• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• 대한방사선기술학회지:방사선기술과학
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• 제24권2호
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• pp.19-22
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• 2001

The purpose of this study is to evaluate time course of signal enhancement on Gadomer-17 enhance MRI, and to correlate the size of enhanced area with that of the infarct area on 2'3'5'-triphenyl tetrazolium chloride(TTC) histochemical examination for the assessment of myocardial viability in reperfused Myocardial Infarction in a cat model. Tan cats(average weight: 3.8 kg) which had undergone 90 minutes of occlusion of the LAD followed by 90 minutes of reperfusion underwent MR T2-weighted imaging, and T1-weighted imaging, enhanced T1-weighted imaging. We used 1.5T Magneton Vision MRI system(Siemens, Erlangen, Germany). Signal intensities were measured in the enhanced and non-enhanced areas of enhanced T1-weighted imaging. and TTC histochemical staining the size of the abnormal signal area on each image was compared with that of the infarct area. Maximum enhancement was detected during a $40{\sim}60$ minute period with an average enhancement of $168{\pm}9.9%$ of normal myocardium. TTC staining revealed that the size of the high signal area on T2-weighted images and of the enhanced area on enhanced T1-weighted images was greater than that of the infarct area($T2=48.1%{\pm}3.7$, enhanced $T1=47.2%{\pm}2.6$, TTC $staining=38.7%{\pm}3.1$ ; p<0.05). In reperfused Myocardial Infarction in a cat model, enhanced MR imaging delineates reversibly and irreversibly damaged myocardium, with a strong enhancement and a broad temporal window. We may therefore expect that enhanced MR image is useful for demonstrating myocardial injury.

• 융합보안논문지
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• 제8권3호
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• pp.33-39
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• 2008

소트프웨어 고장 시간은 테스팅 시간과 관계없이 일정하거나. 단조증가 혹은 단조 감소 추세를 가지고 있다. 이러한 소프트웨어 신뢰모형들을 분석하기 위한 자료척도로 자료에 대한 추세 검정이 개발되어 있다. 추세 분석에는 산술평균 검정과 라플라스 추세 검정등이 있다. 추세분석들은 전체적인 자료의 개요의 정보만 제공한다. 본 논문에서는 고장시간을 측정하는 도중에 지수가중 이동 평균 관리도를 이용하여 관리 상태에 있는 자료만 가지고 정보분석을 해야 효율성이 있을 것으로 판단된다. 따라서 본 논문에서는 기존의 추세 검정과 지수가중이동평균 관리도를 사용하여 실제 소프트웨어 자료를 비교 분석하는 것을 목표로 하고 있다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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• pp.369-376
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• 2002

The stochastic analysis of semi-infinite domain is presented using the weighted integral method, which is improved to include the higher order terms in expanding the displacement vector. To improve the weighted integral method, the Lagrangian remainder is taken into account in the expansion of the status variable with respect to the mean value of the random variables. In the resulting formulae only the 'proportionality coefficients' are introduced in the resulting equation, therefore no additional computation time and memory requirement is needed. The equations are applied in analyzing the semi-infinite domain. The results obtained by the improved weighted integral method are reasonable and are in good agreement with those of the Monte Carlo simulation. To model the semi-infinite domain, the Bettess's infinite element is adopted, where the theoretical decomposition of the strain-displacement matrix to calculate the deviatoric stiffness of the semi-infinite domains is introduced. The calculated value of mean and the covariance of the displacement are revealed to be larger than those given by the finite domain assumptions which is thought to be rational and should be considered in the design of structures on semi-infinite domains.

• Journal of Information Processing Systems
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• 제6권1호
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• pp.79-90
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• 2010

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.193-214
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• 2017

In this paper, we study the problem of network utility maximization in a CSMA based multi-hop wireless network. Existing work in this aspect typically adopted continuous time Markov model for performance modelling, which fails to consider the channel conflict impact in actual CSMA networks. To maximize the utility of a CSMA based wireless network with channel conflict, in this paper, we first model its weighted network capacity (i.e., network capacity weighted by link queue length) and then propose a distributed link scheduling algorithm, called CSMA based Maximal-Weight Scheduling (C-MWS), to maximize the weighted network capacity. We derive the upper and lower bounds of network utility based on C-MWS. The derived bounds can help us to tune the C-MWS parameters for C-MWS to work in a distributed wireless network. Simulation results show that the joint optimization based on C-MWS can achieve near-optimal network utility when appropriate algorithm parameters are chosen and also show that the derived utility upper bound is very tight.

