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Proposing a high performance face detector based on UKF 7

Proposing a high performance face detector based on UKF

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IEEE/SICE International Symposium on System Integration (SII)

作者： Lamsal, Bikash Matsumoto, Naofumi Ashikaga Inst Technol 268-1 Ohmae Cho Ashikaga Tochigi 3268558 Japan

ISBN: (纸本)9781479969449

In this paper, we propose a high performance algorithm for detecting human faces in a still image. Human faces help to communicate and interact in a better way that may be either in human-human interaction or human-machine interaction. The success points for our algorithm is the use of different individual algorithms along with the Unscented Kalman filter (UKF) process, as a novelty of our process. We have modified the algorithms as well. We used Viola Jones eye detector, skin color detector and the Haar cascade classifier for face detection process. Finally, we have conducted a benchmark test for our proposed algorithm using image databases of CMU-MIT, MIT training sets, INRIA Graz-01, and FDDB database. Then, we clarify its effectiveness using ROC curves.

关键词： Kalman filters eye face recognition human computer interaction image classification image colour analysis nonlinear filters object detection skin visual databases CMU-MIT database FDDB database Haar cascade classifier INRIA Graz-01 database MIT training sets ROC curves UKF Viola Jones eye detector benchmark test high performance face detector human face detection human-human interaction human-machine interaction image databases skin color detector still image unscented Kalman filter Classification algorithms Databases Detectors Face Face detection Image color analysis Skin visual databases Facial Recognition image colour analysis Benchmark testing Still images Kalman filters ROC Curve Face Nonlinear filters sorting algorithm computer human interaction skin Object detection Image classification Face

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Direct AC charging of EV Reconfigurable Cascaded Multilevel Converter

Direct AC charging of EV Reconfigurable Cascaded Multilevel ...

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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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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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Generalized Learning Vector Quantization Particle Swarm Optimization (GLVQ-PSO) FPGA Implementation for Real-Time Electrocardiogram

Generalized Learning Vector Quantization Particle Swarm Opti...

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International Workshop on Big Data and Information Security (IWBIS)

作者： Wardhana, Yulistiyan Jatmiko, Wisnu Rachmadi, M. Febrian Univ Indonesia Fac Comp Sci Depok Indonesia Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9781509034772

Cardiovascular system is the most important part of human body which has role as distribution system of Oxygen and body's wastes. To do the job, there are more than 60.000 miles of blood vessels participated which can caused a problem if one of them are being clogged. Unfortunately, the conditions of clogged blood vessels or diseases caused by cardiovascular malfunction could not be detected in a plain view. In this matter, we proposed a design of wearable device which can detect the conditions. The device is equipped with a newly neural network algorithm, GLVQ-PSO, which can give recommendation of the heart status based on learned data. After the research is conducted, the algorithm produce better accuracy than LVQ, GLVQ and FNGLVQ in the high level language implementation. Yet, GLVQ-PSO still has relatively worse performance in its FPGA implementation.

关键词： algorithm design and analysis Field programmable gate arrays Clustering algorithms Classification algorithms Particle swarm optimization Heart High level languages Field programmable gate arrays high level language(hll) algorithm design and analysis sorting algorithm Clustering algorithms Particle swarm optimization Heart learning vector quantization Human Body Blood Vessels Cardiovascular System

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A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks 13

A hypothesize-and-verify framework for Text Recognition usin...

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

作者： Ray, Anupama Rajeswar, Sai Chaudhury, Santanu Indian Inst Technol Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781479918058

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors.

关键词： image matching image segmentation image sequences recurrent neural nets text detection tree searching abstraction best first search bidirectional long short term memory deep BLSTM deep recurrent neural network hybrid text recognizer hypothesize-and-verify framework language model line sequences multihypotheses tree architecture recognition errors segmentation algorithms sub-string match unicode sequences Classification algorithms Training Transforms modelling languages recurrent neural nets Printed text recognition Tree searching sorting algorithm Abstraction image matching image sequences Transform best-first search image segmentation segmentation Functional training

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Fault Detection in Kerman Combined Cycle Power Plant Boilers by Means of Support Vector Machine Classifier algorithms and PCA 3

Fault Detection in Kerman Combined Cycle Power Plant Boilers...

