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Novel Context-aware Classification for Highly Accurate Automatic Toll Collection 30

Novel Context-aware Classification for Highly Accurate Autom...

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30th IEEE Intelligent Vehicles Symposium (IV)

作者： Khata, Marouan Shvai, Nadiya Hasnat, Abul Llanza, Arcadi Sanogo, Assan Meicler, Antoine Nakib, Amir VINCI Autoroutes Cyclope AI Rueil Malmaison France Univ Paris Est Lab LISSI Vitry Sur Seine France

ISBN: (纸本)9781728105604

Toll Vehicle Classification is an important task. Indeed, it has many uses in traffic management and toll collection systems. In this paper, Vinci Autoroutes group Networks (the biggest French Highways concession) are considered, where every year, millions of vehicles are classified in real-time. Then, a small decrease in classification performance can have serious economic losses. Therefore, the accuracy and the time complexity become critical for the toll collection system. The current classification algorithm uses the scene features' to detect vehicles classes. However, it requires a large labeled datasets, and has a limitations when multiple vehicles are in the scene. Herein, we propose a novel context-aware vehicle classification method that takes profit from the semantic spatial relationship of the objects. The experiments show that our method is performing as accurately as the existing model with significantly lower labeled datasets (74 times smaller). Moreover,the obtained accuracy of the proposed method is 99.97% compared to 99.79% achieved by the current method when using the same training set.

关键词： feature extraction image classification object detection road vehicles traffic engineering computing ubiquitous computing road vehicles Object detection Ubiquitous computing Image classification Feature extraction Toll collection Vehicle classification engineering computing Land transportation Context awareness vehicle complexity classes Traffic management sorting algorithm

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Classification of sonar data for a mobile robot using neural networks

Classification of sonar data for a mobile robot using neural...

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IEEE International Joint Symposia on Intelligence and Systems

作者： Diep, D Johannet, A Bonnefoy, P Harroy, F Loiseau, P LGI2P EERIE EMA F-30000 Nimes France

ISBN: (纸本)0818685484

We study in this paper an innovative architecture of ultrasonic sensor, in conjunction with a neural network-based classification algorithm, in order to recognize some geometric obstacles encountered by a mobile robot. The ultrasonic sensor is made of the association of an array of ultrasonic transducers, building an acoustic antenna, and providing acoustic scans with a fine resolution. The neural network is a multilayer perceptron which was trained with a set of features extracted from the sonar data. Results show that, by selecting appropriate features, the network can be trained to classify some geometric shapes, like wall, corners and edges.

关键词： Ultrasonic sensors Mobile robots Neural network sonar Multilayer perceptrons Geometric forms ultrasonic transducers sorting algorithm Acoustics

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Using ABC algorithm for Classification and Analysis on Effects of Control Parameters

Using ABC Algorithm for Classification and Analysis on Effec...

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18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Dilmac, Selim Korurek, Mehmet Istanbul Tech Univ Fen Bilimleri Enstitusu Maslak Sariyer Is Turkey

ISBN: (纸本)9781479975723

In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH ECG database. In addition to it, validity indices and effects of some control parameters such as MCN, Limit are examined. It is observed that, selected features have significiant effect on classification success rate of classifier. If there is high overlap between the classes, success rate of classifier decreases. However observed results indicate that ABC algorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, MABC algorithm based classifier provides higher classification success rates versus ABC algorithm, independent from Limit and MCN values.

关键词： classification data analysis electrocardiography feature extraction feature selection learning (artificial intelligence) medical information systems medical signal processing signal classification swarm intelligence ABC algorithm effectiveness BUPA IRIS MABC algorithm based classifier MCN parameter MITBIH ECG database PIMA SCTR control parameter UCI Machine Learning Repository WINE analysis algorithm artificial bee colony algorithm based classifier class overlap classification algorithm classification success rate control parameter effect feature effect limit parameter multidimensional dataset classification validity index Classification algorithms Educational institutions Electrocardiography Indexes Iris Machine learning algorithms Particle swarm optimization Feature extraction sorting algorithm Electrocardiography Particle swarm optimization Wine medical information systems control parameter INTEGRATED RISK INFORMATION SYSTEM Machine learning algorithms medical signal processing signal classification Educational institutions Classifiers activity based costing Iris

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Lymph diseases diagnosis approach based on support vector machines with different kernel functions 9

Lymph diseases diagnosis approach based on support vector ma...

