Procedia Computer Science最新文献

英文中文

3D Target Detection Algorithm of Laser Point Cloud Based on Artificial Intelligence 基于人工智能的激光点云三维目标检测算法

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.039

Xiangyuan Tong, Xiaoyan Huang, Ziyan Li

Traditional target detection methods based on manual features have limitations, and it is difficult to adapt to complex scenes and various types of targets. Recently, three-dimensional target detection tasks have achieved good performance using artificial intelligence technology that is based on deep learning methods. Based on this, this paper presents a three-dimensional laser point cloud target detection algorithm based on deep learning. First, the point cloud segmentation network is designed for data semantic segmentation of the original point cloud, and the point cloud is categorized in semantic regions. Then, a point cloud region proposal network to generate region proposals according to the categorization results. Finally, the target recognition network of the point cloud is designed to achieve the detection of three-dimensional objects, specifically by conducting proposal classification and position regression. The entire algorithm integrates key modules such as point cloud segmentation, area proposal, and target recognition end-to-end, and fully excavates the geometric characteristics and semantic information of point cloud data. Experimental verification is carried out on the public benchmark data set, and the results show that the proposed algorithm has achieved state-of-the-art performance in the three-dimensional target detection task, and the detection accuracy and recall rate have reached a high level.

传统的基于人工特征的目标检测方法存在局限性，难以适应复杂场景和各种类型的目标。近年来，基于深度学习方法的人工智能技术在三维目标检测任务中取得了良好的效果。基于此，本文提出了一种基于深度学习的三维激光点云目标检测算法。首先，设计了点云分割网络，对原始点云进行数据语义分割，并将点云划分为语义区域。然后，设计点云区域建议网络，根据分类结果生成区域建议。最后，设计点云目标识别网络，具体通过进行提案分类和位置回归，实现对三维物体的检测。整个算法端到端集成了点云分割、区域建议和目标识别等关键模块，充分挖掘了点云数据的几何特征和语义信息。在公共基准数据集上进行了实验验证，结果表明所提出的算法在三维目标检测任务中取得了一流的性能，检测准确率和召回率都达到了较高水平。

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引用次数: 0

A Single Automatic Generation Optimization Algorithm Based On Maximum Likelihood Estimation for UAV Inspection Worker Computer Vision Technology 基于最大似然估计的无人机检测工人计算机视觉技术的单次自动生成优化算法

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.033

Xiaoya Chen, Xuanyu Chen

Because of its high-speed flight and wide field of view, UAV can carry out comprehensive monitoring and target searching in a wide area, high altitude and long distance environment, and become an important tool of modern information inspection. Drone inspection technology has been widely used in many industries such as power, logistics, agriculture and security, especially in forest inspection, which plays a key role in protecting forest resources and maintaining ecological balance. The traditional manual inspection method has some problems, such as low efficiency, high missing rate and personnel safety risk, so it is difficult to meet the needs of modern inspection. This paper presents and implements a UAV inspection system based on computer vision. The system carries out inspection through the autonomous route planning of the UAV, collects image data and transmits it to the embedded device for analysis to extract the target monitoring information. Finally, the system generates the corresponding work order and sends it to the client, realizing the efficient, accurate and safe UAV inspection. This paper not only optimizes the UAV inspection algorithm design, but also improves the accuracy and efficiency of image recognition by applying the maximum likelihood estimation method, which provides reliable technical support for various inspection tasks.

