• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3A
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• pp.205-214
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• 2011

In 3rd generation project partnership long term evolution, wireless multicast techniques which send the same data to multiple users under single frequency networks have attracted much attention. In the multicast system, the transmission mode needs to be selected for efficient data transfer while satisfying the multicast coverage requirement. To achieve this, users' channel state information (CSI) should be available at the transmitter. However, it requires too much uplink feedback resource if all the users are allowed to transmit their CSI at all the time. To solve this problem, in this paper, the multicast coverage prediction is suggested. In the proposed algorithm, each user measures its transition probabilities between the success and the fail state of the decoding. Then, it periodically transmits its CSI to the basestation. Using these feedbacks, the basestation can predict the multicast coverage. From the simulation results, we demonstrate that the proposed scheme can predict the multicast system coverage.

• Nuclear Engineering and Technology
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• v.23 no.1
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• pp.56-65
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• 1991

The LOFT LOCE L2-3 was simulated using the RELAP5/MOD2 Cycle 36.04 code to assess its capability in predicting the thermal-hydraulic phenomena in LBLOCA of a PWR. The reactor vessel was simulated with two core channels and split downcomer modeling for a base case calculation using the frozen code. The result of the base calculation showed that the code predicted the hydraulic behavior, and the blowdown thermal response at high power region of the core reasonably and that the code had deficiencies in the critical How model during subcooled-two-phase transition period, in the CHF correlation at high mass flux and in the blowdown rewet criteria. An overprediction of coolant inventory due to the deficiencies yielded the poor prediction of reflood thermal response. Improvement of the code, RELAP5 / MOD2 Cycle 36.04, based on the sensitivity study increased the accuracy of the prediction of the rewet phenomena.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.451-468
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• 2021

Predicting hazardous ground conditions ahead of a TBM (Tunnel Boring Machine) tunnel face is essential for efficient and stable TBM advance. Although there have been several studies on the electrical resistivity survey method for TBM tunnelling, sufficient experimental data considering TBM advance were not established yet. Therefore, in this study, the laboratory-scale model experiments for simulating TBM excavation were carried out to analyze the applicability of an electrical resistivity survey for predicting hazardous ground conditions ahead of a TBM tunnel face. The trend of electrical resistivity during TBM advance was experimentally evaluated under various hazardous ground conditions (fault zone, seawater intruded zone, soil to rock transition zone, and rock to soil transition zone) ahead of a tunnel face. In the course of the experiments, a scale-down rock ground was provided using granite blocks to simulate the rock TBM tunnelling. Based on the experimental data, the electrical resistivity tends to decrease as the tunnel approaches the fault zone. While the seawater intruded zone follows a similar trend with the fault zone, the resistivity value of the seawater intrude zone decreased significantly compared to that of the fault zone. In case of the soil-to-rock transition zone, the electrical resistivity increases as the TBM approaches the rock with relatively high electrical resistivity. Conversely, in case of the rock-to-soil transition zone, the opposite trend was observed. That is, electrical resistivity decreases as the tunnel face approaches the rock with relatively low electrical resistivity. The experiment results represent that hazardous ground conditions (fault zone, seawater intruded zone, soil-to-rock transition zone, rock-to-soil transition zone) can be efficiently predicted by utilizing an electrical resistivity survey during TBM tunnelling.

• Korea journal of population studies
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• v.19 no.1
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• pp.5-44
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• 1996

Wornen's lahor market participation as well as the policy concern for wider utilization of married women, have continuously grown up. However, research efforts on the determinants of women's labor market participation, in the context of the relationship hetween life courses and active entry into lahor market, has been far behind the growing interest in this field. This study has conducted an event histoiry analysis of women's labor market transition utilizing personal occupational history data collected by the Korea Institute for Women's Development in 1991. The analysis is divided into tow parts: First part introduces logit regression to analyze the determinants of women's labor market participation and exit. The second part employs Cox regression to see the variation of transition rate between employment and non-employment. The result shows that there is a wide variation in women's labor market participation according to age, cohort, and family formation. Special note is needed for the significantly negative effect of marriage and child birth on labor market participation. The transition pattern of lower class women with less education fits well to the prediction of neo-classical economics; but the tendency of highly educated women's regression to non-employment reveals the strong influence of the unfavorable labor market structure, which can be better explained by the neo-structuralist perspective. There is a strong trade-off between productive and reproductive labor of women, which can only be corrected by strong policy implementation, such as extended child care facilities, abolition of discriminatory employment practices, and expansion of flexible part-time employment.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.195-198
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• 2007

The present work focuses on the prediction of birefringence in injection-molded plastic part and its improvement by rapid mold heating. To calculate birefringence, flow-induced residual stress is computed through a fully three-dimensional injection molding analysis. Then the stress-optical law is applied from which the order of birefringence can be evaluated and visualized. The birefringence patterns are predicted for a rectangular plate with a variation of mold temperatures, which shows that the amount of molecular orientation and birefringence level decreases with an increase of mold temperature. The effect of mold temperature on the order of birefringence is also studied for a thin-walled rectangular strip, and compared with experimental measurements. Both predicted and experimental patterns of birefringence are in agreements on the observation that the birefringence level diminishes significantly when the mold temperature is raised to above the glass transition temperature.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.490-494
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• 2008

We simulated a forest fire which was occurred in Yangyang area on 2005 and compared a results between two different weather conditions(real weather condition and mean weather condition since 1968) using FARSITE, which is a forest fire spread simulator for preventing and predicting fire in USDA. And, we researched a problem in the transition for introducing, so we serve the basic method for prevention and attacking fire. In the result, severe weather condition on 2005 effected a forest fire behavior. The rate of spread under real weather condition was about 4 times faster than mean weather condition. Damaged area was about 10 time than mean weather condition. Therefore, Climate change will make a more sever fire season. As we will encounter to need for accurate prediction in near future, it will be necessary to predict a forest fire linked with future wether and fuel condition.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2971-2976
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• 2007

The mode change from Taylor cone-jet to dripping in electrospraying has been analytically investigated. The change has been predicted by the dynamic behavior of a liquid drop at the tip of the cone-jet. Conservation laws are applied to determine the upward motion of the drop, and an instability model of electrified jets is used to determine the jet breakup. Finally, for the first time, the analysis enables prediction of the transition in terms of the Weber number and electric Bond number. The predictions are in good agreement with experimental data.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.31-33
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• 2004

웹 사용에 대한 다음 요구 사항을 예측하기 위한 마이닝 방법으로 연관규칙이나 순차 패턴 등이 많이 사용되고 있지만, 이러한 방법들은 생성된 규칙들의 지지도(Support)나 신뢰도(Confidence)에 의한 예측만을 고려하기 때문에 정확한 예측을 하기 어려운 단점을 가지고 있다. 따라서, 본 논문에서는 빈도 수에 의한 Markov model을 기반으로 하여 웹 로그 파일에 저장된 사용자들의 행동 패턴에 따라 생성되어지는 여러 형태의 규칙 유형을 찾아내고, 사용 빈도 수를 이용한 전이 확률 행렬에 따른 다음 요구사항을 정확하게 예측할 수 있는 모델을 제시하고자 한다. 그 결과 여러 형태의 규칙 유형을 $K^{th}$ -order Markov 과정에서 효율적으로 발견해 낼 수 있다.