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

作者： Elshazly, Hanaa Ismail Elkorany, Abeer Mohamed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Giza Egypt

ISBN: (纸本)9781479965946

In this paper, a Genetic algorithm (GA) hased supporting vector machine classifier (GA-SVM) is proposed for lymph diseases diagnosis. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to six features using GA. In the second stage, a support vector machine with different kernel functions including linear, Quadratic and Gaussian was utilized as a classifier. The Lymphography database was obtained from the University Medical Center, Institute of Oncology, Ljubljana, Yugoslavia. The obtained classification accuracy was very promising with regard to the other classification applications in the literature for this problem. The performance of SVM classifier with each kernel function was evaluated by using performance indices such as accuracy, sensitivity, specificity, area under curve (AUC) or (ROC), Matthews Correlation Coefficient (MCC) and F-Measure. Linear kernel function obtained highest results which verifies the efficiency of GA-linear stategy.

关键词： diseases genetic algorithms medical diagnostic computing patient diagnosis pattern classification support vector machines GA based supporting vector machine classifier GA-SVM GA-linear stategy Gaussian kernel function Institute of Oncology Ljubljana Lymphography database SVM classifier performance University Medical Center Yugoslavia classification accuracy classification applications genetic algorithm linear kernel function lymph disease dataset lymph disease diagnosis approach performance indices quadratic kernel function algorithm design and analysis Classification algorithms Extraterrestrial measurements Indexes Kernel Prediction algorithms Support vector machines Support Vector Network KERNEL FUNCTIONS Genetic algorithm (GA) medical diagnostic computing Extraterrestrial measurements algorithm design and analysis Academic Medical Centers performance indicators patient diagnosis Prediction algorithms sorting algorithm Pattern recognition Disease diagnosis Kernel maladjustment

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Open-Circuit Fault Diagnosis and Fault-Tolerant Control with Sequential Indirect Model Predictive Control for Modular Multilevel Converters 4

Open-Circuit Fault Diagnosis and Fault-Tolerant Control with...

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4th IEEE Workshop on the Electronic Grid (eGRID)

作者： Sun, Yuanxiang Li, Zhen Zhang, Zhenbin Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China Shandong Univ Suzhou Inst Suzhou 215123 Peoples R China

ISBN: (纸本)9781728157047

Fault diagnosis and fault-tolerant control are significant for the reliability of an MMC system, which usually has a large number of switching devices and high potential of operation failure. This paper proposes a simple fault diagnosis method for sub-module open-circuit faults and a new fault-tolerant control method. The proposed methods combine a novel sequential indirect MPC structure with sorting algorithm. Different from the well-known solutions, we detect and locate open-circuit faults in any sub-module efficiently with the maximum voltage output of the capacitor voltage sorting result, which is based on the physical characteristics of circuits when capacitor voltage rises. Thereafter, a new fault-tolerant control solution based on MPC is implemented through modifying circuit structure and control algorithm. The proposed methods are easy to implement and add little computational complexity to the control algorithm. Finally, the MATLAB/Simulink verification results confirm the effectiveness of the proposed solutions.

关键词： fault diagnosis fault-tolerant control MMC sequential indirect MPC sorting algorithm

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A Novel Optimal Modulation Strategy for Modular Multilevel Converter Based HVDC Systems 2

A Novel Optimal Modulation Strategy for Modular Multilevel C...

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IEEE 2nd International Conference on Renewable Energy and Power Engineering (REPE)

作者： Khanal, Saroj Disfani, Vahid R. Univ Tennessee Chattanooga TN 37403 USA

ISBN: (纸本)9781728145624

Unlike conventional converters, modular multilevel converter (MMC) has a higher switching frequency - which has direct implication on important parameters like converter loss and reliability - mainly due to increased number of switching components. However, conventional switching techniques, where submodule sorting is just based on capacitor voltage balancing, are not able to achieve switching frequency reduction objective. A novel modulation algorithm for modular multilevel converters (MMCs) is proposed in this paper to reduce the switching frequency of MMC operation by defining a constrained multiobjective optimization model. The optimized switching algorithm incorporates all control objectives required for the proper operation of MMC and adds new constraints to limit the number of submodule switching events at each time step. Variation of severity of the constraints leads to a desired level of controllability in MMC switching algorithm to trade-off between capacitor voltage regulation and switching frequency reduction. Finally, performance of the proposed algorithm is validated against a seven-level back-to-back MMC-HVDC system under various operating conditions.

关键词： capacitor voltage balancing high voltage direct current (HVDC) model predictive control (MPC) optimal switching sorting algorithm

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ECG Classification Based on Time and Frequency Domain Features Using Random Forests 44

ECG Classification Based on Time and Frequency Domain Featur...

