• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.369-372
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• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes comprising a sensing part and a communication part are developed to detect ground movement. Sensing part is designed to measure inclination angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15.1) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of experimental studies was performed at a small-scale earth slope equipped with an artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope starts to move. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.369-376
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• 2003

In this paper, a nonlinear structural analysis for the composite structure composed of the spent nuclear fuel disposal canister and the 50㎝ thick bentonite buffer is carried out to predict the collapse of the canister while the horizontal symmetric sudden rock movement of 10㎝ is applied on the composite structure. This sudden rock movement is anticipated by the earthquake etc. at a deep underground. Elastoplastic material model is adopted. Drucket-Prager yield criterion is used for the material yield prediction of the bentonite buffer and von-Mises yield criterion is used for the material yield prediction of the canister(cast iron, copper). Analysis results show that even though very large deformations occur beyond the yield point in the bentonite buffet, the canister structure still endures elastic small strains and stresses below the yield strength. Hence, the 50㎝ thick bentonite buffet can protect the canister safely against the 10㎝ sudden rock movement by earthquake etc.. Analysis results also show that bending deformations occur in the canister structure due to the shear deformation of the bentonite buffer.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.13 no.1
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• pp.37-43
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• 1991

Cephalometric prediction tracing is the preoperative double checking procedure which can predict bony and soft tissue change. Soft tissue profile prediction is routinely performed according to the known ratios of the soft to hard tissue movement which can vary considerably in each individual. Besides interindividual variation of the ratios of the soft to hard tissue change, actual results of the postoperative soft tissue profile can reflect other important modifying factors if it is compared with prediction tracing used. The purpose of this study is to compare soft tissue prediction tracing used with postoperative tracing and to find intervening modifying factor via serial tracing. Review of 30 prediction tracing showed that the most important factor contributing to prodiction tracing inaccuracy was the skeletal and dental relapse. And, some factors which may be responsible for prediction tracing inaccuracy were discussed.

• Physical Therapy Korea
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• v.27 no.2
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• pp.140-148
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• 2020

Background: Scapular winging (SW) could be caused by tightness or weakness of the periscapular muscles. Although data mining techniques are useful in classifying or predicting risk of musculoskeletal disorder, predictive models for risk of musculoskeletal disorder using the results of clinical test or quantitative data are scarce. Objects: This study aimed to (1) investigate the difference between young women with and without SW, (2) establish a predictive model for presence of SW, and (3) determine the cutoff value of each variable for predicting the risk of SW using the decision tree method. Methods: Fifty young female subjects participated in this study. To classify the presence of SW as the outcome variable, scapular protractor strength, elbow flexor strength, shoulder internal rotation, and whether the scapula is in the dominant or nondominant side were determined. Results: The classification tree selected scapular protractor strength, shoulder internal rotation range of motion, and whether the scapula is in the dominant or nondominant side as predictor variables. The classification tree model correctly classified 78.79% (p = 0.02) of the training data set. The accuracy obtained by the classification tree on the test data set was 82.35% (p = 0.04). Conclusion: The classification tree showed acceptable accuracy (82.35%) and high specificity (95.65%) but low sensitivity (54.55%). Based on the predictive model in this study, we suggested that 20% of body weight in scapular protractor strength is a meaningful cutoff value for presence of SW.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.10
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• pp.1345-1350
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• 2010

Sit-to-stand movement is a basic movement in daily activities. On the basis of this movement, the biomechanical functions of a person can be evaluated. The study of the joint kinematics, moment, and muscle coordination is necessary to understand the characteristics of the sit-to-stand movement. We have developed a motion-based program for inverse dynamics analysis and the electromyogram-based program for muscle force prediction. The joint kinematics and the kinetic results estimated on the basis of obtained motion data, ground reaction force, and electromyogram signals were compared with those reported in previous studies, and the muscle forces determined by the two methods were compared with each other. The methods and programs developed in this study can be used to understand biomechanics and muscle coordination involved in basic movements in daily activities.

• Journal of Korean Tunnelling and Underground Space Association
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• v.8 no.1
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• pp.21-30
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• 2006

In case of an urban tunnel, the displacement of ground base controls the tunnel design because it is built on shallow and unconsolidated ground many times. There are more insufficiency to describe the ground movement which coincides in the measured result of the situ because the design of an urban tunnel is dependent on the method of numerical analysis used to the existing elastic and elasto-plastic models. We studied about the predict ion for the ground movement of a shallow tunnel in unconsolidated ground, mechanism of collapse, and settlement. Also this paper shows comparison with the existing elastic and elasto-plastic model using the unlinear analysis of the strain-softening model. We can model the real ground movement as the increasement of ground surface inclination or occurrence of shear band by using strain-softening model for the result of ground movement of an urban NATM tunnel.

• Asia Pacific Journal of Corpus Research
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• v.5 no.1
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• pp.23-36
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• 2024

This research centers on the Taiwan Eye-Movement Corpus of Spanish (TECS), a specially created corpus comprising eye-tracking data from Chinese-speaking learners of Spanish as a third language in Taiwan. Its primary purpose is to explore the broad utility of TECS in understanding language learning processes, particularly the initial stages of language learning. Constructing this corpus involves gathering data on eye-tracking, reading comprehension, and language proficiency to develop a machine-learning model that predicts learner behaviors, and subsequently undergoes a predictability test for validation. The focus is on examining attention in input processing and their relationship to language learning outcomes. The TECS eye-tracking data consists of indicators derived from eye movement recordings while reading Spanish sentences with temporal references. These indicators are obtained from eye movement experiments focusing on tense verbal inflections and temporal adverbs. Chinese expresses tense using aspect markers, lexical references, and contextual cues, differing significantly from inflectional languages like Spanish. Chinese-speaking learners of Spanish face particular challenges in learning verbal morphology and tenses. The data from eye movement experiments were structured into feature vectors, with learner behaviors serving as class labels. After categorizing the collected data, we used two types of machine learning methods for classification and regression: Random Forests and the k-nearest neighbors algorithm (KNN). By leveraging these algorithms, we predicted learner behaviors and conducted performance evaluations to enhance our understanding of the nexus between learner behaviors and language learning process. Future research may further enrich TECS by gathering data from subsequent eye-movement experiments, specifically targeting various Spanish tenses and temporal lexical references during text reading. These endeavors promise to broaden and refine the corpus, advancing our understanding of language processing.

• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.136-145
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• 2007

A computation was made to predict the movement of the thrust center position due to the rocket nozzle deflection. Three dimensional computations were done for the nozzle deflection angles of 0/1/3 degrees, and the oscillation of aerodynamic coefficients, not observed for the axisymmetric cases, was encountered. The position of the thrust center was found to be at -16 mm and -4 mm for the deflection angles of 1 and 3 degrees, respectively, and it can be concluded that the thrust center movement due to nozzle deflection is negligible. In addition to the computational results, the mechanism of thrust generation in a rocket engine is described with a brief mathematical derivation as it is sometimes mistaken. Also presented are some descriptions on the problem of pressure center definition for symmetric cases such as a rocket external flow problem and the nozzle deflection case.