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IEEE International Conference on Systems, Man and Cybernetics

作者： Chen, Chao-Rong Huang, Kuo-Hua Teng, Hsiang-Chung Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan Chunghwa Telecom Co Taipei Taiwan

ISBN: (纸本)9781424400997

The capacity of lead-acid storage battery for communication system has been long estimated by constant current discharge method in the past. It spends a lot of time and labor and wastes more energy. This paper proposes a new method combining the measured data of battery discharge and the back-propagation neural network. After they are trained and learned, the back-propagation neural network can estimate the capacity of lead-acid storage battery after half hour discharge test. Therefore, the advantages of this paper are less discharge time of storage battery, less working hour and saving energy. The practical results show that the method has good performances.

关键词： back-propagation artificial neural network (BP ANN) battery capacity lead-acid storage battery

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Simulation of Micro-grid Energy Storage Inverter with neural network Control 29

Simulation of Micro-grid Energy Storage Inverter with Neural...

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第29届中国控制与决策会议

作者： Huaqing Zeng Changliang Liu Automation Department North China Electric Power University

ISBN: (纸本)9781509046584

The study for inverter control was researched under the simulation system of single deviation-grid micro-grid energy storage inverter, the inverter output voltage and current waveforms under various conditions were obtained. The inverse model of the micro-grid energy storage inverter was identified based on the algorithm of back-propagation artificial neural network, and then the neural network internal model control scheme based on the design of inverse system was adopted to improve the power quality of the inverter output. The control scheme of the micro-grid energy storage inverter was realized by the Matlab/simulink platform, a simulation study was carried out. Results show that the neural network internal model control scheme based on the design of the inverse system has a better effect on controlling inverter compared to the conventional PI controller, and can be applied in the control system design of the micro-grid energy storage inverter.

关键词： Micro-grid energy storage inverter Output voltage and current Inverse model back-propagation artificial neural network

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A novel non-reference image quality assessment algorithm 2

A novel non-reference image quality assessment algorithm

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2017 IEEE 2nd Information Technology, networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Yong Ma Yingyun Yang Yao Lyu Jiawei He Information Engineering School Communication University of China

ISBN: (纸本)9781509064151;9781509064144

Due to the poor performance of many nonreference evaluation algorithms for the TID2013 image library, it is a great challenge to evaluate non-reference image quality for such library. In this paper, a novel non-reference image quality assessment(NR-IQA) algorithm based on wavelet energy, structure and texture features and color signature is proposed. The wavelet energy is calculated by combining visual saliency map and four level wavelet transform. The textural and structural map are obtained by Rudin-Osher-Fatemi algorithm while the features of these are extracted by local binary pattern(LBP) method. Two types of characteristic parameters of distorted image are analyzed, among which one is acquired in gray-scale image and the other is obtained in two color channel(Cb and Cr). Finally, back propagation artificial neural network and Radial Basis Function neural network are used to learn the mapping between feature space and subjective opinion scores. Experimental results on two benchmark image quality databases show that the proposed method has highly competitive performance in the state-of-the-art NR-IQA theories, especially in TID2013 IQA database.

关键词： Non-reference image quality assessment wavelet energy texture features structure features back-propagation artificial neural network radial basis function neural network

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Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy

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METHODSX 2018年 5卷 890-908页

作者： Nain, Somvir Singh Sihag, Parveen Luthra, Sunil CMR Coll Engn & Technol Dept Mech Engn Ctr Excellence Mat & Mfg Hyderabad 501401 Telangana India Natl Inst Technol Dept Civil Engn Kurukshetra 136119 Haryana India State Engn Coll Dept Mech Engn Nilokheri Haryana India

The main purpose of this research is to check the relative importance of methods fuzzy-logic and back- propagation neural network to evaluate the performance of wire electric discharge machine (WEDM) of aeronautics super alloy. It has been confirmed that BP-ANN method reveals significant result over the fuzzy logic method for the evaluation of surface roughness and waviness of the WEDM of aeronautic super alloy. On the basis of Taguchi analysis, it has been established that the variable pulse-on, interaction amid the pulse-on and pulse-off time, wire tension and spark-gap voltage have a superlative influence on the surface roughness. The waviness is influenced prominently by pulse-on time, pulse-off time and spark-gap voltage. The thickness of recast layer is minimized up to 9.434 mu m. (C) 2018 Published by Elsevier B.V.

