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Direct AC charging of EV Reconfigurable Cascaded Multilevel Converter

Direct AC charging of EV Reconfigurable Cascaded Multilevel ...

14th Annual IEEE Energy Conversion Congress and Exposition (IEEE ECCE) / IEEE-Industry-Applications-Society (IEEE-IAS) Annual Meeting

作者： Tresca, Giulia Formentini, Andrea Granata, Samuele Leuzzi, Riccardo Zanchetta, Pericle Elect Comp & Biomed Engn Dept Pavia Italy Elect Elect Telecommun Engn & Naval Architect Dep Genoa Italy Elect & Elect Engn Dept Nottingham England

ISBN: (数字)9781728193878

ISBN: (纸本)9781728193878

This paper presents the Reconfigurable Cascaded Multilevel Converter (RCMC), employed for EV powertrain applications, in charging configuration. The converter is directly connected to an AC three-phase power system and the battery modules are charged by dynamically controlling the reconfigurable battery modules. The intrinsic structure of the converter gives the advantage to implement different charging algorithms, according to customizable requirements, without the need of extra middle power stages. Two charging methods are proposed and explained in detail: the first one prioritizes the SOC balancing between the battery modules, the second one the power losses reduction. Finally, a charging time estimation is computed for the RCMC and for a classic battery pack, assuming to increase their initial state of charge of 20%. The computation results show that the Reconfigurable Cascaded Multilevel Converter requires 40% less time than the battery pack.

关键词： AC Charging Charging Time Electric Vehicle Filter design Multilevel converter Reconfigurable Cascaded Multilevel Converter sorting algorithm

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Insertion Sort Correction of Two-Way Merge Sort algorithm for Balancing Capacitor Voltages in MMC with Reduced Computational Load 8

Insertion Sort Correction of Two-Way Merge Sort Algorithm fo...

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8th IEEE International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)

作者： Zhao, Fangzhou Xiao, Guochun Song, Zhishun Peng, Cheng Xi An Jiao Tong Univ Sch Elect Engn Xian Shanxi Peoples R China Xian XD Power Syst Co Ltd Xian Shanxi Peoples R China

ISBN: (纸本)9781509012107

The Modular Multilevel Converter (MMC) is very suitable for high-power applications like High-Voltage Direct Current (HVDC) transmission system, in which the number of submodules is commonly very large to reach high voltage. Therefore, the sorting algorithm of the conventional voltage balancing strategy causes too much computational load, greatly increasing the hardware cost and challenging the controller design. This paper introduces a two-way merge sort that evidently reduces computation weight and accelerates the sorting, combining with the prediction of capacitor voltages in ideal conditions. Furthermore, to solve the issue of non-ideal conditions in practical applications, this paper proposes an insertion sort correction algorithm with little and controllable computational load on the basis of the two-way merge sort. The proposed sorting algorithm takes advantage of the features of MMC control strategy, and consequently is much faster than conventional sorting such as quicksort. 401-level MMC simulation results in PSCAD/EMTDC show the validity of the proposed sorting algorithms, and the reduced computational load with shorter execution time is verified by DSP controller TMS320F28335.

关键词： Modular multilevel converter capacitor voltage balancing sorting algorithm two-way merge sort insertion sort

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Classification of AF and Other Arrhythmias from a Short Segment of ECG Using Dynamic Time Warping 44

Classification of AF and Other Arrhythmias from a Short Segm...

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44th Computing in Cardiology Conference (CinC)

作者： Tziakouri, Maria Pitris, Costas Orphanidou, Christina Univ Cyprus Dept Elect & Comp Engn 1 Panepistimiou Ave2109 AglanziaPOB 20537 CY-1678 Nicosia Cyprus Univ Cyprus Kios Res & Innovat Ctr Excellence Nicosia Cyprus Oxygen Res Ltd Limassol Cyprus

ISBN: (纸本)9781538645550;9781538666302

Atrial Fibrillation(AF) is a major public health risk but its identification is challenging because it may be episodic and non-symptomatic. Automatically identifying episodes of AF from a short segment of ECG would, thus, be beneficial. As a response to the Physionet/Computing in Cardiology Challenge 2017 we have implemented a three-stage classifier which can classify segments of ECG into Noisy, Normal, AF or Other Rhythm. We employ a state-of-the-art SQI for identifying noisy segments and then learn two different Support Vector Machine (SVM) classifiers using features extracted from the ECG. The features used are derived using a template matching approach via Dynamic Time Warping and using the statistical characteristics of the R-R intervals. Our average F1 score on the validation set was 0.66.

