Subjects: Information Science and Systems Science >> Systematic Application of Information Technology submitted time 2022-10-26 Cooperative journals: 《桂林电子科技大学学报》
Abstract： The rise of a new generation of information technology and the rapid development of the internet industry have led
to an explosive growth in the amount of data. In order to meet the needs of billions of users to obtain effective information
from massive data quickly, it is of great significance to improve the retrieval quality and query efficiency of search engines,
but it also faces challenges. On the one hand, the query words of users are becoming more and more complex, and the characteristics
of the morphological variation of language vocabulary lead to the diversification of search words, while existing
stemming algorithms generally suffer from under stemming and unsatisfactory stemming accuracy; On the other hand, it is
a very time-consuming task to retrieve document results that meet user query requirements from massive data, and existing
methods of dividing documents into multiple servers to handle query latency often suffer from tail latency problems. In view
of the above problems, in the text preprocessing stage, the word form normalization algorithm APS (advanced porter stemmer)
is designed, the rule function is recoded, and the feature word extraction is optimized; In the related ranking stage, the
anytime ranking algorithm SAR (SAAT anytime ranking) is designed based on the score-at-a-Time query processing strategy,
which can terminate the query process in advance after a given time budget or processing a specified number of inverted
segments and control the query delay effectively. Experiments are carried out on multiple real datasets to verify the effectiveness of the APS algorithm in improving the accuracy of stemming and the authenticity of the SAR algorithm in controlling
query latency.
Subjects: Information Science and Systems Science >> Systematic Application of Information Technology submitted time 2022-10-26 Cooperative journals: 《桂林电子科技大学学报》
Abstract： In order to solve the data matching problem between sports camera and lidar, a joint calibration optimization method
of sports camera and lidar based on Levenberg-Marquard (LM) algorithm is designed. First, the calibration board is
placed in the common field of view of the lidar and the sports camera, and the laser point cloud and image data of the calibration
object at different positions are collected by changing the position of the calibration board. Then the fisheye distortion
correction function is called through OpenCV to correct the image distortion, and obtain multiple sets of pixel coordinates of
the corner points of the calibration plate image. At the same time, point cloud filtering and point cloud registration are performed
on the laser point cloud, and the laser point cloud is segmented by a combination of manual and automatic methods,
and then the point cloud center iterative algorithm is used to solve the calibration board point cloud center coordinates and
The point cloud coordinates of each corner point. Finally, through multiple sets of point cloud coordinates representing the
corner points of the calibration board and the corresponding image pixel coordinates, the direct linear transformation method
(DLT) is used to calculate the initial value of the joint calibration between the two sensors, and the difference between the
point cloud reprojection coordinates and the image pixel coordinates is constructed. The least squares function of, the function
is optimized by the LM algorithm that introduces the damping factor, and the optimized joint calibration result is
solved. Experiments show that the joint calibration result reduces the reprojection error by 35% compared with the initial
value. The joint calibration result is used to achieve laser point cloud and image fusion based on the principle of collinear equations, which verifies the accuracy and effectiveness of the method
Subjects: Information Science and Systems Science >> Systematic Application of Information Technology submitted time 2022-09-27 Cooperative journals: 《桂林电子科技大学学报》
Abstract： As a typical "low, small, slow" target, UAV has the characteristics of slow flight speed, low altitude, and small radar reflection area (RCS), making it difficult to detect and identify UAV targets. In view of the problem of low signal-tonoise ratio and difficult detection of UAVs in complex environments, a knowledge-aided constant false alarm rate (CFAR) detection method for UAV targets is proposed. This method first analyzes three common ground clutter distribution models and mean CFAR detectors, and then adopts CFAR detection methods for the echo signals under the three clutter distributions, and uses the method with the best detection performance as the clutter distribution The optimal CFAR detection method is stored in the knowledge base to establish the CFAR knowledge base; by estimating the clutter distribution of the echo signal of the target to be detected, the clutter distribution model is judged, and the distribution is obtained from the radar knowledge base Select the corresponding CFAR algorithm to complete the echo signal detection. Finally, the actual measurement data collected by radar is used to verify the feasibility and effectiveness of the method.
Subjects: Information Science and Systems Science >> Systematic Application of Information Technology submitted time 2022-09-27 Cooperative journals: 《桂林电子科技大学学报》
Abstract： Classification that assigns label for pixel in HSI dataset is an important pre-processed method in hyperspectral image (HSI) processing, label information is useful for application such as recognition and exploration. A graph based semisupervised classification method is proposed to tackle problems of large data volume, high data dimension, and small known sample size in HSI classification task. Dataset was modeled with graph for dimensional reducing, then the task is formulated as an unconstrained optimization problem in this method. Matrix inverse is inevitable for solving such problem, and complexity would increase with large scale. In order to avoid large scale matrix inversion, a quasi-Newton method which approximates inversion operation according to decomposition of Hessian matrix is used, such method can be implemented in distributed manner. Simulations demonstrate that, compared with existing methods, proposed algorithm has lower complexity and higher accuracy in large scale and multi-class HSI classification task.
Subjects: Information Science and Systems Science >> Systematic Application of Information Technology submitted time 2019-03-07
Abstract： We study the fabric spreading and cutting problem in apparel factories. For the sake of saving the material costs, the cutting requirement should be met exactly without producing ad#2;ditional garment components. For reducing the production costs, the number of lays that corresponds to the frequency of using the cutting beds should be minimized. We propose an iterated greedy algorithm for solving the fabric spreading and cutting problem. This algorithm contains a constructive procedure and an improving loop. Firstly the constructive procedure creates a set of lays in sequence, and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay set. The improving loop will run until it cannot obtain any small lay set or the time limit is due. The experiment results on 500 cases shows that the proposed algorithm is effective and efficient.
Peer Review Status:
Awaiting Review
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