Showing papers in "Ieej Transactions on Electrical and Electronic Engineering in 2016"

Short‐circuit current calculation and performance requirement of HVDC breakers for MMC‐MTDC systems

Abstract: To evaluate the performance requirement of high-voltage direct current (HVDC) breakers for modular multilevel converter (MMC)-MTDC (multi-terminal high voltage direct current) systems with high efficiency, the equivalent model for calculating the maximum short-circuit current is presented in this paper. First, the short-circuit current is decomposed into the steady-state component and the fault component according to its physical dynamics. Second, the steady-state component is determined by solving the direct current (DC) network; the fault component is calculated by an equivalent network in which the converters are replaced by a reactance, a resistance, and a capacitance in series. Then, the complete procedure for evaluating the performance requirement of HVDC breakers is described based on short-circuit current calculation. Verifications have been carried out based on a three-terminal 800 MW/±400 kV bipolar MMC-MTDC system. The results show that the proposed methodology is efficient and effective. Lastly, based on the same system, the performance requirement of HVDC breakers and the influence by the sub-module (SM) capacitance and the smoothing reactor have been studied with the proposed methodology. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Detection of abnormal MR brains based on wavelet entropy and feature selection

Abstract: An accurate diagnosis is important for the medical treatment of patients suffering from brain diseases. Magnetic resonance (MR) images are commonly used by technicians to assist preclinical diagnosis. The classification of MR images of normal and pathological brains poses a challenge from the technological point of view, since MR imaging generates a large information set that reflects the conditions of the brain. In this paper, we present a computer-assisted diagnosis method based on wavelet entropy (WE) of the feature space approach and a feed-forward neural network (FNN) classification method for improving the brain diagnosis accuracy by means of MR images. The most relevant image feature is selected as the WE, which is used to train an FNN classifier. The results using tenfold cross validation of 64 images show that the average accuracy attainable is 100.00%. It can be easily seen from the data that the proposed classifier can detect abnormal brains from normal controls with excellent performance, which can compete with the latest methods. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

ECG biometric recognition using SVM-based approach

Abstract: This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twenty-one features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT-BIH Arrhythmia and MIT-BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Additive noise reduction in natural images using second-generation wavelet transform hidden Markov models

Abstract: Noise reduction or denoising is required for visual improvement or as a preprocessing step for subsequent processing tasks, such as image compression and analysis. Therefore, denoising has become a highly desirable and essential process in multimedia applications. The aim of all denoising processes, especially in natural images, is to uncover the true image from the observed noisy image, ideally removing the additive white Gaussian noise (AWGN) while producing a sharp, useful image without losing finer details. Generally, most of the noise obtained during acquisition and transmission of the natural images is assumed to be AWGN. In this study, we propose a new adaptive denoising framework based on second-generation wavelet domain using hidden Markov models (SGWD-HMMs) with some new local structure, utilizing the fact that the images are nonstationary with singularities and some smooth areas that can be considered as stationary. The dependencies among wavelet coefficients can be efficiently captured by HMMs since the dependence between two wavelet coefficients dies down quickly as their distance becomes big. Quite remarkably, experimental results verify the effectiveness of SGWD-HMMs in benchmark images when compared with other state-of-the-art denoising algorithms. It gives competitive results in the subjective and objective assessments, but it is computationally more expensive. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Immune algorithm combined with estimation of distribution for traveling salesman problem

Abstract: This paper describes an artificial immune algorithm (IA) combined with estimation of distribution algorithm (EDA), named IA-EDA, for the traveling salesman problem (TSP). Two components are incorporated in IA-EDA to further improve the performance of the conventional IA. First, aiming to strengthen the information exchange during different solutions, two kinds of EDAs involving univariate marginal distribution algorithm and population-based incremental learning are altered based on the permutation representation of TSP. It is expected that new promising candidate solutions can be sampled from the constructed probabilistic model of EDA. Second, a heuristic refinement local search operator is proposed to repair the infeasible solutions sampled by EDA. Therefore, IA-EDA can alleviate the deficiencies of the conventional IA and can find better solutions for TSP by well balancing the exploitation and exploration of the search. Experiments are conducted based on a number of benchmark instances with size up to 100 000 cities. Simulation results show that IA-EDA is effective for improving the performance of the conventional IA and can produce better or competitive solutions than other hybrid algorithms. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Gradient descent learning rule for complex‐valued associative memories with large constant terms