关键词： Electrocardiography Rhythm Feature extraction Support vector machines Classification algorithms Cardiology Noise measurement Electrocardiography Support Vector Network Noise measurement dynamic time warping sorting algorithm electrochemical grinding Rhythms Cardiology Feature extraction air feed equipment TEST SETS

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High-Accuracy Recognition of Muscle Activation Patterns Using a Hierarchical Classifier 5

High-Accuracy Recognition of Muscle Activation Patterns Usin...

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5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Rivera, Luis A. Smith, Nicholas R. DeSouza, Guilherme N. Univ Missouri Dept Elect & Comp Engn Vis Guided & Intelligent Robot Lab Columbia MO 65211 USA

ISBN: (纸本)9781479931262

Systems based on Surface Electromyography (sEMG) signals require some form of machine learning algorithm for recognition and classification of specific patterns of muscle activity. These algorithms vary in terms of the number of signals, feature selection, and the classification algorithm used. In our previous work, a technique for recognizing muscle patterns using a single sEMG signal, called Guided Under-determined Source Signal Separation (GUSSS), was introduced. This technique relied on a very small number of features to achieve good classification accuracies for a small number of gestures. In this paper, an enhanced version called Hierarchical GUSSS (HiGUSSS) was developed to allow for the classification of a large number of hand gestures while preserving a high classification accuracy.

关键词： Accuracy Feature extraction Muscles Sensors Support vector machines Training Vectors muscles Feature extraction Support Vector Network Sensors Functional training Gestures sorting algorithm Machine learning algorithms SEMA5B gene Recognition (Psychology) signals Training Cloning Vectors Hierarchical

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Multi-sensor and multi-platform data fusion for buried objects detection and localization 9

Multi-sensor and multi-platform data fusion for buried objec...

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9th IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Prado, Jose Marques, Lino Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn P-3030290 Coimbra Portugal

ISBN: (纸本)9781467369916

This paper describes a multi-sensor data-fusion approach to detect and localize landmines and unexploded ordnances (UXO) in a field using multiple mobile sensor carriers covering the area of interest. A procedure for high-accuracy geo-referencing the field-data acquired by multiple platforms is proposed. Later, the geo-referenced sensor data is fused by a multi-step algorithm consisting on the detection of heterogeneities corresponding to objects followed by the classification of those objects through a decision-level fusion. The proposed approach was tested and validated in a military field containing more than twenty different objects buried on the ground, using two different metal detector (MD) arrays and one ground penetrating radar (GPR) array. Although the preliminary results can be considered encouraging, since it was possible to detect and classify all the buried objects, further tests are necessary in terms of classification, since the classification algorithm was trained with data collected previously from the same soil.

关键词： Bayesian classification Field robotics Multi-sensor data fusion Robotics demining multi-sensor data fusion multi sensor Buried object detection underground sorting algorithm platform-independent Sensor Data ANTITANK MINES Data fusion data integration ground penetrating radar

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Association Rules Mining with Categories and Regular Expressions based Pruning 11

Association Rules Mining with Categories and Regular Express...

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11th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Slavescu, Radu Razvan Persa, Raul Alexandru Slavescu, Kinga Cristina Tech Univ Cluj Napoca Dept Comp Sci Baritiu 28 RO-400027 Cluj Napoca Romania Iuliu Hatieganu Univ Med & Pharm Pediat Clin 2 RO-400027 Cluj Napoca Romania

ISBN: (纸本)9781467382007

We present an extended version of the well known Apriori algorithm used for mining association rules. Its novelty consists in employing regular expressions in order to specify the form of the rules intended to be generated. These are further used for pruning in the rule generation step, thus speeding up the process. We show that the new version of the algorithm was used for generating medical hypotheses for some problems in the field without risking to loose generality.

关键词： data mining Apriori algorithm association rules mining expressions based pruning Association rules Classification algorithms Correlation Diseases Itemsets Yttrium Association rules Itemsets sorting algorithm Rare earths yttrium data mining tree surgery maladjustment mining learning (artificial intelligence) Information Extraction

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DUAL PIVOT QUICKSORT

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DISCRETE MATHEMATICS algorithmS AND APPLICATIONS 2012年第3期4卷 1250041-1-1250041-13页

作者： Iliopoulos, Vasileios Penman, David B. Univ Essex Dept Math Sci Wivenhoe Pk Colchester CO4 3SQ Essex England

In this paper, we analyze the dual pivot Quicksort, a variant of the standard Quicksort algorithm, in which two pivots are used for the partitioning of the array. We are solving recurrences of the expected number of key comparisons and exchanges performed by the algorithm, obtaining the exact and asymptotic total average values contributing to its time complexity. Further, we compute the average number of partitioning stages and the variance of the number of key comparisons. In terms of mean values, dual pivot Quicksort does not appear to be faster than ordinary algorithm.

