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HYDRODYNAMIC ANALYSIS OF SHORELINE OWC TYPE WAVE ENERGY CONVERTERS

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Journal of Hydrodynamics 2002年第1期14卷 8-15页

作者： Wang, Dong-Jiao Katory, Mahmoud Bakountouzis, lambros Dept. of Naval Arch. and Ocean Eng. South China Univ. of Technol. Guangzhou 510640 China

A numerical model to predict the hydrodynamic performance of a shoreline-mounted wave energy converter, normally referred to as Oscillating Water Column (OWC), was established. Based on the 3D boundary integral equation method, the 3D Green's function was utilized to describe the hydrodynamic flow around the device. Using geometrical data of a wave absorber device symmetrically placed in a channel with various barrier depths, the hydrodynamic efficiencies were calculated for regular waves. The results were then compared with analytical results published elsewhere Malmo[8]. And the comparison is shown to be good, thus validating the proposed numerical model. Furthermore, extensive experiments were also made in a wave tank with a 1:12 scale model of an OWC device of different geometrical configurations. The experimental measurements were compared with calculations using the newly developed numerical model. The comparison, which is also shown to be satisfactory, provides further support of the correctness and the accuracy of the numerical method presented.

关键词： Hydrodynamics Mathematical models

Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

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Computer Modeling in Engineering & Sciences 2020年第5期123卷 631-646页

作者： Caining Zhang Xiaopeng Guo Xiaoya Guo David Molony Huaguang Li Habib Samady Don PGiddens lambros Athanasiou Dalin Tang Rencan Nie Jinde Cao School of Biological Science and Medical Engineering Southeast UniversityNanjing210096China School of Information Science and Engineering Yunnan UniversityKunming650091China School of Science Nanjing University of Posts and TelecommunicationsNanjing210023China Department of Medicine Emory University School of MedicineAtlantaGA30307USA The Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of TechnologyAtlantaGA30332USA Institute for Medical Engineering and Science Massachusetts Institute of TechnologyCambridgeMA02139USA Mathematical Sciences Department Worcester Polytechnic InstituteWorcesterMA 01609USA School of Mathematics Southeast UniversityNanjing210096China

Optical coherence tomography(OCT)is a new intravascular imaging technique with high resolution and could provide accurate morphological information for plaques in coronary ***,its segmentation is still commonly performed manually by experts which is *** aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network(CNN)with U-Net architecture,CNN with Fully convolutional DenseNet(FC-DenseNet)architecture and support vector machine(SVM).In vivo OCT and intravascular ultrasound(IVUS)images were acquired from two patients at Emory University with informed consent *** OCT image slices which included lipid core and with acceptable image quality were selected for our *** segmentation from imaging experts was used as the gold standard for model training and *** OCT has limited penetration,virtual histology IVUS was combined with OCT data to improve reliability.A 3-fold cross-validation method was used for model training and *** overall tissue classification accuracy for the 18 slices studied(total classification database sample size was 8580096 pixels)was 96.36%and 92.72%for U-Net and FC-DenseNet,*** best average prediction accuracy for lipid was 91.29%based on SVM,compared to 82.84%and 78.91%from U-Net and FC-DenseNet,*** overall average accuracy(Acc)differentiating lipid and fibrous tissue were 95.58%,92.33%and 81.84%for U-Net,FC-DenseNet and SVM,*** average errors of U-Net,FC-DenseNet and SVM from the 18 slices for cap thickness quantification were 8.83%,10.71%and 15.85%.The average relative errors of minimum cap thickness from 18 slices of U-Net,FC-DenseNet and SVM were 17.46%,13.06%and 22.20%,*** conclude,CNN-based segmentation methods can better characterize plaque compositions and quantify plaque cap thickness on

关键词： Image segmentation plaque cap thickness OCT CNN SVM

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Master-Slave Islanding Method

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Journal of Power and Energy Engineering 2016年第10期4卷 1-24页

