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In the process of moving target detection, the background is complex and the target is easily occluded. This paper proposes an autonomous detection algorithm for moving targets based on unsupervised missing value prediction. The missed targets are regarded as missing values in the tag data. According to the prior knowledge of the category and number of objects to be detected, the unsupervised generative adversarial imputation networks (GAIN) are used to predict the missing values through the acquired tag data, which greatly improves the recall rate at the expense of less accuracy. The experimental results on the small sample dataset of the characteristic parts of cattle show when the missing rate of tag data is less than 40%, the accuracy of missing value prediction is about 95%, and the average F1 score of detection is 0.92 for different degrees of occluded targets. This method has good detection performance for moving targets under the condition of small samples, which can reduce the uncertainty in practical application and the dependence of the algorithm on sample data, and improve the problem of missing detection in the process of moving target detection. [ABSTRACT FROM AUTHOR]

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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] With the rapid popularization of new energy vehicles and the vigorous development of autonomous driving technology, 3D object detection algorithms play a pivotal role in real road scenes. The LiDAR point clouds contain precise position and geometric structure information of the object, which can accurately describe the 3D space position of the target. Moreover, LiDAR makes environmental perception and route planning of unmanned vehicles a reality. However, cars in real scenes often fall into complex and difficult situations, such as occlusion and truncation of objects, which contribute to highly sparse clouds and incomplete contours. Therefore, the effective use of disordered and unevenly distributed point clouds for accurate 3D object detection has important research significance and practical value for the safety of autonomous driving. [Methods] This paper uses LiDAR point clouds in an autonomous driving scene to conduct in-depth research on a high-performance 3D object detection algorithm based on deep learning. The 3D object detection algorithm based on multibranch feature fusion (PSANet) is designed to improve the capacity and viability of autonomous driving technology. After the disordered point clouds are divided into regular voxels, the voxel feature coding module and convolutional neural network are used to learn the voxel features, and the sparse 3D data are compressed into a dense 2D bird's-eye view. Furthermore, the multiscale bird's-eye view features are deeply fused through the coarse and fine branches of the 2D backbone network. The splitting and aggregation feature pyramid module in the fine branch splits and aggregates the bird's-eye view features at different levels and realizes the deep fusion of semantic information, texture information, and context information of multiscale features to obtain more expressive features. The multiscale features in the coarse branch are fused after transposed convolution, and the precise original spatial location information is preserved. After the feature extraction of coarse and fine branches, the element-wise addition method can obtain more accurate and complete features for object classification, position regression, and orientation prediction. [Results] The experimental results on the KITTI dataset show that the average precision of PSANet in 3D object detection and bird's-eye view object detection tasks reach 81.72% and 88.25%, respectively. The inference speed on a single GTX 1080Ti GPU can reach 24 frames per second, and it shows strong robustness in complex scenes. Compared with the two-stage target detection algorithms MV3D, AVOD-FPN, F-PointNet, and IPOD, the average accuracy of 3D target detection of this algorithm increased by 18.21%, 5.89%, 8.94%, and 3.12%, respectively. Compared with the one-stage target detection algorithms VoxelNet, SECOND, PointPillars, and VoTr-SSD, the average accuracy of 3D target detection of this algorithm increased by 11.63%, 4.05%, 3.19%, 0.98%, 1.16%, and 0.7%, respectively. The detection speed of this algorithm improved by 14 frames per second compared with the two-stage algorithm PointRCNN with similar accuracy. [Conclusions] In comparison with other advanced algorithms, this algorithm exhibits strong performance, in which it can better balance the accuracy and speed of target detection in autonomous driving scenarios. Higher accuracy is required in the algorithm, whether one- or two-stage, for novel applications. The method with the highest efficiency must be used in an autonomous car. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The environmental perception of intelligent vehicles is an important part of achieving autonomous driving, and object detection is one of the crucial functions of the environmental perception systems. Accurate and rapid detection of targets nearby is essentail for the smart vehicles. The faster and more accurately the detection algorithm can detect surrounding targets, the better. However, the detection speed and accuracy of existing perception algorithms still need improving. The distance information between the surrounding objects and the vehicle itself during its operation should also be obtained. The widely used YOLO series of algorithms, as representatives of one-stage object detection methods, have achieved a satisfactory balance between speed and detection accuracy, but there is stiil great potential for further improvement. This paper proposes an end-to-end object detection algorithm based on YOLO algorithm improvement-dual YOLO, which detects and measures the major participants in road traffic ( cars, pedestrians, cyclists) . The attention mechanism acts in the same way as humans observe things, focusing on more important information. The basic idea of combining attention mechanism with convolutional neural networks is to allow the network to concentrate on effective feature information in the image and ignore invalid information, thus improving the overall detection precision. Its mechanism of action is achieved by changing the weights of neurons in the network. This paper is based on the improvement of YOLOV5 algorithm, adding a channel attention mechanism to the backbone network to increase the learning weight of the neural network for effective features, thereby enhancing detection accuracy. In the meantime, a distance measurement module for binocular cameras is introduced to obtain target distance information. The basic principle of binocular camera ranging is triangulation, which employs the visual difference between two cameras to determine the distance of an object. Firstly, the camera is calibrated and distortion is corrected, and then stereo matching using the Semi Global Block Matching (SGBM) algorithm is performed to obtain disparity. Finally, the distance information of the target is calculated. The test results show the dual YOLO object detection algorithm achieves a detection speed of 60 fps and an average accuracy of 85. 99% in identifying the major participants in road traffic, which are far more superior to other algorithms in detecting cyclists and pedestrians. The proposed algorithm well meets the real-time detection and distance measurement requirements of intelligent vehicles. [ABSTRACT FROM AUTHOR]