关键词： Quicksort sorting algorithm

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Classification with a reject option under Concept Drift: the Droplets algorithm

Classification with a reject option under Concept Drift: the...

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Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2015)

作者： Loeffel, Pierre-Xavier Marsala, Christophe Detyniecki, Marcin UPMC Univ Paris 06 Sorbonne Univ CNRS LIP6 UMR 7606 4 Pl Jussieu F-75005 Paris France Polish Acad Sci IBS PAN Warsaw Poland

ISBN: (纸本)9781467382731

In this paper a new on-line algorithm is proposed (the Droplets algorithm) for dealing with concept drifts and to produce reliable predictions. The two main characteristics of this algorithm are that it is able to adapt to different types of drifts without making any assumptions regarding their type or when they occur, and can provide reliable predictions in a non-stationary environment without using a fixed confidence threshold. Experimental results on five datasets based on Random RBF and Rotating Hyperplane generators as well as a new semi-synthetic dataset based weather temperatures show that, by discarding difficult observations, the Droplets algorithm manages to obtain the best average accuracy against ten classifiers. The results also indicate that the algorithm manages to provide reliable prediction by accurately distinguishing which observations are easily classifiable.

关键词： algorithm design and analysis Bagging Classification algorithms Indexes Machine learning algorithms Prediction algorithms Reliability algorithm design and analysis Machine learning algorithms Prediction algorithms sorting algorithm Concept Drift bagging PURSE Confidence Threshold droplets

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Performance of Adaptive RAT Selection algorithms in 5G Heterogeneous Wireless Networks 26

Performance of Adaptive RAT Selection Algorithms in 5G Heter...

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26th International Telecommunication Networks and Applications Conference (ITNAC)

作者： Nguyen, Duong D. Nguyen, Hung X. White, Langford B. Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia Univ Adelaide Teletraff Res Ctr Adelaide SA 5005 Australia

ISBN: (纸本)9781509009190

Radio access technology (RAT) selection has recently received much attention from the research community due to the increasing deployment of heterogeneous wireless networks. Most prior works mainly focus on proposing an efficient algorithm that yields good performances, and evaluate their solutions on a certain network model, particularly in cases when every user can connect to all the available base stations (BSs). In this paper, we evaluate the impact of different aspects of network models, including (i) network topology and (ii) bandwidth allocation, on the performance of RAT selection algorithms to fully understand their limitations. Using simulations on realistic network models, this paper evaluates how different network parameters such as link density, user density or bandwidth distribution can lead to significant differences in algorithm performance. The paper demonstrates that among all the parameters, the performance of adaptive RAT selection algorithms are most significantly effected by the number of base stations that a user can connect to.

关键词： Rats Network topology Throughput Bandwidth Convergence Classification algorithms Adaptation models Network topology sorting algorithm Bandwidth network model Throughput Wireless networks Converge Adaptation models network parameters Rats BASE STATION

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Traffic Video Surveillance: Vehicle Detection and Classification

Traffic Video Surveillance: Vehicle Detection and Classifica...

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International Conference on Control Communication & Computing India (ICCC)

作者： Saran, K. B. Sreelekha, G. Coll Engn Vadakara Dept Elect & Commun Engn Calicut Kerala India Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode Kerala India

ISBN: (纸本)9781467373494

Vehicle detection and classification is the most important and challenging stage of traffic surveillance using computer vision techniques. The videos captured using the closed circuit television (CCTV) cameras placed in roadsides or driveways are used for the surveillance. The surveillance system includes detection of moving vehicles, counting the number of vehicles and the classification of the detected vehicles. The main challenge of the computer vision technique is the real time applicability of the algorithms used. In this work a vehicle detection and classification algorithm which works in real time is proposed. The detection is carried out by the method of background subtraction where the background is modeled using the mixture of Gaussians and the detected vehicles are classified using the Artificial Neural Network (ANN) with a new set of features, Histograms of Oriented Gradients (HOG) and geometric measures of the vehicles. Experimental results shows that the proposed method with the new combination of features as training parameters for ANN give better result as compared to other popular algorithms.

关键词： Vehicle detection images computer vision vehicle new combination Artificial neural networks Drive ways traffic monitoring Gauss sorting algorithm video monitoring SURVEILLANCE SYSTEMS Roadside

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