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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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networking and Resource Allocation for industrial Internet Scenarios base on 5G networks

Networking and Resource Allocation for Industrial Internet S...

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Chinese Control and Decision Conference, CCDC

作者： Kun Tong Cunqian Yu Changyu Zhu Junyu Dou Bing Han Xiaolong Yu Big Data and Industrial Intelligence Networking Technology Laboratory Hangzhou Innovation Institute Beihang University Hangzhou China School of Computer Science Shenyang Aerospace University Shenyang China Pinming Technology Co. Ltd Hangzhou China General Administration Office Hangzhou Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In industrial Internet systems, data is the core, networks are the foundation, and security is the guarantee. In order to achieve the overall goal of upgrading industrial Internet technology, efficiently utilizing production factors and improving the level of Internet industry, the emerging technology of Internet scenario networking has emerged, which links and integrates all elements of the industrial production process. This paper is based on the existing networking mode of industrial Internet and explores the fusion networking mode of 5G+time-sensitive networking (TSN), analyzes the enhanced technology of 5G+new radio (NR) basic configuration. We strive to meet the requirements of industrial Internet scenarios with the optimal resource allocation strategy and improve the success rate of executing computing tasks in Internet systems. And we promote the deep integration of TSN technology and 5G technology, and lay the foundation for adapting to the trend of industrial Internet scenario networking. The advantages of combining edge-cloud fusion computing technology with elastic optical transport network in terms of migration delay, migration cost, and resource balance are verified through the example of industrial automated guided vehicles (AGVs).

关键词： 5G mobile communication Simulation Production Containers Streaming media Internet Delays

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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

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IEEE Internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media Internet of Things Collaboration Visual analytics Feature extraction

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Flexible Gossamer Simulation Based on Improved Particle-Spring Model

Flexible Gossamer Simulation Based on Improved Particle-Spri...

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第34届中国控制与决策会议

作者： Jiping Xu Kun Yang Lu Jiang Zhaoyang Wang Xiaoyi Wang Cheng Chi School of Artificial Intelligence Beijing Technology and Business University Key Laboratory of China Light Industry Internet and Big Data Beijing Institute Of Fashion Technology Institute of Industrial Internet and Internet of Things China Academy of Information and Communications

The quality of model establishment directly affects the stability and efficiency of the flexible gossamer *** at the problem of large calculation amount of particle-spring model and low simulation efficiency in flexible gossamer 3 D simulation,a method of dynamically introducing extension spring and bending spring is *** method can reduce the amount of system calculation while ensuring the details of wrinkles,which proves simulation ***,a force-based super-elastic optimization method is proposed for the super-elastic problem of the particle-spring *** method uses the average value of the spring force around the super-elastic spring as the correction value to correct the spring force where the super-elastic *** can solve the super elastic problem of the particle-spring model while ensuring the model is stable and undistorted and reduce the excessive stretch during flexible gossamer *** comparision with the existing super-elastic correction method also shows that the method in this paper has better stability and higher efficiency,and can perform flexible gossamer simulation and 3 D simulation display better and faster.

关键词： flexible gossamer simulation particle-spring model super-elastic problem average value

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Waveform optimization for MIMO joint communication and radio sensing systems with training overhead

arXiv

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arXiv 2020年

作者： Yuan, Xin Feng, Zhiyong Andrew Zhang, J. Ni, Wei Liu, Ren Ping Wei, Zhiqing Xu, Changqiao Key Laboratory of the Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications China Global Big Data Technologies Center University of Technology Sydney Australia Key Laboratory of the Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications China Global Big Data Technologies Center University of Technology Sydney Australia Commonwealth Scientific and Industrial Research Organization Australia State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

In this paper, we study optimal waveform design to maximize mutual information (MI) for a joint communication and (radio) sensing (JCAS, a.k.a., radar-communication) multi-input multi-output (MIMO) downlink system. We consider a typical packet-based signal structure which includes training and data symbols. We first derive the conditional MI for both sensing and communication under correlated channels by considering the training overhead and channel estimation error (CEE). Then, we derive a lower bound for the channel estimation error and optimize the power allocation between the training and data symbols to minimize the CEE. Based on the optimal power allocation, we provide optimal waveform design methods for three scenarios, including maximizing MI for communication only and for sensing only, and maximizing a weighted sum MI for both communication and sensing. We also present extensive simulation results that provide insights on waveform design and validate the effectiveness of the proposed designs. Copyright © 2020, The Authors. All rights reserved.

关键词： Waveform analysis

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Research on Point-of-Interest Recommendation Method based on Graph Autoencoders and Long Short-Term Preferences 23

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

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

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

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MAVEN-ERE: A Unified Large-scale dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreferen...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, Xiaozhi Chen, Yulin Ding, Ning Peng, Hao Wang, Zimu Lin, Yankai Han, Xu Hou, Lei Li, Juanzi Liu, Zhiyuan Li, Peng Zhou, Jie Department of Computer Science and Technology BNRist Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Beijing China THU-Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT Tsinghua University Beijing China Tsinghua University Beijing China Xi'an Jiaotong-Liverpool University Suzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://***/THU-KEG/MAVEN-ERE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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Precipitation Nowcasting Using Diffusion Transformer with Causal Attention

arXiv

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arXiv 2024年

作者： Li, ChaoRong Ling, XuDong Xue, YiLan Luo, Wenjie Zhu, LiHong Qin, FengQing Zhou, Yaodong Huang, Yuanyuan Faculty of Artificial Intelligence and Big Data Yibin University Yibin644000 China Key Laboratory of Urban-Rural Industrial Integration and Intelligent Decision-Making Sichuan Province For Social Sciences China Chongqing University of Technology Chongqing400054 China Chengdu University of Information Technology Chengdu610225 China

Short-term precipitation forecasting remains challenging due to the difficulty in capturing long-term spatiotemporal dependencies. Current deep learning methods fall short in establishing effective dependencies between conditions and forecast results, while also lacking interpretability. To address this issue, we propose a Precipitation Nowcasting Using Diffusion Transformer with Causal Attention model. Our model leverages Transformer and combines causal attention mechanisms to establish spatiotemporal queries between conditional information (causes) and forecast results (results). This design enables the model to effectively capture long-term dependencies, allowing forecast results to maintain strong causal relationships with input conditions over a wide range of time and space. We explore four variants of spatiotemporal information interactions for DTCA, demonstrating that global spatiotemporal labeling interactions yield the best performance. In addition, we introduce a Channel-To-Batch shift operation to further enhance the model’s ability to represent complex rainfall dynamics. We conducted experiments on two datasets. Compared to state-of-the-art U-Net-based methods, our approach improved the CSI (Critical Success Index) for predicting heavy precipitation by approximately 15% and 8% respectively, achieving state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Rain

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Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross Correlation for Fast and Accurate Stereo Matching on Embedded GPU

Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross C...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Qiong Chang Aolong Zha Weimin Wang Xin Liu Masaki Onishi Tsutomu Maruyama Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z 2 - ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.

关键词： Correlation Power demand Graphics processing units Hardware System-on-chip Acceleration Field programmable gate arrays

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