Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems最新文献_第2页

Survey of the Formal Verification of Operating Systems in Power Monitoring System 电力监控系统中操作系统的形式化验证研究

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609714

Kangle Yang, Jianye Yu, Xinshen Wei, Feng You, Haidong Huang, Xuesong Huo

{"title":"Survey of the Formal Verification of Operating Systems in Power Monitoring System","authors":"Kangle Yang, Jianye Yu, Xinshen Wei, Feng You, Haidong Huang, Xuesong Huo","doi":"10.1145/3609703.3609714","DOIUrl":"https://doi.org/10.1145/3609703.3609714","url":null,"abstract":"The formal verification of the operating systems in power monitoring system is an important means to ensure the security of the operating system in power monitoring system. This paper introduces the verification principles and framework of formal verification of operating systems in power monitoring system, the languages and tools used in formal verification, and some classic projects of formal verification of operating systems. Through the introduction of the related content of the formalization of these operating systems, some ideas and future development trends of the formal verification of the current operating systems are explained. It has completed the verification process, beginning with weak type safety and progressing to functional correctness, proof of the high-level abstract protocol, and modification of the low-level code. These gain from the constant advancement and refinement of tools and technologies for formal verification of operating systems, but it is also subject to formal verification tools and techniques, and cannot genuinely go towards the last practical link of production. The automated research on formal verification tools and technologies will continue to be a significant advance in operating system formal verification.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"586 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116176002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Policy Updating Methods of Q Learning for Two Player Bargaining Game 二人议价博弈Q学习的策略更新方法

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609722

Jianing Xu, Bei Zhou, Nanlin Jin

引用次数: 0

An Improved Self-Adaptive Teaching-learning Based Optimization for Multi-area Economic Dispatch 基于改进自适应教学的多区域经济调度优化

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609716

Qun Niu, Gui Xu, L. Tang

引用次数: 0

Three-Dimensional Sphere Recognition and Tracking Based on YOLO 基于YOLO的三维球面识别与跟踪

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609706

Luying Li, Wenjun Huang

{"title":"Three-Dimensional Sphere Recognition and Tracking Based on YOLO","authors":"Luying Li, Wenjun Huang","doi":"10.1145/3609703.3609706","DOIUrl":"https://doi.org/10.1145/3609703.3609706","url":null,"abstract":"Traditional art exhibitions are usually dominated by relatively static displays such as text, pictures and common multimedia technology. Subject to technical limitations, the exhibition means are relatively simple and the content is relatively thin, which cannot fully meet the exhibition needs of the organizers, nor can it mobilize the enthusiasm of the visitors, and fails to fully show the communication of the exhibition. Therefore, an object detection model based on You Only Look Once(YOLO) network is proposed in this paper to recognize and track the spheres made by felt process. First, the YOLO network was pre-trained using the open source data set, and then the pre-training model was fine-tuned according to the felt sphere image training set. Before fine tuning, the k-means clustering algorithm was used to cluster the marking information of the sphere training set made by felt process. Secondly, for the display of the effect after recognition, OpenCV image processing is used for image special effect processing of the specific recognition area. Through the experimental results, the object detection based on YOLO network proposed in this paper can reach 80.95% in detection accuracy mAP@0.5:0.95 and detection speed up to 20ms, showing excellent performance in detection accuracy and detection speed. It can fit the background interactive display effect of felt art well.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"22 6S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122810921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frequency-Split Inception Transformer for Image Super-Resolution 用于图像超分辨率的分频启始变压器

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609708

Wei Xu

{"title":"Frequency-Split Inception Transformer for Image Super-Resolution","authors":"Wei Xu","doi":"10.1145/3609703.3609708","DOIUrl":"https://doi.org/10.1145/3609703.3609708","url":null,"abstract":"Transformer models have shown remarkable effectiveness in capturing long-range dependencies and extracting features for single image super-resolution. However, their deployment on edge devices is hindered by their high computational complexity. To address this challenge, we propose Inception Swin Transformer (IST), a novel model that leverages frequency domain separation to reduce redundant computations.In IST, we exploit the strengths of both CNN-based networks and Transformer variants to handle high-frequency and low-frequency features, respectively. By dynamically utilizing frequency factors to separate feature maps, IST ensures that different components are processed appropriately. Additionally, IST maintains a balanced trade-off between model speed and performance by gradually reducing the proportion of high-frequency components.Our experiments demonstrate that IST effectively reduces the FLOPs while preserving high performance. The combination of Transformers’ accuracy and CNN variants’ efficiency enables IST to significantly reduce computational strain without compromising quality. Comparative analysis reveals that IST outperforms other models, achieving superior results with less FLOPs.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124346982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An early warning system for height limit based on multi-sensor information fusion 基于多传感器信息融合的限高预警系统

