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

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

ISBN: (纸本)9781510660915

In the hierarchical storage system, the data value needed to be evaluated to formulate rules data for hierarchical storage and migration. To further research the data hierarchical storage system in railway construction period and how to allocate the resources of the storage media with different performances, the researches on evaluation of the data value in railway construction period were pursued in the paper. Based on the business and non-business characteristics of the railway construction period, the evaluation indicator system of data value in railway construction period were designed, which can provide a comprehensive reference for the model. Combined with the quantification standard of evaluation indicators of data value in railway construction period designed in this paper, the complexity of indicator measurement can be reduced. In order to further improve the accuracy of model evaluation and meet data storage requirements, a comprehensive data value evaluation of railway construction period based on analytic hierarchy process (AHP)-grey clustering method was proposed. A simulation was carried out based on the data actually stored in a railway construction period, and the applicability of the model in the hierarchical storage system was proved by the geometric distribution of the results. In order to further verify the accuracy of the data value evaluation model proposed in this paper, its evaluation results were compared with that of the traditional model based on the same group of data. Experimental results shown that the data value model proposed in this paper was more accurate and adaptable than the traditional model in railway construction period, and can better meet the application requirements of the actual railway hierarchical storage system. © 2022 SPIE. All rights reserved.

关键词： Analytic hierarchy process

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Tunnel Disease Detection Based on Spark and Deep Learning 8th

Tunnel Disease Detection Based on Spark and Deep Learning

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8th International Conference on Artificial intelligence and Security , ICAIS 2022

作者： Zhao, Xia Zheng, Ying Yu, Chongchong You, Xiangjun Zhao, Lu China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University Beijing100048 China School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China Zhejiang HuaZhan Institute of Engineering Research and Design Ningbo315012 China Secregen Technology San MateoCA94402 United States

ISBN: (纸本)9783031067662

Aiming at metro shield disease detection with LiDAR scanning data, this paper proposes an approach of distributed parallel point cloud data processing and shield disease detection. The approach is based on HDFS and Spark, and converts the LiDAR point cloud data into the orthographic projection space, and generates projected image data efficiently. Based on the optimized deep neural network YOLOv5 target detection model, our approach detects four types of metro shield diseases and labels their locations and the classifications on the orthographic projection image. The experiments show that our distributed parallel process framework archives the speedup ratio of 4.96 to the approach of a single server on the LiDAR data processing. Using the optimized YOLO v5 model, with the appropriate IOU threshold, the accuracy of the water seepage and spalling detection box is higher than 90%, and that of the cracks and the cracks on joints is higher than 80%. Our approach can improve the efficiency and accuracy of metro shield disease detection significantly, and promote the intelligence of metro operation and maintenance in the rail transit industry. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical radar

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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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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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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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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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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LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices

arXiv

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arXiv 2024年

作者： Ding, Chuntao Cao, Xu Xie, Jianhang Fan, Linlin Wang, Shangguang Lu, Zhichao School of Artificial Intelligence Beijing Normal University Beijing China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education with School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer and Information Engineering Henan Normal University Henan453007 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science City University of Hong Kong HKSAR Hong Kong

Efficient fine-tuning of pre-trained convolutional neural network (CNN) models using local data is essential for providing high-quality services to users using ubiquitous and resource-limited Internet of Things (IoT) devices. Low-Rank Adaptation (LoRA) fine-tuning has attracted widespread attention from industry and academia because it is simple, efficient, and does not incur any additional reasoning burden. However, most of the existing advanced methods use LoRA to fine-tune Transformer, and there are few studies on using LoRA to fine-tune CNN. The CNN model is widely deployed on IoT devices for application due to its advantages in comprehensive resource occupancy and performance. Moreover, IoT devices are widely deployed outdoors and usually process data affected by the environment (such as fog, snow, rain, etc.). The goal of this paper is to use LoRA technology to efficiently improve the robustness of the CNN model. To this end, this paper first proposes a strong, robust CNN fine-tuning method for IoT devices, LoRA-C, which performs low-rank decomposition in convolutional layers rather than kernel units to reduce the number of fine-tuning parameters. Then, this paper analyzes two different rank settings in detail and observes that the best performance is usually achieved when α/r is a constant in either standard data or corrupted data. This discovery provides experience for the widespread application of LoRA-C. Finally, this paper conducts many experiments based on pre-trained models. Experimental results on CIFAR-10, CIFAR-100, CIFAR-10-C, and Icons50 datasets show that the proposed LoRA-Cs outperforms standard ResNets. Specifically, on the CIFAR-10-C dataset, the accuracy of LoRA-C-ResNet-101 achieves 83.44% accuracy, surpassing the standard ResNet-101 result by +9.5%. On the Icons-50 dataset, the accuracy of LoRA-C-ResNet-34 achieves 96.9% accuracy, surpassing the standard ResNet-34 result by +8.48%. In addition, compared with full parameter fine-tuning, LoRA-

关键词： Quality of service

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A Comparative Study of Cross-Sentence Features for Named Entity Recognition

A Comparative Study of Cross-Sentence Features for Named Ent...

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Innovations and Development of Information Technologies and Robotics (IDITR), International Conference on

作者： Sheng-Fu Wang Jing Huang Baohua Zhang Jia Li School of Computer Science and Technology Beijing Institute of Technology Beijing China Huajian eTone Language Technology (Beijing) Co. Ltd State Key Laboratory of Media Convergence Production Technology and Systems China Xinhua New Media Culture Communication Co. Ltd Big Data&Artificial Intelligence Laboratory Industrial and Commercial Bank of China Shanghai China

Recently, a growing number of Named Entity Recognition (NER) methods utilize cross-sentence features (also known as contexts) to improve the performance of NER models, instead of using single-sentence information alone. As far as we know, most NER models choose to exploit pre- and post-sentences to capture cross-sentence features. Generally, current NER studies focus only on the model architecture to capture better token representations. However, there is no in-depth exploration on how to better model cross-sentence features. In this paper, based on the span classification model, we investigate the effect of cross-sentence features under different settings. Specifically, we evaluate the impact of context stitching, context window size, context window padding, and classifier token of pre-trained language model (PLM) on model performance. Comparative experimental results show that appropriate incorporation of document-level contexts can considerably improve the NER metrics. Furthermore, we find that several factors can be used to improve the performance of NER models: (1) use domain-specific PLMs, but not classifier tokens; (2) use only preceding contexts for generic text, and random contexts for specialized text; (3) truncate overly long contexts when the context window is small, and preserve sentence integrity when the window is large; (4) set the context window size to about 200 for the basic size PLM.

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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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