Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition最新文献_第8页

Research on Raman fiber amplifier using neural network combining PSO algorithm 基于神经网络结合粒子群算法的拉曼光纤放大器研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573956

J. Gong, Jiaojiao Lu, Ruijie Gao

引用次数: 0

DOA Estimation of Shifted Coprime Array Based on Covariance Matrix Reconstruction 基于协方差矩阵重构的移位协素阵列DOA估计

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574112

Wei Yang, Dongming Xu, Jiaqi Xue

引用次数: 0

Target Recognition of Laser Imaging Fuze Based on Corner Features 基于角点特征的激光成像引信目标识别

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574081

Lina Liu, W. He

{"title":"Target Recognition of Laser Imaging Fuze Based on Corner Features","authors":"Lina Liu, W. He","doi":"10.1145/3573942.3574081","DOIUrl":"https://doi.org/10.1145/3573942.3574081","url":null,"abstract":"Aiming at the problem that the laser imaging fuze needs to process a large amount of data and the algorithm is complex, so it is difficult to eliminate the cloud interference quickly and accurately, a target recognition method based on the combination of improved Harris corner detection algorithm and rectangularity is proposed. Firstly, B-spline function is used to replace the Gaussian window function in the original corner detection algorithm for smoothing filtering; Secondly, the gray value of the central pixel is compared with its 8 neighborhood, and the diagonal points are pre screened. After eliminating the pseudo corners, the corners are determined by using the improved corner response function and non maximum suppression; Finally, count the number of corners and calculate the rectangularity of the corner area, take the number of corners and rectangularity as the feature vector, and select the linear analysis method with the highest accuracy and the fastest reasoning speed for classification and recognition. The experimental results show that the accuracy of the target recognition method can reach 95.02%. The target recognition method proposed in this paper can quickly and accurately distinguish fighter from cloud.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121561234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Text Classification Based on Graph Convolution Neural Network and Attention Mechanism 基于图卷积神经网络和注意机制的文本分类

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573963

Sheping Zhai, Wenqing Zhang, Dabao Cheng, Xiaoxia Bai

{"title":"Text Classification Based on Graph Convolution Neural Network and Attention Mechanism","authors":"Sheping Zhai, Wenqing Zhang, Dabao Cheng, Xiaoxia Bai","doi":"10.1145/3573942.3573963","DOIUrl":"https://doi.org/10.1145/3573942.3573963","url":null,"abstract":"Extracting and representing text features is the most important part of text classification. Aiming at the problem of incomplete feature extraction in traditional text classification methods, a text classification model based on graph convolution neural network and attention mechanism is proposed. Firstly, the text is input into BERT (Bi-directional Encoder Representations from Transformers) model to obtain the word vector representation, the context semantic information of the given text is learned by the BiGRU (Bi-directional Gated Recurrent Unit), and the important information is screened by attention mechanism and used as node features. Secondly, the dependency syntax diagram and the corresponding adjacency matrix of the input text are constructed. Thirdly, the GCN (Graph Convolution Neural Network) is used to learn the node features and adjacency matrix. Finally, the obtained text features are input into the classifier for text classification. Experiments on two datasets show that the proposed model achieves a good classification effect, and better accuracy is achieved in comparison with baseline models.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117086549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Research of Retinopathy Image Recognition Method Based on Vit 基于Vit的视网膜病变图像识别方法研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574083

Zongyu Xu, Xuebin Xu, Zihao Huang

引用次数: 0

SECOND-Order Encoder and Restore Detail Decoder Network for Image Semantic Segmentation 用于图像语义分割的二阶编码器和恢复细节解码器网络

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574045

Nan Dai, Zhiqiang Hou, Minjie Cheng

引用次数: 0

Research on Apparel Trend Prediction Based on CNN-BiLSTM-Attention Model 基于cnn - bilstm -注意力模型的服装趋势预测研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573953