Abstract: Complex-valued associative memories (CAMs) are one of the most promising associative memory models by neural networks. However, the low noise tolerance of CAMs is often a serious problem. A projection learning rule with large constant terms improves the noise tolerance of CAMs. However, the projection learning rule can be applied only to CAMs with full connections. In this paper, we propose a gradient descent learning rule with large constant terms, which is not restricted by network topology. We realize large constant terms by regularization to connection weights. By computer simulations, we prove that the proposed learning algorithm improves noise tolerance. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Emergency scheduling for forest fires subject to limited rescue team resources and priority disaster areas

Abstract: To enable immediate and efficient emergency scheduling during forest fires, we propose a novel emergency scheduling model for such fires subject to priority disaster areas and limited rescue team resources to minimize the total travel distance for rescue teams. Moreover, a hybrid intelligent algorithm integrating genetic algorithm (GA) and particle swarm optimization (PSO) is adopted to solve the proposed model. A case study is presented to illustrate the proposed model and the effectiveness of the proposed algorithm. The goal of this work is to analyze the emergency scheduling problem of forest fires subject to limited rescue teams and priority disaster areas. Both theoretical and simulation results demonstrate that the proposed model can perform effectively the quantitative analysis of an emergency involving forest fires. Such results can help decision makers to make better judgment when dealing with an emergency involving fires. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Discrete wavelet transform and support vector machine‐based parallel transmission line faults classification

Abstract: This paper presents a scheme for classification of faults on double circuit parallel transmission lines using combination of discrete wavelet transform and support vector machine (SVM). Only one cycle post fault of the phase currents was employed to predict the fault type. Two features for each phase current were extracted using discrete wavelet transform. Thus, a total of 12 features were extracted for the six phase currents. The training data were collected, and SVM was employed to establish the fault classification unit. After that, the fault classification unit was tested for different fault states. The power system simulation was conducted using the MATLAB/Simulink program. The proposed technique took into account the mutual coupling between the parallel transmission lines and the randomness of the faults on transmission line considering time of occurrence, fault location, fault type, fault resistance, and loading conditions. The results show that the proposed technique can classify all the faults on the parallel transmission lines correctly. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Quasi‐ARX neural network based adaptive predictive control for nonlinear systems

Abstract: In this paper, a new switching mechanism is proposed based on the state of dynamic tracking error so that more information will be provided –not only the error but also a one up to pth differential error will be available as the switching variable. The switching index is based on the Lyapunov stability theory. Thus the switching mechanism can work more effectively and efficiently. A simplified quasi-ARX neural-network (QARXNN) model presented by a state-dependent parameter estimation (SDPE) is used to derive the controller formulation to deal with its computational complexity. The switching works inside the model by utilizing the linear and nonlinear parts of an SDPE. First, a QARXNN is used as an estimator to estimate an SDPE. Second, by using SDPE, the state of dynamic tracking error is calculated to derive the switching index. Additionally, the switching formula can use an SDPE as the switching variable more easily. Finally, numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance-rejection performances. Experimental results demonstrate its effectiveness. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Analysis of water quality in Miharu dam reservoir, Japan, using UAV data

Abstract: This letter reports on the water quality conditions of the Miharu Dam Reservoir. The water quality conditions appear in near-infrared (NIR) data collected by an unmanned aerial vehicle (UAV). Because blue-green algae occur in summer, the water quality of the small lake will be significantly worse at that time. On the basis of the experimental results for data from the months of July and August obtained using a fuzzy regression model, it is found that the UAV data is useful in assessing the water quality conditions in Lake Sakurako, and that the NIR data is effective in estimating the water surface conditions of the lake, especially caused by the occurrence of blue-green algae. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A design guideline of parasitic inductance for preventing oscillatory false triggering of fast switching GaN-FET