关键词： Wire electric discharge machining Fuzzy-logic back-propagation artificial neural network SEM Taguchi

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An Improved CFD-synthesis Method for Predicting Inlet Dynamic Distortion

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Chinese Journal of Aeronautics 2009年第5期22卷 467-473页

作者： Zhang Lifen Liu Zhenxia Qu Jiyun Wang Xiaofeng School of Power and Energy Northwestern Polytechnical University Xi 'an 710072 China Chinese Flight Test Establishment Xi'an 710089 China

This article presents an improved computational fluid dynamics （CFD）-synthesis method to predict dynamic distortion. A steady-state flow field is derived from a CFD solution, through which are acquired total pressure, density, turbulence kinetic energy and others in steady-state at an aerodynamic interface plane （AIP）. back-propagation artificial neural network （BP ANN） is used to find out the relationship between the measured flight turbulence and the CFD-computed turbulence parameters. The dynamic pressure is obtained by incorporating CFD-found total steady-state pressure with fluctuating pressure. Finally, the dynamic distortion is predicted by means of the synthesized dynamic pressure. The fairly good agreement between the computed inlet surface pressure and the flight test data bears out the reliability of CFD solution used in this article. To validate the proposed method, six sets of flight test data are used and the results show that the predicted dynamic distortion is well in line with the distortion displayed in flight tests. An examination of the traditional method is also accomplished and the comparison also shows that the proposed method is superior to the traditional one in higher consistency with flight test data.

关键词： computational fluid dynamics dynamic pressure synthesis turbulence correlation back-propagation artificial neural network dynamic distortion

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Effect of Meteorological Factors on Incidence of Tuberculosis:A 15-Year Retrospective Study Based on Chinese Medicine Theory of Five Circuits and Six Qi

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Chinese Journal of Integrative Medicine 2015年第10期21卷 751-758页

作者：张轩郝宇费占洋贺娟 School of Basic Medicial Science Beijing University of Chinese Medicine

ABSTRACT Objective： To analyze the correlations between the incidence of tuberculosis and meteorological factors over the same period and previous periods including 1, 2 and 3 years ago, defined according to the Chinese medicine theory of five circuits （Wu Yun） and six qi, to establish medical-meteorological forecast models for the Beijing area of China. Methods： Data regarding the incidence of tuberculosis between 1990 and 2004 were obtained from the Beijing Center for Disease Control and Prevention, and the data regarding the meteorological factors （including daily average temperatures, wind speeds, precipitations, relative humidities, vapor pressures and low cloud covers） between 1987 and 2004 were collected from the Beijing Meteorological Observatory and analyzed. Descriptive statistics and a back-propagation artificial neural network were adopted to analyze the data. Results： There were significant correlations between the incidence of tuberculosis and the meteorological factors in the corresponding year and previous years. Among these correlations, wind speed was the factor with the strongest influence on tuberculosis （the standardized significance was 100%）. Additionally, all prediction models would successfully established, suggesting the use of a collection of meteorological factors spanning from three years ago to the present is superior to the use of single data. Conclusions： The incidence of tuberculosis in Beijing area is correlated to meteorological factors in the current year and previous years, which verifies the practicality of the theory of five circuits and six qi.

关键词： tuberculosis meteorological factors Chinese medicine five circuits and six qi back-propagation artificial neural network Beijing

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Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

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Transactions of Nonferrous Metals Society of China 2021年第6期31卷 1665-1679页

作者： Shuang-xi SHI Xiu-sheng LIU Xiao-yong ZHANG Ke-chao ZHOU State Key Laboratory of Powder Metallurgy Central South UniversityChangsha 410083China Wuhan Research Institute of Materials Protection Wuhan 430030China Shenzhen Research Institute Central South UniversityShenzhen 518057China

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and *** stress−strain data extended by two models were implanted into finite element to simulate hot compression *** results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing *** the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of *** cross-validation tests also confirm that the BPANN model has high prediction *** models are effective and feasible in simulation,but BPANN model is superior in accuracy.

关键词： Ti-55511 alloy flow stress Arrhenius constitutive equation back-propagation artificial neural network finite element

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Rapid Soil Classification with Laser Induced Breakdown Spectroscopy

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SPECTROSCOPY AND SPECTRAL ANALYSIS 2017年第1期37卷 241-246页

作者： De-shuo, Meng Nan-jing, Zhao Ming-jun, Ma Yan-hong, Gu Yang, Yu Li, Fang Yuan-yuan, Wang Yao, Jia Wen-qing, Liu Jian-guo, Liu Chinese Acad Sci Anhui Inst Opt & Fine Mech Hefei 230031 Peoples R China

