• ETRI Journal
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• 제45권6호
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.340-367
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• 2018

Storage services integration can be done for achieving high availability, improving data access performance and scalability while preventing vendor lock-in. However, multiple services environment management and interoperability have become a critical issue as a result of service architectures and communication interfaces heterogeneity. Storage federation model provides the integration of multiple heterogeneous and self-sufficient storage systems with a single control point and automated decision making about data distribution. In order to integrate diverse heterogeneous storage services into a single storage pool, we are proposing a storage service federation framework named BoxBroker. Moreover, an automated decision model based on a policy-driven data distribution algorithm and a service evaluation method is proposed enabling BoxBroker to make optimal decisions. Finally, a demonstration of our proposal capabilities is presented and discussed.

• ETRI Journal
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• 제36권5호
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.

• 한국전산유체공학회지
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• 제3권1호
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• pp.11-21
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• 1998

A vorticity-based method for the numerical solution of the two-dimensional incompressible Navier-Stokes equations is presented. The governing equations for vorticity, velocity and pressure variables are expressed in an integro-differential form. The global coupling between the vorticity and the pressure boundary conditions is fully considered in an iterative procedure when numerical schemes are employed. The finite volume method of the second order TVD scheme is implemented to integrate the vorticity transport equation with the dynamic vorticity boundary condition. The velocity field is obtained by using the Biot-Savart integral. The Green's scalar identity is used to solve the total pressure in an integral approach similar to the surface panel methods which have been well established for potential flow analysis. The present formulation is validated by comparison with data from the literature for the two-dimensional cavity flow driven by shear in a square cavity. We take two types of the cavity now: (ⅰ) driven by non-uniform shear on top lid and body forces for which the exact solution exists, and (ⅱ) driven only by uniform shear (of the classical type).

• Computers and Concrete
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• 제15권1호
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• pp.55-71
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• 2015

This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data of the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the stress increase in externally prestressed tendon, ${\Delta}f_{ps}$, is then reported. The contributions of this paper is two folds; (i) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (ii) it provides a novel solution for estimating the ${\Delta}f_{ps}$ even without a complete physical knowledge of unbonded tendons.

• Steel and Composite Structures
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• 제52권4호
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• pp.461-473
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• 2024

This study focuses on the reliability of a transmission line under wind excitation and evaluates the failure probability using explicit data resources. The data-driven framework for calculating the failure probability of a transmission line subjected to wind loading is presented, and a probabilistic method for estimating the yearly extreme wind speeds in each wind direction is provided to compensate for the incompleteness of meteorological data. Meteorological data from the Xuwen National Weather Station are used to analyze the distribution characteristics of wind speed and wind direction, fitted with the generalized extreme value distribution. Then, the most vulnerable tower is identified to obtain the fragility curves in all wind directions based on uncertainty analysis. Finally, the failure probabilities are calculated based on the presented method. The simulation results reveal that the failure probability of the employed tower increases over time and that the joint probability distribution of the wind speed and wind direction must be considered to avoid overestimating the failure probability. Additionally, the mixed wind climates (synoptic wind and typhoon) have great influence on the estimation of structural failure probability and should be considered.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제8권3호
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• pp.57-64
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• 2019

본 논문에서는 이벤트구동 게이트수준 타이밍 시뮬레이션의 성능 향상 및 디버깅 효율성 크게 높일 수 있는 공간적 부분시뮬레이션 전략이 적용된 효율적인 예측기반 병렬 시뮬레이션 기법을 제안한다. 제안된 기법은 병렬 이벤트구동 로컬시뮬레이션들의 입력값과 출력값에 대한 빠르면서도 정확한 예측을 달성하기 위해서, 공간적 부분시뮬레이션 전략을 추상화 상위수준 시뮬레이션에 적용하여 정확한 예측 데이터를 빠르고 즉각적으로 생성해낸다. 공간적 부분시뮬레이션 전략이 적용된 예측기반 병렬 게이트수준 타이밍 시뮬레이션은 성능 평가를 위하여 사용된 6개의 벤치마크 설계들에 대하여 제일 일반적인 순차 이벤트구동 게이트수준 타이밍 시뮬레이션에 비하여 평균 약 3.7배, 상용화된 멀티코어 기반의 병렬 이벤트구동 게이트수준 타이밍 시뮬레이션에 비해서는 평균 9.7배, 그리고 기존의 가장 우수한 예측기반 병렬 이벤트구동 게이트 수준 타이밍 시뮬레이션 결과에 비해서도 평균 2.7배의 시뮬레이션 성능이 향상됨을 확인할 수 있었다.

• 음성과학
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• 제9권1호
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• pp.39-47
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• 2002

This paper proposes an effective method to remedy the acoustic modeling problem inherent in the usual log-normal Parallel Model Composition intended for achieving robust speech recognition. In particular, the Gaussian kernels under the prescribed log-normal PMC cannot sufficiently express the corrupted speech distributions. The proposed scheme corrects this deficiency by judiciously selecting the 'fairly' corrupted component and by re-estimating it as a mixture of two distributions using data-driven PMC. As a result, some components become merged while equal number of components split. The determination for splitting or merging is achieved by means of measuring the similarity of the corrupted speech model to those of the clean model and the noise model. The experimental results indicate that the suggested algorithm is effective in representing the corrupted speech distributions and attains consistent improvement over various SNR and noise cases.

• 센서학회지
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• 제32권6호
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Ecology and Resilient Infrastructure
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• 제9권1호
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• pp.24-35
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• 2022

수질분야에서 물재해 안정성 강화를 위해 과거와 현재의 수질을 분석하여 예측하는 기술을 지속적으로 고도화하는 것이 필요하며 데이터 기반의 예측 모형이 하나의 대안으로 대두되고 있다. 데이터 기반 모형은 복잡하고 광범위한 자료의 양을 기반으로 구축되기 때문에 보다 신뢰도 있는 결과를 얻을 수 있는 입력자료의 조합을 위한 상관관계 분석방법의 적용이 필수적이다. 본 연구에서는 보다 신속하고 정확한 데이터 기반의 수질 예측 모형을 구성하기 위한 선행단계로 Gamma Test를 적용하였다. 먼저 팔당댐의 다양한 수문조건에 따른 해당 유역의 복잡성과 정밀성이 재현된 과거와 현재의 일단위 수질을 최대한 확보하고자 물리적 기반 모형 (HSPF, EFDC)을 구동하였다. 팔당댐 수질예측지점과 팔당댐으로 유입되는 주요 하천의 수질을 대상으로 Gamma Test를 수행한 후 해석결과 (Gamma, Gradient, Standar Error, V-Ratio)를 통해 최적의 자료조합을 선정하는 방법을 제시하였다. 본 연구의 결과는 데이터 기반 모형 구축 시 반복적인 수행과정을 생략하여 시간을 단축하면서 보다 효율적으로 최적의 입력자료를 선정할 수 있는 정량적인 기준을 보여준다.