共查询到20条相似文献，搜索用时 31 毫秒
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《Information processing & management》2022,59(1):102726
In synthetic aperture radar (SAR) image change detection, the deep learning has attracted increasingly more attention because the difference images (DIs) of traditional unsupervised technology are vulnerable to speckle noise. However, most of the existing deep networks do not constrain the distributional characteristics of the hidden space, which may affect the feature representation performance. This paper proposes a variational autoencoder (VAE) network with the siamese structure to detect changes in SAR images. The VAE encodes the input as a probability distribution in the hidden space to obtain regular hidden layer features with a good representation ability. Furthermore, subnetworks with the same parameters and structure can extract the spatial consistency features of the original image, which is conducive to the subsequent classification. The proposed method includes three main steps. First, the training samples are selected based on the false labels generated by a clustering algorithm. Then, we train the proposed model with the semisupervised learning strategy, including unsupervised feature learning and supervised network fine-tuning. Finally, input the original data instead of the DIs in the trained network to obtain the change detection results. The experimental results on four real SAR datasets show the effectiveness and robustness of the proposed method. 相似文献
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《Information processing & management》2023,60(2):103212
The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges. 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100477
Increasing numbers of devices that output large amounts of geographically referenced data are being deployed as the Internet of Things (IoT) continues to expand. Partly as a result of the IoT's dynamic, decentralized, and heterogeneous architecture. These are all examples of the Internet of items (IoT), despite the fact that we might be thinking that one of these items is different from the others. The physical and digital worlds are connected by the Internet of Things (IoT). Nowadays, one of the key goals of the Internet is its own development. This paper provides an in-depth analysis of IoT-based data quality and data preparation strategies developed with multinational corporations in mind. The goal is to make IoT data more trustworthy and practical so that MNCs may use it to their advantage in making educated business decisions. The proposed structure consists of three distinct actions: gathering data, evaluating data quality, and cleaning up raw data. Data preprocessing research is essential since it decides and significantly affects the accuracy of predictions made in later stages. Thus, the recommendation for a special and useful combination in the framework of different data preprocessing task types, which includes the following four technical elements and is briefly justified, is made. The Internet of Things (IoT) is a design pattern in which commonplace items can be equipped with classification, sensing, networking, and processing capabilities that will enable them to communicate with one another over the Internet to fulfill a specific function. The Internet of Things will eventually change physical objects into virtual objects with intelligence. In addition to a detailed analysis of the IoT layer, this article gives an overview of the existing Internet of Things (IoT), technical specifics, and applications in this recently growing field. However, this publication will provide future scholars who desire to conduct study in this area of Internet of Things with a better knowledge. 相似文献
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《Journal of The Franklin Institute》2021,358(16):8772-8785
Conventional grant-based random access scheme is inappropriate to massive Internet of Things (IoT) connectivity since massive devices results in large number of collisions. This is unacceptable for the low latency requirement in 5 G and future networks. It is also not possible to assign orthogonal pilot sequences to all users to perform user activity detection (UAD) due to the massive number of devices and limited channel coherence time. In this paper, a novel grant-free (GF) UAD scheme is proposed with extremely low complexity and latency in an IoT network with a massive number of users. We exploit multiple antennas at the base station (BS) to produce spatial filtering by a fixed beamforming network (FBN), there then the inter-beam interference can be mitigated. Moreover, intra-beam interference is removed in temporal domain by orthogonal multiple access (OMA) technology. Joint UAD and multiuser detection (MUD) is realized by a bank of spatial-temporal matched filters at BS. The proposed method is efficient and the complexity is much less than the existing compressed sensing (CS)-based GF non-orthogonal multiple access (GFNOMA) algorithms. Performances of the proposed method is extensively analyzed in terms of the successful activity detection rate (SADR) as well as the Receiver operating characteristic (ROC) based on Neyman-Pearson (NP) decision rule. Numerical results demonstrate that it is comparable to the recently proposed iterative Maximum Likelihood (ML) algorithm, yet the computation load of the proposed scheme is extensively reduced. 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100468