Abstract: Gallium nitride field-effect transistors (GaN-FETs) are attractive devices because of its low on-state resistance and fast switching capability. However, they can suffer from false triggering caused by fast switching. Particularly, a disastrous oscillation of repetitive false triggering can occur after a turn-off, which may deteriorate the reliability of power converters. To address this issue, we give a design guideline to prevent this phenomenon. We analyze a simple circuit model to derive the condition of occurrence of this phenomenon, which is then verified experimentally. Results show that the parasitic inductance of the gating circuit, Lg, and that of the decoupling circuit, Ld, should be designed so that the LC resonance frequency of Lg and the gate–source capacitance of the GaN-FET does not coincide with that of Ld and the drain–source capacitance, respectively. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Nondestructive inspection of thermal barrier coating of gas turbine high temperature components

Abstract: A thermal barrier coating (TBC) is applied to high-temperature components in gas turbines, and consists of a ceramic topcoat and a metallic bondcoat. Various kinds of TBC degradation and damage occur in high-temperature components during service, such as topcoat thinning, topcoat delamination, and formation of a thermally grown oxide (TGO) layer below the topcoat, each of which can be examined using a suitable nondestructive inspection technique. Topcoat thinning can be detected by topcoat thickness measurement using terahertz waves, which are electromagnetic waves in the frequency region between optical and radio waves. The measurement resolution is about 10 μm, which is comparable to microscopic observation of the cross section in destructive inspection. Topcoat delamination can be detected by active thermography, in which the topcoat surface is scanned by a heating laser and the surface temperature distribution is measured by a thermal infrared camera. The combination of temperature peak and residual thermal image detection is effective in eliminating false detection. The TGO layer can be detected using photoluminescence, in which the Cr3+ ions included as an impurity in Al2O3 are detected. Since delamination tends to occur at locations at which the TGO layer has grown, TGO layer detection provides an effective method to select regions where delamination has occurred or is likely to occur. An inspection flow based on these techniques is proposed, which is expected to aid the establishment of condition-based maintenance strategies of high-temperature components. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Multi-label emotion recognition of weblog sentence based on Bayesian networks

Abstract: An increasing number of common users, in the Internet age, tend to express their emotions on the Web about everything they like or dislike. As a consequence, the number of all kinds of reviews, such as weblogs, production reviews, and news reviews, grows rapidly. This makes it difficult for people to understand the opinions of the reviews and obtain useful emotion information from such a huge number of reviews. Many scientists and researchers have attached more attention to emotion analysis of online information in the natural language processing field. Different from previous works, which just focused on the single-label emotion analysis, this paper takes into account rich and delicate emotions and gives special regard to multi-label emotion recognition for weblog sentences based on the Chinese emotion corpus (Ren-CECps). Using the theory of Bayesian networks and probabilistic graphical model, the latent emotion variable and topic variable are employed to find out the complex emotions of weblog sentences. Our experimental results on the multi-label emotion topic model demonstrate the effectiveness of the model in recognizing the polarity of sentence emotions. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Smart grid real‐time pricing optimization management on power consumption of building multi‐type air‐conditioners

Abstract: Real-time pricing will be one of the demand response methods for the future smart grid. Assuming an advanced building energy management system for air-conditioning facilities in commercial buildings, a simulated annealing optimization that minimizes an evaluation function consisting of power cost and comfort degradation terms is studied. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Investigations on the suitability of rice bran oil and corn oil as alternative insulating liquids for transformers

Abstract: Increasing power demand forces the development of the high-rated power transformers. In a transformer, petroleum-based mineral oil is used as insulation currently. Transformer oil (TO) produces environmental and health issues because it is nonbiodegradable. The availability of petroleum products is decreasing in the present century. So it encourages researchers to find potential replacements for them. In this work, two natural ester oils (rice bran oil and corn oil) were taken up for investigation to find their suitability for use in transformers. Aging analysis was performed on the TO and natural esters. For this, properties like breakdown voltage, flash point, fire point, viscosity, acidity, resistivity, and loss factor of the natural esters were considered. From the investigations, it was evident that rice bran and corn oil have the ability to be used as alternatives for TO. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Optimum design of a regenerative braking system for electric vehicles based on fuzzy control strategy

