发布求助
文献互助 智能选刊 最新文献

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)最新文献

筛选
英文 中文
Nonparametric Statistical Anomaly Detection Approach for ATMS DDoS Attack ATMS DDoS攻击的非参数统计异常检测方法
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00055
Yunpeng Zhang, Anish Patel, Liang-Chieh Cheng, Jiming Peng
{"title":"Nonparametric Statistical Anomaly Detection Approach for ATMS DDoS Attack","authors":"Yunpeng Zhang, Anish Patel, Liang-Chieh Cheng, Jiming Peng","doi":"10.1109/SDPC.2019.00055","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00055","url":null,"abstract":"Distributed Denial of Service (DDoS) attack is a standout amongst the most prominent attacks types going for the accessibility of framework. We consider the convenient identification and alleviation of DDoS attacks in Automated Traffic Management Systems (ATMS). Utilizing diverse attack traffic designs, it is conceivable to watch the conduct of the algorithm under investigation. The principle objective of this paper is to break down the recursive nonparametric CUSUM, since it is new to the information organize network and it guarantees to have a great deal of future applications in the region. A novel system for recognizing and relieving low-rate DDoS attacks in ITS dependent on nonparametric statistical anomaly/hybrid detection is proposed. The outcome will demonstrate that our proposed technique significantly beats two parametric strategies for opportune identification dependent on the Cumulative Sum (CUSUM) test, just as the conventional information filtering approach as far as normal recognition delay and false alert rate.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114412048","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: 1
Lamb Wave-based Monitoring of Fatigue Crack Propagation using Principal Component Regression 基于Lamb波的疲劳裂纹扩展主成分回归监测
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00011
Xiaopeng Liu, Weifang Zhang, Xiangyu Wang, W. Dai, Guicui Fu
{"title":"Lamb Wave-based Monitoring of Fatigue Crack Propagation using Principal Component Regression","authors":"Xiaopeng Liu, Weifang Zhang, Xiangyu Wang, W. Dai, Guicui Fu","doi":"10.1109/SDPC.2019.00011","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00011","url":null,"abstract":"Fatigue crack is an important factor affecting structural safety, and it is of great significance for accurate monitoring of fatigue crack propagation. This paper presents a Lamb Wave-based method for quantitative monitoring of fatigue crack propagation. In this method, various types of damage features are extracted in both time and frequency domains to comprehensively describe the Lamb wave changes. To address the problem of multicollinearity in damage features, principal component regression (PCR) is adopted to establish a quantitative model between damage features and crack size. The PCR model is established and validated by the experimental data of aluminum alloy plates. Experimental results reveal that the proposed PCR model is able to accurately monitor the fatigue crack propagation, and it performs far better than traditional multiple linear regression (MLR) model.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115714748","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
Research on Fault Diagnosis Method of Aircraft Secondary Distribution System Based on RBF Neural Network 基于RBF神经网络的飞机二次配电系统故障诊断方法研究
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00075
Yetong Qian, Li Wang, Qingwen Chen
{"title":"Research on Fault Diagnosis Method of Aircraft Secondary Distribution System Based on RBF Neural Network","authors":"Yetong Qian, Li Wang, Qingwen Chen","doi":"10.1109/SDPC.2019.00075","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00075","url":null,"abstract":"This paper studies on the fault diagnosis of aircraft secondary power distribution system based on RBF neural network. Firstly, the basic principle and model of radial basis function neural network (RBF) are expounded,then the common fault types of the aircraft secondary distribution system are analyzed and the characteristic parameters extraction of the corresponding fault modes is studied. Then, based on the multisensor information fusion problem of the aircraft secondary distribution system, an RBF network model suitable for the aircraft secondary distribution system is established. The model was trained by MATLAB software, and an online fault diagnosis platform for aircraft secondary power distribution system is established.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116165490","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: 1
Measurement Error Compensation Method for Parameters of Rear Torsion Beam With PSO-BP 基于PSO-BP的后扭力梁参数测量误差补偿方法
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00032
Kangkang Zhang, Bo Liu
