Earticle

본 연구에서는 공간구문론의 인간행태에 대한 예측력을 높이기 위해 공간구문론에 물리적 거리를 가중 개념으로 적용하는 방법론을 제안하는 것을 목적으로 한다. 이를 위해 선행연구 고찰을 통해 기존의 거리 가중 적용 방식이 갖는 문제점을 확인하였고, 공간구문론의 핵심 개념인 ‘깊이’에 ‘접근성’의 개념이 내포되어 있음에 착안하여, 물리적 거리가 접근성에 미치는 영향을 가중 함수로 구현하여 적용하는 접근을 시도하였다. 물리적 거리가 접근성에 미치는 영향요인을 보행자 이동성의 관점과 인지거리의 관점에서 각각 바라보았고, 물리적 거리와 이동성의 관계, 물리적 거리와 인지거리의 관계를 ‘깊이’의 가중 함수로 각각 구현하였다. 이렇게 도출된 거리가중 함수들을 공간구문론 분석에 적용하고 그 결과와 실제 보행패턴과의 상관관계를 통계 분석한 결과 기존 거리가중 적용 방식과는 달리 유의미한 수준의 예측력을 보임을 확인하였다.

In order to improve the predictability of space syntax on human behavior, this study proposes a method of applying metric distance as weighting concept to space syntax. Through examining existing researches, this study finds clues in factors that metric distance has influence on accessibility, considering that the concept of 'accessibility' is implied in 'depth', the key index of space syntax. Thus, the effect of metric distance on accessibility was implemented as functions of depth on metric distance which were applied to space syntax techniques. The effect of metric distance on accessibility was viewed from the viewpoint of pedestrian mobility and cognitive distance, and the relationship between metric distance and mobility and the relationship between metric distance and cognitive distance were implemented as distance weighting functions. Unlike existing methods, the statistical analyses of the correlation tests between pedestrian movement pattern and results of space syntax analysis with distance weighting, were found to be more significant.

In order to solve such problems as excessive enhancement and chessboard effect, difficult image brightness keeping and distortion in the image enhancement algorithm based on histogram equalization, an anti-distortion image contrast enhancement algorithm based on fuzzy statistics and sub-histogram equalization is proposed in this article. Specifically, the fuzzy set theory is introduced therein to convert the image into fuzzy matrix; then, by virtue of the membership function and the probability of the image gradation, the weighting function is embedded to construct the weighted fuzzy histogram calculation model; then, the mid-value of the initial image is adopted to divide the fuzzy histogram into two sub-histograms, and the corresponding cumulative density functions are defined, and the transformation models thereof are also constructed; then, the inverse transformation function is established to realize defuzzification and output the enhanced image. The experimental data show: compared with the present image enhancement algorithm based on histogram equalization, this algorithm can significantly eliminate excessive enhancement and noise amplification, thus to not only have better visual enhancement quality and anti-distortion performance, but also have maximum AIC (Average Information Contents) value and minimum NIQE (Natural Image Quality Evaluator) value.

The current methods of estimating contest distribution function in contextual clarifier are to "classify and count", GTGM (ground-truth-guided-method) and unbiased estimator. In this paper we propose a new contextual classifier echoes context distribution is replaced by context probability that is estimated from transition probability. The classification accuracy increases considerably compared with the classical one.

특이 가중함수로 표현된 shepard interpolant와 일관조건을 사용하여 무요소법 형성함수를 도출하였다. 따라서 통상의 EFGM(Element Free Galerkin Method)과는 달리 변위로 주어지는 경계조건을 자연스럽게 부과할 수 있다. 수치계산 예로서 외팔보 문제를 다루었는데 보이론과 비교하여 매우 잘 맞는 결과를 보여주고, 유한요소법과의 결합도 자연스럽게 이루어짐을 보인다. 또 penny-shaped 균열을 다루는데, 응력확대계수는 균열 표면의 변위로부처 직접 계산하여 해석해와 비교한다.

This paper proposes a new approach to identifying the unknown parameters of continuous LTI systems. For parameter identification in continuous-time systems, the Linear Integral Filter (LIF) method generally has been used in the beginning. Especially, one of the most efficient LIF methods in the literature is to use a weighting function satisfying specific three constraints. In high order systems, even though the weighting function satisfies the three constraints, it is impossible to identify the exact parameters of the systems because of information loss arising from a great amount of magnitude differences among the weighting function and its high-order derivatives. This paper, using an LMI technique, shows the limitation in designing the weighting function of the existing methods, and ...

In this note, we extends some of the results of Liu [Fuzzy Sets and systems 157 (2006) 869-878]. This extension consists of a simple proof involving weighted functions and their preference index. We also give an elementary simple proof of the maximum entropy weighting function problem with a given preference index value without using any advanced theory like variational principles or without using Lagrangian multiplier methods.

Recently, Liu (2005) proposed a special type of weighting function under a given preference index level with the minimal variability similar to the minimal variability OWA operator weights problem proposed by Fuller and Majlender (2003). He solved this problem using a result of classical optimal control theory. In this note, we give a direct elementary proof of this problem without using any known results.

