• 대한원격탐사학회지
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• 제25권6호
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제10권2호
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• pp.53-58
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• 2021

본 논문에서는 제어 입력이 있는 이산 시간 상태 공간 모델에 대한 유한기억구조(Finite Memory Structure, FMS) 스무딩 필터(Smoothing filter)를 개발한다. FMS 스무딩 필터는 가장 최근 윈도우의 유한 관측값과 제어 입력값만을 이용하여 비편향성 제약조건하에서 최소 분산 성능 지표의 최적화 문제를 직접 해결함으로써 얻어진다. FMS 스무딩 필터는 비편향성(Unbiasedness), 무진동성(Deadbeat) 및 시불변성(Time-invariance)과 같은 내재적으로 좋은 특성을 갖는다. 또한, 관측값과 추정값이 구해지는 시간 사이의 지연 길이에 따라 FMS 스무딩 필터는 기존의 FMS 필터들과 동등함을 보인다. 마지막으로, 컴퓨터 시뮬레이션을 통해 제안된 FMS 스무딩 필터의 내재적인 강인성(Robustness)을 검증하기 위해 일시적인 모델 불확실성을 가진 시스템에 FMS 스무딩 필터를 적용해본다. 시뮬레이션 결과를 통해 제안된 FMS 스무딩 필터가 기존의 FMS 필터와 칼만(Kalman) 필터보다 우수할 수 있음을 보여준다.

• Advances in aircraft and spacecraft science
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• 제10권6호
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• pp.495-519
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• 2023

The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Korean Chemical Engineering Research
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• 제49권2호
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• pp.129-136
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• 2011

SMB 공정은 주로 4개의 구역으로 나뉘어지는 다수의 크로마토그래피 컬럼으로 구성된다. 이러한 특성은 회분식 크로마토그래피 공정보다 우수한 이성분계 물질의 연속적인 분리를 구현한다. SMB는 회분식 크로마토그래피에 비해 연속성 및 높은 생산성과 순도로 목적물질을 분리해 낼 수 있는 장점을 갖는다. 경제적이며 효율적인 공정의 운용을 위해 반응과 회수를 결합시키는 연구가 보고되고 있으며, 이와 같은 연구 중 SMBR은 연속분리공정인 SMB와 반응기가 결합된 공정이다. 다양한 반응을 적용한 SMBR에 대해 많은 연구가 진행되고 있으며, 촉매반응, 효소반응, 이온 교환 수지를 통한 화학반응이 주를 이루고 있다. 초기의 SMBR은 촉매를 사용한 고정층의 형태이며, 유동성 효소를 사용하는 SMBR, 고정화 효소를 사용하는 SMBR, 반응구역과 흡착구역이 분리되어 있는 SMBR순으로 발전하였다. 공정 설계에 있어서 필수적인 모델링 및 최적화를 위하여 대류현상만을 고려한 간단한 기법이 있지만, 실제 물질거동을 설명하기 위해서는 축 방향 분산이나 물질전달 저항을 고려한 복잡한 해석을 필요로 한다. SMBR같이 반응과 분리가 결합된 공정의 경우 설비의 간소화를 통한 시설비용의 축소뿐 아니라 가역반응평형의 극복을 통해 물질의 순도와 수율을 향상시킬 수 있는 장점이 있다.

• 지능정보연구
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• 제17권4호
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• pp.157-173
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• 2011

최근 인터넷 사용의 증가에 따라 네트워크에 연결된 시스템에 대한 악의적인 해킹과 침입이 빈번하게 발생하고 있으며, 각종 시스템을 운영하는 정부기관, 관공서, 기업 등에서는 이러한 해킹 및 침입에 의해 치명적인 타격을 입을 수 있는 상황에 놓여 있다. 이에 따라 인가되지 않았거나 비정상적인 활동들을 탐지, 식별하여 적절하게 대응하는 침입탐지 시스템에 대한 관심과 수요가 높아지고 있으며, 침입탐지 시스템의 예측성능을 개선하려는 연구 또한 활발하게 이루어지고 있다. 본 연구 역시 침입탐지 시스템의 예측성능을 개선하기 위한 새로운 지능형 침입탐지모형을 제안한다. 본 연구의 제안모형은 비교적 높은 예측력을 나타내면서 동시에 일반화 능력이 우수한 것으로 알려진 Support Vector Machine(SVM)을 기반으로, 비대칭 오류비용을 고려한 분류기준값 최적화를 함께 반영하여 침입을 효과적으로 차단할 수 있도록 설계되었다. 제안모형의 우수성을 확인하기 위해, 기존 기법인 로지스틱 회귀분석, 의사결정나무, 인공신경망과의 결과를 비교하였으며 그 결과 제안하는 SVM 모형이 다른 기법에 비해 상대적으로 우수한 성과를 보임을 확인할 수 있었다.