无人机因其高速飞行、视野开阔等特点，可在大范围、高空、远距离环境下进行全方位监测和目标搜索，成为现代信息巡检的重要工具。无人机巡检技术已广泛应用于电力、物流、农业、安防等多个行业，特别是在森林巡检方面，对保护森林资源、维护生态平衡起着关键作用。传统的人工巡检方式存在效率低、遗漏率高、人员安全风险大等问题，难以满足现代巡检的需要。本文提出并实现了一种基于计算机视觉的无人机巡检系统。该系统通过无人机自主规划航线进行巡检，采集图像数据并传输至嵌入式设备进行分析，提取目标监测信息。最后，系统生成相应的工单并发送给客户端，实现无人机巡检的高效、准确和安全。本文不仅优化了无人机巡检算法设计，还应用最大似然估计方法提高了图像识别的精度和效率，为各种巡检任务提供了可靠的技术支持。

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引用次数: 0

Application of BIM Technology in Building Water Supply and Drainage Network Security Monitoring Engineering BIM 技术在建筑给排水管网安全监测工程中的应用

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.029

Hui Liu

With the acceleration of urbanization, we are paying more and more attention to the safety and reliability of building water supply and drainage systems. Faced with various limitations of traditional monitoring methods, such as insufficient real-time performance and difficulties in data collection, we propose a new solution that combines BIM (Building Information Modeling) technology and the Internet of Things. This system not only enables real-time monitoring, but also quickly addresses security risks, greatly improving management efficiency and system security. In terms of system stability, the standard deviation of water pressure based on BIM technology is 1.1 kPa and the standard deviation of water flow rate is 0.2 L/s. Moreover, the building water supply and drainage network security monitoring project based on BIM technology can quickly and accurately respond to various security incidents. From the data conclusion, it can be seen that the system based on BIM technology has efficient, accurate, stable, and secure monitoring capabilities.

随着城市化进程的加快，我们越来越重视建筑给排水系统的安全性和可靠性。面对传统监测方法实时性不足、数据采集困难等种种局限，我们提出了一种结合 BIM（建筑信息模型）技术和物联网的全新解决方案。该系统不仅能实现实时监控，还能快速应对安全风险，大大提高了管理效率和系统安全性。在系统稳定性方面，基于 BIM 技术的水压标准偏差为 1.1 kPa，水流量标准偏差为 0.2 L/s。此外，基于 BIM 技术的建筑给排水管网安全监控项目能够快速、准确地应对各种安全事故。从数据结论可以看出，基于 BIM 技术的系统具有高效、准确、稳定、安全的监测能力。

引用次数: 0

Build an Audit Framework for Data Privacy Protection in Cloud Environment 构建云环境中数据隐私保护的审计框架

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.020

Yu Liu

With the rapid development of cloud computing technology, the data outsourcing service model in cloud environment is becoming increasingly popular. However, while cloud storage technology provides users with efficient services, its complex architecture also brings significant challenges to data privacy security. Because cloud service providers are not completely trusted, there is a risk of tampering or leaking user data, and third-party audits or malicious attacks by unauthorized users may also lead to data damage or loss. Therefore, how to realize the privacy protection and integrity audit of outsourced data in the cloud environment has become an important research topic of information security management. This paper focuses on the information security management method in the cloud environment, and deeply discusses the privacy protection and integrity audit technology of outsourced data. Aiming at the possible risk of tampering or leakage in the process of managing outsourced data by cloud service providers, a variety of privacy protection schemes based on data staining, data segmentation confusion and secure access control of private data are proposed. We study and propose a multi-copy integrity audit method that supports dynamic update of data, which realizes efficient dynamic operation and security verification through signature algorithm and random mask technology. We construct a privacy-protected data dynamic update integrity audit method, which utilizes hierarchical multi-branch tree data structure and random mask technology to significantly improve audit efficiency and security. In this paper, hash message authentication code (HMAC) and indistinguishable obfuscation (IO) techniques are used to propose a low-cost and efficient third-party audit scheme, which further reduces the computational overhead and verification cost.