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44th Computing in Cardiology Conference (CinC)

作者： Kropf, Martin Hayn, Dieter Schreier, Guenter Charite Virchow Klinikum Berlin Germany AIT Graz Austria Graz Univ Technol Graz Austria

ISBN: (纸本)9781538645550;9781538666302

We present a combined method of classical signal analysis and machine learning algorithms for the automated classification of 1-lead ECG recordings, which was developed in the course of the Computing in Cardiology Challenge 2017. To classify ECG recordings into the four classes as defined for the Challenge (normal, suspicious to AF, suspicious to other arrhythmia, noise) we used MATLABand a set of algorithms for detection of beats, wave point detection on detected beats, quality evaluation of the detection, averaging of beats, beat classification, rhythm classification and many more. We extracted a variety of features from both time and frequency domain etc. as input features for the classifier. A total of 380 features were used to train a Random Forest - based classifier (bagged decision trees). Since classes for the Challenge were severely unbalanced, weights based on the class distribution were applied. To train the classifier and for our internal evaluation we used cross-validation on all available ECGS from the training-set. 10-fold cross-validated F-1 score on the training set is 0.83. Final F-1 score from the official challenge evaluation on the enhanced dataset is 0.81, which is quite close to the other top performing algorithms.

关键词： Electrocardiography Feature extraction Classification algorithms Machine learning algorithms Training Predictive models Cardiology Machine learning algorithms Electrocardiography sorting algorithm Cardiology Prediction models Feature extraction Functional training Frequency-domain analysis Signal analysis Classifiers electrochemical grinding Beat generation

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kNN estimation of the unilateral dependency measure between random variables 5

kNN estimation of the unilateral dependency measure between ...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Cataron, Angel Andonie, Razvan Chueh, Yvonne Transylvania Univ Brasov Elect & Comp Dept Brasov Romania Cent Washington Univ Dept Comp Sci Ellensburg WA USA Cent Washington Univ Dept Math Ellensburg WA USA

ISBN: (纸本)9781479945184

The informational energy (IE) can be interpreted as a measure of average certainty. In previous work, we have introduced a non-parametric asymptotically unbiased and consistent estimator of the IE. Our method was based on the kth nearest neighbor (kNN) method, and it can be applied to both continuous and discrete spaces, meaning that we can use it both in classification and regression algorithms. Based on the IE, we have introduced a unilateral dependency measure between random variables. In the present paper, we show how to estimate this unilateral dependency measure from an available sample set of discrete or continuous variables, using the kNN and the naive histogram estimators. We experimentally compare the two estimators. Then, in a real-world application, we apply the kNN and the histogram estimators to approximate the unilateral dependency between random variables which describe the temperatures of sensors placed in a refrigerating room.

关键词： pattern classification regression analysis IE classification algorithms informational energy kth nearest neighbor kNN estimation kNN method nai?ve histogram estimators random variables regression algorithms unilateral dependency Approximation methods Estimation Histograms Ice Probability density function Random variables Temperature sensors Random variable histograms Temperature Sensor Device Component Approximation method Pattern recognition Probability density function Ice Nearest neighbor Joint Research Centre regression analysis sorting algorithm Unilateral

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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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Determining the impact of demographic features in predicting student success in Croatia

Determining the impact of demographic features in predicting...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Trstenjak, Bruno Donko, Dzenana Medimurje Univ Appl Sci Cakovec Dept Comp Engn Cakovec Croatia Fac Elect Engn Dept Comp Sci Sarajevo Bosnia & Herceg

ISBN: (纸本)9789532330816

Predicting the success of students is a topic which has been studied for a long time in different scientific fields. Evaluation of importance of the features used in the prediction and their subsequent selection is an immensely important step in the process of classification and data mining. This paper presents a study on the importance of student demographic features in the process of predicting. The study and performed analyses used the demographic data collected from the Information System for Higher Education (ISVU). For determining the importance of demographic features in the study the following methods have been used: Information Gain (IG), Gain Ratio (GR), Sequential Backward Selection (SBS), Sequential Forward Selection (SFS). The results show the features rank, their importance weight in the prediction and comparison of the results and the use of different methods. Two classification algorithms for evaluating the impact of ranking features to the quality of prediction are used: Naive Bayes i Support Vector Machine (SVM). Final results provide guidelines for the development of a new prediction model.

关键词： Accuracy Classification algorithms Educational institutions Machine learning algorithms Prediction algorithms Support vector machines sorting algorithm Machine learning algorithms Support Vector Network Prediction algorithms Educational institutions Students Croatia Computer based information systems

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