Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learn...Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).展开更多
Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring sy...Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid.In contradiction to conventional methods,transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit(PMU)at one of the generator buses.This minimal requirement makes the proposed methodology ideal for providing backup protection.Spectral analysis of equivalent power factor angle(EPFA)variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid.Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence.The proposed system can considerably assist system protection center(SPC)in fault localization and to restore the line at the earliest.Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.展开更多
The current detection technology for vegetable pests mainly relies on artificial statistics,which exists many shortages such as requiring a large amount of labor,low efficiency,feedback delay and artificial faults.By ...The current detection technology for vegetable pests mainly relies on artificial statistics,which exists many shortages such as requiring a large amount of labor,low efficiency,feedback delay and artificial faults.By rapid detection and image processing technology targeting at vegetable pests,not only can reduce manpower and pesticide use,but also provide decision support for precise spraying and improve the quality of vegetables.Practical research achievements are still relatively lacking on the rapid identification technology based on image processing technology in vegetable pests.Given the above background,this paper presents a classification and recognition scheme based on the bag-of-words model and support vector machine(BOF-SVM)on four important southern vegetable pests including Whiteflies,Phyllotreta Striolata,Plutella Xylostella and Thrips.This paper consists of four sub-algorithms.The first sub-algorithm is to compute the character description of pest images based on scale-invariant feature transformation.The second sub-algorithm is to compute the visual vocabulary based on bag of features.The third sub-algorithm is to compute the classifier of pests based on support vector machines.The last one is to classify the pest images using the classifier.In this study,C++and Python language were used as implementation technologies with OpenCV and LibSVM function library based on BOF-SVM classification algorithm.Experiments showed that the average recognition accuracy was 91.56%for a single image category judgment with 80 images from the real environment,and the average time was 0.39 seconds.This algorithm has achieved the ideal operating speed and precision.It can provide decision support for UAV precise spraying,and also has good application prospect in agriculture.展开更多
文摘Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).
文摘Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid.In contradiction to conventional methods,transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit(PMU)at one of the generator buses.This minimal requirement makes the proposed methodology ideal for providing backup protection.Spectral analysis of equivalent power factor angle(EPFA)variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid.Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence.The proposed system can considerably assist system protection center(SPC)in fault localization and to restore the line at the earliest.Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.
基金This work was supported by the National Spark Program(2015GA780002)Guangdong Province Science and Technology Program(2015A020224042).
文摘The current detection technology for vegetable pests mainly relies on artificial statistics,which exists many shortages such as requiring a large amount of labor,low efficiency,feedback delay and artificial faults.By rapid detection and image processing technology targeting at vegetable pests,not only can reduce manpower and pesticide use,but also provide decision support for precise spraying and improve the quality of vegetables.Practical research achievements are still relatively lacking on the rapid identification technology based on image processing technology in vegetable pests.Given the above background,this paper presents a classification and recognition scheme based on the bag-of-words model and support vector machine(BOF-SVM)on four important southern vegetable pests including Whiteflies,Phyllotreta Striolata,Plutella Xylostella and Thrips.This paper consists of four sub-algorithms.The first sub-algorithm is to compute the character description of pest images based on scale-invariant feature transformation.The second sub-algorithm is to compute the visual vocabulary based on bag of features.The third sub-algorithm is to compute the classifier of pests based on support vector machines.The last one is to classify the pest images using the classifier.In this study,C++and Python language were used as implementation technologies with OpenCV and LibSVM function library based on BOF-SVM classification algorithm.Experiments showed that the average recognition accuracy was 91.56%for a single image category judgment with 80 images from the real environment,and the average time was 0.39 seconds.This algorithm has achieved the ideal operating speed and precision.It can provide decision support for UAV precise spraying,and also has good application prospect in agriculture.