随着云计算技术的飞速发展，云环境下的数据外包服务模式日益流行。然而，云存储技术在为用户提供高效服务的同时，其复杂的架构也给数据隐私安全带来了巨大挑战。由于云服务提供商并非完全可信，因此存在篡改或泄露用户数据的风险，而第三方审计或未授权用户的恶意攻击也可能导致数据损坏或丢失。因此，如何在云环境下实现外包数据的隐私保护和完整性审计已成为信息安全管理的重要研究课题。本文重点探讨了云环境下的信息安全管理方法，并对外包数据的隐私保护和完整性审计技术进行了深入探讨。针对云服务提供商在管理外包数据过程中可能存在的篡改或泄露风险，提出了基于数据染色、数据分割混淆和隐私数据安全访问控制的多种隐私保护方案。我们研究并提出了一种支持数据动态更新的多副本完整性审计方法，通过签名算法和随机掩码技术实现了高效的动态操作和安全验证。我们构建了一种隐私保护数据动态更新完整性审计方法，利用分层多分支树数据结构和随机掩码技术显著提高了审计效率和安全性。本文采用哈希信息验证码（HMAC）和不可区分混淆（IO）技术，提出了一种低成本、高效率的第三方审计方案，进一步降低了计算开销和验证成本。

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引用次数: 0

Study on the Relaxation Time Characteristics of Brain Tissue Based on Multi-Parametric Quantitative Magnetic Resonance Imaging 基于多参数定量磁共振成像的脑组织松弛时间特征研究

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.046

Jianhui Ren, Yuqin Zhang

Traditional magnetic resonance imaging (MRI) is qualitative imaging, and doctors need to rely on experience to diagnose diseases, which cannot meet the current needs of precision medicine. As a new quantitative magnetic resonance imaging technology, magnetic resonance fingerprint imaging can obtain a variety of human tissue parameters at the same time through a data acquisition, which greatly improves the imaging speed and improves the impact of noise on image quality. Several pattern matching algorithms are compared, including direct matching method, Bloch response iterative projection method, covering tree and approximate nearest neighbor search method, and improved methods. Absolute error image, mean absolute error (MAE), normalized root means square error (RMSE) and running time are counted in the experimental results. The results show that the improved method is better than the traditional method, which can greatly improve the quality of MR fingerprint multi-parameter images (T1, T2, B0, PD), and make the running time within an acceptable range. In addition, the improved algorithm is insensitive to random additive noise.

传统的磁共振成像（MRI）属于定性成像，医生需要依靠经验诊断疾病，无法满足当前精准医疗的需求。磁共振指纹成像作为一种新型的定量磁共振成像技术，通过一次数据采集可同时获得多种人体组织参数，大大提高了成像速度，改善了噪声对图像质量的影响。比较了几种模式匹配算法，包括直接匹配法、布洛赫响应迭代投影法、覆盖树和近似近邻搜索法以及改进方法。实验结果包括图像绝对误差、平均绝对误差（MAE）、归一化均方根误差（RMSE）和运行时间。结果表明，改进方法优于传统方法，能大大提高磁共振指纹多参数图像（T1、T2、B0、PD）的质量，并使运行时间在可接受的范围内。此外，改进算法对随机加性噪声不敏感。

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引用次数: 0

Towards Intelligent Auditing: Exploring the Future of Artificial Intelligence in Auditing 迈向智能审计：探索人工智能在审计中的未来

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.079

Ling Huang , Dongbing Liu

Recent years have witnessed an increasingly broad application of artificial intelligence (AI) technologies such as speech recognition, computer vision, natural language processing, machine learning, algorithmic framework, cognitive computing, deep learning and neural networks in the field of auditing, producing a far-reaching impact on traditional audit work. However, the application of AI technologies in auditing practices is still in its infancy stage and further exploration and development are needed. Based on an in-depth investigation of AI-powered auditing practices, this paper proposes four innovative paths towards intelligent auditing in response to the key problems and challenges in practices, namely, audit procedure design, audit data processing, audit approach transformation and audit model exploration, with a view of achieving full coverage of intelligent auditing and making it standardized, normalized, popularized and practically effective. These innovations will effectively advance the improvement in auditing competencies and promote the high-quality development of audit work.

