• Journal of Digital Convergence
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• v.14 no.8
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• pp.57-67
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• 2016

As the Internet is becoming more socialized, Korean government, publishing a slogan, Government 3.0, has recently began to find a way to deliver its administration services to the public in more personalized manner. Policy directions to implement this advanced idea, are however still at large, primarily because of the vague nature of 'personalized'. This study, therefore, explores the possibility of getting public administrative services closer to personalization. To achieve this objective, this study attempts to develop a integrative framework of classifying the administration services to the public, based on two dimensions - the degree of citizen-oriented and the degree of government-driven, both of which are perhaps key determinants of personaliztion of services. For each quadrant of the framework, key features, characteristics, and conditions to be met are explained and followed by exemplary cases and policy implications.

• Journal of KIISE
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• v.41 no.12
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• pp.1090-1098
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• 2014

In this paper, an improved ACDE (Adaptive Cauchy Differential Evolution) algorithm with faster convergence speed, called ACDE2, is suggested. The baseline ACDE algorithm uses a "DE/rand/1" mutation strategy to provide good population diversity, and it is appropriate for solving multimodal optimization problems. However, the convergence speed of the mutation strategy is slow, and it is therefore not suitable for solving unimodal optimization problems. The ACDE2 algorithm uses a "DE/current-to-best/1" mutation strategy in order to provide a fast convergence speed, where a control parameter initialization operator is used to avoid converging to local optimization. The operator is executed after every predefined number of generations or when every individual fails to evolve, which assigns a value with a high level of exploration property to the control parameter of each individual, providing additional population diversity. Our experimental results show that the ACDE2 algorithm performs better than some state-of-the-art DE algorithms, particularly in unimodal optimization problems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1274-1285
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• 1991

This paper presents a statistical convergence analysis of the normalized least mean square(NLMS)algorithm that employs a single-pole lowpass filter, In this algorithm the lowpass filter is used to adjust its output towards the estimated value of the input signal power recursively. The estimated input signal power so obtained at each time is then used to normalize the convergence parameter. Under the assumption that the primary and reference inputs to the adaptive filter are zero mean wide sense stationary, and Gaussian random processes, and further making use of the independence assumption. we derive expressions that characterize the mean and maen squared behavior of the filter coefficients as well as the mean squared estimation error. Conditions for the mean and mean squared convergence are explored. Comparisons are also made between the performance of the NLMS algorithm and that of the popular least mean square(LMS) algorithm Finally, experimental results that show very good agreement between the analytical and emprincal results are presented.

• Journal of KSNVE
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• v.5 no.4
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• pp.453-460
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• 1995

본 논문에서는 정현파 소음을 제어하기 위한 filtered-x LMS에 바탕을 둔 새로운 적응 알고리듬을 제안하였다. 이러한 알고리듬은 두개의 연속적인 계수조정 식으로, 제어기의 계수를 조정한다. 서로 독립인 각 주파수별로 처리하기 때문에 빠른 수렴을 얻을 수 있다. 두번째식은 이차경로로 인한 위상지연을 추정한다. 정현 파 신호 주파수보다 4배 이상 빠른 표본화 주파수를 선택하여 추정된 위상지연 추정 값은 $2{\pi}f_0$만큼 오차를 나타내며, 이 값은 $\pi$2보다 작다. 정현파 신호의 주파수를 알면 이러한 오차는 $2{\pi}f_0$를 더함으로써 제거할 수 있다. 이러한 방법은 위상지연이 $\pi$2보다 큰 경우 수렴속도를 증가시킨다는 사실을 실험을 통하 여 알 수 있다. 추정된 위상지연은 제어기 계수값을 조정하는데 필요한 필터링된 참조신호를 발생시키믄데 사용된다. 참조신호의 위상지연이 각 주파수 성분별로 수행 되기 때문에, 콘볼루션 연산이 생략되어 계산량을 줄일 수 있다. 또한 연속적으로 위상지연을 추정하기 때문에 시변 상황에 적용이 가능하다. 조정식의 수렴조건을 유도하였다. 제안된 알고리듬은 제어기 계수를 추정하는데 바이어스가 없으며, 위상 지연추정을 위한 수렴상수의 최대허용치는 제어기계수에 대한 수렴상수에 반비례함을 이론적으로 분석을 통해 알 수 있다. 모의실험을 통하여 제안된 알고리듬이 filtered-x LMS 알고리듬에 바탕을 둔 다른 알고리듬보다 환경변화에 우수한 성능을 보임을 알 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.1985-1997
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• 1997

