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Three-Phase Modular Multilevel Converter (MMC) for Low-voltage Applications: Improved Modulation Technique Toward Less Capacitance Requirement

Three-Phase Modular Multilevel Converter (MMC) for Low-volta...

引用

IEEE Conference on Power Electronics and Renewable Energy (IEEE CPERE)

作者： Younis, Tarek Mattavelli, Paolo Magnone, Paolo Toigo, Igino Corradin, Michele Univ Padua Dept Management & Engn Vicenza Italy Socomec Vicenza Italy

ISBN: (纸本)9781728109107

Modular Multilevel Converter (MMC) has become one of the promising topologies in medium and high power applications. Modularity, scalability, and reliability are the main features that give the MMC some advantages over other multilevel topologies when considering high number of levels. This paper presents an alternative modulation technique for a modified modular multilevel converter (M-MMC) topology for low-voltage applications. By using level-shifted carrier (LSC) modulation and the proposed sorting algorithm, the capacitance requirement for this topology can be reduced. Finally, the proposed modulation scheme and the reduction of capacitance are verified by simulation results using PLECS.

关键词： Modular multilevel converters (MMCs) renewable energy multilevel converters level-shifted carrier (LSC) low-voltage systems modulation sorting algorithm

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An Online System for algorithm Benchmarking 5

An Online System for Algorithm Benchmarking

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5th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Toth, Janos Kovacs, Laszlo Harangi, Balazs Kiss, Csaba Mohacsi, Andras Orosz, Zoltan Hajdu, Andras Univ Debrecen Inst Math POB 12 H-4010 Debrecen Hungary Univ Debrecen Fac Informat H-4010 Debrecen Hungary

We present a platform that will aid researchers in developing algorithms for specific (primarily image processing) tasks by providing training data sets with ground truth and with providing evaluation of the outputs o... 详细信息

ISBN: (纸本)9781479972807

关键词： image processing online algorithm benchmarking system training data sets Benchmark testing Classification algorithms Educational institutions Image processing Retinopathy Standards Training data Image processing Benchmark testing Training data sorting algorithm Educational institutions Retinopathy Online Systems Dataset

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Visual Appearance based Document Classification Methods: Performance Evaluation and Benchmarking 13

Visual Appearance based Document Classification Methods: Per...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Bukhari, Syed Saqib Dengel, Andreas German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany

ISBN: (纸本)9781479918058

Most of the traditional document image classification techniques concentrate on document segmentation and OCR analysis, in spite of so many complexities and limitations involved. Recently, many of the document image classification problems are easily solved just by adapting standard computer vision approaches for natural image retrieval and classification, that are referred as visual appearance based document classification techniques. These approaches have reported better results as compared to the traditional approaches on proprietary datasets. However, so far these approaches are not compared with each other and, despite having potential, they are not evaluated on distorted camera-captured documents, which is one of the challenging requirements in our present commercial document analysis projects. In this paper, we present simple and effective descriptions of different visual appearance based document image classification techniques. We compare their performance on various standard and publicly available datasets, that are differ in degree of image degradations and content variations. We also demonstrate their advantages and limitations. Additionally, we make the implemented versions of these method publicly available to research community for usage and further testing on other domains.

关键词： document image processing image classification image segmentation optical character recognition OCR analysis content variations document segmentation image degradations natural image classification natural image retrieval performance evaluation publicly available datasets standard computer vision visual appearance based document classification method algorithm design and analysis Chlorine Classification algorithms Distortion Image segmentation Optical character recognition software Programming image degradation optical character recognition softwares image segmentation algorithm design and analysis document image processing optical character recognition sorting algorithm Performance Evaluation Image classification abnormal shapes Chlorine Programming distortion document image BENCHMARKS

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Joint Signal Detection and Classification of Mobile WiMAX and LTE OFDM Signals for Cognitive Radio

Joint Signal Detection and Classification of Mobile WiMAX an...