作者： lambros Ekonomou George P. Fotis Vasiliki Vita Valeri Mladenov Department of Electrical and Electronic Engineering Educators A.S.PE.T.E.—School of Pedagogical and Technological Education Athens Greece Department of Theoretical Electrical Engineering Technical University of Sofia Sofia Bulgaria

This study aims to address the feasibility of planned islanding operation and to investigate the effect of unplanned islanding using the master-slave islanding method for controlling the distributed generation units during grid-connected and islanding operation. Neplan desktop power simulation tool was used for the modelling and simulation of a realistic MV network with four different distributed generation technologies (diesel, gas, hydro and wind) along with their excitation and governor control systems, while an exponential model was used to represent the loads in the network. The dynamic and steady state behavior of the four distributed generation technologies were investigated during grid-connected operation and two transition modes to the islanding situation, planned and unplanned. The obtained results that validated through various case studies have shown that a suitable planned islanding transition could provide support to critical loads at the event of electricity utility outages.

关键词： Distributed Generation Distribution Networks Islanding Master-Slave Islanding Method Neplan Simulation Tool

Automatic Segmentation for Intracoronary OCT Image Based on Convolutional Neural Network and Support Vector Machine Methods

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医用生物力学 2019年第A1期34卷 95-96页

作者： Caining Zhang Huaguang Li Xiaoya Guo David Molony Xiaopeng Guo Habib Samady Don PGiddens lambros Athanasiou Rencan Nie Jinde Cao Dalin Tang School of Biological Science&Medical Engineering Southeast UniversityNanjing210096China School of Information Science&Engineering Yunnan UniversityKunming650091China School of Mathe matics Southeast UniversityNanjing210096China Department of Medicine Emory University School of MedicineAtlantaGA30307USA The Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of TechnologyAtlantaGA30332USA Institute for Medical Engineering&Science Massachusetts Institute of TechnologyCambridgeMA02139USA Mathematical Sciences Department Worcester Polytechnic InstituteWorcesterMA01609USA

Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and *** medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited ***,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary *** particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable ***,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and *** overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive *** this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque *** In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent *** were obtained from ILUMIEN OPTIS System(***,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/*** the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/***-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation *** OCT segmentation was performed by experts and used as gold standard in the auto

关键词： atherosclerotic plaques OCT CNN SVM image segmentation

Muscle Aerobic Capacity and Filleting Yield of Farmed Fish Species in the Mediterranean Sea

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Journal of Agricultural Science and Technology(B) 2013年第9期3卷 685-688页

作者： Cosmas Nathanailides lambros Kokokiris Costas Karipoglou Grigorios Kanlis Panagiotis Logothetis Ioannis Mittakos Dimitrios Lenas Department of Aquaculture & Fisheries Technological Educational Institute of Epirus Igoumenitsa Greece Department of Fisheries Technology and Aquaculture Alexander Technological Educational Institute of Thessaloniki Greece

In the present work, we compared indices of metabolism and feed conversion efficiency of a semi benthic and two pelagic mari-cultured fish species in the Mediterranean Sea. The metabolism of fish species varies according to habitat and ecophysiological adaptations. For example, with increasing depth, some species may exhibit adaptations to lower temperature and hypoxic conditions with a reduction in mass specific metabolic rate. Meagre （Argyrosomus regius） is a semi-benthic fish found at depths between 30 m and 300 m. Sea bass （Dicentrarhus labrax） and gilthead sea bream （Sparus auratus） are semi-pelagic fish species widely cultivated in the Mediteranean Sea. The aerobic metabolic potential and feed conversion efficiency of meagre were very different from the sea bass and gilthead sea bream. Compared to these two semic-pelagic fish species, meagre exhibited less efficient feed conversion rate, fillet yield. The results provide an estimate of the quantity of fish required to produce 1 kg of fillet weight, an estimation which is required for the operational management of fish processing companies and for estimating the wastes of fish processing factories during filleting.

关键词： Aquaculture metabolism filleting yield sea bass sea bream meagre.