To confront the difficulties for moving vehicles in measuring the actual heights at various highway height limit facilities, this paper proposes a binocular camera height limit target real-time monitoring algorithm. First, an analysis of the height limit scenarios and facilities is made and a large- scale multi-scenario highway height limit dataset is built. Second, the target region is obtained by the detection and tracking algorithm based on deep learning. The binocular disparity is processed by a multi-step and multi-scale filtering algorithm to obtain the high-confidence height limit facility disparity data and thus calculate the distance and height information. Finally, the robust output strategy is adopted and alarms will be activated based on the real-time collected distance and height results. After multi-scenario height limit experiments, the target detection accuracy stands at 98.77%, the missed detection rateat 0. 87%, the false detection rate at 0. 36%, and the average height measurement error within 60 m below 4%. The results show that our algorithm achieves automatic detection and computation of highway height limit targets, as well as real-time and robust delivery of distance and height measurements and early warnings. [ABSTRACT FROM AUTHOR]

As the existing fuzzy clustering algorithms are easily affected by noise, it is difficult to achieve precise segmentation. This paper proposes an image segmentation based on multi-scale kernel fuzzy clustering algorithm. First, Gaussian filter is employed to blur the segmented images many times to build a multi-scale space. Next, the local spatial and weight information of the image is utilized to modify the local blur factors. In the meantime, a kernel function is introduced to replace the original Euclidean metric with the kernel-induced distance, which increases its robustness to noise points and outliers. Then, the global membership and local membership are weighted to modify the division of membership twice. Finally, the filtered upper image clustering results are used to guide the lower image clustering in turn, which avoids random initialization and effectively suppresses noise to improve the segmentation performance of the algorithm. To verify the effectiveness of the proposed algorithm, a comparative analysis with other eight clustering algorithms show that the proposed one achieves better segmentation results with noisy and complex images. [ABSTRACT FROM AUTHOR]

The use of drones and traditional navigation equipment to detect overhead transmission lines has such limitations as high costs of sensors, difficulties in data processing, and vulnerability to weather and environmental factors. Therefore, they can hardly guarantee the safety of drones and power systems. To improve the intelligent inspection of transmission lines, this paper proposes an autonomous navigation of UAVs with a single camera on transmission lines. Since monocular vision cannot provide in-depth information, a machine learning model Faster R-CNN for obstacle detection is introduced. In the detection model, the image height of the detected power lines represents the distance to the UAV, and the image width between the power lines is employed to obtain the widest unobstructed space. The experimental results show the positioning detection algorithm is highly applicable since it makes marked improvements with its positioning error within the range of the use error. [ABSTRACT FROM AUTHOR]

The existing Conflict-Based Search algorithm (CBS) and its variants require long search time and their planned path is inconsistent with kinematic constraints. To address these limitations, this paper proposes a Dynamic CBS (DCBS) algorithm. The second best factor is introduced to improve the searching efficiency of CBS, and the A* path search in CBS algorithm is upgraded to hybrid A* search so that the planned path is more in line with the kinematic constraints. In the meantime, the distance weighting coefficient and time weighting coefficient are introduced into the underlying path search of CBS algorithm to plan the fast and short path. A comparison with the cutting-edge ECBS algorithm through simulation experiment on ROS platform show the path planned by the dynamic CBS algorithm substantially improves the work efficiency of multiple unmanned vehicles, conforming to their kinematic constraints, increasing the path planning efficiency by 47.06% on average, and cutting the total path cost by 7.64% on average. [ABSTRACT FROM AUTHOR]

The resampling of traditional particle filter algorithm causes particle scarcity and needs to increase the number of particles to improve the estimation accuracy. In this paper, a particle recombination particle filter algorithm based on multi-strategy artificial rabbits optimization algorithm is proposed. First, the introduction of the median line algorithm improves the convergence speed of the artificial rabbit algorithm, and through its foraging and hiding mechanism, the optimal particle guides the particle set to move towards the high likelihood region, thereby improving estimation accuracy. Second, the particle density near the optimal particle is calculated in real-time. Adaptive adjustment is made to the number of iterations when the density exceeds the set threshold whereas a self disturbance strategy is introduced to increase sample diversity when it exceeds the maximum density value. Finally, the resampling stage recombines the retained particles and the remaining particles into new particles to increase particle diversity. The performance of the improved algorithm in SLAM is verified through simulation experiments. The results show the algorithm achieves higher accuracy and better robustness in pose and landmark estimation compared with other three algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Blockchain technology can solve the problems of centralized management and easy data loss in the traditional access control scheme of IoT to achieve distributed and high security access control, but it is easy to ignore the importance of establishing a dynamic and flexible access control mechanism such that the dynamic information of the network cannot be captured automatically when its node is damaged and its authorization policy is adjusted accordingly. This paper design an attribute-based access control mechanism for IoT with a trust and reputation system that assists authorization, and a multidimensional adaptation algorithm (MDAA) is proposed. Firstly, a public blockchain and a private sidechain are used to store sensitive information and public data separately, access threshold policies are defined by service consuming nodes register attributes and service providing nodes. Secondly, the trust and reputation system gradually quantifies the trust and reputation scores of each node in the network, when a service consumer node initiates an access request, the smart contract verifies whether the service consumer node satisfies thresholds of the attributes, trust and reputation required by the access threshold policy, the access privileges is available if satisfied. Finally, the trust and reputation scores of nodes are updated periodically based on interactions between nodes to achieve dynamic verification and authorization. Simulation results show that compared with TARAS algorithm and DADAC algorithm, MDAA supports two- way trust evaluation, has better algorithm convergence and reduces the delay in processing access control while ensuring authorization security, which is applicable. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)