Abstract: A regenerative braking system (RBS) can prolong the driving distance of electric vehicles by converting mechanical energy into electric energy. In this paper, an RBS based on fuzzy control strategy is proposed. By analyzing the characteristics of all factors, under the assurance of safety and stability during braking conditions, a fuzzy control model was established in the MATLAB/SIMULINK environment and verified by using simulation software Advisor2002. In order to recover more energy, the control model was optimized by the Taguchi method, and a new fuzzy control model was established and simulated. The simulation results show that by using the optimized fuzzy control system, more braking energy can be recovered and that the energy recovery efficiency can be increased. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Negative DC corona discharge effects on the ultraviolet photon count in rod to plane air gaps

Abstract: In order to investigate the effects of negative DC corona discharge on ultraviolet (UV) photon count, a corona discharge measurement system based on rod to plane air gaps was established. The variations of positive ion, negative ion, and electron densities were calculated with a fluid model, and the generation process of photons during negative corona discharge was investigated. The differences of photon count and the variation of charged particles between negative and positive corona were also compared. The corona current, Trichel pulses, and corona-generated photons were measured with increasing applied voltage. An approximate parabolic relationship found to exist between the photon count and the corona current, and also an ideal quadratic function was found to exist between the photon count and the frequency of Trichel pulses. These results provide a solid foundation in the application of UV imaging detection of negative DC corona discharge in power equipment. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Adaptive‐search template matching technique based on vehicle acceleration for monocular visual odometry system

Abstract: One of the important tasks of an autonomous mobile vehicle is the reliable and fast estimation of its position over time. This paper presents the development of an adaptive technique to hasten and improve the quality of correlation-based template matching for monocular visual odometry systems that estimate the relative motion of ground vehicles in low-textured environments. Moreover, the factors that can affect the maximum permissible vehicle driving speed were determined and the related equations were derived. The developed system uses a single downward-facing monocular camera installed at an optimum location to avoid the negative effect of directional sunlight and shadows which can disturb the correlation. In addition, the normalized cross-correlation method is implemented to calculate the pixel displacement between image frames. Although this method is highly effective for template matching because of its invariance to linear brightness and contrast variations, it incurs high computational cost. Thus, the optimal sizes of image template and matching search area are selected and their locations are dynamically changed according to vehicle acceleration, in order to achieve a compromise between the performance and the computational cost of correlation. The proposed technique increases the allowable vehicle driving speed and reduces the probability of template false-matching. Moreover, compared to traditional full search matching techniques, the adaptive technique demonstrates high efficiency and accuracy and improves the quality and speed of the correlation with more than 87% of reduction in computational cost. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Vision of new olfactory sensing array

Abstract: A newly synthesized material with highly controllable, but partial selectivity is proposed for the construction of catalytic sensors comprising an olfactory array. The transduction principle proposed for this array is that of a fuel-cell-type amperometric sensor. In the example discussed here, it is based on electrochemical oxidation of lower aliphatic alcohols in an alkaline medium. Since a cyclic voltammogram of each sensor in the olfactory array provides high-dimesional data, a pattern recognition technique including mutidimensional data generation, feature extraction, and classification is indispensable. The combination of the new sensor array with a sophisticated pattern recognition algorithm might lead to the next generation of olfactory sensing array. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

An observer‐based controller for a class of polynomial fuzzy systems with disturbance

Abstract: In this paper, a new observer-based controller for a class of polynomial fuzzy systems with disturbance is proposed. First, by some appropriate equivalence transformation of the polynomial fuzzy model, the first output-sized states can be obtained directly from the output, which eventually enables us to obtain the feedback control gain and observer-related parameters together without considering the so-called separation principle. Second, with the disturbance in mind, a PI-type observer is suggested. Finally, the sum of squares for the stability of the closed-loop observer-based control system is provided. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A framework for adaptive motion control of autonomous sociable guide robot

