• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2224-2236
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• 2014

The performance of an isolated high voltage full bridge converter is improved using a voltage doubler. In a conventional high voltage full bridge converter, the diode of the transformer secondary voltage undergoes a voltage spike due to the leakage inductance of the transformer and the resonance occurring with the parasitic capacitance of the diode. In addition, in the phase shift control, conduction loss largely increases from the freewheeling mode because of the circulating current. The efficiency of the converter is thus reduced. However, in the proposed converter, the high voltage dual converter consists of a voltage doubler because the circulating current of the converter is reduced to increase efficiency. On the other hand, in the proposed converter, an input current is distributed when using parallel input / serial output and the output voltage can be doubled. However, the voltages in the 2 serial DC links might be unbalanced due to line impedance, passive and active components impedance, and sensor error. Considering these problems, DC injection is performed due to the complementary operations of half bridge inverters as well as the disadvantage of the unbalance in the DC link. Therefore, the serial output of the converter needs to control the balance of the algorithm. In this paper, the performance of the conventional converter is improved and a balance control algorithm is proposed for the proposed converter. Also, the system of the 1.5[kW] PCS is verified through an experiment examining the operation and stability.

• Journal of Biomedical Engineering Research
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• v.40 no.5
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• pp.179-188
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• 2019

In ultrasound telemedicine, it is important to reduce the size of the data by compressing the ultrasound image when sending it. Ultrasound images can be divided into image context and other information consisting of patient ID, date, and several letters. Between them, ultrasound context is very important information for diagnosis and should be securely preserved as much as possible. In several previous papers, ultrasound compression methods were proposed to compress ultrasound context and other information into different compression parameters. This ultrasound compression method minimized the loss of ultrasound context while greatly compressing other information. This paper proposed the method of automatic segmentation of ultrasound context to overcome the limitation of the previously described ultrasound compression method. This algorithm was designed to robust for various ultrasound device and to enable real-time operation to maintain the benefits of ultrasound imaging machine. The operation time of extracting ultrasound context through the proposed segmentation method was measured, and it took 311.11 ms. In order to optimize the algorithm, the ultrasound context was segmented with down sampled input image. When the resolution of the input image was reduced by half, the computational time was 126.84 ms. When the resolution was reduced by one-third, it took 45.83 ms to segment the ultrasound context. As a result, we verified through experiments that the proposed method works in real time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.8-14
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• 2009

A Hidden Markov Model(HMM)-based information extraction method is proposed in this paper. The proposed extraction method is applied to extraction of products' prices. The input of the proposed IESHMM is the URLs of a search engine's interface, which contains the names of the product types. The output of the system is the list of extracted slots of each product: name, price, image, and URL. With the observation data set Maximum Likelihood algorithm and Baum-Welch algorithm are used for the training of HMM and The Viterbi algorithm is then applied to find the state sequence of the maximal probability that matches the observation block sequence. When applied to practical problems, the proposed HMM-based system shows improved results over a conventional method, PEWEB, in terms of recall ration and accuracy.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.346-348
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• 2004

In this rarer, 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 structurenamed 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. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. 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 (IG) 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 gas furnace process dataset.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.92-97
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• 2000

An AMR(Adaptive Multi-Rate) speech coding algorithm has been adopted as a standard speech codec for IMT-2000. It is based on the algebraic CELP, and consists of eight speech coding modes having the bit rate from 4.75 kbit/s to 12.2 kbit/s. It also contains the VAD(Voice Activity Detector), SCR (Source Controlled Rate) operation, and error concealment scheme for robustness in a radio channel. The bit rate of AMR is changed on a frame basis depending on the channel condition. In this paper, we introduced AMR speech coding algorithm and performed the real-time implementation using TMS320C6201, i.e., a Texas Instrument's fixed-point DSP. With the ANSI C source code released from ETSI and 3GPP, we convert and optimize the program to make it run in real time using the C compiler and assembly language. It is verified that the decoded result of the implemented speech codec on the DSP is identical with the PC simulation result using ANSI C code for test sequences. Also, actual sound input/output test using microphone and speaker demonstrates its proper real-time operation without distortions or delays.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1071-1084
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• 1992

