• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.675-676
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• 2013

This paper presents a design method for multiple-output combinational digital logic systems using time domain based on multiplexing and common multi-terminal extension decision diagrams. The common multi-terminal extension decision diagrams represents extension valued multiple-output functions, while time domain based on multiplexing systems transmit several signals on a single lines. The proposed method can reduce the 1)hardware, 2)logic levels and 3)pins. In the logic system design, we use two types of decision diagrams, that is the common binary decision diagrams and common multi-terminal extension decision diagrams.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3021-3023
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• 1999

엄격한 시간 제약성에 의해 특성화되는 실시간 시스템의 성능을 평가하기 위해서 퍼지-랜덤 변수가 포함된 이산 사건 모델을 제시한다. 실시간 시스템의 정확성은 출력의 논리적 결과 뿐 아니라 반응시간에도 의존하므로, 본 논문에서는 실시간 시스템의 성능을 유연하게 평가하기 위해서 퍼지-랜덤 변수에 의해 적절하게 변형된 상태 오토마타를 제시하고 그 오토마타를 적용한 수치 예제를 제시한다.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1714-1716
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• 2005

본 논문은 독립형 시스템에 태양광을 집적하기위하여 최대출력점추적기를 적용한 새로운 제어방법을 제시한다. PV 판넬로부터 하나의 변수만을 시스템에 대한 제어변수로서 이용한다. 제안한 방법은 전압, 전류의 명확한 결과를 적용하지 않고 최대 전력점을 계산할 수 있다. 제안된 시스템에 대한 전력회로는 동기정류기를 가지는 승압형 컨버터이다.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.15-25
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• 2004

The regression analysis and Adaptive -Network based Fuzzy-inference system (ANFIS) were applied to the explanation on human's visual texture of cotton fabrics with 7 mechanical properties. The ANFIS uses the structure with fuzzy membership function and neural network. The results obtained by the statistical analysis through the coefficient of correlation and regression analysis showed that subjective texture had a linear relationship with mechanical properties. But It had a relatively low coefficient of determination and was difficult that the statistical analysis explained other relationship with the exception of a lineality and interaction among mechanical properties. Comparing the statistical analysis, the ANFIS was an effective tool to explain human's non-linear perceptions and their interactions. But to apply ANFIS to human's perceptions more effectively, it is necessary to discriminate effective input variables through controlling the properties of samples.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.123-141
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• 2023

A sufficient amount of data with quality is needed for training artificial neural networks (ANNs). However, developing ANN models with a small amount of data often appears in engineering fields. This paper presented an ANN model to improve prediction performance of the ammonia emission amount with 83 data. The ammonia emission rate included eleven inputs and two outputs (maximum ammonia loss, N_max and time to reach half of N_max, K_m). Categorical input variables were transformed into multi-dimensional equal-distance variables, and 13 data were added into 66 training data using a generative adversarial network. Hyperparameters (number of layers, number of neurons, and activation function) of ANN were optimized using Gaussian process. Using 17 test data, the previous ANN model (Lim et al., 2007) showed the mean absolute error (MAE) of K_m and N_max to 0.0668 and 0.1860, respectively. The present ANN outperformed the previous model, reducing MAE by 38% and 56%.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.4
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• pp.47-52
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• 2005

In this paper, the genetic algorithm is used to design a fixed order square decoupling $H_{\infty}$ controllers based on the Two-Degree-of-freedom standard model. The proposed decoupling $H_{\infty}$ controller which is minimizes the maximum energy in the output signal is designed to reduce the coupling properties between the input/output variables which make it difficult to control a system efficiently. A minimal set of assumptions for existence of the decoupling controller formula is described in the state-space formulas. It is verified by an example.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.1-13
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• 2010