• 한국산학기술학회논문지
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• 제13권2호
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• pp.562-574
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• 2012

기존 대부분의 연구에서 사용하고 있는 시뮬레이션 검증 기법은 통계적 분석기법으로, 총 처리량이나 자원 이용률의 평균 및 분산을 통해 분석하여 왔다. 그러나 이러한 방식은 모델의 개별적인 요소들에 대한 신뢰성을 보장하기 어려웠다. 이를 해결하기 위해 제시된 방법이 가중 F-measure를 사용한 검증이다. 하지만 가중 F-measure는 Tact time 값 하나에 대해 하나의 클래스를 할당하기 때문에 수많은 Tact time 값들을 갖는 복잡한 시스템에 적용하기 어려운 문제를 가지고 있다. 한편, 가중치의 범위가 정해져 있지 않기 때문에 평가기준(Threshold)의 선정에 있어서 어느 정도의 수준이 만족할만한 수준인지 정하기가 어려웠다. 따라서 본 논문에서는 이러한 문제점을 개선하기 위해 군집분석을 적용한 가중 F-measure를 제시한다. 군집의 클래스화를 통해 클래스의 수를 현저히 줄일 수 있고 다양한 시스템으로의 적용 또한 가능해진다. 또한 객관성을 저하시키지 않는 범위 내에서 최소한의 가중치를 부여하는 방식으로 가중치의 범위를 지정하여 검증 방법을 향상시켰다. 이를 입증하기 위해 국내 'L사'의 LCD공정설비를 대상으로 시뮬레이션 모델링 및 환경을 구축하였고, 그 결과를 통해 타당성을 증명하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.343-347
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• 1989

In this paper, predictive control method using actual applied input which is the weighted summation of past inputs is presented. In conventional predictive control methods, a set of control inputs is computed and in these only the first element is applied to the process at each time instant. But this predictive control method based on conventional methods considers all computed control inputs. Consequently, the characteristic of response and the reliability of the control scheme in the case of imperfact model are improved.

• Investigative Magnetic Resonance Imaging
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• 제21권2호
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• pp.65-70
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• 2017

Purpose: To optimize the saturation time and maximizing the pH-weighted difference between the normal and ischemic brain regions, on 3-tesla amide proton transfer (APT) imaging using an in vivo rat model. Materials and Methods: Three male Wistar rats underwent middle cerebral artery occlusion, and were examined in a 3-tesla magnetic resonance imaging (MRI) scanner. APT imaging acquisition was performed with 3-dimensional turbo spin-echo imaging, using a 32-channel head coil and 2-channel parallel radiofrequency transmission. An off-resonance radiofrequency pulse was applied with a Sinc-Gauss pulse at a $B_{1,rms}$ amplitude of $1.2{\mu}T$ using a 2-channel parallel transmission. Saturation times of 3, 4, or 5 s were tested. The APT effect was quantified using the magnetization-transfer-ratio asymmetry at 3.5 ppm with respect to the water resonance (APT-weighted signal), and compared with the normal and ischemic regions. The result was then applied to an acute stroke patient to evaluate feasibility. Results: Visual detection of ischemic regions was achieved with the 3-, 4-, and 5-s protocols. Among the different saturation times at $1.2{\mu}T$ power, 4 s showed the maximum difference between the ischemic and normal regions (-0.95%, P = 0.029). The APTw signal difference for 3 and 5 s was -0.9% and -0.7%, respectively. The 4-s saturation time protocol also successfully depicted the pH-weighted differences in an acute stroke patient. Conclusion: For 3-tesla turbo spin-echo APT imaging, the maximal pH-weighted difference achieved when using the $1.2{\mu}T$ power, was with the 4 s saturation time. This protocol will be helpful to depict pH-weighted difference in stroke patients in clinical settings.