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Analysis of Electromyogram (EMG) for Detection of Neuromuscular Disorders 7

Analysis of Electromyogram (EMG) for Detection of Neuromuscu...

World Engineering, Science and Technology Congress (ESTCON) / 7th International Conference on Intelligent and Advanced System (ICIAS)

作者： Hasni, Hazirah Yahya, Norashikin Asirvadam, Vijanth S. Jatoi, Munsif Ali Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar Malaysia Benazir Bhutto Shaheed Univ Technol & Skill Dev Dept Elect Engn Technol Khairpur Mirs Sindh Pakistan

ISBN: (纸本)9781538672693

In this paper, we conducted time domain analysis on EMG signals for the development of neuromuscular disorder classification algorithm based on Support Vector Machine (SVM). The type of EMG data considered are from a healthy individual, from one individual with neuropathy and one individual with myopathy condition. The extracted feature from the EMG data comprises of statistical features namely mean, mean absolute deviation, standard deviation, interquartile range, energy, coefficient of variation and Auto-Regressive (AR) model. The statistical features are then ranked using relieff algorithm in order to identify the most prominent features and to be used for selection of the best features. In classification method, 2-stage cascaded SVM is used in which the 1st-stage SVM involved classification of healthy versus unhealthy (neuropathy + myopathy), whereas the 2nd-stage SVM involve classification of data coming from the 1st-stage SVM. This means, the 2nd-stage SVM will only involve classification of neuropathy versus myopathy EMG data. The proposed EMG classification method gave good performance with average accuracy of 95.7%, average sensitivity of 100% and average specificity of 91% generated over the two SVM stages.

关键词： classification EMG signals feature extraction two-stage SVM relieff algorithm

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Refrigerant charge fault diagnosis in the VRF system using Bayesian artificial neural network combined with relieff filter

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APPLIED THERMAL ENGINEERING 2017年 112卷 698-706页

作者： Shi, Shubiao Li, Guannan Chen, Huanxin Liu, Jiangyan Hu, Yunpeng Xing, Lu Hu, Wenju Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China Beijing Univ Civil Engn & Architecture Beijing Municipal Key Lab HVAC&R Beijing Peoples R China

A proper refrigerant charge amount (RCA) is critical for a variable refrigerant flow (VRF) system since RCA may affect the operational performance. However, there were few studies of RCA fault for the VRF system in the open literature. Therefore VRF systems are calling for a fault diagnosis strategy. This paper develops a highly efficient fault diagnosis model (FDM), which employs the relieff algorithm for feature ranking (FR) and applies the neural network for fault diagnosis. Firstly, the artificial neural network (ANN) model is built on the N-best features data subset and optimized by the Bayesian regularization algorithm. Secondly, the model is verified by testing data subset, the correct diagnosis rates (CDR) using the N-best features data subset can be obtained. The optimal FDM is selected in consideration of CDR and the computational efficiency. Finally, optimal FDM is further optimized by selecting the best hidden neurons. The results show that the CDR of the FDM based on 6-best features is sufficiently high in comparison to the CDR achieved when 22 features are used, while the training time decreases by 98.8%. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Variable refrigerant flow system relieff algorithm Bayesian neural network Refrigerant charge amount fault

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Vibration-Based Terrain Classification for Autonomous Vehicles

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Journal of Beijing Institute of Technology 2017年第4期26卷 440-448页

作者： Kai Zhao Mingming Dong Zhiguo Wang Yanxi Han Liang Gu School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China Beijing North Vehicle Group Corporation Beijing 100072 China

A method for terrain classification based on vibration response resulted from wheel-terrain interaction is presented. Four types of terrains including sine,gravel,cement and pebble were *** vibration data were collected by two single axis accelerometers and a triaxial seat pad accelerometer,and five data sources were utilized. The feature vectors were obtained by combining features extracted from amplitude domain,frequency domain,and time-frequency domain. The relieff algorithm was used to evaluate the importance of attributes; accordingly,the optimal feature subsets were selected. Further,the predicted class was determined by fusion of outputs provided by five data sources. Finally,a voting algorithm,wherein a class with the most frequent occurrence is the predicted class,was employed. In addition,four different classifiers,namely support vector machine,k-nearest neighbors,Nave Bayes,and decision tree,were used to perform the classification and to test the proposed method. The results have shown that performances of all classifiers are ***,the proposed method is proved to be effective.