It is not easy to set RED(Random Early Detection) parameter according to environment in managing Network Device. Especially, it is more difficult to set parameter in the case of maintaining the constant service rate according to the change of environment. In this paper, we hypothesize the router that has Multi-core CPU in output queue and propose AI RED(Artificial Intelligence RED), which directly induces Genetic Algorithm of Artificial Intelligence in the output queue that is appropriate to the optimization of parameter according to RED environment, which is automatically adaptive to workload. As a result, AI RED Is simpler and finer than FuRED(Fuzzy-Logic-based RED), and RED parameter that AI RED searches through simulations is more adaptive to environment than standard RED parameter, providing the effective service. Consequently, the automation of management of RED parameter can provide a manager with the enhancement of efficiency in Network management.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.234-234
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• 2023

유해 남조 대발생(Harmful Algal blooms, HABs)이 담수호에 발생하면 마이크로시스틴과 같은 독성물질과 맛·냄새 물질을 생성하여 상수원이용과 친수활동을 방해한다. 그래서 유해 남조 대발생 전 유해남조 세포수를 예측하여 선제적 대응하는 것은 중요하다. 따라서 본 연구는 머신러닝기반 Random Forest(RF)를 활용하여 팔당댐 앞의 유해남조 세포수를 예측하는 모델을 개발하고 성능을 평가하고자 한다. 모델 구축을 위해 2012년 4월부터 2021년 12월까지의 팔당호(삼봉리, 경안천) 및 남북한강(의암댐~이포보)권역의 조류, 수질, 수리/수문, 기상 자료를 수집하여 입력 및 출력 자료로 이용하였다. 수집된 데이터에는 다양한 입력변수들이 있어 남조 세포수 예측 성능 비교를 위한 전체 26개 변수 적용과 통계학적으로 상관관계가 높은 12개 변수 적용을 통해 모델을 구축하였다. 입력, 출력 자료로 이용한 유해남조 세포수는 로그변환된 값으로 사용하였으며 일반적인 조류 시료 채취기간이 7일이므로 7일 후를 예측하기 위한 모델을 구축하였다. 구축한 모델의 성능은 실측데이터와 예측데이터의 R²로 산출하여 평가하였다. 전체 26개 입력변수로 모델 구축 후 학습 및 검증 수행 결과 R²의 학습 0.803, 검증 0.729로 나타났고, 유해남조 세포수와 유의미한 상관관계를 보이는 12개 입력변수로 모델 구축 후 학습 및 검증 수행 R²은 학습 0.784, 검증 0.731로 나타났다. 두 모델의 성능을 살펴본 결과 입력변수 개수의 변화에 따른 성능차이는 크지 않은 것으로 나타났으며, 남조세포수 예측을 위한 모델로서 활용가능함을 알 수 있었다. 향후 연구에서는 Random Forest 외 다른 기계학습 모델들과 딥러닝 모델을 통해 남조세포수 예측 성능이 높은 모델을 구축해볼 필요성이 있다.

• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.4
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• pp.93-103
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• 1999

The steady-state performance analysis program for turboprop engine which was used for a small, middle industrial aircraft and a basic trainer aircraft was developed and linear Gas Path Analysis method was applied to Engine Health Monitoring for Turboprop engine. This program was compared with TURBOMARCH program which is well known with performance and power according to flight Mach No. at the standard atmospheric condition to prove a steady-state performance analysis program. From the result, inlet, exit temperature and pressure of each component had error within 3% and especially power according to flight Mach No. had error within 2.4% so that this program could be assured. To make sure if linear Gas Path Analysis is reasonable four cases were selected. The first is the case that fouling is occurred in compressor only. The second is the case that fouling is occurred in compressor and erosion is occurred in turbine. The third is the case that erosion is occurred in both compressor and turbine and power turbine at the same time. Finally, the case that fouling and erosion are occurred in compressor, compressor turbine and power turbine was selected. Different parameters were selected impartially among the independent parameters so that the effect of measurement parameter selection was observed. From the result, the more measurement parameters the smaller RMS error and even though the number of measurement parameters was the same, the RMS error was obtained differently according to which measurement parameters were selected. The case using eight instrument parameters of case IV-4 had small error comparably and was economic and it was important to select optimal number of measurement and optimal measurement parameters.