Chunfa Zhang, Ning Chen, Shu-xu Zhao

引用次数: 1

A Prediction Model of Diabetes Based on Ensemble Learning 基于集成学习的糖尿病预测模型

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573949

Lei Qin

{"title":"A Prediction Model of Diabetes Based on Ensemble Learning","authors":"Lei Qin","doi":"10.1145/3573942.3573949","DOIUrl":"https://doi.org/10.1145/3573942.3573949","url":null,"abstract":"Abstract: Diabetes is a common disease that seriously endangers human health, mostly in the middle-aged and the elderly. Predicting the incidence rate of diabetes enables doctors to make a scientific treatment plan in advance, which will significantly improve the cure rate and reduce the incidence rate. Based on this situation, this paper proposes a diabetes prediction model based on ensemble learning, which integrates some classical machine learning algorithms, including Logisticregression, Kneigbors, Decisiontree, GaussianNB, and support vector machine (SVM) The first four low correlation algorithms are constructed as basic learners, and then integrated into meta learner SVM to build an integrated learning model. The advantages of the comprehensive model are evaluated from the following aspects: accuracy, precision, recall rate, AUC, and other evaluation indicators. The experiment was carried out on the Pima Indian diabetes data set (PIDD) published by UCI. First, the XGboost algorithm was used to select the optimal features, and then an integrated learning model was constructed to predict. The experimental results show that the accuracy rate of the integrated learning model is 81.63%, the precision rate is 80%, the recall rate is 80%, and the AUC is 84%. The advantages of the model in accuracy, precision, recall, and AUC are verified. The model will effectively help doctors make more accurate diagnoses and predictions of patients' physical conditions and implement more scientific treatment.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Particle Filter Passive Location Method Based on Differential Squirrel Search Algorithm 基于差分松鼠搜索算法的改进粒子滤波无源定位方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573998

Junliang Yang, Ersen Zhang

{"title":"An Improved Particle Filter Passive Location Method Based on Differential Squirrel Search Algorithm","authors":"Junliang Yang, Ersen Zhang","doi":"10.1145/3573942.3573998","DOIUrl":"https://doi.org/10.1145/3573942.3573998","url":null,"abstract":"Particle filtering is a standard method for parameter estimation in passive location and has great application value in nonlinear and non-Gaussian systems. The standard particle filter (PF) is prone to the problem of particle weight degradation and particle dilution as the number of iterations increases, which affects the overall performance of the location algorithm. Aiming at this problem, a PF algorithm based on differential squirrel search algorithm (DSSA) optimization is proposed. The particles are divided into optimal individuals, sub-optimal individuals, and ordinary individuals according to the weight of the particles. The low-weight particles are moved closer to the position of the high-weight particles by simulating the predation behavior of squirrels, so that the location information of most particles can be retained. And seasonal monitoring condition is used to avoid the algorithm falling into local optimal. The simulation results show that the improved algorithm has a lower root mean square error (RMSE) than the standard PF algorithm and the improved PF algorithms of other intelligent optimization algorithms under non-Gaussian noise. The improved algorithm can accurately achieve the passive location of the moving target.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114978828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correlation Filter Based on Saliency Detection and Channel Selection for Visual Object Tracking 基于显著性检测和通道选择的相关滤波视觉目标跟踪

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574039

Sugang Ma, Zhixian Zhao, Lei Zhang, Lei Pu

{"title":"Correlation Filter Based on Saliency Detection and Channel Selection for Visual Object Tracking","authors":"Sugang Ma, Zhixian Zhao, Lei Zhang, Lei Pu","doi":"10.1145/3573942.3574039","DOIUrl":"https://doi.org/10.1145/3573942.3574039","url":null,"abstract":"To improve the utilization of convolution features by correlation filter trackers and reduce the influence of interference feature channels on algorithm performance, a correlation filter visual object tracking algorithm based on saliency detection and channel selection is proposed in this paper. Firstly, the HOG features and double-layer convolution features are used to represent the target, and the target salient region mask is obtained by the saliency detection method. Secondly, a channel selection mechanism is designed by using the salient region feature energy and the search region feature energy to remove redundant feature channels containing a large number of background information. Extensive evaluation results obtained on the OTB2015 benchmark demonstrate the effectiveness of the proposed method. The success rate and precision of the proposed algorithm are 67.5% and 91.3%, which are 5.4% and 9.1% higher than the benchmark algorithm BACF, respectively. In addition, according to the experimental results, it can be seen that the proposed tracker has a significant improvement in tracking performance compared with competitors in tracking scenarios with challenges such as deformation, background clutter, and rotation.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115564204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0