关键词： relieff algorithm terrain classification vibration voting algorithm

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Method for Predicting Hot Spot Residues at Protein-Protein Interface Based on the Extreme Learning Machine 3

Method for Predicting Hot Spot Residues at Protein-Protein I...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Qiu, Yanzi Ping, Pengyao Wang, Lei Pei, Tingrui Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan Peoples R China

ISBN: (纸本)9781509063529

Protein binding hot spots are those residues that locate at the interfaces of protein-protein interaction, which can influence the interaction of proteins significantly, although they consist of a small part of the interface residues only. Since traditional experimental methods for prediction of hot spots are quite complex and time-consuming, then recently, bioinformatics methods are adopted as efficient tools in the area of hot spots prediction at protein-protein interface. In this paper, a novel prediction model is proposed for the prediction of hot spots at the protein-protein interaction interfaces based on the Extreme Learning Machine (ELM) and a new way for feature selection. Different from existing methods, the selection of the classifier in our method included two parts: the first part aimed for deleting some redundant features from the original features without prediction, and the second part aimed for constructing our final prediction model based on the prediction. Our major contribution was that the ELM and a new prediction model were introduced into the area of hot spots prediction, which could improve the prediction performance remarkably, when comparing with some traditional existing methods. And the simulation results based on two benchmark datasets ASEdb and BID showed that the newly proposed prediction model outperformed some of the existing well known methods such as the Robetta, KFC, and HotPoint, etc.

关键词： hot spots structural information relieff algorithm extreme learning machine

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MODELING THE DEMAND OF A CALORIFIER TO ESTABLISH THE BASELINE BEFORE RETROFITTING IT WITH A COMMERCIAL AIR SOURCE HEAT PUMP 11

MODELING THE DEMAND OF A CALORIFIER TO ESTABLISH THE BASELIN...

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11th International Conference on the Industrial and Commercial Use of Energy (ICUE)

作者： Tangwe, S. Mzolo, N. Simon, M. Meyer, E. Univ Ft Hare South Africa Ft Hare Inst Technol Alice South Africa

ISBN: (纸本)9780992204167

In South Africa, more than 40 % of the electricity generated by Eskom is used by the commercial sector. The universities constitute the primary consumers of electrical energy through the utilization of hot water. The research focused on the construction of a data acquisition system that monitored the demand and hot water profiles of a 12.0 kW Calorifier. The DAS comprised of 1 current transducer that measured current, hence determined the power consumption, 3 temperature sensors that measured the inlet cold water, outlet hot water into the residence and the room temperature and also a flow meter that measured the volume of incoming cold water into the Calorifier. In addition a regression model was also developed correlating the energy consumption during the heating up cycle to the total volume of cold water flowing into the Calorifier, the average room temperature, the average inlet, the outlet water temperature as well as the time taken for the heating up cycle. The relieff algorithm was used to rank the predictors by weight of importance to the energy consumed. The results depicted that on an average weekday for the month of March 2013, a volume of 1953 L of hot water was drawn and an electrical energy of 137.85 kWh was consumed with a load factor of 0.464. Furthermore the relieff algorithm showed that all the predictors were primary factors except of the room temperature. The mathematic model could always be used in adjusting the baseline, when computing the energy saving after retrofitting the Calorifier with an ASHP.

关键词： Calorifier Load factor regression model relieff algorithm Air source heat pump (ASHP) Data acquisition system (DAS)

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A QUANTITATIVE AND QUALITATIVE METHODOLOGY TO EVALUATE PERFORMANCE IMPROVEMENT OF A COAL POWER PLANT IN SOUTH AFRICA BY THE VIRTUE OF ENERGY EFFICIENCY INTERVENTION 11

A QUANTITATIVE AND QUALITATIVE METHODOLOGY TO EVALUATE PERFO...

