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Measurement
Elsevier BV
Measurement

Elsevier BV

0263-2241

Measurement/Journal MeasurementISTPSCIAHCI
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    The track, hotspot and frontier of international hyperspectral remote sensing research 2009-2019-- A bibliometric analysis based on SCI database

    Zhang, WeiZhao, Liang
    11页
    查看更多>>摘要:Hyperspectral remote sensing is widely used in earth observation and environmental survey. However, the research on the development, applications and hot spots of hyperspectral remote sensing is very limited. Metadata taken from 4122 literature records is used to visualize the state of hyperspectral remote sensing research. The records were published between 2009 and 2019, and Citespace software is employed to visualize key data about the research contained on the SCI and SSCI platform. It has the following findings. In recent 10 years, hyperspectral remote sensing related research literature has shown a trend of rapid growth. There are more publications in China, the United States, Germany, and other countries, and the publishing institutions are mostly concentrated in the universities of various countries. Remote Sensing of Environment and IEEE journals are authoritative journals in this field. Anatoly Gitelson, Camps-valls, Asner GP and other authors have made important contributions to basic research. The highly cited documents of hyperspectral remote sensing are distributed in the research directions of spectral imaging technology, support vector machine, and spectral data classification, and 12 research clusters are formed, such as vegetation research, data feature extraction, SVM classification, and de-mixing algorithm. The research hotspots in this field are mainly in image classification, algorithm model, spectral resolution/reflectance and vegetation analysis. The research frontiers include spectral characteristics and reflectance, end element extraction, radar and data dimension reduction, UAV and so on. The development of hyperspectral remote sensing technology promotes the cross research in the fields of environment, ecology, chemistry, computer and so on.

    Analysis of low power wide area network wireless technologies in smart agriculture for large-scale farm monitoring and tractor communications

    Klaina, HichamPicallo Guembe, ImanolLopez-Iturri, PeioCampo-Bescos, Miguel Angel...
    18页
    查看更多>>摘要:In this paper, the assessment of multiple scenario cases for large-scale farm monitoring using Low-Power WideArea Network (LPWAN) based near-ground sensor nodes with the interaction of both tractors and farmers are presented. The proposed scenario under analysis considers multiple communication links, namely nodes to infrastructure, nodes to tractor, nodes to farmer, tractor to infrastructure and farmer to infrastructure communications. Moreover, these scenarios are proposed for tractors and agricultural equipment performance improvement and tracking, as well as resources management within the farm field. Different link type configurations are tested in order to consider the impact of ground, spatial distribution as well as infrastructure elements. The results show that LPWAN-based WSNs can provide better performance in terms of coverage and radio link quality results than ZigBee for a non-flat large-scale farm field in both cases of near-ground fixed nodes and moving tractor and farmer. The proposed systems are validated by cloud-based platforms for LoRaWAN, Sigfox and NB-IoT communications, providing flexible and scalable solutions to enable interactive farming applications.

    In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing

    Li, JingchangCao, LongchaoXu, JieWang, Shengyi...
    15页
    查看更多>>摘要:Selective laser melting (SLM) has shown unique advantages in fabricating metal components. However, the part quality still largely suffered from the porosity defects that are not easily detected and eliminated. In this work, the objective is to realize the porosity classification based on high-speed melt pool images. A coaxial high-speed in situ monitoring system was first developed to capture the melt pool images during the multi-track and multilayer printing process. Then, a novel image and data processing method was proposed to extract the critical and high-level melt pool features data. Three intelligent machine learning algorithms of back propagation neural network (BPNN), support vector machine (SVM), and deep belief network (DBN) were finally developed to match the features data with porosity modes. Results show that it is feasible and effective for the proposed method to realize porosity classification during the SLM process, which can provide a potential to reduce porosity defects.

    A novel machine learning approach for breast cancer diagnosis

    Bacha, SawssenTaouali, Okba
    10页
    查看更多>>摘要:Breast cancer disease is a major public health problem among women worldwide. This article proposes an expert system for the diagnosis of breast cancer disease based on an evolutionary algorithm known as Differential Evolution (DE) of a Radial-Based Function Kernel Extreme Learning Machines (RBF-KELM). In the structure of the RBF-KELM, there are two adjustable parameters of the RBF-kernel which are the penalty parameter C and the RBF-kernel's parameter (sigma). These parameters play a major role in the efficiency of RBF-KELM. In this study, the optimal values of these parameters have been obtained using a differential evolution (DE) algorithm. To validate the effectiveness of the suggested approach, DE-RBF-KELM was examined on the two datasets: The Mammo-graphic Image Analysis Society (MIAS) and the wisconsin breast cancer database (WBCD) and the results were satisfactory compared to conventional approaches.

    Safety and reliability evaluations of bridge behaviors under ambient truck loads through structural health monitoring and identification model approaches

    Kaloop, Mosbeh R.Eldiasty, MohammedHu, Jong Wan
    11页
    查看更多>>摘要:Evaluation and modeling of structures behavior over their service life are crucial parameters for construction safety. This research investigates the use of output-only structural health monitoring (SHM) to evaluate the reliability and safety of highway steel plate girders of WonHyo bridge. Displacement, strain and acceleration measurements were used to assess the bridge behavior under the designed static, semi-static and dynamic truck loads effects. A novel integration method with combined probability, Autoregressive and Integrated Moving Average (ARIMA) identification model was developed to design PARIMA approach, which is introduced and used for analyzing the semi-static and dynamic reliability of the bridge girders. The PARIMA experimentally evaluated structure's reliability and damage based on the output only SHM system. The results showed the effectiveness of PARIMA for detecting structures damages and analyzing structure's reliability in semi-static and dynamic domains. The evaluation of WonHyo bridges showed its performances in time and frequency domains are safe, and the bridge reliability is high.

