Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering最新文献_第8页

Low-Rank Tensor Completion with Total-Variation-Regularized Transformed Tensor Schatten-p Norm for Video Inpainting 基于全变分正则化变换张量schattenp范数的低秩张量补全

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573699

Jiahui Liu, Jialue Tian

引用次数: 0

Direct Position Determination using Distributed Unfold Coprime Arrays with Unknown Mutual Coupling: based on Reduced-Dimension Search 基于降维搜索的未知互耦合分布展开互素阵列直接定位

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573706

Baobao Li, Xiaofei Zhang, Jianfeng Li, Jinke Cao

{"title":"Direct Position Determination using Distributed Unfold Coprime Arrays with Unknown Mutual Coupling: based on Reduced-Dimension Search","authors":"Baobao Li, Xiaofei Zhang, Jianfeng Li, Jinke Cao","doi":"10.1145/3573428.3573706","DOIUrl":"https://doi.org/10.1145/3573428.3573706","url":null,"abstract":"The direct position determination (DPD) approach has higher localization accuracy and better robustness than the classical two-step approach when localizing multiple sources with distributed antenna arrays. This paper focuses on the DPD algorithm using multiple Unfolded Coprime Arrays (UCAs) with unknown mutual coupling. To reduce the adverse effects of the mutual coupling, we first expand the unfolded coprime arrays into the DPD scenario. Subsequently, we introduce the HD-DPD-Capon algorithm, which fuses all inverse covariance matrices of distributed arrays, simultaneously searching for multiple unknown mutual coupling coefficients and source positions. Finally, in advance of the reduced-dimension search, we propose the RMCD-ICF algorithm, which only needs to search the two-dimension position, to reduce the high computational complexity of the HD-DPD-Capon algorithm caused by the high-dimensional search. Simulation results verify the superiority of the proposed algorithm on computation complexity and localization accuracy.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116500019","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

Characteristics Analysis of RODNet ConfMap for MMW Radar Target Detection 用于毫米波雷达目标探测的RODNet ConfMap特性分析

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573552

Zhuang Li, Y. Li, Yanping Wang, Yun Lin, Wenjie Shen

引用次数: 0

Multi-Feature Convergence Network for Acoustic Scene Classification 声学场景分类的多特征收敛网络

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573633

Menglong Wu, Hongxia Dong, Xichang Cai, Ziling Qiao, Cuizhu Qin, Lin Zhang

{"title":"Multi-Feature Convergence Network for Acoustic Scene Classification","authors":"Menglong Wu, Hongxia Dong, Xichang Cai, Ziling Qiao, Cuizhu Qin, Lin Zhang","doi":"10.1145/3573428.3573633","DOIUrl":"https://doi.org/10.1145/3573428.3573633","url":null,"abstract":"This paper investigates a multi-feature convergence network for acoustic scene classification (ASC). A series of neural network models designed with features of the Log Mel spectrogram, Deltas, and Delta-Deltas superimposed on the channel have achieved good classification results. However, the low-frequency part of the speech spectrogram feature extracted from the audio signal has a mosaic shape due to its low resolution, which leads to the loss of information in the low-frequency part of the Log Mel-Deltas-DeltaDeltas feature and reduces the classification accuracy. To solve this problem, the constant Q-transform (CQT) spectrogram is introduced and this feature is superimposed on the channel with the log Mel-Deltas-DeltaDeltas feature to form a 4-channel feature spectrum as the input to the network model. Moreover, the proposed network model is deepened by increasing the 8 residual blocks from the baseline system to 10 residual blocks and a snapshot integration operation is performed on the various models saved during the training process due to the complementary information. And then, a 3-classifier is added based on the ASC's primarily categorized scenes' 10-classifier and chooses the final scene classification by combining the 3–10 two-stage classification scores. The classification accuracy of our proposed network reached 77.4%, which is 5.1% higher than the baseline system set in this paper and 26% higher than the baseline on the official website of DCASE 2020.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132689981","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

A denoising method based on CNN through multi-layer separation 一种基于CNN的多层分离去噪方法

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573490

Jianyu Huang, Nanting Cai, Jian Mao, Jialin Zhang, Xiaochun Zhao

引用次数: 0

Research on Dehazing of Cable Tunnel Image Based on Dark Channel Prior 基于暗信道先验的电缆隧道图像去雾研究

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573702

Qian Li, Yuechao Chen, Junguo Feng, Jun Zhang, Xiaoyun Sun, Yuchao Cao

引用次数: 0

Analysis of The Change of Softmax Value in The Training Process of Neural Network 神经网络训练过程中Softmax值的变化分析

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573763

Yuyang Chen

{"title":"Analysis of The Change of Softmax Value in The Training Process of Neural Network","authors":"Yuyang Chen","doi":"10.1145/3573428.3573763","DOIUrl":"https://doi.org/10.1145/3573428.3573763","url":null,"abstract":"Classification is an essential field in deep learning. Generally, the category corresponding to the maximum value of softmax is mainly used as the prediction result and the softmax value as the prediction probability. However, whether softmax can indeed serve as a prediction probability needs further confirmation. This paper first focuses on the classification of paintings through Convolutional Neural Network. To deal with the imbalanced dataset problem, only those with more than 200 paintings are selected. Besides, class weight is also taken into consideration. Next, data augmentation is applied to enlarge the dataset and add more relevant data. For the modeling and training part, transfer learning is employed to avoid training from scratch on a new dataset and reduce the cost of later training. Techniques such as ‘EarlyStopping’ and ‘ReduceLROnPlateau’ are also used to avoid overfitting. The final prediction accuracy can achieve 99 percent on the training and 87 percent on the validation sets. Furthermore, the paper studies the change of softmax distribution during the training process and the relationship between the average maximum value of softmax and the classification performance of classes. The experiments show that the maximum value of softmax will gradually shift to the corresponding correct label during the training process. Still, there is no correlation between the classification performance and the average maximum value of softmax. Therefore, softmax cannot be used as a probability value for classification.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134554770","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

Deep Cross-modal Hashing Retrieval Based on Semantics Preserving and Vision Transformer 基于语义保持和视觉转换的深度跨模态哈希检索

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573439

Jin Hong, Huayong Liu

引用次数: 0

Research on Data Augmentation Strategy Methods for Image Caption 图像标题数据增强策略方法研究

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573673

Nan Lin, Shuang Li, Yingkang Han, Mengdi Liu

引用次数: 0

Design and Implementation of Course Review System 课程复习系统的设计与实现

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573453

Yang Yi, Yu Liu

引用次数: 0