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11th International Conference on the Industrial and Commercial Use of Energy (ICUE)

作者： Tangwe, S. Simon, M. Meyer, E. Univ Ft Hare Ft Hare Inst Technol Alice South Africa

ISBN: (纸本)9780992204167

The Industrial and Commercial sectors in South Africa consume more than 80% of the electricity generated by Eskom. The generation of electricity to meet the demand in these sectors is primarily derived from the coal thermal power plant. The goal of Eskom is to sustain the demand and equally reduce environmental pollution. Eskom has embarked on energy efficiency initiatives on their coal boiler plant in a bid to decrease the amount of coal burnt and in turn increase the electricity generated. The study focused on the analysis of the before and after outage data obtained from the unit cards in one of the Eskom's "once through" 600 MW coal boiler with a mechanical conversion efficiency of 35 % (from manufacturer specification). Multiple linear regression models were developed and built to predict the power generated and sent out in correlation with the predictors (average air heater temperature, average main stream super heater temperature, average high pressure heater temperature, the total mass of coal burnt, auxiliary power consumption, average of the cold well and hot well condenser temperature and pressure). The data obtained 3 months before and after an outage showed an average power generated of 434.95 MW and 502.08 MW respectively. The results also revealed that the cumulative energy gained was 44000 MWh. Finally, the relieff algorithm ranked all predictors as primary factors with the high pressure heater and main stream super heater temperatures contributing the most by virtue of the weight of importance to the power generated.

关键词： Energy efficiency initiatives Power generated Predictors Multiple linear regression models relieff algorithm once through boiler Eskom

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HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning

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INFORMATION SCIENCES 2013年 222卷 282-301页

作者： Zafra, Amelia Pechenizkiy, Mykola Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Eindhoven Univ Technol Dept Comp Sci NL-5600 MB Eindhoven Netherlands

Feature selection techniques have been successfully applied in many applications for making supervised learning more effective and efficient. These techniques have been widely used and studied in traditional supervised learning settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the label is known for the bag as a whole, but not for the individual instances it consists of. Therefore utilizing these labels for feature selection in MIL becomes less straightforward. In this paper we study a new feature subset selection method for MIL called HyDR-MI (hybrid dimensionality reduction method for multiple instance learning). The hybrid consists of the filter component based on an extension of the relieff algorithm developed for working with MIL and the wrapper component based on a genetic algorithm that optimizes the search for the best feature subset from a reduced set of features, output by the filter component. We conducted an extensive experimental evaluation of our method on five benchmark datasets and 17 classification algorithms for MIL The results of our study show the potential of the proposed hybrid with respect to the desirable effect it produces: a significant improvement of the predictive performance of-many MIL classification techniques as compared to the effect of filter-based feature selection. This is achieved due to the possibility to decide how many of the top ranked features are useful for each particular algorithm and the possibility to discard redundant attributes. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Multiple instance learning Feature selection relieff algorithm Genetic algorithm

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relieff Based Feature Selection In Content-Based Image Retrieval

ReliefF Based Feature Selection In Content-Based Image Retri...

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International MultiConference of Engineers and Computer Scientists (IMECS 2012)

作者： Sarrafzadeh, Abdolhossein Atabay, Habibollah Agh Pedram, Mir Mosen Shanbehzadeh, Jamshid Unitec Dept Comp Auckland New Zealand Tarbiat Moallem Univ Tehran Tehran Iran

ISBN: (纸本)9789881925114

content based image retrieval systems usually extract low level features to retrieve similar images. But in most cases, selection of suitable features according to their impact on the classification accuracy has been less considered. This paper studies the effects of reducing the number of features and selecting the most effective subset of features in the context of content-based image classification and retrieval of objects. We use Legendre moments to extract features, relieff algorithm to select the most relevant and non redundant features and support vector machine to classify images. The experimental results on Coil-20 image dataset, shows that by selecting much lower number of features when employing relieff, we can improve retrieval in terms of speed and accuracy.

关键词： Content Based Image Retrieval Feature Selection relieff algorithm Exact Legendre Moments Support Vector Machine

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