    Additional procedures for characterizing the performance of recycled polymer modified asphalt mixtures

    Viscione, NunzioVeropalumbo, RosaOreto, CristinaBiancardo, Salvatore Antonio...
    14页
    查看更多>>摘要:Great efforts have been made in recent years to improve the mechanical properties of asphalt mixtures by replacing conventional mix components with innovative ones or by adding materials such as polymers. Hence, the innovative-sustainable road materials to be investigated through laboratory tests require articulated procedures, the research here presented aims to provide an experimental-methodological approach to analyse the mechanical performance of untraditional hot asphalt mixtures made up using a polymer compound of recycled plastics. Three Asphalt Concrete 20 (AC20) Hot Asphalt Mixtures (HMA) were analysed by measuring base properties (i.e., indirect tensile strength and moisture damage) and advanced features (i.e., stiffness, fatigue, cracking and rutting resistance). As a result, the addition of polymer compound using dry process might lead firstly to change the laboratory mixing procedure than the traditional hot limestone asphalt solutions. The main benefits derived from the adoption of this innovative technology compared to the conventional ones are as follows: a) an improvement of resistance to moisture damage (at 15 degrees C); b) a suitable stiffness at 10, 20 and 40 degrees C; c) an increment of the cracking resistance (at 10 degrees C) and d) a good rutting resistance in terms of rut depth (at 60 degrees C).

    Fault diagnosis for small samples based on attention mechanism

    Zhang, XinHe, ChaoLu, YanpingChen, Biao...
    14页
    查看更多>>摘要:Aiming at the application of deep learning in fault diagnosis, mechanical rotating equipment components are prone to failure under complex working environment, and the industrial big data suffers from limited labeled samples, different working conditions and noises. In order to explore the problems above, a small sample fault diagnosis method is proposed based on dual path convolution with attention mechanism (DCA) and Bidirectional Gated Recurrent Unit (DCA-BiGRU), whose performance can be effectively mined by the latest regularization training strategies. BiGRU is utilized to realize spatiotemporal feature fusion, where vibration signal fused features with attention weight are extracted by DCA. Besides, global average pooling (GAP) is applied to dimension reduction and fault diagnosis. It is indicated that DCA-BiGRU has exceptional capacities of generalization and robustness by experiments, and can effectively carry out diagnosis under various complicated situations.

    Upgrade and new applications of the automated high-tech scanning facility PAVICOM for data processing of track detectors

    Alexandrov, AndreyKonovalova, NinaOkateva, NataliaPolukhina, Natalia...
    9页
    查看更多>>摘要:The high-tech automated measuring facility PAVICOM is presented, which latest upgrade enables to implement the continuous scanning of solid-state track detectors at the modern world standard level. The unique advantage of the facility is its versatility allowing for measurements and primary analysis of data from all types of track detectors processed on optical microscope. On the PAVICOM facility, search and digitization of the charged particle tracks in the detector material, their recognition and tracking, systematization and primary data processing are carried out in fully automated mode on base of the exclusive author software. The main technical characteristics achieved are given, which provide an increase in efficiency of the data processing and a significant improvement the scanning speed. Examples of using the PAVICOM facility for processing data of accelerator experiments, experiments in the field of high-energy physics, astrophysics and cosmic ray physics, and the most significant results obtained are presented.

    Displacement-strain transformation for a variable cross-section beam based on hypergeometric and Meijer-G functions

    Deng, HuaxiaWu, YiminWang, JunWang, Buyi...
    9页
    查看更多>>摘要:The modal-learning displacement-strain transformation has been proved to be able to predict the strain of the typical uniform beam. However, as the complexity of the structure increases, for example, the shape of the beam cross-section changes. The typical displacement-strain transformation is no longer applicable due to the unknown mode function. To further explore this issue, the hypergeometric and Meijer-G functions are used to establish the mode shape function with good structural adaptability. A displacement-strain transformation method based on the hypergeometric and Meijer-G functions is proposed to predict the dynamic strain of the variable cross-section beam. Experiment results demonstrate that the proposed method can solve the problem of full-field strain prediction for beams with variable sections and boost the displacement-strain transformation's theoretical calculation accuracy. Under sinusoidal excitation and random excitation, the strain prediction accuracy reaches 99.69% and 99.25%, respectively. This approach simplifies the preprocessing of the structure, and provides a welcome boost to developing the displacement-strain transformation method in full-field strain measurement.

    Abrasive tool wear prediction based on an improved hybrid difference grey wolf algorithm for optimizing SVM

    Liang, YuHu, ShanshanGuo, WensenTang, Hongqun...
    13页
    查看更多>>摘要:In the era of intelligent manufacturing, it is necessary to monitor the wear condition of abrasive tools in real time to prevent the deterioration of workpiece quality due to tool breakage and wear. A wear prediction model of abrasive tools based on an improved hybrid differential grey wolf optimization algorithm for optimizing support vector machine (IHDGWO-SVM) is proposed based on the grinding of zirconia ceramic holes by sintered diamond grind bits. The features of the force and vibration signals were extracted by time domain, frequency domain and wavelet analysis. The wear experimental results showed that the prediction accuracy of the IHDGWO-SVM model was 92%, which was significantly higher than the prediction accuracy of 68%, 80% and 72% of SVM, GWO-SVM and DE-SVM. The new IHDGWO-SVM model provides a theoretical and practical method for the on-line wear monitoring of abrasive tools during grinding of NMBM.