In this paper, the method for navigation and obstacle avoidance of the autonomous mobile robot is proposed. The proposed algorithms are based on the fuzzy inference system which is able to deal with imprecise and uncertain information. The self-tuning algorithm, which adopts the simplex method, modifies the parameters of membership functions of the input-output linguistic variables by changing the support of these fuzzy sets according to the integral of absolute error(IAE) of the system response. The wall-follwing navigation and obstacle avoidance of the mobile robot are based on range data measured from the internal sensors(encoder) and the outer sensors(sonar sensor). In addition, the algorithm for the obstacle detection proposed in this paper is based on the expert's experience. Finally, the effectiveness of navigation and obstacle avoidance algorithm is demonstrated through simulation and experiment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.12-19
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• 2015

In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly in the unknown multi-obstacle environment, this paper presented the navigation problem of a wheel mobile robot based on proximity sensors by fuzzy logic controller. Then a genetic algorithm was applied to optimize the membership function of input and output variables and the rule base of the fuzzy controller. Here the environment is unknown for the robot and contains various types of obstacles. The robot should detect the surrounding information by its own sensors only. For the special condition of path deadlock problem, a wall following method named angle compensation method was also developed here. The simulation results showed a good performance for navigation problem of mobile robots.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.111-114
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• 2004

Object-based segmentation is the first essential step for image processing applications. Recently, SAR (Synthetic Aperture Radar) segmentation techniques have been developed, however not enough to preserve the significant information contained in the small regions of the images. The proposed method is to partition an SAR image into homogeneous regions by using a fuzzy hit-or-miss operator with an inherent spatial transformation, which endows to preserve the small regions. In our algorithm, an iterative segmentation technique is formulated as a consequential process. Then, each time in iterating, hypothesis testing is used to evaluate the quality of the segmented regions with a homogeneity index. The segmentation algorithm is unsupervised and employed few parameters, most of which can be calculated from the input data. This comparative study indicates that the new iterative segmentation algorithm provides acceptable results as seen in the tested examples of satellite images.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.897-900
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• 1998

Through fuzzy logic controller is very useful to many areas, it is difficult to build up the rule-base by experience and trial-error. So, effective self-tuning fuzzy controller for the position control of ball and beam is designed. In this paper, we developed the neural network control system with fuzzy-neuron which conducts the adjustment process for the parameters to satisfy have nonlinear property of the ball and beam system. The proposed algorithm is based on a fuzzy logic control system using a neural network learinign algorithm which is a back-propagation algorithm. This system learn membership functions with input variables. The purpose of the design is to control the position of the ball along the track by manipulating the angualr position of the serve. As a result, it is concluded that the neural network control system with fuzzy-neuron is more effective than the conventional fuzzy system.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4E
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• pp.21-27
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• 1996

In this paper, we propose a discriminative training algorithm for the stochastic segment model (SSM) in continuous speech recognition. As the SSM is usually trained by maximum likelihood estimation (MLE), a discriminative training algorithm is required to improve the recognition performance. Since the SSM does not assume the conditional independence of observation sequence as is done in hidden Markov models (HMMs), the search space for decoding an unknown input utterance is increased considerably. To reduce the computational complexity and starch space amount in an iterative training algorithm for discriminative SSMs, a hybrid architecture of SSMs and HMMs is programming using HMMs. Given the segment boundaries, the parameters of the SSM are discriminatively trained by the minimum error classification criterion based on a generalized probabilistic descent (GPD) method. With the discriminative training of the SSM, the word error rate is reduced by 17% compared with the MLE-trained SSM in speaker-independent continuous speech recognition.