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高效率地使用工程车辆是工程项目管理中节约成本的有效方法,无人监管环境下工程车辆的工况识别,是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别,提出一种基于GRU循环神经网络的工程车辆工况识别方法,通过对工程车辆在不同工况下产生的音频信号进行分析,从中提取Mel倒谱系数作为主要特征,构建GRU循环神经网络模型进行训练和识别。实验结果表明,该方法可以实现对工程车辆工况的有效识别。 相似文献
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在总结前人钨中空位及其团簇的能量学和动力学行为的研究成果基础上,采用第一性原理方法系统计算了钨中空位及其团簇的结合能和扩散能垒。研究发现,交换关联泛函PW91和PBE较PBEsol、AM05和LDA更适合用于计算钨空位的能量学性质。基于第一性原理计算结果对文献中单空位形成能、双空位作用性质等争议性问题进行了讨论,并对钨经验势进行了评估。研究结果表明,钨中孤立单空位间总是相互排斥,而空位团簇(Vn>3)对单空位具有很强的吸引作用,其结合能随着所含空位个数增多呈现波动性增大的趋势。空位团簇稳定结构可通过最小化Wigner-Seitz表面积来确定,其结合能与Vn与Vn-1之间的Wigner-Seitz面积之差呈正比。 相似文献
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Smartphones are being used and relied on by people more than ever before. The open connectivity brings with it great convenience and leads to a variety of risks that cannot be overlooked. Smartphone vendors, security policy designers, and security application providers have put a variety of practical efforts to secure smartphones, and researchers have conducted extensive research on threat sources, security techniques, and user security behaviors. Regrettably, smartphone users do not pay enough attention to mobile security, making many efforts futile. This study identifies this gap between technology affordance and user requirements, and attempts to investigate the asymmetric perceptions toward security features between developers and users, between users and users, as well as between different security features. These asymmetric perceptions include perceptions of quality, perceptions of importance, and perceptions of satisfaction. After scoping the range of smartphone security features, this study conducts an improved Kano-based method and exhaustively analyzes the 245 collected samples using correspondence analysis and importance satisfaction analysis. The 14 security features of the smartphone are divided into four Kano quality types and the perceived quality differences between developers and users are compared. Correspondence analysis is utilized to capture the relationship between the perceived importance of security features across different groups of respondents, and results of importance-satisfaction analysis provide the basis for the developmental path and resource reallocation strategy of security features. This article offers new insights for researchers as well as practitioners of smartphone security. 相似文献
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Weronika Gonciarz ukasz Lechowicz Mariusz Urbaniak Wiesaw Kaca Magdalena Chmiela 《International journal of molecular sciences》2021,22(1)
Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm−1 and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans. 相似文献
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Vijayalakshmi A Rajesh Kanna B Vijayalakshmi C 《Microscopy research and technique》2022,85(2):685-696
Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature. The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed. The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its co-occurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the “p” value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased. 相似文献
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国家电网公司信息化程度越来越高,单机运维审计系统产生的数据量日益增多,对海量数据高效率存储分析性能严重下降,系统稳定性降低。为满足国家电网当前对运维审计系统数据存储分析以及系统稳定性的需求,在Hadoop开源架构的基础上,本文提出基于Hadoop集群的海量数据分布式存储方法和基于Heartbeat的心跳检测技术,实现基于Hadoop的电力运维审计系统。〖JP2〗实验测试结果表明,基于Hadoop的电力运维审计系统相比单机系统可用性提高了8.42%,大大提升了存储分析海量数据的性能,具有系统工作稳定和服务不间断等优势。 相似文献