• ETRI Journal
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• 제38권2호
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• pp.337-346
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• 2016

We propose a data-driven kinematic control method for a robotic spatial augmented reality (RSAR) system. We assume a scenario where a robotic device and a projector-camera unit (PCU) are assembled in an ad hoc manner with loose kinematic specifications, which hinders the application of a conventional kinematic control method based on the exact link and joint specifications. In the proposed method, the kinematic relation between a PCU and joints is represented as a set of B-spline surfaces based on sample data rather than analytic or differential equations. The sampling process, which automatically records the values of joint angles and the corresponding external parameters of a PCU, is performed as an off-line process when an RSAR system is installed. In an on-line process, an external parameter of a PCU at a certain joint configuration, which is directly readable from motors, can be computed by evaluating the pre-built B-spline surfaces. We provide details of the proposed method and validate the model through a comparison with an analytic RSAR model with synthetic noises to simulate assembly errors.

• 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.

• 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.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제4권3호
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• pp.85-90
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• 2015

본 논문에서는 새로운 병렬 이벤트구동 로직 시뮬레이션 기법을 제안한다. 제안한 예측에 기반한 병렬 이벤트구동 시뮬레이션 기법은 병렬 이벤트구동 시뮬레이션에서 다른 로컬시뮬레이션과의 연동 과정에서 사용되는 입력값과 출력값에 실제값과 예측값을 함께 사용함으로써 성능 향상의 제약 요소인 동기 오버헤드 및 통신 오버헤드를 크게 감소시킬 수 있다. 본 논문에서 제안한 예측기반 병렬 이벤트구동 로직 시뮬레이션의 유용함은 다수의 디자인들에 적용한 실험을 통하여 확인할 수 있었다.

• Structural Monitoring and Maintenance
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• 제6권2호
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• pp.125-145
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• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• 한국컴퓨터그래픽스학회논문지
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• 제23권2호
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• pp.23-27
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• 2017

본 논문에서는 기존에 발표되었던 데이터 기반보행제어 기법의 단점을 보완하는 다리 간 충돌 회피 기법을 제안한다. 2010년에 제안된 Lee et. al. 의 데이터 기반 이족 보행 제어 기법 [1]은 경우에 따라 보행 중 두 다리가 서로 교차하는 동작을 만들어내기도 하는데, 이는 실제 사람 혹은 이족 보행 로봇의 보행에서는 실현될 수 없는 동작이다. 본 논문에서는 스윙 힙(swing hip)의 각도를 변경하는 피드백 규칙에 스탠스 레그 (stance leg)와의 충돌을 피할 수 있는 추가적인 각도조절을 도입하여 스윙 풋 (swing foot)이 스탠스 풋 (stance foot)을 지난 이후에만 스탠스 풋보다 안쪽으로 움직일 수 있도록 하는 알고리즘을 제안한다. 이를 통해 기존의 제어기 동작 방식에 최소한의 변경과 추가적인 계산만을 더하여 두 다리가 교차하지 않는 안정적인 보행 결과를 만들어 낼 수 있다.

• 한국수자원학회논문집
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• 제43권3호
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• pp.257-264
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• 2010

강수량을 이용해 수문분석 할 경우 강수 자료의 양과 연속성은 분석의 신뢰성에 큰 영향을 미칠 수 있다. 따라서 강수 자료가 짧거나 기계 고장 등으로 인하여 결측된 경우에 강수 자료기간을 늘리거나 결측 자료를 보완하는 것은 매우 기본적인 과정이다. 이에 본 연구에서는 결측 강수량을 보완하기 위해서 적용되는 자료구동(Data-driven) 방법인 선형계획법을 많이 사용되는 7개 기법을 비교 분석하고 우수성을 검정한다. 이를 위해서 적용한 자료는 한강 유역 내에 있는 기상청 관할 관측소 중에 미계측 기간 15년을 포함하는 철원 관측소와 5개 주변 관측소의 17년간 강수량 자료이다. 그리고 검정된 방법을 적용하여 철원 관측소의 미계측 강수량을 보완하고 한강 유역의 32년간 유역 평균 강수량을 산출한다.

• 한국산업융합학회 논문집
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• 제19권4호
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• pp.223-230
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• 2016

This paper presents data-driven thermal protection algorithm for preventing overheating of automotive sunroof motor. When a sunroof motor operates abnormally, its coil is overheated and it is failed. Besides, drivers and passengers are damaged. Hence, the sunroof motor observes its temperature and will be stoped when its temperature reach a predefined level. In order to implement low-cost thermal protection function, we drew a knowledge-based temperature increasing and decreasing curve from the result of experimental test. And then, we implemented data-driven thermal protection algorithm which prevents motor's On/Off operation according to motor operating voltage and motor speed. Finally, we implemented experimental test bed and evaluated its feasibility.

• 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.