• Journal of Power Electronics
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• v.16 no.1
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• pp.277-286
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• 2016

This paper offers two Maximum Power Point Tracking (MPPT) systems for Photovoltaic (PV) applications. The first MPPT method is based on a fixed frequency Model Predictive Control (MPC). The second MPPT technique is based on the Predictive Hysteresis Control (PHC). An experimental demonstration shows that the proposed techniques are fast, accurate and robust in tracking the maximum power under different environmental conditions. A DC/DC converter with a high voltage gain is obligatory to track PV applications at the maximum power and to boost a low voltage to a higher voltage level. For this purpose, a high gain Switched Inductor Quadratic Boost Converter (SIQBC) for PV applications is presented in this paper. The proposed converter has a higher gain than the other transformerless topologies in the literature. It is shown that at a high gain the proposed SIQBC has moderate efficiency.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.925-934
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• 2015

This paper considers the robust controller design problem for a boost DC-DC converter. Based on the Takagi-Sugeno fuzzy model-based approach, a fuzzy controller as well as a fuzzy load conductance observer are designed. Sufficient conditions for the existence of the controller and the observer are derived using Linear Matrix Inequalities (LMIs). LMI parameterizations of the gain matrices are obtained. Additionally, LMI conditions for the existence of the fuzzy controller and the fuzzy load observer guaranteeing α-stability, quadratic performance are derived. The exponential stability of the augmented fuzzy observer-controller system is shown. It is also shown that the fuzzy load observer and the fuzzy controller can be designed independently. Finally, the effectiveness of the proposed method is verified via experimental and simulation results under various conditions.

• Journal of Power Electronics
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• v.12 no.4
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• pp.538-547
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• 2012

This paper presents two different robust controllers for boost converters with two stages in a cascade. The first robust controller is monovariable; that is, the duty-cycle is the same for the two switches. The monovariable controller ensures that some prescribed constraints on pole placement and control effort are met, and optimizes the load disturbance rejection, while takes into account the uncertainty in certain parameters. The first controller is then compared with a multivariable robust controller; that is, with independent duty cycles in each switch. The multivariable controller takes into account the same uncertainty, constraints and optimization function. The comparison shows that the multivariable controller performs better at the expense of a slightly more complex implementation; that is, the multivariable controller provides a better rejection of the load disturbance. The paper also describes simulations and experimental results that are in perfect agreement with theoretical derivations.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Journal of Power Electronics
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• v.19 no.6
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• pp.1366-1379
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• 2019

High-gain DC/DC converters have become one of the key technologies for the grid-connected operation of new energy power generation, and its research provides a significant impetus for the rapid development of new energy power generation. Inspired by the transformer effect and the ripple-suppressed ability of a coupled inductor, a double-coupled inductor high gain DC/DC converter with a ripple absorption circuit is proposed in this paper. By integrating the diode-capacitor voltage multiplying unit into the quadratic Boost converter and assembling the independent inductor into the magnetic core of structure coupled inductors, the adjustable range of the voltage gain can be effectively extended and the limit on duty ratio can be avoided. In addition, the volume of the magnetic element can be reduced. Very small ripples of input current can be obtained by the ripple absorption circuit, which is composed of an auxiliary inductor and a capacitor. The leakage inductance loss can be recovered to the load in a switching period, and the switching-off voltage spikes caused by leakage inductance can be suppressed by absorption in the diode-capacitor voltage multiplying unit. On the basis of the theoretical analysis, the feasibility of the proposed converter is verified by test results obtained by simulations and an experimental prototype.

• Smart Media Journal
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• v.12 no.10
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• pp.9-18
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• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.749-754
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• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.330-339
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• 2008

Ground-based interceptors(GBI) comprise a major element of the strategic defense against hostile targets like Intercontinental Ballistic Missiles(ICBM) and reentry vehicles(RV) dispersed from them. An optimum design of the subsystems is required to increase the performance and reliability of these GBI. Propulsion subsystem design and optimization is the motivation for this effort. This paper describes an effort in which an entire GBI missile system, including a multi-stage solid rocket booster, is considered simultaneously in a Genetic Algorithm(GA) performance optimization process. Single goal, constrained optimization is performed. For specified payload and miss distance, time of flight, the most important component in the optimization process is the booster, for its takeoff weight, time of flight, or a combination of the two. The GBI is assumed to be a multistage missile that uses target location data provided by two ground based RF radar sensors and two low earth orbit(LEO) IR sensors. 3Dimensional model is developed for a multistage target with a boost phase acceleration profile that depends on total mass, propellant mass and the specific impulse in the gravity field. The monostatic radar cross section (RCS) data of a three stage ICBM is used. For preliminary design, GBI is assumed to have a fixed initial position from the target launch point and zero launch delay. GBI carries the Kill Vehicle(KV) to an optimal position in space to allow it to complete the intercept. The objective is to design and optimize the propulsion system for the GBI that will fulfill mission requirements and objectives. The KV weight and volume requirements are specified in the problem definition before the optimization is computed. We have considered only continuous design variables, while considering discrete variables as input. Though the number of stages should also be one of the design variables, however, in this paper it is fixed as three. The elite solution from GA is passed on to(Sequential Quadratic Programming) SQP as near optimal guess. The SQP then performs local convergence to identify the minimum mass of the GBI. The performance of the three staged GBI is validated using a ballistic missile intercept scenario modeled in Matlab/SIMULINK.

• The Journal of Bigdata
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• v.6 no.2
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• pp.109-124
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• 2021

In this paper, the structural characteristics of Industry-University cooperation networks are analyzed using network analysis. Recent studies have shown that technological cooperation and joint research has a positive effect on R&D performance. In order to boost innovation performance, various types of cooperative activities and governmental policy supports for major R&D stakeholders(i.e. universities, laboratories, etc.) are provided. However, despite these efforts, the outcome is still insufficient, so it is time to prepare for a plan to build an innovative network to strengthen university-centered Industry-University cooperation activities. Specifically, this study builds the networks according to the form of Industry-University cooperations(i.e. patent, paper, joint research, and technology transfer), and different types of Industry-University cooperation networks are analyzed from a statistical viewpoint by using QAP correlation and regression analyses. The analysis results show that joint research network is closely related to paper network, and is related to other Industry-University cooperation networks. This study is expected to shed a light on supporting innovation activities such as establishing Industry-University cooperation strategies and discovering cooperative partners necessary for creating new growth engines for universities.