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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 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.)

Synthetic minority oversampling technique (SMOTE) has become one of the mainstream methods for dealing with unbalanced data due to its ability to effectively deal with minority samples, and many SMOTE improvement algorithms have been proposed, but very little research existing considers popular algorithmic-level improvement methods. Therefore a more comprehensive analysis of existing SMOTE class algorithms is provided. Firstly, the basic principles of the SMOTE method are elaborated in detail, and then the SMOTE class algorithms are systematically analyzed mainly from the two levels of data level and algorithmic level, and the new ideas of the hybrid improvement of data level and algorithmic level are introduced. Data-level improvement is to balance the data distribution by deleting or adding data through different operations during preprocessing; algorithmic-level improvement will not change the data distribution, and mainly strengthens the focus on minority samples by modifying or creating algorithms. Comparison between these two kinds of methods shows that, data- level methods are less restricted in their application, and algorithmic-level improvements generally have higher algorithmic robustness. In order to provide more comprehensive basic research material on SMOTE class algorithms, this paper finally lists the commonly used datasets, evaluation metrics, and gives ideas of research in the future to better cope with unbalanced data problem. [ABSTRACT FROM AUTHOR]

[Objective] Trajectory planning is critical for the motion control of a manipulator. The motion time and joint vibration of the manipulator can be reduced by providing a fast and stable reference trajectory to the controller. The single objective trajectory optimization algorithm remains insufficient in meeting the increasing production needs; therefore, it is often necessary to use a multiobjective heuristic algorithm to optimize the motion trajectory of the manipulator while satisfying its kinematic constraints to optimize the motion time and reduce the joint impact or energy loss, improving the production efficiency of the manipulator and ensuring its stability during motion. An improved adaptive multiobjective particle swarm optimization (IAMOPSO) method is studied for the time- and impact-based optimal trajectory planning of a multidegree-of-freedom manipulator. [Methods] First, to obtain physically continuous motion trajectories, including position, velocity, and acceleration, we use a quintic B-spline curve to interpolate the joint path points based on the local controllability of the curve. The trajectory optimization process is a step-by-step approach so that the interpolated solution optimally satisfies the kinematic constraints of the manipulator, and the B-spline curve passes through all interpolation points, rendering the motion trajectory of the manipulator more adaptable. Second, a specific objective function improves the motion efficiency of the manipulator. Under certain kinematic constraints, an expression for the joint impact of the manipulator is designed to improve the trajectory tracking accuracy and ensure the stability of the manipulator during operation tasks. Finally, to increase the diversity of the nondominated solution set while avoiding the local extremum, the multi-objective particle swarm optimization (MOPSO) algorithm is improved through a hybrid strategy of mutation operators, adaptive weights, and dynamic learning factors; consequently, the objective function is optimally solved, and the average optimal solution is selected using a normalization function. A nonlinear mutation operator is adopted to encourage particles to comprehensively explore the decision space, ensuring population diversity in the initial iteration of the algorithm, and an adaptive weight strategy and dynamic learning factor are adopted to balance the exploration and development of the MOPSO algorithm, making it easy for the algorithm to determine the global optimal solution. [Results] 1 The quintic B-spline curve accurately interpolates the path points and ensures the continuity of the acceleration and jerk curves, thus meeting the requirements of smoothness and continuity of the joint trajectory of the manipulator; 2 the optimal trajectory planning method based on the IAMOPSO algorithm can yield a Pareto front with good convergence, select the time- and impact-based optimal trajectory according to the average optimal criterion, and achieve continuity of joint angles and their derivatives. [Conclusions] The optimal trajectory planning method for the manipulator based on IAMOPSO proposed in this paper can improve the motion efficiency and tracking accuracy of the manipulator, obtain Pareto front surfaces with good convergence, ensure smooth and continuous joint curves of the manipulator, and improve the operational efficiency and stability of the manipulator. [ABSTRACT FROM AUTHOR]

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]

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 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.)

[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 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 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.)