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IEEE International Ultrasonics Symposium (IUS)

作者： Li, Qing Tran, Tho N. H. T. Guo, Jialin Xu, Kailiang Li, Boyi Le, Lawrence H. Ta, Dean Fudan Univ Ctr Biomed Engn Shanghai Peoples R China Fudan Univ Acad Engn & Technol Inst Biomed Engn & Technol Shanghai Peoples R China Shanghai First Matern & Infant Hosp Shanghai Peoples R China Univ Alberta Dept Radiol & Diagnost Imaging Edmonton AB Canada

ISBN: (数字)9781665466578

ISBN: (纸本)9781665466578

Bone health assessment is commonly used to evaluate skeletal development and maturation and to diagnose neonatal growth disorders. Implementing an automated ultrasonic system for pediatric bone evaluation, which is radiation-free and cost-effective, to complement conventional radiography is significant for clinical applications. This study mainly proposes a method, which combines the relieff algorithm with fuzzy C-means clustering, for fully automatic detection of wave arrival time and estimation of ultrasonic velocity from axial transmission waveforms measured from the bone of infants without the need for signal preprocessing. The results of in-vivo velocity measurement show good consistency with the ground truth modality, indicating the feasibility of the proposed method for further clinical studies and usages.

关键词： bone quantitative ultrasound axial transmission speed of sound fuzzy C-means clustering relieff algorithm

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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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Disease identification method based on graph features between pulse cycles

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期83卷 000-000页

作者： Fan, Lin Shi, Xuemei Wang, Zhongmin Zhang, Rong Zhang, Jie Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China 618 West Changan St Xian 710121 Shaanxi Peoples R China

The pulse signal at the wrist communicates a range of cardiovascular information and reflects a person's state of health. In recent years pulse signals have been used extensively in the field of disease identification. As a typical quasi-cyclic physiological signal, many researchers have simply used single-cycle pulse signals for disease diagnosis, ignoring the influence of inter-cycle differences on the diagnosis. In this paper, we propose a disease identification method based on graph features between pulse cycles, to focus on the differences in pulse cycles brought about by the disease. The pulse was pre-processing using a Sliding Window, followed by the construction of the correlation coefficient matrix, the setting of a threshold and the construction of the connection graph to obtain the graph features;the Multiscale Permutation Entropy method was used to extract the pulse's entropy features. The relieff algorithm is then used to select a total of 18 features and a classification algorithm is used to distinguish between patients and healthy people. Both the self-constructed dataset and the open dataset from the PLA 211 Hospital had high recognition rates for the experiments. The accuracy of identifying hypertension was 94.16% with a precision of 90.41%;the accuracy of identifying chronic diseases was 97.23% with a precision of 97.81%. The experimental results from the two datasets demonstrate both the validity of the difference between cycles for disease classification and the applicability of the method proposed in this paper.

关键词： Traditional Chinese medicine Wrist pulse Graph features Multiscale permutation entropy relieff algorithm

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An Industrial Process Monitoring Method Based on Entropy Projection Transformation Analysis

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Yang, Yinghua Kong, Qingyan Chao, Zhipeng Li, Peihong Liu, Xiaozhi Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

The feature extraction and selection of process variables and the calculation of monitoring statistics are inevitable problems in industrial process monitoring. A new process detection method is proposed in this article that makes full use of the historical information of process variables for feature extraction. First, a projection transformation grid is proposed for the feature extraction and dimensionality reduction of process variables. Using historical data to construct a joint matrix and paying attention to the correlation between process variables and the joint matrix, an entropy projection transformation component (EPTC) is extracted, and the feature weight is calculated to complete the extraction of the process variable information. On this basis, the feature differences of neighboring samples are further evaluated to select the optimal information entropy. In addition, a statistic based on smooth relative entropy (SRE) and the corresponding threshold calculation method are proposed, improving the model's sensitivity to early fault monitoring. Experiments demonstrate that incorporating optimal information entropy into industrial process monitoring can improve the reliability of fault detection compared with a traditional process monitoring model.

关键词： Entropy Feature extraction Process monitoring Probability Kernel Production Principal component analysis Entropy projection transformation components (EPTCs) feature exaction Kullback-Leibler divergence (KLD) process monitoring relieff algorithm

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SVM-Based Feature Selection for Differential Space Fusion and Its Application to Diabetic Fundus Image Classification

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IEEE ACCESS 2019年 7卷 149493-149502页

作者： Ding, Weiping Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Data mining is one of the most important applications of machine learning. In machine learning algorithms, the fusion kernel principle component analysis (KPCA) and support vector machine (SVM) algorithm is used in complex data classification. To solve the problem that the fusion KPCA and SVM algorithm does not have promising classification performance, the SVM based on a feature selection algorithm for differential space fusion (DSF-FS) is proposed. First, the original data is processed to obtain differential space data by principle component analysis (PCA), and the KPCA algorithm is performed respectively on the original data and differential space data to get the differential space fusion features. Second, the relieff algorithm is used to get the weight of features, and the optimal feature combination is selected by a preliminary classification evaluation metric. Third, the SVM algorithm is used to classify the dimensionality reduction data. Finally, some experimental results on the five UCI datasets show that the proposed DSF-FS algorithm can not only improve the classification accuracy, but it can also reduce the computational complexity of the classification process. Moreover, the DSF-FS algorithm can be successfully applied in diabetic fundus image classification, and the encouraging results further demonstrate its strong feasibility and applicability.

