• Journal of Communications and Networks
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• v.14 no.2
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• pp.151-161
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• 2012

This paper considers a two-user Gaussian interference channel with energy harvesting transmitters. Different than conventional battery powered wireless nodes, energy harvesting transmitters have to adapt transmission to availability of energy at a particular instant. In this setting, the optimal power allocation problem to maximize the sum throughput with a given deadline is formulated. The convergence of the proposed iterative coordinate descent method for the problem is proved and the short-term throughput maximizing offline power allocation policy is found. Examples for interference regions with known sum capacities are given with directional water-filling interpretations. Next, stochastic data arrivals are addressed. Finally, online and/or distributed near-optimal policies are proposed. Performance of the proposed algorithms are demonstrated through simulations.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.98-99
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• 2013

최근 빠른 산업발달에 따른 전력전자기술발전과 더불어 고효율 발전기 시스템에 대한 관심과 요구가 급증하고 있다. 이에 따라 영구자석 발전기 시스템에 대한 연구가 활발히 진행됨과 동시에 무효전력에 따른 전압강하를 방지하기 위한 보상장치에 대한 관심이 날로 증가하고 있다. PMSG(영구자석 동기발전기, Permanent Magnetic Synchronous Generator)는 출력전압을 직접적으로 제어할 수 없기 때문에 출력전압을 제어하는 보조적인 장치가 필요하다. 본 논문은 PMSG에 부하가 걸렸을 때 무효전력으로 인한 전압강하를 보상하기 위한 전력회로와 제어기법을 제안하고 시뮬레이션을 통해 검증하였다.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.505-507
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• 1998

In this paper, a simple hierarchical fuzzy inference system using structured Takagi-Sugeno type fuzzy inference units(SFIUs) is proposed. The number of fuzzy rules of the proposed HFIS is minimum in the sense of that only the number of partitions of each system variables, not of intermediate outputs of layered fuzzy controllers, are concerned. And resulted number of fuzzy rules is a summation of partition in each system variables. Gradient descent algorithm is used for adaptation of fuzzy rules. The ball and beam control is performed in computer simulation to illustrate the performance of the proposed controller.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.537-539
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• 1998

In this paper, we construct the on-line model structure for the nonlinear process systems using the adaptive fuzzy-neural network. Adaptive fuzzy-neural network usually consists of two distinct modifiable structure, with both, the premise and the consequent part. These two parts can be adapted by different optimization methods, which are the hybrid learning procedure combining gradient descent method and least square method. To achieve the on-line model structure, we use the recursive least square method for the consequent parameter identification of nonlinear process. We design the interface between PLC and main computer, and construct the monitoring and control simulator for the nonlinear process. The proposed on-line modeling to real process is carried out to obtain the effective and accurate results.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.606-613
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• 2003

As a technical method for controlling chaotic dynamics, this paper presents a predictive control for chaotic systems based on radial basis function networks(RBFNs). To control the chaotic systems, we employ an on-line identification unit and a nonlinear feedback controller, where the RBFN identifier is based on a suitable NARMA real-time modeling method and the controller is predictive control scheme. In our design method, the identifier and controller are most conveniently implemented using a gradient-descent procedure that represents a generalization of the least mean square(LMS) algorithm. Also, we introduce a projection matrix to determine the control input, which decreases the control performance function very rapidly. And the effectiveness and feasibility of the proposed control method is demonstrated with application to the continuous-time and discrete-time chaotic nonlinear system.

• Speech Sciences
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• v.7 no.4
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• pp.11-26
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• 2000

In this paper, we propose the HMM with the MIN module. Because initial and re-estimated variance vectors are important elements for performance in HMM recognition systems, we propose a method which compensates for the mismatched statistical feature of training and test data. The MIN module function is a differentiable function similar to the sigmoid function. Unlike a continuous density function, it does not include variance vectors of the data set. The proposed hybrid HMM/MIN module is a unified network in which the observation probability in the HMM is replaced by the MIN module neural network. The parameters in the unified network are re-estimated by the gradient descent method for the Maximum Likelihood (ML) criterion. In estimating parameters, the variance vector is not estimated because there is no variance element in the MIN module function. The experiment was performed to compare the performance of the proposed HMM and the conventional HMM. The experiment measured an isolated number for speaker independent recognition.

