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Secure multipath routing using link compromise metric in mobile ad hoc networks 移动自组织网络中基于链路折衷度量的安全多路径路由
2015 Symposium on Recent Advances in Electrical Engineering (RAEE) Pub Date : 2015-12-17 DOI: 10.1109/RAEE.2015.7352761
M. Naheed, H. Mahmood, Iqbal Murtza
{"title":"Secure multipath routing using link compromise metric in mobile ad hoc networks","authors":"M. Naheed, H. Mahmood, Iqbal Murtza","doi":"10.1109/RAEE.2015.7352761","DOIUrl":"https://doi.org/10.1109/RAEE.2015.7352761","url":null,"abstract":"In this paper, we propose a novel routing protocol by overcoming the security issues of node disjoint multipath routing protocols to ensure the reliability by discovering multiple secure paths. The proposed protocol considers link compromise probabilities to generate multiple routes with low compromise probabilities. Contrary to contemporary distance metric multipath routing protocols, our proposed work is. In the end, simulation results show improvement in path compromise probability and reliability in comparison with Distance routing Metric. Simulations show that the ratio of compromised paths is dropped significantly by implementing the proposed routing algorithm and consequently security aspect of the data routing is also improved. The proposed routing protocol is suitable for dynamic ad hoc networks where topology changes frequently.","PeriodicalId":424263,"journal":{"name":"2015 Symposium on Recent Advances in Electrical Engineering (RAEE)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093049","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
Data-driven technique for robust fault detection in generators 发电机鲁棒故障检测的数据驱动技术
2015 Symposium on Recent Advances in Electrical Engineering (RAEE) Pub Date : 2015-12-17 DOI: 10.1109/RAEE.2015.7352759
Abeer Fatima, Abdul Qayyum Khan
{"title":"Data-driven technique for robust fault detection in generators","authors":"Abeer Fatima, Abdul Qayyum Khan","doi":"10.1109/RAEE.2015.7352759","DOIUrl":"https://doi.org/10.1109/RAEE.2015.7352759","url":null,"abstract":"Protection of a synchronous generator presents a very challenging problem because of its simultaneous system connections on three different sides; the prime mover, grid and the source of DC excitation. Generator Model is a very extensive and complex model and model-based fault detection techniques are difficult to implement. For this data-driven techniques can be applied which need only the process data to establish FDD systems. This paper presents application of subspace aided system identification method and robust residual evaluation using the process data directly, to detect actuator faults occuring in synchronous generators.","PeriodicalId":424263,"journal":{"name":"2015 Symposium on Recent Advances in Electrical Engineering (RAEE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122725970","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
Implementation of nonlinear classifiers for adaptive autoregressive EEG features classification 非线性分类器自适应自回归脑电特征分类的实现
2015 Symposium on Recent Advances in Electrical Engineering (RAEE) Pub Date : 2015-12-17 DOI: 10.1109/RAEE.2015.7352749
Muddasir Ahmad, M. Aqil
{"title":"Implementation of nonlinear classifiers for adaptive autoregressive EEG features classification","authors":"Muddasir Ahmad, M. Aqil","doi":"10.1109/RAEE.2015.7352749","DOIUrl":"https://doi.org/10.1109/RAEE.2015.7352749","url":null,"abstract":"The objective of this work is to realize two nonlinear classifiers for the adaptive autoregressive Electroencephalography (EEG) features. The EEG features are modeled as adaptive autoregressive model and estimated using recurring least square algorithm. Nonlinear classification is performed using multilayer perceptron (MLP) and radial basal function neural network to classify extracted features for a two classes experiment. For validation, hands movement imaginations based experiments are conducted using low price EEG EPOC headset. A comparative study, carried out amongst the nonlinear classifiers and with a linear discriminant analysis, demonstrates the dominance of the MLP as nonlinear classifier.","PeriodicalId":424263,"journal":{"name":"2015 Symposium on Recent Advances in Electrical Engineering (RAEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130927718","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
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