Soil classification is an important research content in soil science field. It is the basis of soil survey and resource evaluation which is important to agricultural production. There are many soil classification standards all over the world. China has two kinds of classifications including soil genetic classification and soil system classification. There are great differences between different types of soil elements, so it is feasible for soil classification to use laser induced breakdown spectroscopy. Laser induced breakdown spectroscopy (LIBS) is a new element analysis technology which uses a laser pulse with high energy density to ablate samples. LIBS has been used in many fields including environmental protection and industrial production control. It can directly reflect the difference of element content in different soils. The experimental setup including an Nd: YAG laser, a spectrometer, a computer and a rotating platform. In the experiment 7 kinds of soil (red soil, brick red soil, lateritic red soil, paddy soil, cinnamon, alluvial soil and alpine meadow soil) including 25 samples were used. All soil samples were grinded and sieved before the experiment. Under the same experimental condition, the temperatures of the plasma created by the laser pulses on the surface of the different soil samples have great differences. The lateritic red soil had the highest temperature, and the alpine meadow soil had the lowest. But it was not enough to form the basis for classification. Therefore six constant elements including Si, Fe, Al, Mg, Ca and Ti were selected and their spectral line intensity were treated as classification index. Principal component analysis (PCA) was used to simplify the classification process. The PCA method could simplify the 6 indexes to few independent indexes which could also reflect the spectral information of the 6 elements. The original spectral data was processed by Matlab. The process consisted of spectral background removal, characteristi

关键词： Laser induced breakdown spectroscopy Soil classification Principal component analysis back-propagation artificial neural network

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Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 209卷 106320-106320页

作者： Ferjaoui, Radhia Cherni, Mohamed Ali Boujnah, Sana Kraiem, Nour El Houda Kraiem, Tarek Univ Tunis El Manar Res Lab Biophys & Med Technol LRBTM ISTMT Tunis 1006 Tunisia Univ Tunis LR13 ES03 SIME Lab ENSIT Montfleury 1008 Tunisia Univ Tunis El Manar Natl Engn Sch Tunis Tunis Tunisia Hosp Aziza Othmana Tunis Radiol Dept Tunis 1006 Tunisia Univ Tunis El Manar Fac Med Tunis Tunis 1007 Tunisia

background: After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. Methods: The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test. Results: The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96,5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F-1-measure, and geometric-mean w

关键词： Evolutive lymphoma Residual masses Machine learning Deep learning back-propagation artificial neural network Support vector machine K-nearest neighbours Random forest Relevance vectors machine (RVM) Convolutional neural network (CNN)

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Vis/NIR Chemical Imaging Technique for Predicting Sodium Humate Contents in Aquaculture Environment

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WATER AIR AND SOIL POLLUTION 2017年第5期228卷 177-177页

作者： Qu, Jia-Huan Sun, Da-Wen Pu, Hongbin South China Univ Technol Sch Food Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Guangzhou Higher Educ Mega Ctr Acad Contemporary Food Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Higher Educ Mega Ctr Engn & Technol Res Ctr Guangdong Prov Intelligent Guangzhou 510006 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr FRCFT Dublin 4 Ireland

This study aimed to develop a visible and near-infrared (Vis/NIR) chemical imaging (400-1000 nm) technique to provide rapid prediction of the contents of sodium humate dissolved in aquaculture environment. Gray reference image with 5% reflectance value was first used to correct the obtained raw images in order to promote the reflectance values as compared to that with 99% reflectance for further spectral analysis. Successive projection algorithm (SPA) was introduced to extract four optimal wavelengths, which were then used for the establishment of back-propagation artificial neural network (BP-ANN) models. The results revealed that the BP-ANN model based on the selected four optimal wavelengths better performed (R-C(2) = 0.986, R-CV(2) = 0.985, R-P(2) = 0. 9 9 3, RMSEC = 0. 329 m g /L, RMSECV = 0.433, RMSEP = 0.734 mg/L) than that based on the whole 381 wavelengths (R-C(2) = 0.978, R-CV(2) = 0.996, R-P(2) = 0.977, RMSEC = 0.388 mg/L, RMSECV = 0.625, RMSEP = 0.734 mg/L). Finally, a series of chemical images were developed to clearly display the concentration distribution of the sodium humate dissolved in water, demonstrating that Vis/NIR chemical imaging technique was feasible to quantify the contents of sodium humate in the aquatic environment and could be further used for real-time monitoring the quality of aquaculture water.

关键词： Imaging spectroscopy Vis/NIR Humic Gray reference image Successive projections algorithm back-propagation artificial neural network

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