International Conference on Algorithms, Microchips and Network Applications最新文献_第7页

MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection MSSF-DCNet：利用密集连接网络进行多尺度选择性融合，用于声纳图像目标检测

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3032084

Yu Dong, Jianlei Zhang, Chun-yan Zhang

{"title":"MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection","authors":"Yu Dong, Jianlei Zhang, Chun-yan Zhang","doi":"10.1117/12.3032084","DOIUrl":"https://doi.org/10.1117/12.3032084","url":null,"abstract":"In the field of underwater target recognition, forward-looking sonar images are widely applied in underwater rescue operations. The emergence of object detection technologies powered by deep learning has significantly enhanced the ability to recognize underwater targets. In object detection, the neck network, serving as a critical intermediary component, plays a vital role. However, traditional Feature Pyramid Networks (FPN) have two main problems: 1) During the feature fusion process, FPN does not modify the importance of features across various levels, resulting in imbalanced features at different scales and loss of scale information. 2) Lack of effective information transmission between features of different scales. In this article, we propose a novel neck network architecture, Multi Scale Selective Fusion with Dense Connectivity Network (MSSF-DCNet), which encompasses two components to tackle the previously mentioned challenges. The first one is the Multi Scale Selection Module, which effectively balances the weights of features at different levels during the feature fusion process by calculating and weighting weights for different scales, better preserving scale information. The second one is the Cross Scale Dense Connection module, which exchanges information between different feature layer levels. The model is capable of capturing global context information at every layer. thereby improving the detection capability of the neck network. By replacing the FPN with MSSF-DCNet in the Faster R-CNN framework, our model achieves an increase in Average Precision (AP) by 1.2, 4.0, and 2.6 points using MobileNet-v2, ResNet50, and SwinTransformer backbones, respectively. Furthermore, when employing ResNet50 as the backbone, MSSF-DCNet enhances the RetinaNet by 3.4 AP and ATSS by 4.1 AP. At the same time, we compared different neck networks with MSSF-DCNet on the Faster R-CNN baseline network, and MSSF-DCNet achieved the best performance in all metrics.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368946","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

Design and implementation of an electromagnetic tracing intelligent vehicle based on STC32 基于 STC32 的电磁追踪智能车的设计与实现

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3032092

Zhaocheng Zhang, Mo Shen, Hongtao Guo

引用次数: 0

An improved credit-based shaper for TSN TSN 的改进型基于信用的整形器

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3031997

Zeya Li, Yang Liu, Longqing Gong, Danni Xu, Jinfeng Tang

{"title":"An improved credit-based shaper for TSN","authors":"Zeya Li, Yang Liu, Longqing Gong, Danni Xu, Jinfeng Tang","doi":"10.1117/12.3031997","DOIUrl":"https://doi.org/10.1117/12.3031997","url":null,"abstract":"In response to the growing demands of real-time application scenarios, TSN has emerged as a crucial solution. However, the implementation of a credit-based shaper to ensure low-priority traffic transmission may unintentionally result in burst traffic generation. This could lead to storage overflow and potential data loss, posing a significant challenge to the efficient operation of TSN networks. To address this issue, we propose the ICBS algorithm, an enhancement of the original CBS mechanism, which preserves the fundamental principles of preventing low-priority starvation while mitigating the risk of burst traffic generation. The ICBS algorithm demonstrates enhanced fine-grained operation of data frames by refining the credit calculation method, effectively minimizing the occurrence of bursts in AVB traffic. Furthermore, we have devised an ICBS adaptation evaluation algorithm to assess the rationality of pre-scheduling results for AVB, ensuring optimal resource allocation. The simulation results demonstrate that the proposed ICBS algorithm effectively achieves its objective of low-priority traffic transmission with extra worst-case delay cost, making it a highly suitable substitute for existing TSN shaper solutions. The ICBS algorithm not only enhances the efficiency and reliability of TSN networks but also paves the way for future advancements in real-time application scenarios.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369406","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

GoPlace: chip placement like playing go GoPlace：像下围棋一样放置筹码

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3032070

Jianguo Hu, Shengzhi Shen, Yanyu Ding, Yuhe Wang, Jiakai Pan, Wenjun Huang, Deming Wang

