• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.307-317
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• 2017

In liquid-propellant rockets, POGO instability can occur, in which a fluctuation of propellant supply to the engine, a thrust fluctuation, and a structural vibration are coupled. For the prediction of this instability, it is required to provide dynamic characteristics of the pump represented as the transfer matrix correlating the upstream and downstream pressure and flow rate fluctuations. In the present study, the flow rate fluctuation is evaluated from the fluctuation of pressure difference at the different locations assuming that the fluctuation is caused by the inertia of the flow rate fluctuation. The experiments were performed in some flow conditions, and it was shown that the tendencies of dynamic characteristics are related to excitation frequencies, cavitation numbers and flow rate coefficients.

• 대한기계학회논문집
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• 제19권10호
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• pp.2637-2646
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• 1995

Combustion instability in solid-propellant rocket motors depends on the balance between acoustic energy gains and losses of the system. The objective of this paper is to demonstrate the capability of the program which predicts the standard longitudinal stability using acoustic modes based on linear stability analysis and T-burner test results of propellants. Commercial ANSYS 5.0A program can be used to calculate the acoustic characteristic of a rocket motor. The linear stability prediction was compared with the static firing test results of rocket motors.

• Journal of Mechanical Science and Technology
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• 제19권7호
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• pp.1517-1525
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• 2005

Liquid sloshing displays the highly nonlinear free surface fluctuation when either the external excitation is of large amplitude or its frequency approaches natural sloshing frequencies. Naturally, the accurate tracking of time-varying free surface configuration becomes a key task for the reliable prediction of the sloshing time-history response. However, the numerical instability and dissipation may occur in the nonlinear sloshing analysis, particularly in the long-time beating simulation, when two simulation parameters, the relative time-increment parameter a and the fluid mesh pattern, are not elaborately chosen. This paper intends to examine the effects of these two parameters on the potential-based nonlinear finite element method introduced for the large amplitude sloshing flow.

• 한국정보시스템학회지:정보시스템연구
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• 제29권3호
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• 한국지능시스템학회논문지
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• 제15권4호
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• pp.412-417
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• 2005

오늘날 인터넷을 기반으로 하는 원격 제어 기술이 급속히 발달하고 있다. 그러나 이러한 원거리 네트워크 기반 제어는 데이터를 전송함에 있어서 지연이 불가피하며, 또한 이 지연이 일정하지 않은 문제점을 지니고 있다. 이러한 네트워크 지연은 시스템의 안정성이나 정확도에 영향을 미친다. 본 논문에서는 네트워크상의 데이터 전송 지연을 고려한 이동 로봇의 원격 운용을 위해 TSK (Takagi-Sugeno-Kang) 퍼지 시스템을 이용하여 전송 지연의 확률 분포 함수와 네트워크 모델을 구하고 이를 전송 지연 예측 알고리즘에 적용하였다. 그리고 컴퓨터 시뮬레이션으로부터 제안된 알고리즘의 실효성을 검증하고, 기존의 예측 알고리즘과의 비교분석을 통하여 그 성능을 평가하였다.

• 대한의용생체공학회:의공학회지
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• 제33권2호
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• pp.78-88
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• 2012

Knee joint instability by anterior cruciate ligament(ACL) rupture is allowing the abnormal loading condition at the tibial epiphysis locally, resulting in producing locally different bone bruise. The study examined difference between local alteration patterns of trabecular bone microarchitecture at medial and lateral parts of the tibial epiphysis by ACL rupture. Fourteen SD rats were divided into Control(CON; n = 7) and Anterior Cruciate Ligament Transection(ACLT; n = 7) groups. The tibial joints were then scanned by in vivo ${\mu}$-CT at 0, 4, and 8 weeks post-surgery. The results showed that alteration pattern on trabecular bone microarchitecture at medial part was significantly higher than that at lateral part of the tibial epiphysis in ACLT group from 0 to 8 weeks(P < 0.05). Tb.Th and Tb.Sp distributions were well corresponded with differences between aforementioned trabecular bone microarchitectural alteration pattens at medial and lateral parts of the tibial epiphysis in ACLT group from 0 to 8 weeks(P < 0.05). These findings suggest that the alteration patterns of trabecular bone microarchitecture should be locally and periodically considered, particularly with respect to the prediction of bone fracture risk by ACL rupture. Improved understanding of the alteration patterns at medial and lateral trabecular bone microarchitectures at the tibial epiphysis may assist in developing more targeted treatment interventions for knee joint instability secondary to ACL rupture.

• 한국정밀공학회지
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• 제6권2호
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• pp.77-87
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• 1989

This study is carried out to estimate the influence of cutting speed on the dynamic stability of a drilling machine. The theoretical stabilityu chart is constructed by using the measurd dynamic characteristics of the drilling machine. The critical cutting width and speed predicted from the stability chart show excellent agreements with those measured. Therefore it is confirmed that the analysis technique used in this study is useful for the prediction of the dynamic instability and improvement of the dynamic characteristics of drilling machines.

• Korean Journal of Mathematics
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• 제20권3호
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• pp.323-331
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• 2012

We present a high-order potential ow model for the motion of the impulsively accelerated unstable interface of infinite density jump. The Layzer model for the evolution of the interface is extended to high-order. The time-evolution solutions of the bubble and the spike in the interface are obtained from the high-order model. We show that the high-order model gives improvement on the prediction of the evolution of the bubble and the spike.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.