{"title":"Measurement Error Compensation Method for Parameters of Rear Torsion Beam With PSO-BP","authors":"Kangkang Zhang, Bo Liu","doi":"10.1109/SDPC.2019.00032","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00032","url":null,"abstract":"In the process of inspecting the rear torsion beam, there will be measurement error because of the manufacturing error, vibration of the automatic inspection tool and the deformation of the workpiece. This paper presents an error compensation method for parameter of rear torsion beam based on PSO-BP (particle swarm optimization and back propagation neural network) algorithm. In order to solve the problem that BP neural network converges slowly and is easy to fall into local optimum, the paper uses PSO algorithm to optimize its weight and threshold. The research results show that the PSO-BP algorithm has good error compensation accuracy.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121827867","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: 1
Application of GO Method in Reliability Analysis of Aero-engine GO法在航空发动机可靠性分析中的应用
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00079
Yuxi Tao, Hai-ping Dong, X. Yi, Chenhui Ren
{"title":"Application of GO Method in Reliability Analysis of Aero-engine","authors":"Yuxi Tao, Hai-ping Dong, X. Yi, Chenhui Ren","doi":"10.1109/SDPC.2019.00079","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00079","url":null,"abstract":"An aero-engine fuel system is an important part of an aero-engine, and its reliability directly affects the operation of the aero-engine. To improve the reliability of aero-engines, this paper uses Goal-Oriented (GO) method to analyze the reliability of an aero-engine fuel system with afterburner combustion chamber. Firstly, the GO model is established by the structural schematic diagram of an aero-engine fuel system with an afterburner combustion chamber, and the qualitative analysis and quantitative calculation are carried out according to the operation rules of operators and signal flows in the GO model. Then the minimum cut set and quantitative reliability level of the fuel system are obtained respectively. Finally, the results are compared with the results by the fault tree analysis (FTA) method. The comparison result shows that the reliability analysis of the aero-engine fuel system by the GO method is reasonable and the GO method can be used for the reliability analysis of aero-engine systems.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121934364","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
Real-Time Vehicle Tracking using Convolutional Neural Networks in Aerial Video 基于卷积神经网络的航拍视频实时车辆跟踪
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00052
Yu Yang, Chengpo Mu, Ruixin Yang, Yanjie Wang
{"title":"Real-Time Vehicle Tracking using Convolutional Neural Networks in Aerial Video","authors":"Yu Yang, Chengpo Mu, Ruixin Yang, Yanjie Wang","doi":"10.1109/SDPC.2019.00052","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00052","url":null,"abstract":"Vehicle tracking based on video images has been widely used in military and civilian fields. The tracking method must robust enough to hand the unexpected situations that may occur during the tracking process. In this paper, a novel vehicle tracking method based on convolutional neural networks (CNNs) is proposed to target the accurate and speed demand of vehicle tracking. The proposed method contains two networks with shared weights. It utilizes the residual block to reduce the train error. Offline training is used to achieve real-time tracking. It also use transfer learning to reduce training time. The experimental results under the real aerial video demonstrate that vehicle tracker achieves an accuracy of 70.8% and the speed of 135fps with GPU. The proposed method is robust enough to handle occlusion and other interference conditions.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122126618","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
Constrained Nonnegative Matrix Factorization for Image-based Protein Subcellular Localization Prediction 基于图像的蛋白质亚细胞定位预测约束非负矩阵分解
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00132
Huaqun Zhan, Ping Zhou, Hualin Zhan
{"title":"Constrained Nonnegative Matrix Factorization for Image-based Protein Subcellular Localization Prediction","authors":"Huaqun Zhan, Ping Zhou, Hualin Zhan","doi":"10.1109/SDPC.2019.00132","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00132","url":null,"abstract":"Protein subcellular location is an important biological information for understanding protein’s function in normal cells. Automatic analysis of protein subcellular location based on bioimage has been received much attention in recent years. Since preprocessing is a critical step in the automatic image-based analysis system for source separation, this research focuses on the protein subcellular location. Some problems exist in most existing separation methods, such as, the lack of strong explanation and low accuracy. In this paper, a new separation method called minimum volume constrain nonnegative matrix factorization for image preprocessing has been proposed. To examine the effectiveness of the proposed method, both local and global features are extracted from the separated channels, and multi-label classifier is used to make prediction for subcellular localization. The results show the proposed method can generally improve the accuracy of final prediction compared with other methods.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"81 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131349396","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