近年来，语音识别、计算机视觉、自然语言处理、机器学习、算法框架、认知计算、深度学习和神经网络等人工智能（AI）技术在审计领域的应用日益广泛，对传统审计工作产生了深远影响。然而，人工智能技术在审计实务中的应用仍处于起步阶段，需要进一步探索和发展。本文在深入调研人工智能助力审计实务的基础上，针对实务中存在的关键问题和挑战，提出了审计程序设计、审计数据处理、审计方法转化和审计模型探索四条实现智能审计的创新路径，以期实现智能审计的全覆盖，使其标准化、规范化、大众化和实效化。这些创新将有效推进审计能力提升，促进审计工作高质量发展。

引用次数: 0

Research on Data Cluster Analysis based on Intelligent Algorithm and Big Data Analysis 基于智能算法和大数据分析的数据聚类分析研究

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.080

Bingqing Weng

With the development of the times, computers have been continuously developed, and data analysis based on intelligent algorithms has been gradually applied. Big data analysis has become an indispensable part of various enterprises and institutions, which can help enterprises better analyze the market, optimize the overall resource allocation and decision-making efficiency, and promote the sustainable development of the overall enterprises and institutions. The purpose of this experiment is to use intelligent algorithm and big data analysis technology to cluster hotel data, so as to find the patterns and associations hidden in the data and provide decision support for the hotel industry.

随着时代的发展，计算机不断发展，基于智能算法的数据分析也逐渐得到应用。大数据分析已经成为各类企事业单位不可或缺的一部分，可以帮助企业更好地分析市场，优化整体资源配置和决策效率，促进整体企事业单位的可持续发展。本实验旨在利用智能算法和大数据分析技术对酒店数据进行聚类，从而发现隐藏在数据中的规律和关联，为酒店行业提供决策支持。

引用次数: 0

Design and Implementation of Intelligent Logistics Control Platform Based on Spring Cloud 基于 Spring Cloud 的智能物流控制平台的设计与实现

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.063

Changjuan Guo, Xiongwei Shi, Minmin Ji, Xuecui Ding, Bo Chen

With the continuous progress of science and technology, Internet technology, management concepts and service models have also undergone earth-shaking changes. To address this challenge, this article proposes a solution based on a Spring Cloud application. The platform adopts microservice structure to realize efficient data interactive processing system architecture, mainly including administrator users, logistics personnel, front-end application software development environment, server and other functional modules. Its core goal is to help internal employees quickly and accurately access the company's website, and carry out statistical analysis of various reports to improve work efficiency and quality. After that, the intelligent logistics control platform was tested, and the test results showed that the security range was from 85 to 100, and at time point 8, the safety score reached the highest 99%, and the system security reached the best level. This shows that the background support management function can ensure the security of database information and record various operation logs to meet the management needs of enterprises.

随着科学技术的不断进步，互联网技术、管理理念和服务模式也发生了翻天覆地的变化。针对这一挑战，本文提出了一种基于 Spring Cloud 应用的解决方案。该平台采用微服务结构，实现高效的数据交互处理系统架构，主要包括管理员用户、后勤人员、前端应用软件开发环境、服务器等功能模块。其核心目标是帮助内部员工快速、准确地访问公司网站，并对各类报表进行统计分析，提高工作效率和质量。随后，对智能物流管控平台进行了测试，测试结果显示，安全范围在 85 分至 100 分之间，在时间点 8，安全得分最高达到 99%，系统安全性达到最佳水平。由此可见，后台支持管理功能可以保证数据库信息的安全，记录各种操作日志，满足企业的管理需求。

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引用次数: 0

Multi-Autonomous Mobile Robot traffic Management Based on Layered Costmaps and a modified Dijkstra's Algorithm 基于分层成本图和改进的 Dijkstra 算法的多自主移动机器人交通管理