{"title":"GoPlace: chip placement like playing go","authors":"Jianguo Hu, Shengzhi Shen, Yanyu Ding, Yuhe Wang, Jiakai Pan, Wenjun Huang, Deming Wang","doi":"10.1117/12.3032070","DOIUrl":"https://doi.org/10.1117/12.3032070","url":null,"abstract":"As a critical stage in modern Very-Large-Scale Integrated (VLSI) design, placement plays a crucial role in positioning numerous circuit modules of varying sizes onto a 2D chip canvas to achieve optimal performance. In recent years, applying machine learning methods to placement has emerged as a promising solution for significantly enhancing efficiency and achieving superior results. However, machine learning-driven methods are still in their early stages, facing challenges such as exploration and convergence difficulties. In addition, it is challenging to integrate netlist data with the placement information. This paper proposes a novel approach leveraging deep reinforcement learning to address these challenges. First, a multi-layer chip canvas state representation method is proposed to tackle the challenges of storing and using the placement information. Additionally, a graph neural network is used to assist in generating placement information. Second, this paper proposed a semi-shared policy and value network, and to accommodate the scale and complexity of chip placement, a residual-like neural network is proposed. Third, extensive experiments on eight circuits of public benchmarks show that GoPlace achieves 10% ~ 25% wirelength reduction compared to other reinforcement learning-based methods, lowest congestion, and guarantees zero overlap.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370280","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

A low-complexity FMCW-SAR imaging system and moving target detection method 低复杂度 FMCW-SAR 成像系统和移动目标探测方法

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3031896

Chao Wang, Donghao Feng, Peiyuan Guo, Wuliang Chang, Jiachang Guo, Pengsong Duan, Yangjie Cao

引用次数: 0

Fusing lightweight Retinaface network for fatigue driving detection 融合轻量级 Retinaface 网络进行疲劳驾驶检测

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3031940

Zhiqin Wang

引用次数: 0

Multi-target vital signs detection by fusing radar and optical images 通过融合雷达和光学图像探测多目标生命体征

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3032020

Chao Wang, Xinfeng Hu, Xiaodong Yang, Wuliang Chang, Guangze Cao

引用次数: 0

Research on energy management of building operation and maintenance based on multiple prediction algorithms 基于多重预测算法的建筑运维能源管理研究

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3031948

Ting Lei, Jingyuan Wang, Ming Jiang

{"title":"Research on energy management of building operation and maintenance based on multiple prediction algorithms","authors":"Ting Lei, Jingyuan Wang, Ming Jiang","doi":"10.1117/12.3031948","DOIUrl":"https://doi.org/10.1117/12.3031948","url":null,"abstract":"With the continuous promotion of national energy conservation and emission reduction, the in-depth application of information technology has gradually triggered in-depth changes in the development mode of the country, city and industry. This paper mainly starts from the status quo of national building energy consumption and related energy saving and green development plan, introduces the research status quo of building energy management, and aims at the more popular machine learning algorithm models in recent years, including Random Forest Regression Model, XGBoost Model and Stacking Multi-Algorithmic Fusion Model, and combines with CITIC Design Digital Intelligent Building System in the statistics of a certain office building with a total of 321 days of Combined with the raw data of measured energy consumption of an office building counted in the CITIC Design Digital Intelligent Building System for a total of 321 days, the prediction learning of building operation and maintenance energy consumption is carried out respectively, the prediction effects of the three prediction algorithms are compared and analyzed, and it is recommended to use the Stacking Multi-Algorithmic Fusion Model for predicting the energy consumption of building operation and maintenance and the operation and maintenance mode of building operation and maintenance energy consumption control in advance warning is proposed by combining with energy consumption prediction model.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368452","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

Multi-agent scheduling based on three-dimensional time window 基于三维时间窗的多代理调度

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3031972

Wei Wang, Xuejuan Zhang, Hairui Song, Chi Qiu

引用次数: 0

Requirement analysis of remote conference system based on qualitative and quantitative analysis combination 基于定性与定量分析相结合的远程会议系统需求分析

International Conference on Algorithms, Microchips and Network Applications Pub Date : 2024-06-08 DOI: 10.1117/12.3031999

Yiping Yan

引用次数: 0