• 한국통신학회논문지
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• 제31권12A호
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• pp.1182-1188
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• 2006

적응 변조 OFDM(Orthogonal Frequency Division Multiplexing) 전송 기법은 각 부반송파의 채널 상태에 따라 변조방식을 적절히 변화시켜 무선 채널의 다중 경로 페이딩에 의해 의한 영향을 최소화하여 시스템의 성능을 증가시키는 방식이다. 시스템이 적응적으로 전송하기위해서는 단말기에서 각 부반송파(subcarrier)별 채널 상태 정보 (Channel State Information : CSI)를 되먹임 채널을 통해 실시간으로 기지국으로 전송해 주어야한다. 하지만, 단말기에서 데이터를 처리할 때 걸리는 시간과, 단말기에서 기지국으로 CSI를 되먹임(feedback) 할 때 걸리는 시간으로 인한 되먹임 지연(feedback delay) d가 발생하게 된다. 이 되먹임 지연은 CSI 정보의 불일치를 발생시켜 적응 OFDM 시스템의 성능저하를 일으킨다. 본 논문에서는 CSI의 되먹임 지연 $d(\geq2)$를 적절히 보상하는 주파수 축 멀티 스탭 채널 예측기를 제안하고 이를 적응 전송 OFDM 시스템에 적용하고 모의실험을 통하여 기존의 OFDM 시스템, 기존의 채널 예측방식과의 성능을 MSE(mean square error), 비트오율(bit error rate : BER) 및 채널용량을 바탕으로 비교한다.

• Smart Structures and Systems
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• 제32권1호
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• pp.37-47
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• 2023

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

• Journal of Power Electronics
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• 제18권5호
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• pp.1608-1617
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• 2018

The rapid and correct isolation of faulty submodules (SMs) is of great importance for improving the reliability of modular multilevel converters (MMCs). Therefore, a fast diagnosis method containing fault detection and fault location determination was presented in this paper. An improved incremental predictive model of arm current was proposed to detect failures, and the multi-step prediction method was used to eliminate the negative impact of disturbances. Moreover, a control method was proposed to strengthen the fault characteristics to rapidly locate faulty arms and faulty SMs by detecting the variation rate of the SM capacitor voltage. The proposed method can rapidly and easily locate faulty SMs under different load conditions without the need for additional sensors. The experimental results have validated the effectiveness of the proposed method by using a single-phase MMC with four SMs per arm.

• Structural Engineering and Mechanics
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• 제8권4호
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• pp.411-419
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• 1999

A nonlinear model for time-step analysis of bridges subjected to two orthogonal horizontal components of earthquake motions was developed. The focus of the study was on elastomeric isolators with or without lead cores. The hysteretic behavior of the isolators, the columns, abutments, and shear keys was taken into account. The nonlinear analysis showed that, contrary to linear theory prediction, the use of isolators does not necessarily increase the displacement of the superstructure. Furthermore, it was shown that properly designed isolators can reduce the ductility demand in RC bridge columns substantially.

• 대한전기학회논문지
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• 제39권4호
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• pp.364-370
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• 1990

In this paper, we propose a self-tuning PID controller for unknown systems with time-varying delay. Using pole placement equations, we derive the controller that can be extended to the multi-step time delay case. The time-varying delays are estimated by a prediction error delay method using multiple predictors. Since the order of the estimation vector is not increased, the persistant exciting condition of control input is alleviated. Since the least square method gives biased parameter estimates for colored noise cases, the recursive instrumental variable method is used to estimate system parameters. The computational burden of the proposed method is less than the conventional adaptive methods. Computer simulations are performed to illustrate the efficiency of the proposed method.

• 한국통신학회논문지
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• 제21권6호
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• pp.1587-1595
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• 1996

We obtained the predicted and measured results for the reflection coefficient and radiation pattern of Ka-band (20- GHz) corrugated horn, which is usually used for feeder of reflector antenna for satellite communication, using suface impedance condition. In order to predict the reture losses of corrugated horn, we analyzed propagation constant of hybrid mode in the corrugated waveguide and then obtained the total reflection coefficient using the circuti theory of multi-step transformer. We also got the radiation pattern of corrugated horn with small flare angle, considering the phase deviation and integrate transverse field on aperture. A test model of corrugated horn antenna for Ka-band designed using theory and program displayed performance and the results agree with the theoretical prediction.