영광 2호기 9주기 노심을 대상으로 다양한 운전조건에서 노외계측기 weighting function을 계산하고 영향 인자들에 대한 민감도 분석을 수행하였다. Weighting function 계산은 2차원 각분할 수송코드인 DORT 2.8.14를 사용하였고 핵단면적 라이브러리는 ENDF/B-VI에 근거한 BUGLE93 라이브러리를 사용하였다. Weighting function은 축방향 weighting function(R-Z 모델)과 집합체별 weighting function(R- 모델)을 계산하였고, 민감도 분석에 사용한 인자는 출력준위, 연소도, 제어봉 삽입, 붕소농도이다. 민감도 분석결과 노외계측기 weighting function은 출력 준위에 민감하고 그외 모든 인자의 영향은 무시할 수 있을 만큼 작았다. 또한 출력분포와 weighting function으로부터 계산되는 단순노외계측기 교정법의 계측기반응상수는 출력준위와 연소도를 고려하여 생산해야함을 확인하였다.

Iterative learning controllers combined with existing feedback controllers have prominent capability of improving tracking performance in repeated tasks. However, the iterative learning controller has been designed without utilizing effective information such as the performance weighting function to design a feedback controller. In this paper, we deal with a robust iterative learning controller design problem for an uncertain feedback control system using its explicit performance information. We first propose a robust convergence condition in the $L_2$-norm sense for an iterative learning control (ILC) scheme. We present a method to design an iterative learning controller using the information on the performance of the existing feedback control system such as performance weighting functions and frequency ranges of desired trajectories. From the obtained results, several design criteria for iterative learning controller are provided. Through analysis on the remaining error, the loop properties before and after learning are compared. We also show that, in the $L_2$-norm sense, the remaining error can be less than the initial error under certain conditions. Finally, to show the validity of the proposed method, simulation studies are performed.

This paper describes a new type of the spatial filter detector (SFD) with variable and multi-valued weighting function. This SFD called variable spatial filter detector with multi-valued weighting function (VSFDwMWF) uses current-mode circuits for noise resistance and high-resolution weighting values. Total weighting values consist of 7bit, 6-signal bit and 1-sign bit. We fabricate VSFDwMWF chip using Rohm 0.35${\mu}$m CMOS process. VSFDwMWF chip includes two-dimensional 10${\times}$13 photodiode array and current-mode weighting control circuit. Simulation shows the weighting values are varied and multi-valued by external switching operation. The layout of VSFDwMWF chip is shown.

Via extension of the classical double-Newton method, we propose high-order family of two-point methods in this paper. Theoretical and computational properties of the proposed methods are fully investigated along with a main theorem describing methodology and convergence analysis. Typical numerical examples are thoroughly treated to verify the underlying theory.

본 논문에서는 혼합 norm을 이용한 가중치 함수 기반의 비국부 평균 노이즈 제거 방식을 제안한다. 비국부 평균 노이즈 제거 방식에서 중심 패치와 참조 패치의 오차에 대한 신뢰도는 노이즈 양 및 국부 활동성에 의존적인 특성을 갖고 있다. 본 논문에서는 혼합 norm 기반의 새로운 가중치 함수를 제안하고, 혼합 norm의 차수를 노이즈 정도 및 중심 패치의 국부 활동성에 의해 적응적으로 결정하여 비국부 평균 노이즈 제거 방식의 성능을 개선하고자 하였다. 실험 결과를 통해 기존의 비국부 평균 노이즈 제거 방식과 비교하여 제안 방식의 정량적 및 정성적 성능의 우수성을 확인할 수 있었다. 더불어, 제안 방식은 표준 유클리드 norm 기반의 다른 형태의 비국부 평균 노이즈 방식의 성능을 개선할 수 있는 능력이 있음을 확인할 수 있었다.

This paper presents a non-local means (NLM) denoising algorithm based on a new weighting function using a mixed norm. The fidelity of the difference between an anchor patch and the reference patch in the NLM denoising depends on noise level and local activity. This paper introduces a new weighting function based on a mixed norm type of which the order is determined by noise level and local activity of an anchor patch, so that the performance of the NLM denoising can be enhanced. Experimental results demonstrate the objective and subjective capability of the proposed algorithm. In addition, it was verified that the proposed algorithm can be used to improve the performance of the other $l_2$ norm based non-local means denoising algorithms

In this paper, we are applied the Genetic Algorithm(GA) to design of the robust $H^{\infty}$ speed controller by auto-tuning of the weighting function. GA is used to design of the weighting functions in the robust $H^{\infty}$ controller. To evaluate the performances of the proposed robust $H^{\infty}$ controller, we make an experiment on $H^{\infty}$ speed controller of an actual DC servo- motor system with nonlinear characteristics. Experimental results show that proposed controller have better performance than those of PD controller.

In general, spatial filtering method was proposed to simplify measurement system through parallel Processing hardware. Spatial filtering is a method of detection that we can get a spatial pattern information, as we process a special space pattern, to say, as we process spatial parallel process by using the spatial weighting function. The important processing characteristics will be depended in according to how ire design a spatial weighting function, a spatial sensitive distribution. The form of the weighting function which is realized from the generally used spatial filtering is fixed and the weighting value was already became a binary-value. In this paper, we propose a new method in order to construct adaptive measurement systems. This method is a weighting function design to make multi-valued and variable.