基于ZLG500的智能卡门禁系统设计

介绍了基于智能卡读写模块ZLG500的门禁系统设计原理与方法,主要分析了该智能卡门禁系统中的数据存储与传输模块、系统主模块和时钟模块等重要组成模块的工作原理,同时给出了ZLG500与MCU的硬件接口及部分软件代码。本系统经过实际运行,具有稳定可靠、操作简便等特点。     

动态增量聚类的设计与实现

传统聚类算法往往只适用于静态数据集的聚类。对于动态数据集,新增数据后,前期的聚类结果不再可靠,运用此类算法则需要重新聚类,这样会造成效率低下和计算资源浪费。在基于密度和自适应密度可达聚类算法的基础上,提出了一种新的增量聚类算法。理论分析和实验结果证明该算法能够有效地处理动态数据集,提高聚类效率和资源的利用率。     

时序NDVI数据集重建方法评价与实例研究

时序NDVI数据集已经成功地应用于全球与区域环境变化、植被动态变化、土地覆盖变化和植物生物物理量参数反演等方面的研究。受到大气条件和传感器自身因素的制约,虽然经过严格的预处理,时序NDVI数据集仍包含很多噪声,影响其进一步的应用。首先介绍了近几年来普遍使用的6种时序NDVI数据集的重建方法：改进的最佳指数斜率提取法、均值迭代滤波法、Savitzky-Golay滤波法、傅立叶变换法、非对称高斯函数拟合法和时间序列谐波分析法 |然后采用这几种方法对张掖地区2007年和2008年10 d最大值合成的SPOT/VEGETATION的时序NDVI数据进行了重建,对重建结果进行了比较和评价 |最后对人为的噪声序列进行重建,对重建结果的优缺点进行评价。     

Web检索查询意图分类技术综述

查询分类是近年来信息检索领域的研究热点,并且在很多领域得到了广泛地关注。主要讨论根据查询的意图进行分类的研究工作,从查询分类的诞生背景、关键技术、所使用的分类方法和评价方法方面进行综述评论,提出了查询意图分类面临的问题和挑战。认为缺乏权威的评测标准、在大规模数据集上的未经全面测试的性能、如何准确地获取查询的特征以及如何证明分类体系的完备性和独立性是目前查询意图分类研究的关键问题。     

基于Delaunay三角网的空间离群挖掘

空间离群是指非空间属性与其空间邻居显著不同的空间对象。空间数据的特殊性决定了空间离群挖掘需要充分考虑空间数据的特点,才能挖掘出有现实意义的离群。本文对现有主要的空间数据离群挖掘算法进行了研究分析,针对k-邻域法确定空间邻域的缺点,基于Delaunay三角网在表达空间邻近关系的有效性,通过构建Delaunay三角网确定空间邻域并生成空间权重矩阵,据此提出了基于Delaunay三角网的空间离群挖掘算法DT_SOF,并以实际生态地球化学数据进行实验检验。结果表明,算法具有较低的用户依赖性,能准确挖掘空间离群。     

T形异形柱的非线性有限元分析

分析了T形异形柱的受力问题,利用ABAQUS有限元软件对T形异形柱进行了弹塑性非线性有限元分析.着重从混凝土的非线性本构关系和破坏准则,受拉开裂后的行为,钢筋的本构关系几方面进行分析,通过计算绘制出了梁柱混凝土的应力云图、裂缝图及钢筋的应力云图.用ABAQUS计算所得的裂缝图与拟静力试验结果裂缝进行比较,两者结果吻合.结果表明:应用ABAQUS对异形柱进行非线性分析是可靠的,从而论证了软件的实际应用能力,也为进一步进行更复杂的非线性分析打下了基础.     

Multi-scale-based joint super-resolution and inverse tone-mapping with data synthesis for UHD HDR video

The performance of current joint super-resolution (SR) and inverse tone-mapping (ITM) frameworks is limited since they only account for information in small local receptive fields. Moreover, the SDR images in the existing dataset for joint SR-ITM are oversaturated compared to the HDR ground truths. The models trained on this dataset produce HDR images with color shift. This paper proposes a multi-scale-based joint SR-ITM model to reconstruct HR HDR videos. Image features are downsampled to different resolutions to increase the local receptive fields. Thus the proposed model can react to more complex patterns of input images. And we design a novel multi-path residual dense block (MRDB) as the model’s fundamental component to extract features. The proposed MRDBs can improve performance by combining dense feature learning and novel multi-path residual learning. Experiments show that the proposed model outperforms the state-of-the-art methods in terms of quantitative and qualitative results. Furthermore, we propose a data synthesis pipeline to generate perceptually identical SDR images from their HDR counterparts. These SDR-HDR pairs are used to create a new dataset. Experiments demonstrate that models trained with our new dataset prevent color shift and preserve creative intent.     

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.     

Passive infrared sensor dataset and deep learning models for device-free indoor localization and tracking

Location estimation or localization is one of the key components in IoT applications such as remote health monitoring and smart homes. Amongst device-free localization technologies, passive infrared (PIR) sensors are one of the promising options due to their low cost, low energy consumption, and good accuracy. However, most of the existing systems are complexly designed and difficult to deploy in real life, in addition, there is no public dataset available for researchers to benchmark their proposed localization and tracking methods. In this paper, we propose a system and a dataset collected from our PIR system consisting of commercial-of-the-shelf (COTS) sensors without any modification. Our dataset includes profile data of 36 classes that have over 1,000 samples of different walking directions and test data consisting of multiple scenarios with a sequence length of over 2,000 timesteps. To evaluate our system and dataset, we implement various deep learning methods such as CNN, RNN, and CNN–RNN. Our results prove the applicability and feasibility of our system and illustrate the viability of deep learning methods for PIR-based localization and tracking. We also show that our dataset can be converted for coordinate estimation so that deep learning methods and particle filter approaches can be applied to estimate coordinates. As a result, the best performer achieves a distance error of 0.25 m.     

Crack detection of structures using modified extreme learning machine (MELM)

In this paper, crack detection and estimation method is presented in structures using modified extreme learning machine. For this purpose, extreme learning machine was modified using modified weights and biases. By using the first three frequencies and mode shapes as input, crack was detected as output. Performance of the proposed method was evaluated by using some numerical examples consisting of a simply supported beam, cantilever beam and fixed-simply supported beam. In addition, noise effect (3% noise level) on the measured frequencies and mode shapes have been investigated. In another work, a portal frame has been studied. The results indicated that the proposed method is effective and fast in crack detection and estimation of structures.