• International Journal of Aeronautical and Space Sciences
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• v.15 no.4
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• pp.345-355
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• 2014

This paper focuses on aerodynamic and inertial modeling of the propeller for its applications in flight dynamics analyses of a propeller-driven airplane. Unsteady aerodynamic and inertial loads generated by the propeller are formulated using the blade element method, where the local velocity and acceleration vectors for each blade element are obtained from exact kinematic relations for general maneuvering conditions. Vortex theory is applied to obtain the flow velocities induced by the propeller wake, which are used in the computation of the aerodynamic forces and moments generated by the propeller and other aerodynamic surfaces. The vortex lattice method is adopted to obtain the induced velocity over the wing and empennage components and the related influence coefficients are computed, taking into account the propeller induced velocities by tracing the wake trajectory trailing from each of the propeller blades. Aerodynamic forces and moments of the fuselage and other aerodynamic surfaces are computed by using the wind tunnel database and applying strip theory to incorporate viscous flow effects. The propeller models proposed in this paper are applied to predict isolated propeller performances under steady flight conditions. Trimmed level forward and turn flights are analyzed to investigate the effects of the propeller on the flight characteristics of a propeller-driven light-sports airplane. Flight test results for a series of maneuvering flights using a scaled model are employed to run the flight dynamic analysis program for the proposed propeller models. The simulations are compared with the flight test results to validate the usefulness of the approach. The resultant good correlations between the two data sets shows the propeller models proposed in this paper can predict flight characteristics with good accuracy.

• Journal of the Korean Society of Visualization
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• v.15 no.2
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• pp.59-67
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• 2017

To verify floatability of ABB (Air bubble barrier), we compared bubble swarm behavior with and without the air-driven ejector. Experiment was conducted using the fabricated air-driven ejector with 5 mm nozzle on the bottom of 1 m3 water tank. Reynolds number of air in the nozzle was ranged 1766-13248. We analyzed data with statistical method using image processing, particle mage velocimetry (PIV) and proper orthogonal decomposition (POD) analysis. As a result of POD analysis, there was no significant eigenmode in bubbly flow generated from the ejector. It means that more complex turbulent flows were formed by the ejector, thereby (1) making bubbles finer, (2) promoting three-dimensional energy transfer between bubble and water, and (3) making evenly distributed velocity profile of water. It is concluded that the air-driven ejector could enhance the performance of ABB.

• ETRI Journal
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• v.36 no.5
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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.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• Speech Sciences
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• v.11 no.2
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• pp.247-257
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• 2004

There has been many research efforts to overcome the problems of speech recognition in the noisy conditions. Among them, the model-based compensation methods such as the parallel model combination (PMC) and vector Taylor series (VTS) have been found to perform efficiently compared with the previous speech enhancement methods or the feature-based approaches. In this paper, a data-driven model compensation approach that adapts the HMM(hidden Markv model) parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional model-based methods such as the PMC, the statistics necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared with the PMC for the noisy speech recognition.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.3
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• pp.226-233
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• 1994

The present paper considers the method to estimate the bottom friction driven by waves and current on rough turbulent flow. Parameter adjusting technique is suggested for the computation of bed shear stress driven by uni-directional flow. and the value of parameter is determined by comparing the computational results against Bijker's laboratory data. For the computation of combined flow bottom shear stress, two methods are presented; one is the modified Bijker approach (BYO Model) and the other is the modified Fredsoe approach (FY Model). both of which are refined by the present writer. Both models are again refined in two aspects, and tested against the Bijker's laboratory data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.367-370
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• 2024

본 논문에서는 데이터-드리븐 기법을 이용해 가상환경에서 사용자의 동작에 따라 아바타의 저글링 움직임을 자연스럽게 처리할 수 있는 방법을 제안한다. 사용자의 저글링 동작 정보를 이용하여 아바타의 움직임을 제어할 뿐만 아니라 가상 공의 궤적을 실시간으로 표현할 수 있다. 이 과정에서 사용자의 손위치 정보를 모두 활용하는 것이 아닌, 한 쪽 손의 데이터를 기반으로 다른 쪽 손의 궤적을 합성한다. 또한 계산량이 큰 물리 기반 최적화 과정이 아닌, 상대적으로 경량화된 기법인 포물선 운동을 활용해 가상 공의 궤적으로 실시간으로 표현할 수 있는 결과를 보여준다.

• Journal of Hydrogen and New Energy
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• v.35 no.4
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• pp.437-450
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• 2024

Sustainable shipping depends on eco-friendly energy solutions. This paper reviews methods for predicting marine fuel cell performance, including empirical approaches, physical modeling, data-driven techniques, and hybrid methods. Accurate prediction models tailored to the marine environment's unique conditions are crucial for operational efficiency. By evaluating the strengths and weaknesses of each method, this study provides a comprehensive analysis of effective strategies for forecasting polymer electrolyte membrane fuel cell and solid oxide fuel cell performance in marine applications. These insights contribute to the advancement of eco-friendly shipping technologies and enhance fuel cell performance in challenging marine environments.

• Journal of Aerospace System Engineering
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• v.15 no.4
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• pp.7-10
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• 2021

Including 425 Program, which is Korean military surveillance and reconnaissance satellite, there were mostly civil-driven space programs in Korea. However, there are increasing numbers of military demand-driven space program in nowadays. Furthermore, it is positive effects on launch vehicle development in Korea that the termination of Korea-U.S. missile guideline. In this paper, it emphasizes the needs of system engineering(SE) management method which meets both defense system's characteristics and space's characteristics. These characteristics are such as non-fixable after the launch, the security issue in defense system. And it also introduces SE tool, methodology and its philosophy. There are several functions that data management, issue management, risk management, and technical requirement management. Also describing its implications and direction of improvement.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.332-338
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• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.