The Experts below are selected from a list of 41700 Experts worldwide ranked by ideXlab platform
Wei Wang - One of the best experts on this subject based on the ideXlab platform.
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iteration path length error correction approach to subreflector shaping for distortion compensation of large reflector antenna
2019Co-Authors: You Ban, Congsi Wang, Shufei Feng, Baoyan Duan, Wei WangAbstract:At the stage of structural optimization, the subreflector structure should be designed such that it compensates for the residual error or relaxes the Accuracy Requirement of the main reflector to a certain extent. However, there is no fast and effective means to shape the subreflector during the structural optimization process. This communication is based on the application of geometrical optics (GO)/physical optics (PO) analysis to shape a subreflector for antenna distortion compensation. Thus, a method performing an iterative approximation to the GO/PO analysis is proposed. Unlike GO shaping or optimization, the proposed method is not formulated by simultaneous nonlinear ordinary differential equations. Therefore, the main contribution of this communication is that the method does not entail computationally intensive calculation and optimization, and thus, it does not require a large amount of time to complete the calculation. The proposed method is very suitable for a large reflector antenna with a small-amplitude deformation and smoothly varying errors such that the shaped subreflector does not have a significant effect on the amplitude of the main reflector’s electric field. An example with regard to the symmetric system is presented. The obtained results demonstrate that the proposed method is effective.
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malware traffic classification using convolutional neural network for representation learning
2017Co-Authors: Wei Wang, Xuewen Zeng, Xiaozhou Ye, Yiqiang ShengAbstract:Traffic classification is the first step for network anomaly detection or network based intrusion detection system and plays an important role in network security domain. In this paper we first presented a new taxonomy of traffic classification from an artificial intelligence perspective, and then proposed a malware traffic classification method using convolutional neural network by taking traffic data as images. This method needed no hand-designed features but directly took raw traffic as input data of classifier. To the best of our knowledge this interesting attempt is the first time of applying representation learning approach to malware traffic classification using raw traffic data. We determined that the best type of traffic representation is session with all layers through eight experiments. The method is validated in two scenarios including three types of classifiers and the experiment results show that our proposed method can satisfy the Accuracy Requirement of practical application.
Eugene Wong - One of the best experts on this subject based on the ideXlab platform.
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the regional monte carlo method a dose calculation method based on Accuracy Requirement
1998Co-Authors: Milton K Woo, D J Scora, Eugene WongAbstract:In this work we propose the regional Monte Carlo (RMC) method of dose calculation. This method combines the Monte Carlo(MC) algorithm and a non-MC algorithm (such as the convolution method) for optimal speed and Accuracy in dose calculation for both photon and electron beams and for various irradiation and patient geometries. For specific regions in the geometry where high Accuracy is required but difficult to obtain with analytical or empirical calculations, such as critical organs surrounded by complicated inhomogeneities, the MC algorithm is used. For regions with simple geometries, or where a high degree of dose Accuracy is not critical, the non-MC algorithm is used to increase speed. There are two aspects of the RMC method. The first one involves determining critical regions and boundaries, and the other involves the actual implementation and mixing of the two computational algorithms. Two examples of different geometries are used to illustrate the different ways to apply the RMC method. The possibility to extend the method to more complicated geometries and inhomogeneities, as well as the ability of the method to incorporate different calculation algorithms, are also discussed.
Ananthram Swami - One of the best experts on this subject based on the ideXlab platform.
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average consensus with minimum energy consumption optimal topology and power allocation
2010Co-Authors: Stefania Sardellitti, S Barbarossa, Ananthram SwamiAbstract:Consensus algorithms have generated a lot of interest due to their simplicity in computing globally relevant statistics exploiting only local communications among sensors. However, the inherent iterative nature of consensus algorithms makes them prone to a possibly large energy consumption. Because of the strong energy constraints of wireless sensor networks, it is then of interest to minimize energy consumption necessary to achieve consensus, within a prescribed Accuracy Requirement. In this work, we propose a method for optimizing the network topology and power allocation over each link, in order to minimize energy consumption, while ensuring that the network reaches a global consensus. Interestingly, we show how to introduce a relaxation in the topology optimization that converts a combinatorial problem into a convex-concave fractional problem. The results show how the sparsity of the resulting network depends on the propagation model.
Chuang Shi - One of the best experts on this subject based on the ideXlab platform.
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Requirement assessment of the relative spatial Accuracy of a motion constrained gnss ins in shortwave track irregularity measurement
2019Co-Authors: Quan Zhang, Qijin Chen, Xiaoji Niu, Chuang ShiAbstract:Modern railway track health monitoring requires high Accuracy measurements to ensure comfort and safety. Although Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integration has been extended to track geometry measurements to improve the work efficiency, it has been questioned due to its positioning Accuracy at the centimeter or millimeter level. We propose the relative spatial Accuracy based on the Accuracy Requirement of track health monitoring. A Requirement assessment of the spatial relative Accuracy is conducted for shortwave track irregularity measurements based on evaluation indicators and relative Accuracy calculations. The threshold values of the relative spatial Accuracy that satisfy the constraints of shortwave track irregularity measurements are derived. Motion-constrained GNSS/INS integration is performed to improve the navigation Accuracy considering the dynamic characteristics of the track geometry measurement trolley. The results of field tests show that the mean square error and the Allan deviation of the relative position errors of motion-constrained GNSS/INS integration are smaller than 0.67 mm and 0.16 mm, respectively, which indicates that this approach meets the Accuracy Requirements of shortwave track irregularities, especially vertical irregularities. This work can provide support for the application of GNSS/INS systems in track irregularity measurement.
Milton K Woo - One of the best experts on this subject based on the ideXlab platform.
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the regional monte carlo method a dose calculation method based on Accuracy Requirement
1998Co-Authors: Milton K Woo, D J Scora, Eugene WongAbstract:In this work we propose the regional Monte Carlo (RMC) method of dose calculation. This method combines the Monte Carlo(MC) algorithm and a non-MC algorithm (such as the convolution method) for optimal speed and Accuracy in dose calculation for both photon and electron beams and for various irradiation and patient geometries. For specific regions in the geometry where high Accuracy is required but difficult to obtain with analytical or empirical calculations, such as critical organs surrounded by complicated inhomogeneities, the MC algorithm is used. For regions with simple geometries, or where a high degree of dose Accuracy is not critical, the non-MC algorithm is used to increase speed. There are two aspects of the RMC method. The first one involves determining critical regions and boundaries, and the other involves the actual implementation and mixing of the two computational algorithms. Two examples of different geometries are used to illustrate the different ways to apply the RMC method. The possibility to extend the method to more complicated geometries and inhomogeneities, as well as the ability of the method to incorporate different calculation algorithms, are also discussed.
