• 제목, 요약, 키워드: discrete optimization

검색결과 455건 처리시간 0.038초

Optimization of the anti-snow performance of a high-speed train based on passive flow control

  • Gao, Guangjun;Tian, Zhen;Wang, Jiabin;Zhang, Yan;Su, Xinchao;Zhang, Jie
    • Wind and Structures
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    • v.30 no.4
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    • pp.325-338
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    • 2020
  • In this paper, the improvement of the anti-snow performance of a high-speed train (HST) is studied using the unsteady Reynolds-Averaged Navier-Stokes simulations (URANS) coupled with the Discrete Phase Model (DPM). The influences of the proposed flow control scheme on the velocity distribution of the airflow and snow particles, snow concentration level and accumulated mass in the bogie cavities are analyzed. The results show that the front anti-snow structures can effectively deflect downward the airflow and snow particles at the entrance of the cavities and alleviate the strong impact on the bogie bottom, thereby decrease the local accumulated snow. The rotational rear plates with the deflecting angle of 45° are found to present well deflecting effect on the particles' trajectories and force more snow to flow out of the cavities, and thus significantly reduce the accretion distribution on the bogie top. Furthermore, running speeds of HST are shown to have a great effect on the snow-resistance capability of the flow control scheme. The proposed flow control scheme achieves more snow reduction for HST at higher train's running speed in the cold regions.

Wavelet Thresholding Techniques to Support Multi-Scale Decomposition for Financial Forecasting Systems

  • Shin, Taeksoo;Han, Ingoo
    • 한국데이타베이스학회:학술대회논문집
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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.

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Wavelet Thresholding Techniques to Support Multi-Scale Decomposition for Financial Forecasting Systems

  • Shin, Taek-Soo;Han, In-Goo
    • 한국지능정보시스템학회:학술대회논문집
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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.

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Simulated Moving Bed Reactor(SMBR)의 원리 (Principles of Simulated Moving Bed Reactor(SMBR))

  • 송재룡;김진일;구윤모
    • Korean Chemical Engineering Research
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    • v.49 no.2
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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.

비대칭 오류비용을 고려한 분류기준값 최적화와 SVM에 기반한 지능형 침입탐지모형 (An Intelligent Intrusion Detection Model Based on Support Vector Machines and the Classification Threshold Optimization for Considering the Asymmetric Error Cost)

  • 이현욱;안현철
    • 지능정보연구
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    • v.17 no.4
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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.