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3rd International Conference on Control, Instrumentation, and Automation (ICCIA)

作者： Berahman, M. Safavi, A. A. Shahrbabaki, M. Rostami Kerman Combined Cycle Power Plant Kerman Iran Shiraz Univ Power & Control Engn Dept Shiraz Iran

ISBN: (纸本)9781479931170

In this paper, fault detection in HP drum of boilers in Kerman combined cycle power plant is explored by means of support vector machine (SVM) algorithm and principal component analysis (PCA). Initially, SVM classifier algorithm and PCA are discussed and then based on the collecting data on normal and abnormal operating the conditions of boilers, fault detection is carried out via explained methods. Finally, a comparison of these techniques and other routine methods is made to show the superiority with the proposed approaches in Kerman power plant.

关键词： Boilers Classification algorithms Fault detection Indexes Power generation Principal component analysis Support vector machines Boilers Defect detection principal components analysis Support Vector Network sorting algorithm boilers Power production Combined cycle power plants Passive Cutaneous Anaphylaxis

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New approach for feature selection based on rough set and bat algorithm 9

New approach for feature selection based on rough set and ba...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Emary, E. Yamany, Waleed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Fayoum Univ Fac Comp & Informat Al Fayyum Egypt SRGE Cairo Egypt

ISBN: (纸本)9781479965946

This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. A fitness function based on rough-sets is designed as a target for the optimization. The used fitness function incorporates both the classification accuracy and number of selected features and hence balances the classification performance and reduction size. This paper make use of four initialisation strategies for starting the optimization and studies its effect on bat performance. The used initialization reflects forward and backward feature selection and combination of both. Experimentation is carried out using VCI data sets which compares the proposed algorithm with a GA-based and PSO approaches for feature reduction based on rough-set algorithms. The results on different data sets shows that bat algorithm is efficient for rough set-based feature selection. The used rough-set based fitness function ensures better classification result keeping also minor feature size.

关键词： data reduction feature selection optimisation pattern classification rough set theory BA bat algorithm classification performance fitness function optimization reduction size rough set Accuracy algorithm design and analysis Classification algorithms Equations Indium phosphide Integrated circuits Feature extraction Rough sets algorithm design and analysis Indium phosphide sorting algorithm barium Rough set theory Pattern recognition Bats data reduction bat Chiroptera Dataset

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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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Derive Local Invariance Transformations from SVM

Derive Local Invariance Transformations from SVM

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International Joint Conference on Neural Networks

作者： Ling Ping Wang Zhe Wang Xi Zhou Chun-guang Jilin Univ Coll Comp Sci & Technol Key Lab Symbol Computat & Knowledge Engn Natl Educ Minist Changchun 130012 Peoples R China

ISBN: (纸本)9781424418206

Invariance transformation (IT) is a rewarding technique to facilitate classification. But it is often difficult to derive its definition. This paper derives a local invariance transformation definition from SVM decision function. The corresponding IT-distance definition is consequently designed in both input space and feature space. And a classification algorithm based on IT and Nearest Neighbor rule is proposed, named as ITNN. ITNN exploits hyper sphere centers as class prototypes and labels data using a weighted voting strategy. ITNN is of computational ease brought by training dataset reduction and hyper parameter self-tuning. We describe experimental evidence of classification performance improved by ITNN on real datasets over state of the arts.

关键词： Support Vector Network sorting algorithm invariance state of the art Eigenvector Corresponding information technology tuning Dataset

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Using Classification for Cost Reduction of Applying Mutation Testing

Using Classification for Cost Reduction of Applying Mutation...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Strug, Joanna Strug, Barbara Cracow Univ Technol Dept Elect & Comp Engn Ul Warszawska 24 PL-30059 Krakow Poland Jagiellonian Univ Dept Phys Astron & Appl Comp Sci Ul Lojasiewicza 11 PL-30348 Krakow Poland

ISBN: (纸本)9788394625375

The paper uses machine learning methods to deal with the problem of reducing the cost of applying mutation testing. A method of classifying mutants of a program using structural similarity is proposed. To calculate such a similarity each mutant is firstly converted into a hierarchical graph, which represents the mutant's control flow, variables and conditions. Then using such a graph form graph kernels are introduced to calculate similarity among mutants. The classification algorithm is then applied for prediction. This approach helps to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented.

关键词： problem reducing cost COST REDUCTION Mutants Line graph sorting algorithm

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