• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.433-436
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• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.431-437
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• 2004

This paper propose a facial recognition method based on an ubiquitous computing that is one of next generation intelligence technology fields. The facial images are acquired by a mobile device so-called cellular phone camera. We consider a mobile security using facial feature extraction and recognition process. Facial recognition is performed by the PCA and fuzzy LDA algorithm. Applying the discrete wavelet based on multi-resolution analysis, we compress the image data for mobile system environment. Euclidean metric is applied to measure the similarity among acquired features and then obtain the recognition rate. Finally we use the mobile equipment to show the efficiency of method. From various experiments, we find that our proposed method shows better results, even though the resolution of mobile camera is lower than conventional camera.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.2
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• pp.142-149
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• 2009

This paper investigates operating characteristics of three-phase matrix converter with unity input power factor by direct duty-ratio pulse-width modulation in the case of balanced and unbalanced load. It can be found from the system analysis that (1) The control algorithm for unity power factor is not related to the variables of load sides but the input voltages, (2) With the balanced three-phase load except for the pure reactive load, the unity input power factor can be achieved, (3) In the case of the unbalanced linear load, the equivalent input characteristics of the matrix converter can be seen like the nonlinear resister, (4) When the input frequency and the output frequency have the specific relationship, each input phases have the same sharing of the average power. The feasibility and validity of the analysis were verified by simulation and experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.140-148
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• 2004

Natural images can be classified into edge or flat region. Edges have also various shapes such as long edge, texture and so on. Because the conventional deinterlacing methods commonly use one specific algorithm, they are faced with the difficulty that does not adapt various shapes of images. In this paper, a selective deinterlacing method based on the characteristics of local region of image is proposed. An input image is classified into three regions; flat region, complex edge, long edge. And then for each region, the proper method is assigned according to the characteristic of the local feature. For long edge region, the modified $NEDI(New Edge Directed Interpolation)^{[1]}$ method that interpolates long edge very well is used. The linear $filter^{[2]}$ that enhances high frequency components is used for complex edge, and the bilinear interpolation method is applied to flat region. The proposed method shows improved performance in PSNR and subjective evaluation compared with previous algorithms.

• Convergence Security Journal
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• v.16 no.7
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• pp.113-120
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• 2016

This paper presents a new high-speed SHA-1 pipeline architecture having a computation delay close to the maximum critical path delay of the original SHA-1. The typical SHA-1 pipelines are based on either a hash operation or unfolded hash operations. Their throughputs are greatly enhanced by the parallel processing in the pipeline, but the maximum critical path delay will be increased in comparison with the unfolding of all hash operations in each round. The pipeline stage logics in the proposed SHA-1 has the latency is similar with the result of dividing the maximum threshold delay of a round by the number of iterations. Experimental results show that the proposed SHA-1 pipeline structure is 0.99 and 1.62 at the operating speed ratio according to circuit size, which is superior to the conventional structure. The proposed pipeline architecture is expected to be applicable to various cryptographic and signal processing circuits with iterative operations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.53-60
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• 2018

In this paper, we described the unknown parameter identification using Dual Extended Kalman Filter for precise control of 2-link manipulator. 2-link manipulator has highly non-linear characteristic with changed parameter thought tasks. The parameter kinds of mass and inertia of system is important to handle with the manipulator robustly. To solve the control problem by estimating the state and unknown parameters of the system through the proposed method. In order to verify the performance of proposed method, we simulate the implementation using Matlab and compare with results of RLS algorithm. At the results, proposed method has a better performance than those of RLS and verify the estimation performance in the parameter estimation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.92-100
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• 2009

In this paper, we analyze the hardware architecture of Low Density Parity Check (LDPC) decoder using Log Likelihood Ration-Belief Propagation (LLR-BP) decoding algorithm. Various design issues that affect the decoding performance and the hardware complexity are discussed and the tradeoffs between the hardware complexity and the performance are analyzed. The message data for passing error probability is quantized to 7 bits and among them the fractional part is 4 bits. To maintain the decoding performance, the integer and fractional parts for the intrinsic information is 2 bits and 4 bits respectively. We discuss the alternate implementation of $\Psi$(x) function using piecewise linear approximation. Also, we improve the hardware complexity and the decoding time by applying overlapped scheduling.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.124-129
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• 1990

This paper describes a new method to eliminate some selected harmonics (5,7,11) in PWM waveforms using Walsh and related orthogonal functions. Previous analyses of PWM waveforms are based on the nonlinear equations requiring iterative solution methods which are not practical in real-time systems. In addition, synthesis of low harmonics waveform at high power system is not easy to implement with power electronic hardware. The goal of this paper is to achieve the harmonics elimination in a PWM waveform by replacing the nonlinear equations in Fourier analysis with linear algebraic equations resulting from the use of orthogonal Walsh equation. This paper also describes how to synthesize low ordered harmonic waveforms with practical power electronic hardware. Walsh and Radmacher functions are easily manipulated by Harmuth's array generator, and those algorithms are accurate, computationally efficient and faster than algorithm based on Fourier analysis. In addition, this method is simulated to synthesize periodic PWM waveforms. From the experi-mental results, it is shown that single-phase PWM waveform are identified with the proposed method. And these methods are also extended to three-phase PWM waveforms in this paper.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.9-16
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• 2004

This paper provides an observer-based Η$\infty$ output feedback controller design method for descriptor systems with time-varying delay by just one LMI(linear matrix inequality) condition. The sufficient condition for the existence of controller and the controller design method are presented by perfect LMI approach which can be solved efficiently by convex optimization. The design procedure involves solving an LMI. Since the obtained condition can be expressed as an LMI form all variables including feedback gain and observer gain can be calculated simultaneously by Schur complement changes of variables, and singular value decomposition. Moreover, The proposed controller design algorithm can be extended to the observer-based robust Η$\infty$ output feedback controller design method for descriptor systems with parameter uncertainty and time delay. An example is given to illustrate the results.