from solar radiation, the heat transferred via ventilation, conduction, long-wave radiation, and the heat storaged by wall, a mathematical model of the greenhouse was constructed. Secondly, a classical proportion-integrationdifferentiation (cPID) controller was deployed for the air temperature control of the greenhouse based on ventilation. Then an enhancement for cPID, a single neuron proportion-integration-differentiation (SNPID) controller was proposed to improve the control effect, and the former cPID controller acted as a comparison. Next, a MATLAB/Simulink simulation was carried out based on the solar greenhouse model and the PID controller models. As the experimental results show, with sunlight input and Gaussian noise interference in the controlled greenhouse model, the maximum positive and negative temperature offset of SNPID are both smaller than the cPID results, and after interference at the simulated time t=3.33 h, the adjustment time of SNPID is shortened by at least 45.0% compared with cPID. The temperature curve under SNPID control is closer to the target and more stable than the curve of cPID. Thus, the accuracy and robustness of control in this greenhouse are improved. © 2022 SPIE. All rights reserved.

关键词： Greenhouses

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ReFrame: A Resource-Friendly Cloud-Assisted On-Device Deep Learning Framework for Vision Services

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IEEE Transactions on Services Computing 2025年

作者： Xie, Jianhang Ding, Chuntao Guan, Qingji Zhou, Ao Li, Yidong Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

Cloud-assisted Internet of Things (IoT) device deployment of deep neural networks (DNNs) promotes On-device deep learning to provide users with ubiquitous high-quality services by solving the contradiction between insufficient IoT device resources and intensive demand for high-performance DNN resources. However, most existing methods optimize DNNs by considering one or two terms of transmission, computation, and storage resources, but do not consider all three terms at the same time in cloud-assisted IoT device deployment and updating DNNs. To this end, we propose a non-learnable module-based ResNet and a cloud-assisted on-device deep learning framework, ReFrame, based on the consideration of three indicators: model transmission parameters, computation resources, and storage resources. In the proposed method, we first specify that some parameters in DNNs are non-learnable and randomly initialized, so that, these parameters can be saved and reproduced with a few random seeds. By doing so, the cloud only transmits random seeds and learnable parameters to reduce the number of parameter transmissions. Secondly, we reduce the computation resource consumption of the model by introducing computation-friendly operators, such as pooling, to replace vanilla convolutions. Finally, since random seeds are used to save non-learnable model parameters, on IoT devices we only need to store random seeds and learnable parameters to reproduce the well-trained model. Compared with saving the complete model, our method greatly reduces IoT device storage resource consumption. Experimental results on image classification, object detection, and semantic segmentation tasks demonstrate the effectiveness of the proposed method. Specifically, on the CIFAR-10, our proposed method reduces approximately 89% of FLOPs and 90% of transmitted data in the prototype system compared to ResNet-18. © 2008-2012 IEEE.

关键词： Cloud platforms

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Research on Point-of-Interest Recommendation Method based on Graph Autoencoders and Long Short-Term Preferences 4

Research on Point-of-Interest Recommendation Method based on...

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4th International Conference on Artificial intelligence and Computer Engineering, ICAICE 2023

作者： Lian, Xiaoqin Mi, Jiachen Gao, Chao Chen, Xiang Mai, Weimin Guan, Wenyang Gong, Yonggang Li, Jin School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China School of Electronics and Information Technology Sun Yat-sen University Guangdong Province Guangzhou510006 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9798400708831

Point-of-Interest (POI) recommendation is one of the most important tasks in the research of Location-based Social Networks (LBSNs). To solve the spatial sparsity problem in POI recommendation, this study proposed a recommendation model based on graph autoencoder (GAE) and long short-term preferences applied to location recommendation. Firstly, the POI transfer map of the check-in sequence is constructed, from which the hidden links between each POI are extracted using GAE to alleviate the troubles caused by data sparsity. Secondly, a long short-term preference extraction module is incorporated into the model, considering the influence of user preferences on user decisions. Finally, the proposed GELS model was tested on the Foursquare dataset and compared with results from other models. The GELS model in this study achieved Recall@10 scores of 0.6933 and 0.5099 on two different datasets, surpassing other models and demonstrating its superior performance in POI recommendation. The results show that GELS model can provide new ideas for POI recommendations. © 2023 ACM.

关键词： Recommender systems

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DASCDC: Dual Attention-based Sparse Common Deep Clustering Method 9

DASCDC: Dual Attention-based Sparse Common Deep Clustering M...

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9th IEEE International Conference on Behavioural and Social Computing, BESC 2022

作者： Dong, Yao Shi, Jin Deng, Yahan Dong, Yongfeng Wang, Ziqiu School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Computing Tianjin300401 China Data Driven Industrial Intelligent Hebei Engineering Research Center Tianjin300401 China

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

Graph convolutional network (GCN) has been successfully applied in deep clustering on the high dimensional dataset, which is usually processed by extracting the feature and structure information. However, it lacks an effective fusion mechanism of common information between the feature and structure information and does not perform well on the sparse dataset. Therefore, we propose a dual attention-based sparse common deep clustering method(DASCDC). Firstly, we advance a sparse feature representation method with an adversarial loss function to solve the data sparsity problem and improve the discriminator's performance. Then, an information interaction operator is designed to deeply mine the common information and enhance the information fusion capability. Finally, considering the different contribution degrees of various neighbour nodes and information, we promote a dual self-attention mechanism from the node and model levels. Experiment results indicate that the proposed DASCDC algorithm overperforms other state-of-art deep clustering algorithms on non-graph and graph datasets with high dimension and sparsity. © 2022 IEEE.

关键词： Cluster analysis

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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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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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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Research on EEG Emotion Recognition Based on Multi-scale Residual Convolutional Neural Network

Research on EEG Emotion Recognition Based on Multi-scale Res...

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

作者： Xiaoqin Lian Ziqi Shi Chao Gao Guochun Ma Yelan Wu Wenyang Guan 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

Emotion recognition based on EEG signals has become an essential method in fields such as human-computer interaction and disease assessment. To further improve the recognition effect of human emotions, this paper fused the spatial and frequency information in EEG signals. It proposed a multi-scale residual convolutional neural network (ECA-Resnet-CNN) with an ECA attention mechanism to perform multi-scale information fusion on the constructed original features. The method first divides the EEG signals into six frequency bands; then, the differential entropy features are extracted from each frequency band and converted into a spatial-band three-dimensional feature matrix; then, the deep information of the three-dimensional feature matrix is extracted through a multi-scale residual convolutional neural network; finally, the weights of different channels of the feature map are adjusted by introducing the ECA attention mechanism, and the emotion classification is completed. The results show that on the Deap dataset, the arousal dimension and valence dimension binary classification accuracy of this method can reach $97.16 \%$ and $\mathbf{9 6. 3 0 \%}$ respectively, which is a significant improvement compared with the existing mainstream emotion recognition models, proving the effectiveness of this method.

关键词： Emotion recognition Attention mechanisms Accuracy Feature extraction Brain modeling Electroencephalography data mining Convolutional neural networks Matrix converters Residual neural networks

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