As time went on, technological progress inevitably altered our daily routines. Many new technologies, such as the Internet of Things (IoT) and cryptocurrency, offer revolutionary possibilities. To put it simply, the blockchain is a distributed, public, and auditable database that can be used to record financial transactions. The IoT, or “Internet of Things,” is a system of interconnected electronic devices that can communicate with one another and be remotely monitored and handled. This paper reviews the most recent findings in the field of blockchain and Internet of Things with the goal of examining blockchain as a possible answer to secure IoT data management within supply networks. There is a dearth of literature in the early stages of both blockchain and IoT study because they are such novel topics. The study's findings suggest that in order to improve their leadership quality to intentionally impact employee performance, industry managers should pay attention to human resource management indicators like collaboration, involvement, actualization, perception, and teamwork. This is primarily because of the inherent limitations of IoT devices and the distributed ledger architecture of the blockchain technology. There is potential for IoT to provide many advantages if blockchain capabilities can be optimized for it. 相似文献
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随着网络技术飞速发展和网络规模的不断扩大,网络安全已经成为全球性的重要问题之一。概述了网络入侵检测技术的发展历史及其通用模型,对入侵检测系统的分类和入侵检测的方法进行了分析,讨论了该领域尚存在的问题。 相似文献
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基于我国物联网产业发展具有代表性的9个省(市)的相关统计数据,构建物联网产业技术研发评价指标体系,运用曼奎斯特(Malmquist)指数方法对2007—2013年间我国物联网产业的技术研发效率进行动态评价。通过研究发现:我国物联网产业技术研发效率较为低下,且各地区研发效率差异较大;技术衰退是导致物联网产业研发效率下降的主要原因。 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100456
The Internet of Things (IoT) has sparked a revolution in the manufacturing sector, providing numerous advantages to companies that adopt it. Using IoT, factories can boost productivity, cut expenses, and develop a more sustainable business model. The rise of digital networking and real-time communication are compelling manufacturers to adopt cutting-edge technologies in order to compete in today's fast-paced, international marketplace. The Internet of Things (IoT) to facilitate the virtualization of manufacturing processes and the gathering of real-time data to guarantee seamless supply chain operations. There has been abductive qualitative research done. Case studies of the heavy-duty vehicle sector provided empirical data, while a review of the relevant literature provided the theoretical underpinnings. Information system issues and people and structure issues were cited as barriers to analytics adoption. In this study works on challenges and security of manufacturing. Finally, suitable themes for analysis have been derived using a thematic analysis. The results show that manufacturing firms can benefit from analytics solutions for production activities even if they are not highly automated or complicated. The Internet of Things (IoT) offers numerous opportunities for growth in the business models of manufacturing companies. Businesses can boost efficiency, cut expenses, and develop a more robust business model by implementing IoT. Successfully integrating IoT, however, calls for meticulous preparation and execution. 相似文献
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为去除网络入侵数据集中的冗余和噪声特征,降低数据处理难度和提高检测性能,提出一种基于特征选择和支持向量机的入侵检测方法。该方法采用提出的特征选择算法选取最优特征组合,并以支持向量机为分类器建立模型,应用于入侵检测系统。仿真结果表明,本文方法不仅可以减少特征维数,降低训练和测试时间,还能提高入侵检测的分类准确率。 相似文献
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物联网是什么？物联网不是互联网、传感网、产品电子代码,也不单纯是一种技术应用。物联网将“互联网”和“物”连接在一起,就意味着把破坏性创新引进到当今的信息和通信技术世界。与互联网不同,物联网是物、网络、语义等视角的综合而形成的集网络、应用服务于一体的技术融合系统。在物联网语境中,物联网技术像人一样形成了人为的自主特征。物联网意味着一种潜在的技术异化的环境：个人隐私以多种方式受到威胁。而现有对隐私的制度规约存在诸多的不完备性。 相似文献
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基于IPV6的网络安全入侵检测技术研究 总被引：1，自引：0，他引：1
主要研究了一种基于IPV6入侵检测技术。首先介绍了传统IPV6网络的几种网络协议,然后提出了一种采用BP神经网络技术的IPV6网络入侵检测算法。与传统网络入侵检测系统模型的对比,得到的实验数据突出了本文提出的改进型算法,有较高的优势,不管在时间上,还是在识别率上都得到了较好地提高,误检率低。 相似文献
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介绍了集成学习入侵检测系统设计的总体思路、总体结构和各模块功能,重点研究了基于遗传算法的集成学习分类引擎工作原理,通过仿真试验说明集成神经网络能克服单个神经网络的缺陷,具有高速数据处理与自学习功能. 相似文献
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粒子群算法网络异常检测技术研究 总被引：1，自引：0，他引：1
提出了一种新的基于粒子群算法入侵检测方法模型。算法采用粒子群优化算法,有效地降低网络拓扑路径长度,通过优化算法来寻找聚类的中心。实验结果表明,提出的改进算法与传统的入侵检测算法相比,具有更好的入侵识别率和检测率。 相似文献
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物联网是一门新兴的高科技产业,是继计算机、互联网与移动通信网之后的又一次信息产业浪潮。这篇文章运用数据包络分析（DEA）方法评价21家物联网上市公司绩效,得出该行业的总效率、纯技术效率、规模效率以及规模效率增减性．并在此基础上提出了优化资源配置和提高上市公司绩效的对策建议。从分析的数据及结果显示可以看出：我国物流上市公司总体效率不高．并且在效率上存在显著差异：从上述分析我们可以得出,造成我国物流企业总体绩效不佳的主要原因是纯技术无效率． 相似文献
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首先对入侵检测系统的相关技术进行了深入地讨论和分析,并系统阐述了常见网络攻击手段和防范措施,对入侵防御系统的现状和发展方向论述,并详细叙述了入侵防御系统总体设计方案。形成了一种入侵防御系统的预取模型。通过实验分析,该系统能针对不同种类攻击而对系统实现多方位的信息安全保护,很大程度上提高了受保护网络系统的安全性。 相似文献
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针对钢板表面缺陷图像分类传统深度学习算法中需要大量标签数据的问题,提出一种基于主动学习的高效分类方法。该方法包含一个轻量级的卷积神经网络和一个基于不确定性的主动学习样本筛选策略。神经网络采用简化的convolutional base进行特征提取,然后用全局池化层替换掉传统密集连接分类器中的隐藏层来减轻过拟合。为了更好的衡量模型对未标签图像样本所属类别的不确定性,首先将未标签图像样本传入到用标签图像样本训练好的模型,得到模型对每一个未标签样本关于标签的概率分布(probability distribution over classes, PDC),然后用此模型对标签样本进行预测并得到模型对每个标签的平均PDC。将两类分布的KL-divergence值作为不确定性指标来筛选未标签图像进行人工标注。根据在NEU-CLS开源缺陷数据集上的对比实验,该方法可以通过44%的标签数据实现97%的准确率,极大降低标注成本。 相似文献