The Experts below are selected from a list of 932283 Experts worldwide ranked by ideXlab platform
Zhang Yue - One of the best experts on this subject based on the ideXlab platform.
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Detection Method of ST Segment Change in Real-time Electrocardiography Monitoring
Computer Engineering, 2009Co-Authors: Zhang YueAbstract:This paper proposes a Detection Method of ST segment change in real-time Electrocardiography(ECG) monitoring, which uses line-fitting to extract the baseline, uses wavelet transform to monitor QRS complex, and combines J+X Method and T wave Detection Method to attain the feature point of ST segment.A ST segment recorded message is designed in real-time monitoring.Through a long period of standard database simulation and clinical tests, this Method is proved to be accuracy and useful.
Yang Luxi - One of the best experts on this subject based on the ideXlab platform.
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An Optimized Neural Network Tree Based Anomaly Intrusion Detection Method
Signal Processing, 2010Co-Authors: Xu Qin-zhen, Yang LuxiAbstract:This paper dedicates to propose an optimized neural network tree(ONNT) based anomaly Detection Method that is capable to improve the understandability and interpretability on the Detection results of the trained learning model as well as the anomaly Detection accuracy.ONNT is a binary-tree-structured hybrid learning model whose interior nodes split according to the criterion of information gain ratio.The simple perceptron neural network embedded in each interior node is trained on the current samples.A limited number of input features are selected on current samples in accordance to instruction signal for the perceptron neural network to build a local decision hyper-plane with low complexity.The proposed anomaly Detection Method involves two optimization items.Firstly,the complexity of local decision hyper-plane is decreased by optimizing each interior node.The trained neural network in an interior node with simple structure enables the learning result to be interpreted into low complexity Boolean functions or rule set followed by acceptable computation cost,and thereby lay a good basis for the interpretability of the learning results.Secondly,the tree structure of the learning model is optimized,i.e.,the neural network tree(NNT) is pruned to condense the precondition in disjunctive description of all interior nodes,which makes the extracted rule set as understandable as possible.The experimental results compared with those of NNT based Detection Method suggest that the ONNT based anomaly intrusion Detection Method allows better understandability and interpretability on the anomaly Detection results as a result of simpler structured neural network in interior nodes and reduced complexity of tree structure. The experimental results compared with those obtained by other parallel Methods show that the ONNT based anomaly Detection Method achieves competitive recognition accuracy as well as lower false alarm rate.And what is more,the proposed anomaly Detection Method presents the information of those features which make greater contribution to the Detection result.
Toshihiro Yamauchi - One of the best experts on this subject based on the ideXlab platform.
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CANDAR - Malware Detection Method Focusing on Anti-debugging Functions
2014 Second International Symposium on Computing and Networking, 2014Co-Authors: Kota Yoshizaki, Toshihiro YamauchiAbstract:Malware has received much attention in recent years. Antivirus software is widely used as a countermeasure against malware. However, some kinds of malware can evade Detection by antivirus software, hence, a new Detection Method is required. In this paper, we propose a malware Detection Method that focuses on Anti-Debugging functions. An Anti-Debugging function is a Method that prevents malware analysts from analyzing an application program (AP). The function can form part of benign as well as malicious APs. Our Method focuses on a behavioral difference between benign and malicious APs and detects malware by comparing the two behavioral patterns. Evaluation results with malware confirmed our Method to be capable of successfully detecting malware.
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CANDAR - Malware Detection Method Focusing on Anti-debugging Functions
2014 Second International Symposium on Computing and Networking, 2014Co-Authors: Kota Yoshizaki, Toshihiro YamauchiAbstract:Malware has received much attention in recent years. Antivirus software is widely used as a countermeasure against malware. However, some kinds of malware can evade Detection by antivirus software, hence, a new Detection Method is required. In this paper, we propose a malware Detection Method that focuses on Anti-Debugging functions. An Anti-Debugging function is a Method that prevents malware analysts from analyzing an application program (AP). The function can form part of benign as well as malicious APs. Our Method focuses on a behavioral difference between benign and malicious APs and detects malware by comparing the two behavioral patterns. Evaluation results with malware confirmed our Method to be capable of successfully detecting malware.
Alison Sutinen - One of the best experts on this subject based on the ideXlab platform.
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discovering inappropriate billings with local density based outlier Detection Method
Australasian Data Mining Conference, 2009Co-Authors: Yin Shan, Wayne D Murray, Alison SutinenAbstract:This paper presents an application of a local density based outlier Detection Method in compliance in the context of public health service management. Public health systems have consumed a significant portion of many governments' expenditure. Thus, it is important to ensure the money is spent appropriately. In this research, we studied the potentials of applying an outlier Detection Method to medical specialist groups to discover inappropriate billings. The results were validated by specialist compliance history and direct domain expert evaluation. It shows that the local density based outlier Detection Method significantly outperforms basic benchmarking Method and is at least comparable, in term of performance, to a domain knowledge based Method. The results suggest that the density based outlier Detection Method is an effective Method of identifying inappropriate billing patterns and therefore is a valuable tool in monitoring medical practitioner billing compliance in the provision of health services.
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AusDM - Discovering inappropriate billings with local density based outlier Detection Method
2009Co-Authors: Yin Shan, D. Wayne Murray, Alison SutinenAbstract:This paper presents an application of a local density based outlier Detection Method in compliance in the context of public health service management. Public health systems have consumed a significant portion of many governments' expenditure. Thus, it is important to ensure the money is spent appropriately. In this research, we studied the potentials of applying an outlier Detection Method to medical specialist groups to discover inappropriate billings. The results were validated by specialist compliance history and direct domain expert evaluation. It shows that the local density based outlier Detection Method significantly outperforms basic benchmarking Method and is at least comparable, in term of performance, to a domain knowledge based Method. The results suggest that the density based outlier Detection Method is an effective Method of identifying inappropriate billing patterns and therefore is a valuable tool in monitoring medical practitioner billing compliance in the provision of health services.
Rongfeng Yang - One of the best experts on this subject based on the ideXlab platform.
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research on a novel Detection Method for fundamental positive sequence active current
Conference on Industrial Electronics and Applications, 2014Co-Authors: Yunguang Fang, Rongfeng YangAbstract:The rapid and accurate fundamental positive sequence active current Detection Method is very important for the power quality control. However, many traditional Methods need to use the phase locked loop (PLL), the low pass filter (LPF) or αβ and dq coordinate transforms which increase the complexity of the Detection and have the large Detection errors in three-phase unsymmetrical system. In view of these problems, this paper proposed a novel Detection Method. Firstly, this Method, with the theory of the fan-in vector transformation, transfers a three-phase system into a single-phase system which simplifies the Detection Method. In addition, it designs the extractors for fundamental positive sequence current and voltage with the analyzing of the selective frequency characteristic of an amplitude integration signal. Finally, through the simple calculation, the fundamental positive sequence active current was detected. This Detection Method needs neither a PLL nor a LPF, and can omit some complex calculations for using αβ and dq coordinate transforms. It not only greatly reduces the calculation quantities and makes a better real-time Detection but also achieves the accurate result in three-phase unsymmetrical system. Results of the simulation MATLAB/Simulink verify the validity and feasibility of the proposed Method.