关键词： Classification algorithms Support vector machines Feature extraction Kernel Data mining Machine learning algorithms Principal component analysis Kernel principle component analysis SVM differential space data relieff algorithm diabetic fundus image classification

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Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 195卷 105535-105535页

作者： Aydin, Eda Akman Gazi Univ Fac Technol Dept Elect & Elect Engn TR-06500 Ankara Turkey

Background and Objective: Brain-computer interfaces (BCIs) enable people to control an external device by analyzing the brain's neural activity. Functional near-infrared spectroscopy (fNIRS), which is an emerging optical imaging technique, is frequently used in non-invasive BCIs. Determining the subject-specific features is an important concern in enhancing the classification accuracy as well as reducing the complexity of fNIRS based BCI systems. In this study, the effectiveness of subject-specific feature selection on classification accuracy of fNIRS signals is examined. Methods: In order to determine the subject-specific optimal feature subsets, stepwise regression analysis based on sequential feature selection (SWR-SFS) and relieff methods were employed. Feature selection is applied on time-domain features of fNIRS signals such as mean, slope, peak, skewness and kurtosis values of signals. Linear discriminant analysis, k nearest neighborhood and support vector machines are employed to evaluate the performance of the selected feature subsets. The proposed techniques are validated on benchmark motor imagery (MI) and mental arithmetic (MA) based fNIRS datasets collected from 29 healthy subjects. Results: Both SWR-SFS and relieff feature selection methods have significantly improved the classification accuracy. However, the best results (88.67% (HbR) and 86.43% (HbO) for MA dataset and 77.01% (HbR) and 71.32% (HbO) for MI dataset) were achieved using SWR-SFS while feature selection provided extremely high feature reduction rates (89.50% (HbR) and 93.99% (HbO) for MA dataset and 94.04% (HbR) and 97.73% (HbO) for MI dataset). Conclusions: The results of the study indicate that employing feature selection improves both MA and MI-based fNIRS signals classification performance significantly. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Near-infrared spectroscopy Mental arithmetic Motor imagery Brain-computer interfaces Feature selection Stepwise regression analysis relieff algorithm

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An intelligent fault diagnosis method for PV arrays based on an improved rotation forest algorithm

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Energy Procedia 2019年 158卷 6132-6138页

作者： Fuchang Han Zhicong Chen Lijun Wu Chao Long Jinling Yu Peijie Lin Shuying Cheng College of Physics and Information Engineering Fuzhou University 2 XueYuan Road 350116 Fuzhou China Institute of Energy School of Engineering Cardiff University Cardiff CF24 3AA UK

With the exponential growth of global photovoltaic (PV) power capacity, it is essential to monitor, detect and diagnose the faults in PV arrays for optimal operation. This paper presents an improved rotation forest (RoF) algorithm classifiers ensemble hybridized with extreme learning machine (ELM) for fault diagnosis of PV arrays, which mainly consists of feature selection and classification. In the feature selection step, all the attributes are ranked by the relieff algorithm and the top-ranked attributes are chosen to create the new training data subset. In the classification step, the base classifier decision tree of the RoF is replaced by the extreme learning machine to form a new hybrid RoF-ELM ensemble classifier. In the RoF-ELM algorithm, the feature space is first split into several subspaces and the best number of feature subsets is found through the traversal search method. Then, the bootstrap algorithm is employed to carry out bootstrap resampling for each feature subspace, and the principal component analysis (PCA) is then used to transform the resampled samples. Finally, the ELM base classifier is exploited to build each classification model and the final decision is determined by the simple voting approach. By combining the RoF ensemble method with the ELM classifier, the proposed RoF-ELM algorithm not only overcomes the overfitting problem of the basic RoF algorithm, but also improves the generalization ability of the basic ELM. In order to experimentally verify the proposed approach, different types and levels of faults have been created in a laboratory small scale grid-connected PV power system to obtain the fault data samples. Experimental results demonstrate that the RoF-ELM can achieve higher diagnosis accuracy and reliability compared to the basic RoF and ELM algorithms.

关键词： PV arrays Fault diagnosis relieff algorithm Rotation Forest Extreme learning machine

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Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

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BMC PULMONARY MEDICINE 2021年第1期21卷 321-321页

作者： Yu, Hui Zhao, Jing Liu, Dongyi Chen, Zhen Sun, Jinglai Zhao, Xiaoyun Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China Tianjin Univ Dept Biomed Engn Tianjin Key Lab Biomed Detecting Tech & Instrumen Tianjin 300072 Peoples R China Tianjin Univ Chest Hosp Tianjin 300051 Peoples R China

Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people's health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. Results This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert-Huang transform. And then, channels and features were screened by the relieff algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. Conclusion This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.

关键词： Hilbert-Huang tansform relieff algorithm RespiratoryDatabase @ TR Chronic obstructive pulmonary disease

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