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44th Asilomar Conference on Signals, Systems and Computers

作者： Al-Habashna, A. Dobre, O. A. Venkatesan, R. Popescu, D. C. Mem Univ Newfoundland Elect & Comp Engn St John NF A1C 5S7 Canada Old Dominion Univ Dept Elect & Comp Engn Norfolk VA USA

ISBN: (纸本)9781424497218

Spectrum awareness is one of the most challenging tasks in cognitive radio (CR). To adequately adapt to the changing radio environment, it is necessary for the CR to be able to perform joint detection and classification of low signal-to-noise ratio (SNR) signals without requiring much a priori information on the signal parameters. In this paper, we propose a joint detection and classification algorithm for the mobile Worldwide Interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE) signals. The algorithm has the advantage that it requires relaxed information on the signal parameters. Simulation results are presented, which show the efficiency of the proposed algorithm under diverse scenarios.

关键词： WiMax signal parameter Cognitive radio signal acquisition Long Term Evolution signal to noise ratios Local thermodynamic equilibrium sorting algorithm Joints Joints signals Mobile

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Application of Neural Networks in Early Detection and Diagnosis of Parkinson's Disease

Application of Neural Networks in Early Detection and Diagno...

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International Conference on Cyber and it Service Management (CITSM)

作者： Olanrewaju, Rashidah Funke Sahari, Nur Syarafina Musa, Aibinu A. Hakiem, Nashrul Int Islamic Univ Malaysia Dept Elect & Comp Engn Fac Engn Kuala Lumpur Malaysia Fed Univ Technol Minna Minna Niger State Nigeria UIN Syarif Hidayatullah Jakarta Fac Sci & Technol Dept Informat Engn South Tangerang Indonesia

ISBN: (纸本)9781479979738

Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using K-Means Clustering algorithm. The performance of this classifier was evaluated based on the three parameters;sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.

关键词： backpropagation diseases medical computing medical disorders multilayer perceptrons neurophysiology pattern clustering BP algorithm MLFNN Matlab software Oxford Parkinson's disease detection dataset PD detection PD diagnosis PD output Parkinson's disease detection Parkinson's disease diagnosis accuracy parameter back-propagation algorithm chemical dopamine chronic neurological progressive disorder healthy output k-means clustering algorithm multilayer feedforward neural network performance evaluation sensitivity parameter specificity parameter Accuracy Classification algorithms Clustering algorithms Diseases Neural networks Neurons Sensitivity Multilayer feedforward neural network MATLAB back propagation maladjustment Parkinson Disease Clustering algorithms medical computing medical disorders Neural network Multilayer perceptrons sorting algorithm Neurophysiology Cluster Analysis Performance Evaluation Early Diagnosis

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Embedded and Modular Video Processing Design Platform

Embedded and Modular Video Processing Design Platform

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IEEE International Conference on Electro/Information Technology (EIT)

作者： Gonnot, Thomas Saniie, Jafar IIT Dept Elect & Comp Engn Chicago IL 60616 USA

ISBN: (纸本)9781479947744

Research and industry increasingly rely on image processing to analyze an environment. Most image processing requires significant computing power and consequently a complex processing unit. This paper presents a hardware specific platform that computes common image processing algorithms and extracts the information from a video stream. By this arrangement, the image processing can be customized for a specific application, using the same embedded system. This platform also simplifies the development of image processing systems and algorithms by focusing on higher level algorithmic operations.

关键词： algorithm design and analysis Cameras Classification algorithms Colored noise Feature extraction Image color analysis algorithm design and analysis image colour analysis Colored noise sorting algorithm Cameras Feature extraction Image processing Process design Image processing systems Platform

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Mapping tree cover in European cities: comparison of classification algorithms for an operational production framework

Mapping tree cover in European cities: comparison of classif...

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Joint Urban Remote Sensing Event

作者： Lefebvre, Antoine Picand, Pierre-Antoine Sannier, Christophe SIRS Parc Cimaise Villeneuve Dascq France

ISBN: (纸本)9781479966523

In the framework of the Urban Atlas 2012 production, this paper investigated a set of generative models (Maximum likelihood, k-means) and discriminative models (k Nearest Neighbors, Support Vector Machine and Neural Network) to extract urban-tree cover at a European scale. Based on SPOT-5 images and a training on a large coarse resolution dataset, this study tested the performance of these algorithms on 3 cities regarding their geographical location, urban morphology and acquisition dates. Result reveals that discriminative models are more robust than generative ones. It shows that overall accuracy varies from 75% for the k-means classifier to 85% for the neural network. It also shows that neural networks provide the most balanced results (ratio between commission and omission errors) leading to be most suitable algorithm to process different cities with heterogeneous data.