This paper proposed a regularized iterative image restoration by using method of conjugate gradient. Compared with conventional iterative methods, method of conjugate gradient has a merit to converte toward a solution as a super-linear convergence speed. But because of those properties, there are several artifacts like ringing effects and the partial magnification of the noise in the course of restoring the images that are degraded by a defocusing blur and additive noise. So, we proposed the regularized method of conjugate gradient applying constraints. By applying the projectiong constraint and regularization parameter into that method, it is possible to suppress the magnification of the additive noise. As a experimental results, we showed the superior convergence ratio of the proposed mehtod compared with conventional iterative regularized methods.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.380-384
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• 2007

MLP(Multi-Layer Perceptron)를 이용한 학습은 간단한 구조에도 비선형 분류가 가능하다는 장점을 가지고 있다. 하지만 오류역전파 알고리즘을 사용함으로써 시간의 소모가 크고 원치 않는 결과값으로의 수렴가능성을 배제할 수 없다는 단점을 가지고 있다. 이는 초기설정의 의존도가 높기 때문에 발생하는 문제들로 좋은 결과값에 근접한 곳으로 초기화가 이루어지면 좋은 학습 성능을 보이지만 반대로 좋은 결과값으로부터 멀리 떨어진 곳으로 신경망의 초기화가 이루어지면 학습 성능이 현저히 낮아지는 현상을 보인다. 본 논문에서는 MLP 전체의 층을 대상으로 하는 본 학습이 이루어지기 전에 RBM(Restricted Boltzmann Machine)을 이용, 층간 선행학습을 행하고 그 결과로 얻어지는 가중치와 바이어스를 본 MLP 학습의 초기화 데이터로 사용하는 개선 MLP 학습 알고리즘을 제안한다. 이 방법을 사용함으로써 MLP 학습 속도향상은 물론 원치 않는 지역해로의 수렴까지 방지할 수 있어 전체적인 학습 성능향상이 가능하게 된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2091-2101
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• 1997

Conventional method for fractal image decoding requires high-degree computational complexity in decoding propocess, because of iterated contractive transformations applied to whole range blocks. In this paper, we propose a fast decoding algorithm of fractal image using data depence in order to reduce computational complexity for iterated contractive transformations. Range of reconstruction image is divided into a region referenced with domain, called referenced range, and a region without reference to domain, called unreferenced range. The referenced range is converged with iterated contractive transformations, and the unreferenced range can be decoded by convergence of the referenced range. Thus the unreferenced range is called data dependence region. We show that the data dependence region can be deconded by one transformation when the referenced range is converged. Consequently, the proposed method reduces computational complexity in decoding process by executing iterated contractive transformations for the referenced range only.

• Agrochemical news magazine
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• v.17 no.1 s.130
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• pp.10-12
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• 1996

• School Mathematics
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• v.16 no.2
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• pp.317-334
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• 2014

The purpose of this study is to grasp pre-service secondary teachers' understanding-realities for Taylor series convergence conceptions and to examine a teaching way using GeoGebra based on the understanding-realities. In this study, most pre-service teachers have abilities to calculate the Taylor series and radius of convergence, but they are vulnerable to conceptual problems which give meaning of the equality between a given function and its Taylor series at any point. Also they have some weakness in determining the change of radius of convergence according to the change of Taylor series' center. To improve their weakness, we explore a teaching way using dynamic and CAS functionality of GeoGebra. This study is expected to improve the pedagogical content knowledge of pre-service secondary mathematics teachers for infinite series treated in high school mathematics.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.231-238
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• 2004

This paper presents the high speed noise reduction processing system using the modified discrete fourier transform(MDFT) on the frequency domain. The proposed filter uses the linear prediction coefficients of the adaptive line enhance(ALE) method based on the Sign algorithm The signals with a random noise tracking performance are examined through computer simulations. It is confirmed that the fast adaptive digital filter is realized by the high speed adaptive noise reduction(HANR) algorithm with rapid convergence on the frequency domain(FD).