Abstract: We present a framework by which the motion of an autonomous mobile guide robot is adaptively controlled. A sociable robot should adapt its speed and path to suit the users' activities, without restricting the user movement. By generating adaptive artificial potential fields for the users and the subgoal separately, and integrating them with the basic potential fields generated from obstacles, our robot can adapt to the users' activities and provide sociable tour-guide services. The robot predicts a user's moving speed and adapts to it to maintain the social distance. Moreover, with the proposed framework, users can deviate from the guided path temporarily and return to the original task afterward. Instead of waiting for the users and taking the risk of losing them, the robot deviates from its original path to follow the users and also prepares for returning to the guiding task. The robot restarts the guiding task at that place, which ensures the least cost to reach the goal. Simulation and experimental results show that our framework can automatically generate suitable motion patterns to control the robot adaptively, making it sociable while providing tour guide services. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Fruit juice–alcohol mixture analysis using machine learning and electronic nose

Abstract: The aim of this study is to analyze the raw data collected from a fruit juice–alcohol mixture (a fruit juice–alcohol mixture and a fruit juice–multiple alcohol mixture) and the Halal authentication of a fruit juice–alcohol mixture with electronic nose. Machine learning techniques such as naive Bayesian classifier, K-nearest neighbors (K-NN), linear discriminant analysis (LDA), decision tree, artificial neural network (ANN), and support vector machine (SVM) were used to classify the feature of these collected raw data. There are three types of classification: the first one is a fruit juice and an alcohol mixture type; the second is a fruit juice and multiple alcohol mixture types, and the third is a Halal authentication of a fruit juice and alcohol mixture. We aimed at making cocktails with more successful results on the first two types of classification in the work. Also, we focused on Halal authentication of fruit juice–alcohol mixture in the third classification. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Wireless power transfer for electric vehicle at the kilohertz band

Abstract: Wireless power transfer (WPT) technology has attracted considerable attention in recent years, and there have been numerous reports about this technology. This has led to ambiguity over the actual state of WPT technology for electrical vehicles (EV) in Japan. Therefore, this paper describes the WPT technology using the kilohertz band for EVs available in Japan. WPT requirement for EV, circuit topology, coil types, control method, component, foreign object detection, and dynamic charging in Japan are summarized in this study. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Stabilization of neural network by combination with AR model in FastADR control of building air-conditioner facilities

Abstract: Fast automated demand response (FastADR) will be one of possible technologies to realize smart grid ancillary services in future. For FastADR control, a statistical prediction model on the nonlinear response of facility loads is necessary. Although neural networks (NNs) can be utilized for nonlinear prediction, they can have harmful exceptional prediction due to bias of the NN on the training data. In this letter, we propose a combination of an autoregressive (AR) model and an NN to avoid the harmful behavior, and discuss the theoretical basis of the method. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Sympathetic inrush current in a transformer and a method for its identification

Abstract: This paper studies the sympathetic interaction between transformers. The influencing factors of the sympathetic inrush current, including the system impedance, closing angle and the initial remnant flux of the added transformer, the system voltage, and the neutral grounding mode of the transformers, are analyzed in detail through dynamic simulation. Based on this, a method for identifying the sympathetic inrush current is proposed based on the essential curve feature of the magnetizing currents of the transformers during the sympathetic interaction, taking advantage of the information sharing between substations. The results from dynamic simulation and field-recorded data show the validity of the proposed method. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Swarm intelligence based robust active queue management design for congestion control in TCP network

Abstract: Active queue management (AQM) is an effective solution for the congestion control problem. It can achieve high quality of service (QoS) by reducing the packet dropping probability and network utilization. Three robust control algorithms are proposed in this paper in order to design robust AQM schemes: conventional controller, robust particle swarm optimization (PSO)-based PID (proportional–integral–derivative) (PSOPID) controller, and robust ant-colony optimization (ACO)-based PID (ACOPID) controller. PSO and ACO methods are used to tune the PID controller parameters subject to constraints to achieve the required robustness of the network. Robust PSOPID and ACOPID controllers can achieve desirable time-response specifications with a simple design procedure and low-order controller in comparison to the conventional controller. Wide ranges of system parameters change are used to show the robustness of the designed controllers. The ability of the designed controllers to meet the specified performance is demonstrated using MATLAB 7. 11, (R2010b): The MathWorks, Inc.3 Apple Hill Drive Natick, MA USA. On the other hand, to verify the effectiveness of the designed controller, nonlinear simulation is performed using the NS2 package. Finally, it is shown by comparison that the proposed robust ACOPID can achieve more desirable performance than the PSOPID controller and the controllers that have been proposed in previous works. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.