关键词： Canopies Neural network city sorting algorithm Frameworks

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CONSTRUCTION ACTIVITY RECOGNITION FOR SIMULATION INPUT MODELING USING MACHINE LEARNING CLASSIFIERS

CONSTRUCTION ACTIVITY RECOGNITION FOR SIMULATION INPUT MODEL...

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Winter Simulation Conference

作者： Akhavian, Reza Behzadan, Amir H. Univ Cent Florida 4000 Cent Florida Blvd Orlando FL 32816 USA

ISBN: (纸本)9781479974863

Despite recent advancements, the time, skill, and monetary investment necessary for hardware setup and calibration are still major prohibitive factors in field data sensing. The presented research is an effort to alleviate this problem by exploring whether built-in mobile sensors such as global positioning system (GPS), accelerometer, and gyroscope can be used as ubiquitous data collection and transmission nodes to extract activity durations for construction simulation input modeling. Collected sensory data are classified using machine learning algorithms for detecting various construction equipment actions. The ability of the designed methodology in correctly detecting and classifying equipment actions was validated using sensory data collected from a front-end loader. Ultimately, the developed algorithms can supplement conventional simulation input modeling by providing knowledge such as activity durations and precedence, and site layout. The resulting data-driven simulations will be more reliable and can improve the quality and timeliness of operational decisions.

关键词： civil engineering computing construction equipment digital simulation learning (artificial intelligence) loading equipment pattern classification GPS accelerometer built-in mobile sensors construction activity recognition construction equipment action classification construction equipment action detection construction simulation input modeling field data sensing front-end loader global positioning system gyroscope machine learning classifiers ubiquitous data collection nodes ubiquitous data transmission nodes Accelerometers Classification algorithms Data models Feature extraction Global Positioning System Gyroscopes Sensors Global Positioning Systems Gyroscopes accelerometers Front end loaders loading equipment Construction equipment sorting algorithm data models Digital simulation Computer Simulation Pattern recognition Feature extraction Sensors

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State Estimation for Switched linear systems 12

State Estimation for Switched linear systems

引用

International Multi-Conference on Systems, Signals and Devices (SSD)

作者： Sellami, Lamaa Abderrahim, Kamel Univ Gabes Res Unit Numer Control Ind Proc Natl Engn Sch Gabes Gabes 6029 Tunisia

ISBN: (纸本)9781479917587

In this paper, we address the problem of state estimation of linear switched discrete time models from a finite set of input-output data. This is a challenging problem since it requires estimating the active discrete state and its continuous observer. In fact, we propose a hybrid state observer design which consists of two stages. The first allows to determine the active discrete state using a classification algorithm that associated the current data to its appropriate submodel. The second stage is used to estimate the corresponding continuous observer. Simulation results are presented to illustrate the performance of the proposed method.

关键词： State estimation Linear system sorting algorithm Current data input/output data submodel

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Modular Multilevel Converter for Multifunctional Battery Management System of Electric Vehicle 44

Modular Multilevel Converter for Multifunctional Battery Man...

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44th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Sangiri, Jeemut Bahan Rajesh, V Chattopadhyay, Sumit K. Chakraborty, Chandan IIT Kharagpur Sch Energy Sci & Engn Kharagpur W Bengal India IIT Kharagpur Dept Elect Engn Kharagpur W Bengal India IIT Delhi Ctr Energy Studies New Delhi India

ISBN: (纸本)9781509066841

Modular multilevel converter (MMC) has become a promising technology for medium/high voltage high power applications. Electric vehicles on the other hand, have a large number of series connected battery cells to enhance the output voltage to drive the connected motor. This paper applies the concept of modular multilevel converter for low power electric vehicle applications where, the batteries of the EV help in exchanging power with the motor. The batteries can discharge when feeding power to the motor and get charged when the motor runs into regenerative mode. Multi-carrier PWM technique is employed to generate the multilevel voltage waveforms and sorting technique is used to balance the battery voltages. Simulation study has been done in MATLAB/SIMULINK platform and it is observed that the voltages and State-of-Charge (SOC) of batteries are well maintained.

关键词： Modular multilevel converter nearest level control sorting algorithm battery voltage balancing phase-shifted carrier PWM battery management system electric vehicle charger

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