Advances in computer, signals and systems最新文献

Review of deep learning-driven MRI brain tumor detection and segmentation methods 深度学习驱动的MRI脑肿瘤检测与分割方法综述

Advances in computer, signals and systems Pub Date : 2023-09-01 DOI: 10.23977/acss.2023.070803

Rong Zhang, Hongliang Luo, Weijie Chen, Yongqiang Bai

引用次数: 0

The Application and Research of Internet of Things Technology in the Field of Electronic Communication 物联网技术在电子通信领域的应用与研究

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070905

引用次数: 0

User Requirement Analysis of Resilient PNT System 弹性PNT系统用户需求分析

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070712

{"title":"User Requirement Analysis of Resilient PNT System","authors":"","doi":"10.23977/acss.2023.070712","DOIUrl":"https://doi.org/10.23977/acss.2023.070712","url":null,"abstract":"Firstly, this paper provided a brief introduction to the concepts of Positioning, Navigation and Timing (PNT) and Resilient, and proposed that the fundamental premise for building a resilient PNT system is to identify the diverse needs of users in terms of accuracy, availability, continuity, and other indicators across different typical scenarios. Subsequently, it analyzed user requirements for various scenarios, including aviation routes, maritime navigation, agricultural surveying, train control, vehicle navigation, and emergency response, in different environments such as near-earth, urban, jungle, indoor, and underwater environments. The analysis took into account factors such as the accuracy, availability, continuity, integrity, terminal cost, and form of PNT. Based on this analysis, this paper summarized the user requirements for resilient PNT systems in different scenarios, presenting a comprehensive table of typical user requirements. Furthermore, it suggested that resilient PNT terminals should be cost-effective, compact, low-power, and highly compatible. Finally, the diverse user requirements were summarized and analyzed, providing a research foundation for developing resilient PNT solutions for different typical scenarios, such as near-earth, urban, jungle, indoor, and underwater environments.","PeriodicalId":495216,"journal":{"name":"Advances in computer, signals and systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135498069","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 review of machine learning-based prediction of lncRNA subcellular localization 基于机器学习的lncRNA亚细胞定位预测综述

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070908

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Design of Data Acquisition System for Clothing Production Line 服装生产线数据采集系统的设计

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070613

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The 3D Point Cloud Registration Algorithm Based on Harris-DLFS 基于Harris-DLFS的三维点云配准算法

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070614

{"title":"The 3D Point Cloud Registration Algorithm Based on Harris-DLFS","authors":"","doi":"10.23977/acss.2023.070614","DOIUrl":"https://doi.org/10.23977/acss.2023.070614","url":null,"abstract":"Three-dimensional model reconstruction is a pivotal technology in the realm of computer vision. Point cloud registration serves as its integral step, which decisively impacts the efficiency and precision of the entire reconstruction process. However, existing point cloud registration algorithms often face issues. These include prolonged processing time, inadequate accuracy, and poor robustness. To address these problems, this paper proposes a novel point cloud registration algorithm based on corner detection (Harris) and partition-based local feature statistics (DLFS). The main steps are as follows: Firstly, the Harris corner detection algorithm is employed. This step is crucial for extracting key points and enhancing the efficiency of the registration process. Secondly, the DLFS method is used to describe the features of each key point, generating feature vectors. Subsequently, matching point pairs are filtered based on rigid distance constraints, and an coarse registration is performed using the Random Sample Consensus (RANSAC) algorithm. Finally, the Iterative Closest Point (ICP) algorithm is applied for fine registration. Experimental results demonstrated the effectiveness of this method. It significantly improved registration accuracy, robustness, and computational efficiency. Therefore, it holds substantial value for practical point cloud registration applications.","PeriodicalId":495216,"journal":{"name":"Advances in computer, signals and systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135496748","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

Concrete Slump Prediction Based on Hybrid Optimization XGBoost Algorithm 基于混合优化XGBoost算法的混凝土坍落度预测

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070610

引用次数: 0

Research on Vehicle Security Chip Application and Testing Based on Fault Injection 基于故障注入的汽车安全芯片应用与测试研究

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070813

引用次数: 0

Research on flight technology evaluation based on machine learning algorithm 基于机器学习算法的飞行技术评估研究

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070616

{"title":"Research on flight technology evaluation based on machine learning algorithm","authors":"","doi":"10.23977/acss.2023.070616","DOIUrl":"https://doi.org/10.23977/acss.2023.070616","url":null,"abstract":"In China's civil aviation transportation industry, flight safety has been the focus of attention. In this paper, a flight technology assessment model and an automated early warning model are established for aviation safety. First, data pre-processing is performed. Then the suitable indicators are continuously screened by multiple machine learning classifications, and then the screened data are fitted to continuously screen the suitable indicators, and the aircraft technology assessment is found to be more suitable for the integrated learning classification model. Subsequently, three unoptimized optimal models were derived as LightGBM, XGboost and Random Forest classification models. The results of these models are then fused by Stacking model to combine their advantages to build the final aircraft technology assessment prediction model. For the automated early warning mechanism, the aviation early warning mechanism needs to be established first by subclassing these data with the K-mean clustering model and visualizing the key data items such as avg (COG NORM ACCEL) based on the normal distribution, combined with the differentiated distribution for each category to set the implausible warning level to establish the aviation automated early warning model.","PeriodicalId":495216,"journal":{"name":"Advances in computer, signals and systems","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135496744","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 Self-Organizing Multimodal Multi-Objective Coati Optimization Algorithm 一种自组织多模态多目标Coati优化算法

Advances in computer, signals and systems Pub Date : 2023-01-01 DOI: 10.23977/acss.2023.070703

{"title":"A Self-Organizing Multimodal Multi-Objective Coati Optimization Algorithm","authors":"","doi":"10.23977/acss.2023.070703","DOIUrl":"https://doi.org/10.23977/acss.2023.070703","url":null,"abstract":"The Coati Optimization Algorithm (COA) has emerged as a prominent evolutionary algorithm renowned for its efficacy in addressing real-world problems. Its wide-ranging applicability across diverse domains is a testament to its exceptional performance and versatility. Compared to other evolutionary algorithms, COA has been proven to possess excellent global and local search capabilities. This paper introduces a novel self-organizing multimodal multi-objective Coati Optimization Algorithm (MMOCOA) designed specifically to tackle multimodal multi-objective problems. The proposed algorithm aims to effectively handle the complexities associated with such problems by incorporating self-organizing mechanisms into the Coati optimization framework. Primarily, MMOCOA utilizes a self-organizing speciation method as its primary approach to identify the Pareto optimal solutions. This speciation tactic can establish stable niches and continually updates them to actively search for and preserve the optimal Pareto solutions. Furthermore, an improved self-organization mechanism is proposed to enhance the generation speed of the niches. Additionally, MMOCOA incorporates a non-dominated sorting method and a specialized crowding distance technique to effectively preserve the diversity of both the decision and objective space. To assess the effectiveness of MMOCOA, this study presents a comprehensive evaluation using eleven multimodal multi-objective test functions. Additionally, MMOCOA is benchmarked against five state-of-the-art multimodal multi-objective optimization algorithms. The experimental results highlight the superior performance of MMOCOA, as it demonstrates the capability to discover a larger number of Pareto solutions compared to the other algorithms under consideration.","PeriodicalId":495216,"journal":{"name":"Advances in computer, signals and systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135498073","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