• Clinical and Experimental Reproductive Medicine
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• v.4 no.1
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• pp.33-39
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• 1977

During the last 10 years 8 months, clinical observation has been done on 43 cases of cryptorchisms. 1. The cryptorchism has relatively high incidence rate among the anomalies of genitourinary tract(27.2%). 2. The age group, most frequently seen, was between 6 to 10 and the average age visited the hospital at the first time is 11.4 years. It means 4 to 6 years later than age for the adequate treatment required. 3. Atrophy of the seminiferous tubules, interstitial fibrosis and poor or absent spermatogenesis were noted on the 8 cases of cryptorchid biopsies. 4. Hormonal therapy(Puberogen) was done on 16 cases and 8 cases were responded. But there was no complete descent of testis through this hormonal therapy.

• Korean Journal of Bronchoesophagology
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• v.3 no.2
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• pp.318-322
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• 1997

Lingual thyroid is the term applied to a mass of ectopic thyroid tissue located on the base of the tongue in the midline. It may be found anywhere between the circumvallate papillae and the epiglottis. It is believed to be caused by developmental anomalies involving the descent of the embryologic gland anlage from its position posterior to the tuberculum impar to its normal pretracheal location between week 3 and week 7 of embryologic development. Differential diagnosis of the lingual thyroid would include lingual tonsillar hypertrophy, vallecular cyst, thyroglossal duct cyst, epidermal cyst, lymphoma. Lingual thyroid is found in approximately 1 in 100,000 people, and affected individuals have no other thyroid tissue in 70% to 100% of cases. Recently, we have experienced a case of lingual thyroid with mild dysphagia in a 48-year-old male. Now we report the case with literature review.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.679-683
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• 2010

본 연구에서는 공간적 변수인 조도계수를 기지의 수위값을 이용하여 최적값을 결정하는 방법에 대해서 검토하고자 한다. 최적화 기법에 의한 조도계수는 기지의 수위값과 수치모의에 의한 결과 값의 전체 오차를 최소화하는 값으로 결정된다. 본 연구에서는 3가지 최적화 기법을 이용하였으며 가상 수로에 대해서 적용하였다. 수위계산은 표준축차법에 의해 수행하였으며 사용된 최적화 기법은 quasi-Newton 방법이다. 1차원 모형은 Matlab을 이용하여 표준축자법으로 구성하였으며 BFGS 기법, L-BFGS 기법, Steepest Gradient Descent 기법 등도 Matlab으로 구성하였다. 표준축차법은 조도계수가 입력되면 기지의 수위값과의 2-norm을 계산하도록 구성하였다. 계산 결과에 의하면 세가 기법 모두 20 23회 정도의 반복계산을 수행하고 값이 수렴되었는데, L-BFGS의 경우에는 정확하게 음수의 조도계수로 수렴하였으며, BFGS기법과 Steepest Gradient 기법의 경우에는 양의 값으로 정확하게 수렴하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.560-567
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• 1998

This paper concerned about a imputed color printed material image in camera to decrease noise and distortion by processing median filtering with input image to identical condition. Also this paper proposed the way of compares a normal printed material with an abnormal printed material color tone with trained a learning of the error back-propagation to block classification by extracting five place from identical block(3${\times}$3) of color printed material R, G, B value. As a representative algorithm of multi-layer perceptron the error Back-propagation technique used to solve complex problems. However, the Error Back-propagation is algorithm which basically used a gradient descent method which can be converged to local minimum and the Back Propagation train include problems, and that may converge in a local minimum rather than get a global minimum. The network structure appropriate for a given problem. In this paper, a good result is obtained by improve initial condition and adjust th number of hidden layer to solve the problem of real time process, learning and train.