• Korean Journal of Remote Sensing
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• 제7권1호
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.

• Structural Engineering and Mechanics
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• 제71권4호
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• pp.329-339
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• 2019

Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In this approach, the original numerical model is replaced by a surrogate model which is usually explicit and much faster to evaluate. The current paper proposed an efficient reliability method based on Monte Carlo simulation (MCS) and multi-gene genetic programming (MGGP) as a robust variant of genetic programming (GP). GP has been applied in different fields; however, its application to structural reliability has not been tested. The current study investigated the performance of MGGP as a surrogate model in structural reliability problems and compares it with other surrogate models. An adaptive Metropolis algorithm is utilized to obtain the training data with which to build the MGGP model. The failure probability is estimated by combining MCS and MGGP. The efficiency and accuracy of the proposed method were investigated with the help of five numerical examples.

• IEMEK Journal of Embedded Systems and Applications
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• 제14권2호
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• pp.79-86
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• 2019

For decades, in-memory data structures have been designed for DRAM-based main memory that provides symmetric read/write performances and has no limited write endurance. However, such data structures provide sub-optimal performance for NVM as it has different characteristics to DRAM. With this motivation, we rethink a conventional red-black tree in terms of its efficacy under NVM settings. The original red-black tree constantly rebalances sub-trees so as to export fast access time over dataset, but it inevitably increases the write traffic, adversely affecting the performance for NVM with a long write latency and limited endurance. To resolve this problem, we present a variant of the red-black tree called a hierarchical balanced binary search tree. The proposed structure maintains multiple keys in a single node so as to amortize the rebalancing cost. The performance study reveals that the proposed hierarchical binary search tree effectively reduces the write traffic by effectively reaping the high capacity of NVM.

• Annual Conference of KIPS
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.393-394
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• 2009

최근 컴퓨팅 성능의 급격한 발전으로 이를 통해 생산되는 데이터의 크기 역시 매우 커지고 있다. 이는 로터 시뮬레이션 분야에서도 마찬가지인데, 과거에는 백만개 정도의 격자 데이터 정도만을 다루었던 것에 비해 최근에는 1억개 이상의 격자 데이터를 다루려는 시도가 계속되고 있다. 그러나 이렇게 생산된 대용량의 시변환(time-variant) 유동 데이터는 그 크기가 매우 크기 때문에 일반 PC에서는 실시간으로 가시화하기에 곤란한 경우가 많다. 또, 이러한 로터 시뮬레이션 데이터는 매우 복잡한 구조를 가지고 있기 때문에 초보자가 이 데이터에서 vortex와 같은 중요한 정보를 뽑아서 가시화하는 데에는 많은 어려움이 있어 왔다. 본 논문에서는 일반 PC에서 가시화하기 어려운 대용량 로터 시뮬레이션 데이터를 고성능 가시화 컴퓨터와 VTK를 이용해서 빠르게 가시화하기 위한 방법을 서술한다. 또, 복잡한 데이터 내부의 중요한 정보들을 자동으로 빠르고 간편하게 표출하기 위한 방법을 제안한다.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.24-30
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• 2024

This paper investigates the identification and modeling of a climate greenhouse. Given real climate data from greenhouse installed in the LAPER laboratory in Tunisia, the objective of this paper is to propose a solution of the problem of nonlinear time variant inputs and outputs of greenhouse internal climate. Based on fuzzy logic technique combined with least mean squares (lms) a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results are presented to demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model based Algorithm.

• Journal of Environmental Science International
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• 제21권8호
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• pp.965-975
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• 2012

Loading of NPS pollutant was valued through simulation by using BASINS/HSPF model which can simulate runoff volume in rainfall by time. For the verification of the model, it was analyzed the scatter diagram of the simulation value and measure value of water quality and runoff volume in Dongcheon estuary. Using the built model, a study on the time-variant characteristics of runoff and water quality was simulated by being classified into four cases. The result showed the simulation value was nearly same as that of the measured runoff. In the result of fit level test for measured value and simulated value, correlation of runoff volume was computed high by average 0.86 and in the water quality items, fit level of simulation and measurements was high by BOD 0.82, T-N 0.85 and T-P 0.79.

• Journal of the Korean Institute of Telematics and Electronics B
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• 제31B권11호
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• pp.163-169
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• 1994

We propose a new learning method of recurrent neural networks as an effort to solve local minima problem. In this method the network with fixed connection weights is run for a given period time under given time-variant external inputs and initial conditions. The weights are changed in the direction that the total error is maximally decreased by using the steepest gradient method. If the obtained error is not sufficiently small even after iterating this procedure, additional feedback connections are introduced. Then, the external input signal is redefined. And we execute experiments on the recognition of Korean spoken digits as an application of the proposed network.

• Journal of Korea Water Resources Association
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• 제33권6호
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• pp.689-699
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• 2000

This paper presents the integrated storage function model (ISFM) to improve the accuracy of the storage function model (SFM) which is widely employed for flood runoff analysis and its forecasting in Korea. In order to achieve this objective, the optimization method is applied for estimation of parameters of the model which dominate the accuracy of the analysis, which is usually taken by empirical formulae, and they are treated as time dependent variables. The fuzzy control technique is used to detennine the time variant parameters. In addition, the ISFM can be applied to the combined routing of the watershed and the channel with a residual watershed.ershed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• 제22권4호
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• pp.755-760
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• 2013

The dynamics of the winder roller of a roll-to-roll printing system for printed electronics is a time-varying system because of the variation of the winder roller radius owing to rewinding or unwinding of the web. Therefore, an adaptive control method considering the time-variant characteristics is required for precise tension control. In this study, the variable PID gain method is applied to the actual roll-to-roll system and verified by experiments for unwinder tension control. The required value of the winder roller radius for the application of the variable PID gain is estimated from the measurement of the winder tension and winder motor torque. The simulation results as well as experimental results show that the fixed PID gain control cannot stabilize the tension of the winder roller with varying winder roller radius. On the other hand, the variable PID gain method can control the tension of the winder roller regardless of the winder roller radius.

• MALSORI
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• 제65호
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• pp.93-103
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• 2008

In this paper, we propose a method for optimizing a multiple pronunciation dictionary used for modeling pronunciation variations of non-native speech. The proposed method removes some confusable pronunciation variants in the dictionary, resulting in a reduced dictionary size and less decoding time for automatic speech recognition (ASR). To this end, a confusability measure is first defined based on the Levenshtein distance between two different pronunciation variants. Then, the number of phonemes for each pronunciation variant is incorporated into the confusability measure to compensate for ASR errors due to words of a shorter length. We investigate the effect of the proposed method on ASR performance, where Korean is selected as the target language and Korean utterances spoken by Chinese native speakers are considered as non-native speech. It is shown from the experiments that an ASR system using the multiple pronunciation dictionary optimized by the proposed method can provide a relative average word error rate reduction of 6.25%, with 11.67% less ASR decoding time, as compared with that using a multiple pronunciation dictionary without the optimization.