• Nuclear Engineering and Technology
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• v.35 no.2
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• pp.91-107
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• 2003

Since DBA(Design Basis Accidents) has been studied rather separately from SA(Severe Accidents) in the conventional nuclear reactor safety analysis, the thermal hydraulics during transition between DBA and SA has not been identified so much as each accident itself. Thus, in this study, the thermal hydraulic behavior from DBA to the commencement of SA has been experimentally and analytically investigated for the long-term cooling phase of LB-LOCA(Large-Break Loss-of-Coolant Accident). Experiments were conducted for both cases of the loop seal open and closed in an integral test loop, named as SNUF (Seoul National University Facility), which was scaled down to l/6.4 in length and 1/178 in area of the APR1400 (Advanced Power Reactor 1400MWe). The core mixture level was a main measured value since it took major role in the fuel heat-up rate, the location of fuel melting initiation and the channel blockage by melting material during SA. Experimental results were compared to MAAP4.03 to assess its model of calculating the core mixture level. MAAP4.03 overestimates the core two- phase mixture level because sweep-out and spill-over and the measures to simulate the status of loop seal are not included, which is against the conservatism. Thus, it is recommended that MAAP4.03 should be improved to simulate the thermal hydraulic phenomena, such as sweep-out, spill-over and the status of loop seal.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.73-80
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• 2021

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.467-468
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• 2010

Over the past few decades, considerable numbers of studies on the durability of concrete have been carried out extensively. The majority of these researches have been performed on sound uncracked concrete, although most of in-situ concrete structures have more or less micro-cracks. It is only recent approach that the attention has shifted towards the influence of cracks and crack width on the penetration of chloride into concrete. The penetration of chlorides into concrete through the cracks can make a significant harmful effect on reinforcement corrosion. Author of this study examined the effect of cracks on chloride penetration by short term experiment. However, it is necessary to accomplish the effect by long term experiment to get reliable goal. In this study, the long term and short term experiments were carried out. This can be useful for establishing new species model of chloride penetration through cracks in concrete.

• The Korean Journal of Food And Nutrition
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• v.27 no.2
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• pp.225-230
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• 2014

The objective of this study was to investigate the effects of soy hydrolysate fractions on appetite suppression and ghrelin releasing. In a short-term experiment, the cumulative food intake and serum ghrelin level were decreased significantly (p<0.05) during a 4-hr period after the interperitoneal injection of soy hydrolysate fractions (0.5, 1 g/kg BW), following a 12-hr period of food deprivation. In a long-term experiment, food efficiency ratio (FER) was also reduced significantly (p<0.05), when soy hydrolysate fractions (0.5, 1% in drinking water) were given orally for 8 wks. Therefore, we found that soy hydrolysate fractions affected food intake through appetite and ghrelin releasing in short-term and long-term experiments. In conclusion, this study indicated that soy hydrolysate fractions would diminish the sensation of hunger by reducing the secretion of orexigenic factors such as ghrelin that send satiety signals to the brain, terminating food intake.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.433-441
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• 2019

In this paper, a method of estimating speech parameters for ultrasonic Doppler signals reflected from the articulatory muscles using LSTM (Long Short Term Memory) RNN (Recurrent Neural Networks) was introduced and compared with the method using MLP (Multi-Layer Perceptrons). LSTM RNN were used to estimate the Fourier transform coefficients of speech signals from the ultrasonic Doppler signals. The log energy value of the Mel frequency band and the Fourier transform coefficients, which were extracted respectively from the ultrasonic Doppler signal and the speech signal, were used as the input and reference for training LSTM RNN. The performance of LSTM RNN and MLP was evaluated and compared by experiments using test data, and the RMSE (Root Mean Squared Error) was used as a measure. The RMSE of each experiment was 0.5810 and 0.7380, respectively. The difference was about 0.1570, so that it confirmed that the performance of the method using the LSTM RNN was better.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.1
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• pp.125-132
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• 2010

In the recent construction industry, an external strengthening method using fiber reinforced polymers has been widely used. Since reinforced concrete structures strengthened with fiber reinforced polymers are always under sustained loads, influence of creep and shrinkage on the structures is inevitable. Due to the creep and shrinkage, behaviors of the structures, such as deflection, deformation, recovery capability, strength and so on are also under the influence of creep and shrinkage. Thus, in order to estimate efficacy, creep recovery and residual strength of FRP strengthened RC beams, long-term flexural experiments and static flexural experiments were carried out. As the result of the experiments, FRP strengthened RC beams were very effective in terms of deflection control. Furthermore, the strengthened beams had higher immediate deformation recovery than immediate deformation. Through the static flexural experiments, it was shown that the CFRP strengthened beam had high residual strength. It seems that the sustained loads did not affect bond and residual strength of the beams.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.343-348
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• 2002

We are concerned with a long-term replenishment contract for the ARIMA demand process in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy but allows us to contract future replenishments at a time with a price discount. Due to the larger forecast error of future demand, the buyer should keep a higher level of safety stock to provide the same level of service as the usual (s, Q) policy. However, the buyer can reduce his purchase cost by ordering a larger quantity at a discounted price. Hence, there exists a trade-off between the price discount and the inventory holding cost. For the ARIMA demand process, we present a model for the contract and an algorithm to find the number of the future replenishments. Numerical experiments show that the proposed algorithm is efficient and accurate.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.451-467
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• 1997

Tabu search (TS) is a useful strategy that has been successfully applied to a number of complex combinatorial optimization problems. By guiding the search using flexible memory processes and accepting disimproved solutions at some iterations, TS helps alleviate the risk of being trapped at a local optimum. In this article, we propose TS-based heuristics for solving a class of clustering problems, and compare the relative performances of the TS-based heuristic and the simulated annealing (SA) algorithm. Computational experiments show that the TS-based heuristic with a long-term memory offers a higher possibility of finding a better solution, while the TS-based heuristic without a long-term memory performs better than the others in terms of the combined measure of solution quality and computing effort required.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.385-386
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• 2019

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification.