Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： The linear sweeping jamming is one of the common jamming patterns in the battlefield. It is of great significance to detect the sweeping jamming and make effective anti-jamming decision. In this paper, the authors proposed a detection algorithm for sweeping jamming with low complexity under the constraint of the probability of false alarm and missed detection, and theoretically analyzed the performance of the detection algorithm to provide the basis for judging the disturbance of the actual communication system. Then, the authors proposed an anti-jamming learning algorithm based on Q-Learning, which can be used to autonomously select the best communication channel and the maximal reliable transmission time when the wireless communication system is threatened by sweeping jamming. Finally, the simulation results show that the proposed detection algorithm can effectively detect the linear sweeping jamming, and the detection performance is basically consistent with the theoretical analysis results with low complexity. The anti-jamming learning algorithm can adaptively determine the optimal communication channel, and effectively avoid the sweeping jamming to realize the reliable information transmission.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： To further improve the performance of personalized tag recommendation, this paper argued that traditional methods ignore the potential and informative tags hidden in the context of users and items. Aimed at this, this paper proposed a novel personalized tag recommendation method BPR-PITF-P based on potential tag mining and fine-grained preference. Firstly, BPR-PITF-P leverages the context information of both users and items to mine potential and useful tags, and gets three kinds of tags: positive tags, potential tags, and negative tags. Based on the above, it translates the traditional pairwise preference into fine-grained preference relationship among user-item post and tags. This kind of treatment helps alleviate the sparse problem of tagging data. Second, combined with pairwise interaction tensor factorization method to predict preference value, BPR-PITF-P models the preference relationship based on the optimization criteria of Bayesian personalized ranking, and develops a personalized tag recommendation model followed by optimization algorithm. The comparison results show that our proposed method could improve tag recommendation performance in the premise of guarantee convergence speed.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problems that the existing target detection framework based on big data and depth learning has poor recognition effect on low-resolution small targets in high-resolution complex large-field scenes, and the accuracy and real-time performance of multi-target detection are difficult to balance, improve the single shot multi-box detector based on depth learning, and propose an improved multi-target detection framework DRZ - SSD (dynamic region zoom - in, DRZ) , which is dedicated to multi-target detection in complex large traffic scenes. The detection is carried out in a coarse-to-fine strategy, training a low-resolution coarse detector and a high-resolution fine detector respectively, downsampling the high-resolution image to obtain a low-resolution version, designing a dynamic region zoom - in network based on enhanced learning, dynamically enlarging the low-resolution small target region to a high-resolution and then using the fine detector to carry out detection and identification, and detecting the remaining image region by using the coarse detector, so that the detection and identification accuracy of the small target and the improvement effect of the operation efficiency are obvious; Adopting fuzzy threshold method to adjust the adaptive threshold strategy can not only avoid adapting to the data set but also improve the decision-making ability of the model and significantly reduce the detection missed alarm rate and false alarm rate. Experiments show that the improved drz - SSD can achieve good results when dealing with weak targets, multi - targets, cluttered background, occlusion and other difficult detection situations. Through testing on the specified data set, compared with other target detection frameworks based on in-depth learning, the average accuracy rate of various types of target recognition has increased by 4~15 %, the average accuracy rate has increased by 9~16 %, the multi-target detection rate has increased by 13~34 %, and the detection and recognition rate has reached 38 frames / s, realizing the balance between the accuracy of the algorithm and the running rate.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》
Abstract： In order to realize automatic, universal and accurate identification and guidance of unstructured roads for unmanned equipment in the field environment, propose a road guidance algorithm for field scenes based on graph reasoning model and intelligent optimization. Firstly, segmentting the image into homogeneous superpixel blocks, and fusing multi-features of the superpixel blocks to construct a training set. Improving the traditional laplace support vector machine algorithm, combining the location information of superpixel blocks, dynamically selecting superpixel seed blocks in road areas, and training multi-class classifier regressors of superpixel blocks and consistency regressors of adjacent superpixels; Combining the regression values of two kinds of regressors, constructing the energy function of Markov random field and then using the standard graph cutting algorithm to iteratively obtain the minimized energy function to realize the initial road reasoning segmentation. Combining the initial segmentation results of roads, the objective function is constructed according to the constraints set by people's intuitive perception of roads, and using the differential immune clonal evolution algorithm to intelligently optimize and extract the guide lines of roads. The data collected in Zhushan, Nanjing and DARPA grand challenge database are tested, and comparing the results qualitatively and quantitatively with those of classical algorithms. The results show that the extraction accuracy of the guide line of unstructured roads by this algorithm in the field environment is over 91.79 %, compared with classical algorithms, the detection accuracy is increased by 48.1 % and 35.5 % respectively, and the processing efficiency of the algorithm is increased by 98.6 % and 97.8 % respectively, which balances the real-time performance and accuracy of detection and has a strong application prospect.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-24 Cooperative journals: 《计算机应用研究》
Abstract： To solve the problem of large-scale network attack detection, this paper proposed a gated memory network method, based on word vector feature representation and recurrent neural network. Firstly, the proposed method transformed the network request data into low-dimension real-value vector sequence representation. And then, it extracted the features of request data by using the memory ability of gated recurrent neural network. Finally, it adopted the logistic regression classifier to achieve automatic detection of network attack. On the CSIC2010 public data set, this method achieves 98.5% 10-fold cross-validation F1-score. Comparing with traditional methods, it can effectively improve the precision and recall rates for detecting network attack. The proposed method can detect network attacks automatically and has good detection results.
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