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.01.006

Simon Mouritsen Langbak , Casper Schou , Karl Damkjær Hansen

In recent times, the use of autonomous mobile robots (AMRs) has increased in many industries. As more AMRs roam the same area, traffic management becomes essential to prevent congestion and deadlocks. In related work, traffic management is often achieved using sophisticated, centralised planning approaches, albeit this often suffers from scalability issues. This paper therefore explores an approach where multi-AMR traffic management is achieved with layered costmaps and a modified Dijkstra's algorithm. This keeps the global path planner in the individual AMRs, thus not suffering scalability issues. To achieve multi-AMR coordination, traffic lanes and restricted areas are added to the AMRs’ global costmaps. Furthermore, the AMRs also use a modified Dijkstra's algorithm that supports implementation of traffic directions. A proof-of-concept solution is implemented in Robot Operating System 2 with Nav2 and Gazebo. The implemented solution was tested against a standard solution without any traffic management in three scenarios designed to provoke collisions. The results indicate that the implemented solution can prevent a set of collisions better than one without traffic management.

近来，自动移动机器人（AMR）在许多行业的使用都在增加。随着越来越多的自动移动机器人在同一区域漫游，交通管理对于防止拥堵和死锁变得至关重要。在相关工作中，交通管理通常采用复杂的集中式规划方法来实现，但这种方法往往存在可扩展性问题。因此，本文探讨了一种利用分层成本图和改进的 Dijkstra 算法实现多AMR 流量管理的方法。这种方法将全局路径规划器保留在单个 AMR 中，从而避免了可扩展性问题。为了实现多 AMR 协调，在 AMR 的全局成本图中添加了交通线和限制区域。此外，AMR 还使用了改进的 Dijkstra 算法，该算法支持交通方向的实施。通过 Nav2 和 Gazebo 在机器人操作系统 2 中实施了概念验证解决方案。在三个旨在引发碰撞的场景中，实施的解决方案与没有任何交通管理的标准解决方案进行了对比测试。结果表明，与没有交通管理的解决方案相比，所实施的解决方案能更好地防止碰撞。

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引用次数: 0

Sensor and data: key elements of human-machine interaction for human-centric smart manufacturing 传感器和数据：以人为本的智能制造中人机互动的关键要素

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.01.019

Jialu Yang , Ying Liu , Phillip L. Morgan

The proposal of Industry 5.0 has made sustainability, human-centric and resilience the core of digital manufacturing, which also puts forward new requirements for the human-machine interaction (HMI) paradigm in human-centric smart manufacturing (HCSM). In the manufacturing scenario, the process of HMI can be divided into four parts: 1) Sensors and hardware, where the environment information and input signals are collected, 2) Data processing, where the signals are converted into data, 3) Transmission mechanism, where the data is transmitted to the processing centre, and 4) Interaction and collaboration. Among them, sensors and data are expected to become breakthrough points in optimising HMI. This is not only due to the emergence of new research, innovation and technologies but also because they are closely influenced by the new design concepts brought about by Industry 5.0. This paper analyses the latest studies and technologies in the sensor field and their possible applications in HCSM scenarios. Then, opportunities and challenges of data analysis in the HMI in Industry 5.0 are discussed. Finally, based on the design concepts and requirements of Industry 5.0, this paper demonstrates how they will become the key points for future HMI development.

工业 5.0 的提出将可持续性、以人为本和弹性作为数字化制造的核心，这也对以人为本的智能制造（HCSM）中的人机交互（HMI）范式提出了新的要求。在制造场景中，人机交互的过程可分为四个部分：1）传感器和硬件，收集环境信息和输入信号；2）数据处理，将信号转换为数据；3）传输机制，将数据传输到处理中心；4）交互与协作。其中，传感器和数据有望成为优化人机界面的突破点。这不仅是因为新的研究、创新和技术不断涌现，还因为它们受到工业 5.0 带来的新设计理念的密切影响。本文分析了传感器领域的最新研究和技术及其在 HCSM 场景中的可能应用。然后，讨论了工业 5.0 中人机界面数据分析的机遇和挑战。最后，本文基于工业 5.0 的设计理念和要求，论证了它们将如何成为未来人机界面发展的关键点。

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