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609718

Chengjun Feng, Chao Wu, Yifan Wu, Dongyi He, Peng Zhou

{"title":"An early warning system for height limit based on multi-sensor information fusion","authors":"Chengjun Feng, Chao Wu, Yifan Wu, Dongyi He, Peng Zhou","doi":"10.1145/3609703.3609718","DOIUrl":"https://doi.org/10.1145/3609703.3609718","url":null,"abstract":"With the continuous development of social economy, infrastructure such as highways and bridges continues to develop, and overloaded vehicles are repeatedly prohibited. Therefore, many places have set up \"height limit gantry frames\" at the entrances of bridges and overpasses, artificially limiting the types and sizes of vehicles, increasing the safety of bridge structures and roads, but also bringing many safety hazards. In recent years, accidents of ultra-high vehicles colliding with \"height limit gantry\" have occurred frequently, causing serious personnel and property losses. Therefore, it is particularly important to identify and prompt super high vehicles in advance to avoid such accidents. To address such issues, a height limit information early warning system based on V2X multi-sensor information fusion has been developed. Using LiDAR to identify the height of vehicles, using cameras to identify vehicle license plates, and then conducting information fusion, combined with the above information, warning dangerous vehicles through various channels such as V2X or roadside LED screens.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126464104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network equipment recommended placement based on entropy weight method and improved ideal point method distribution scheme design 基于熵权法和改进理想点法的网络设备推荐放置方案设计

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609720

Size Liu, Zhenxing Qi, Xinpei Liu, Fangke Lu

{"title":"Network equipment recommended placement based on entropy weight method and improved ideal point method distribution scheme design","authors":"Size Liu, Zhenxing Qi, Xinpei Liu, Fangke Lu","doi":"10.1145/3609703.3609720","DOIUrl":"https://doi.org/10.1145/3609703.3609720","url":null,"abstract":"At present, data center security and energy consumption have been continuously concerned and discussed. There are some new technology to reduce energy consumption of data center, but few studies focus on make full use of the resources of the existing data center during routine maintenance to solve urgent problems such as low interest rate of distributed power supply resources, mismatch of power supply resources and space resources in cabinet, and unequal distribution of AC power supply systems. This paper focus on problems existing in data center, design a general distribution scheme to recommend network equipment optimal cabinet and location based on entropy weight method ,which combined with entropy weight method to optimize important attributes such as cabinet power utilization, cabinet space utilization, cabinet resource imbalance, and three-phase imbalance, recommend network equipment optimal cabinet and location totally depending on objective data, avoid the problem maintenance staff lack of experience or inconsiderate to make wrong decision effectively, achieve full utilization of data center power system resources and operation optimization. The scheme is verified to be effective, has certain guiding significance for network equipment location selected management in data center.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration of transfer learning capability of multilingual models for text classification 多语言文本分类模型迁移学习能力的探索

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609711

Maddineni Bhargava, K. Vijayan, Oshin Anand, Gaurav Raina

{"title":"Exploration of transfer learning capability of multilingual models for text classification","authors":"Maddineni Bhargava, K. Vijayan, Oshin Anand, Gaurav Raina","doi":"10.1145/3609703.3609711","DOIUrl":"https://doi.org/10.1145/3609703.3609711","url":null,"abstract":"The use of multilingual models for natural language processing is becoming increasingly popular in industrial and business applications, particularly in multilingual societies. In this study, we investigate the transfer learning capabilities of multilingual language models like mBERT and XLM-R across several Indian languages. We study the performance characteristics of a classifier model with mBERT/XLM-R as the front-end, which is trained only in one language for two tasks: text categorization of news articles and sentiment analysis of product reviews. News articles, on the same event but in different languages, are representative of what may be termed as ‘inherently parallel’ data; i.e. data that exhibits similar content across multiple languages, though not necessarily in parallel sentences. Other examples of such data would be customer inquiries/reviews about the same product, social media activity pertaining to the same topic, etcetera. After training in one language, we study the performance characteristics of this classifier model when applied to other languages. Our experiments reveal that by exploiting the inherently parallel nature of the data, XLM-R performs remarkably well when adapted for any Indian language dataset. Further, our study reveals the importance of simultaneously fine-tuning multilingual models with in-domain data from one language in order to express their cross-lingual and domain transfer learning abilities together.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114725072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLFormer: An Efficient and Real-time LiDAR Lane Detection Method based on Transformer LLFormer:一种基于变压器的高效实时激光雷达车道检测方法

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609707

Haoxiang Jie, Xinyi Zuo, Jian Gao, W. Liu, Jun-Dong Hu, Shuai Cheng

{"title":"LLFormer: An Efficient and Real-time LiDAR Lane Detection Method based on Transformer","authors":"Haoxiang Jie, Xinyi Zuo, Jian Gao, W. Liu, Jun-Dong Hu, Shuai Cheng","doi":"10.1145/3609703.3609707","DOIUrl":"https://doi.org/10.1145/3609703.3609707","url":null,"abstract":"Lane detection has been one of the most important functions in the autonomous driving perception module. Most of the current research require complex post-processing and curve fitting processes before they can be used by subsequent regulation modules. In this paper, we propose the LLFormer algorithm combining CNN and Transformer structure, which is the first attempt to perform end-to-end lane detection based on laser point cloud and output its cubic polynomial coefficients. In addition, this paper modifies the structure of the conventional transformer and proposes the Generating Lane Query (GLQ) module. The output of encoder is plugged into GLQ for initialization of lane query in decoder, preserving the uniqueness of each frame of point cloud data. We test the performance of the proposed algorithm in the public dataset K-Lane, and the results show that the accuracy of the proposed LLFormer is close to the existing SOTA algorithm. The number of model parameters of LLFormer is only 9.01M, and the amount of operations is only 0.19GFLOPs, which are 1/26 and 1/2937 of the existing SOTA algorithm, respectively. The frequency of inference calculation is 35.9FPS, which can fully meet the real-time requirements for industrial deployment.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"71 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116041605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems 2023第五届模式识别与智能系统国际会议论文集

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 1900-01-01 DOI: 10.1145/3609703

引用次数: 0