• 한국컴퓨터정보학회논문지
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• 제27권2호
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• pp.171-177
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• 2022

본 논문에서는 다출처 농업 데이터를 통찰할 수 있는 지식체계를 마련하고, 시간 흐름을 가지는 환경인자 분석 정보를 클러스터링 할 수 있는, 농작물 환경 인자 큐레이션 서비스 방법을 제안한다. 제안하는 큐레이션 서비스는 크게 수집, 전처리, 저장, 분석의 네 단계로 구성된다. 첫째, 수집 단계에서는 OpenAPI 기반의 웹크롤러를 이용하여 다출처 농업 데이터에 대한 수집 및 정리를 수행한다. 둘째, 전처리 단계에서는 데이터 측정 오차를 감소시키기 위해 데이터 평활화를 수행한다. 이때 온실, 노지 등의 시설 특성에 따른 오차율을 고려하여 시설 유형별 평활화 방법을 적용한다. 셋째, 저장단계에서는 대용량 농업 데이터 관리를 위해, 농업 데이터 통합 스키마 및 Hadoop HDFS 기반의 저장 구조를 제안한다. 마지막으로 분석 단계에서는 농업 디지털 데이터의 시계열 특성을 고려한 DTW 기반의 시계열 분류를 수행한다. DTW 기반 시계열 분류를 통해 시계열 데이터의 특성을 손실 없이 반영하여 예측 결과 정확도를 향상시킨다. 향후 연구로는 제안한 서비스 방법을 구현하여 스마트팜 온실에 적용하고, 테스트 및 검증을 수행할 예정이다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Structural Engineering and Mechanics
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• 제66권4호
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• pp.505-513
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• 2018

Steam flooding is widely used in heavy oil reservoir with coupling effects among the formation temperature change, fluid flow and solid deformation. The effective stress, porosity and permeability in this process can be affected by the multi-physical coupling of thermal, hydraulic and mechanical processes (THM), resulting in a complex interaction of geomechanical effects and multiphase flow in the porous media. Quantification of the state of deformation and stress in the reservoir is therefore essential for the correct prediction of reservoir efficiency and productivity. This paper presents a coupled fluid flow, thermal and geomechanical model employing a program (MATLAB interface code), which was developed to couple conventional reservoir (ECLIPSE) and geomechanical (ABAQUS) simulators for coupled THM processes in multiphase reservoir modeling. In each simulation cycle, time dependent reservoir pressure and temperature fields obtained from three dimensional compositional reservoir models were transferred into finite element reservoir geomechanical models in ABAQUS as multi-phase flow in deforming reservoirs cannot be performed within ABAQUS and new porosity and permeability are obtained using volumetric strains for the next analysis step. Finally, the proposed approach is illustrated on a complex coupled problem related to steam flooding in an oil reservoir. The reservoir coupled study showed that permeability and porosity increase during the injection scenario and increasing rate around injection wells exceed those of other similar comparable cases. Also, during injection, the uplift occurred very fast just above the injection wells resulting in plastic deformation.

• 한국수자원학회논문집
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• 제51권11호
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• pp.1031-1041
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• 2018

본 연구에서는 ANFIS 기반 GloSea5 앙상블 기상전망 개선 기법을 개발하고 평가하였다. 대상유역은 국내 주요 다목적댐인 충주댐 유역을 선정하였으며, 개선 기법은 ANFIS 기반의 전 후처리기법으로 구성된다. 전처리 기법에서 GloSea5의 앙상블 멤버에 가중치를 부여하며(OWM), 후처리 과정에서는 전처리결과를 편의보정 한다(MOS). 평가결과 편의보정된 GloSea5에 비해 예측성능이 개선되었으며, CASE3, CASE1, CASE2 순으로 모의성능이 우수하였다. 전처리 기법은 강수의 변동성이 큰 계절에 개선효과가 우수하였으며, 후처리 기법은 전처리로 개선하지 못한 오차를 줄 일 수 있는 것으로 나타났다. 따라서 본 연구에서 개발한 ANFIS 기반 GloSea5 앙상블 기상전망 개선 기법은 전 후처리 기법을 함께 사용하는 것이 가장 좋으며, 특히 여름철과 같이 강수의 변동성이 큰 계절에 활용성이 높을 것으로 판단된다.