2022 9th International Conference on Dependable Systems and Their Applications (DSA)最新文献_第10页

Cognitive Radio Spectrum Sensing Technology 认知无线电频谱传感技术

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00144

Yandie Yang

引用次数: 0

Cooperative Area Coverage Path Planning for Multiple UAVs Over Large Areas 多无人机大面积协同区域覆盖路径规划

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00053

Furong Zhang, Xiaopan Zhang

{"title":"Cooperative Area Coverage Path Planning for Multiple UAVs Over Large Areas","authors":"Furong Zhang, Xiaopan Zhang","doi":"10.1109/DSA56465.2022.00053","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00053","url":null,"abstract":"With the popularity and development of Unmanned Aerial Vehicle (UAV) technology, the UAV coverage path planning problem has also emerged. In this paper, we propose a new model of UA V coverage path planning problem, which uses multiple UAVs to cover a certain large area cooperatively, and considers the energy constraint of UA Vs on flight distance, UA V energy replenishment and UA V recovery in the coverage process. In this paper, we propose a two-stage approach to solve the problem. In the first stage, we propose a region decomposition method based on a regular triangular grid, which converts the search region into a graph composed of nodes and edges. In the second stage, we use mixed linear integer programming (MILP) method to plan the coverage path based on the graph generated in the first stage. We use the actual parameters such as the drone's own camera, speed, energy, etc. to dissecte a rectangular area and accurately solve the MILP model, in the process of solving we designed three different scenarios to verify the feasibility and versatility of the model, and the solution results meet the expectations. We also observed the effect of UA V base location on the solving speed and the effect of UA V flight altitude on the model solving target value.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131415420","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

Malware API Sequence Detection Model based on Pre-trained BERT in Professional domain 基于专业领域预训练BERT的恶意软件API序列检测模型

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00162

Rongheng Xu, Jilin Zhang, Li Zhou

引用次数: 0

Multi-UAV Path Planning Model with Multiple Battery Recharge Points 多电池充电点的多无人机路径规划模型

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00117

Mengjie Shan, Xiaopan Zhang

引用次数: 0

A LDPC Decoding Algorithm based on Convolutional Neural Network 一种基于卷积神经网络的LDPC解码算法

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00165

Jiamei Gao, Bo Zhang, Bin Wang, Yang Liu

引用次数: 0

Speech Separation based on As3-2mix Hybrid Strategy Combined Training Convolutional Time Domain Neural Network 基于As3-2mix混合策略结合训练卷积时域神经网络的语音分离

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00099

Pengxu Wang, Haijun Zhang

{"title":"Speech Separation based on As3-2mix Hybrid Strategy Combined Training Convolutional Time Domain Neural Network","authors":"Pengxu Wang, Haijun Zhang","doi":"10.1109/DSA56465.2022.00099","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00099","url":null,"abstract":"In recent years, time-domain speech separation methods have made great progress. The existing time-domain speech separation methods have shown good separation performance on wsj-2mix datasets. However, the performance of these models on Chinese speech datasets has not been studied in detail. To solve this problem, this paper makes a speech separation dataset based on aishell-3 open-source hi-fi Mandarin speech corpus, which we call as3-2mix. As3-2mix not only considers the original features of mixed speech, but also adopts two mixing strategies: same-sex mixing and opposite sex mixing. Based on as3-2mix dataset and different training strategies, we evaluate the generalization ability of convolutional time-domain neural network, and analyze the separated speech through PESQ, STOI, SDRi and SI-SNRi. The experimental results show that our PESQ reaches 2.48 and 2.26 on as3mm1-2mix and as3ff1-2mix datasets respectively, while our STOI reaches 2.46, 0.89 and 0.83 on as3mm1-2mix, as3ff1-2mix and as3fm1-2mix datasets respectively, it is higher than other methods on the same type of dataset. Although the performance of SDRi and SI-SNRi in Chinese dataset is not as good as that in English dataset, they still achieved 13.56dB and 13.21dB good scores, which also shows that different languages may affect some characteristics of speech and then affect the separation effect to a certain extent. Finally, when analyzing the speech amplitude, we find that the speech with large amplitude is conducive to improve PESQ and STOI, and the speech with small amplitude is conducive to improve the SDRi and SI-SNRi.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278663","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

Improved Convolutional Neural Network based Feature Extraction Method 基于改进卷积神经网络的特征提取方法

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00148

Yuanyuan Han, Jingchao Li, Jialan Shen, Bin Zhang

{"title":"Improved Convolutional Neural Network based Feature Extraction Method","authors":"Yuanyuan Han, Jingchao Li, Jialan Shen, Bin Zhang","doi":"10.1109/DSA56465.2022.00148","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00148","url":null,"abstract":"Deep learning algorithms based on convolutional neural networks have been widely researched and developed in the field of images. This helps in more accurate classification and recognition of images. In order to improve the recognition accuracy of convolutional neural network and optimize the learning performance of neural network, an improved dynamic adaptive pooling algorithm is proposed. First, an overview of the basic structure of convolutional neural networks, convolutional layers and pooling layer operations. Second, build a convolutional neural network model, study and compare different network pooling models. Finally, an improved dynamic adaptive pooling model is constructed for the case where the existing algorithm has a slow convergence speed. Experiment on handwritten database. The simulation results show that as the number of iterations continues to increase, the mean square error continues to decrease, and the recognition accuracy of the model continues to improve. The improved pooling method not only makes the feature extraction of the convolutional neural network more accurate, but also improves the convergence speed and model accuracy, and achieves the purpose of optimizing the network learning performance. This approach can be further extended to other models related to convolutional neural networks.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124355636","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

Transform Domain End-to-end Learning Communication System 转换域端到端学习通信系统

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00161

Cheng Chang, Hui Zhou, Chao He, Zilong Zhao, Wulong Li

引用次数: 0

Online Learning based Self-updating Incremental Malware Detection Model 基于在线学习的自更新增量恶意软件检测模型

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00145

Donghui Zhao, Liang Kou, Jilin Zhang

引用次数: 0

Feature Envy Detection with Deep Learning and Snapshot Ensemble 基于深度学习和快照集成的特征嫉妒检测

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00037

Minnan Zhang, Jingdong Jia

{"title":"Feature Envy Detection with Deep Learning and Snapshot Ensemble","authors":"Minnan Zhang, Jingdong Jia","doi":"10.1109/DSA56465.2022.00037","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00037","url":null,"abstract":"Code Smell is a code symptom of deep-seated quality problems caused by design defects or improper coding habits in software. It may not directly affect the operation of program, but it affects the readability, understandability and maintainability of code. Therefore, the identification, location and reconstruction of code smell are becoming increasingly significant. Combined with the deep learning methods, based on the existing model for detecting Feature Envy, this paper optimizes it from three aspects: introducing the attention mechanism, expanding and modifying the model structure, and applying the snapshot ensemble. The experimental results show that compared with the standard results, the model proposed in this paper gets a better performance on four evaluation metrics: precision, recall, F1 measure and AUC. Based on the research results of this experiment, we can see the effectiveness of deep learning in the field of code smell detection and the prospect of theories in natural language processing to be utilized in code smell detection, which provides a practical cornerstone for the research of deep learning based smell detection methods in the future.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121233463","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: 2