Remaining Useful Life Prediction Based on Deep Residual Attention Network 基于深度剩余注意网络的剩余使用寿命预测
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00023
Biao Wang, Tianyu Han, Y. Lei, Naipeng Li
{"title":"Remaining Useful Life Prediction Based on Deep Residual Attention Network","authors":"Biao Wang, Tianyu Han, Y. Lei, Naipeng Li","doi":"10.1109/SDPC.2019.00023","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00023","url":null,"abstract":"Deep learning is gaining growing interests in the field of remaining useful life (RUL) prediction and has achieved state-of-the-art results. Current deep learning-based prognostics approaches, however, do not consider the distinctions of different sensor data during representation learning, which affects their prediction accuracy and limits their generalization. To overcome this weakness, a new deep prognostics network called deep residual attention network (DRAN) is proposed in this paper. DRAN is composed of representation learning sub-network and RUL prediction sub-network. In particular, a new module, i.e., attention module, is constructed in DRAN, aiming to emphasize the important degradation information hidden in sensor data and suppress the useless information during representation learning. The proposed DRAN is validated using the vibration signals acquired by accelerated degradation tests of rolling element bearings. The experimental results show that the proposed DRAN is able to provide accurate RUL prediction results and is superior to some existing convolutional networks.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131748060","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: 3
An Optimization Method of AGV Body Topology Based on Improved Variable Density Method 基于改进变密度法的AGV车身拓扑优化方法
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00151
Huadong Zhao, Nan-Yun Jiang, Chaofan Lei
{"title":"An Optimization Method of AGV Body Topology Based on Improved Variable Density Method","authors":"Huadong Zhao, Nan-Yun Jiang, Chaofan Lei","doi":"10.1109/SDPC.2019.00151","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00151","url":null,"abstract":"Aiming at the problem of large weight coefficient of AGV (Automated guided vehicle) car body at present, taking equal strength and light weight as the research target, the mechanical analysis of car body structure was carried out. The relationship between load distribution and wheel position was obtained by mathematical programming, and the constraint conditions of the topology optimization problem were obtained. An adaptive mechanism was proposed to adjust the moving window according to the change of design variables before and after iteration. An adaptive improved variable density method based on sliding window was established to optimize the topological structure of AGV car body. Finally, the structure simulation and verification were carried out to verify the effectiveness and practicability of the method, which can optimize the performance of the AGV car body structure, reduce the weight of the structure, and then improve the efficiency and rationality of the AGV operation.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121076594","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
Probabilistic and Non-probabilistic Synthetic Reliability Model of Structures 结构的概率与非概率综合可靠度模型
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00200
Jiao Shi, Dongpao Hong, Peihao He, Guangyu Jing
{"title":"Probabilistic and Non-probabilistic Synthetic Reliability Model of Structures","authors":"Jiao Shi, Dongpao Hong, Peihao He, Guangyu Jing","doi":"10.1109/SDPC.2019.00200","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00200","url":null,"abstract":"As an alternative to reliability analysis, the non-probabilistic model is an effective supplement when the interval information exists. We describe the uncertain parameters of the structures with interval variables, and establish a non-probabilistic reliability model of structures. Then, we analyze the relation between the typical interference mode and the reliability according to the structure stress-strength interference model, and propose a new measure of structure non-probabilistic reliability. Furthermore we describe other uncertain parameters with random variables when probabilistic information also exists. For the complex structures including both random variables and interval variables, we propose a probabilistic and non-probabilistic synthetic reliability model. The illustrative example shows that the presented model is feasible for structure reliability analysis and design.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121126980","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: 1
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信