• Korean Journal of Poultry Science
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• v.20 no.3
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• pp.161-170
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• 1993

Prospects are briefly reviewed for further advances in current poultry industry technology in the foreseeable future. It is concluded that in the most advanced industries progress should continue at a similar rate to the recent past in conventional genetics and breeding, nutrition and disease control. Significant benefits will also follow in the short-term from the application of molecular biotechnology to disease diagnosis and vaccine production. Technical advances now make it possible to produce transgenic chickens at acceptable success rates but applications of this technology to poultry breeding will not become significant till we have sufficient knowledge of the poultry genome, and especially the genes involved in production performance. For the undeveloped and less advanced industries it is argued that the level of advanced technologies to be implemented in those countries should be decided largely on market forces, informed by objective assessment of the diverse options available. The need for urgent international action on conservation of poultry genetic resources is also stressed.

• Advances in Traditional Medicine
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• v.5 no.4
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• pp.294-300
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• 2005

Uncaria Tomentosa and Maytenus are known to have anti-inflammatory and anti-rheumatoidal effects after either a single application or application over a short-term period. We applied these natural products to Wister rats every day for two weeks and investigated the effects of this long-term application on inflammation. This was done by measuring footpad edema, which was induced by a locally injected carrageenan. There was a dramatic reduction in edema in both U. Tomentosa- and Maytenus-treated rats; furthermore, the reduction lasted as long as three days for rats treated with U. Tomentosa. When the Balb/C mice underwent similar treatment for one month, the level of IgM in the blood of U. Tomentosa-treated mice decreased while the level of IgG in Maytenus-treated mice increased. This suggests that the long lasting effects of U. Tomentosa may be related to a low level of IgM and the subclass switch from IgM to IgG. Since the anti-inflammatory effects of U. Tomentosa lasts for three days, it may prove useful in treating rheumatoid arthritis when applied for an extended period of time, especially since this product is known to have minimal side effects.

• Computers and Concrete
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• v.24 no.6
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• Journal of IKEEE
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• v.26 no.4
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• pp.545-552
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• 2022

The number of patent application filing for a specific technology has a good relation with the technology's life cycle and future industry development on that area. So industry and governments are highly interested in forecasting the number of patent application filing in order to take appropriate preparations in advance. In this paper, a new method based on the bidirectional long short-term memory(LSTM), a kind of recurrent neural network(RNN), is proposed to improve the forecasting accuracy compared to related methods. Compared with the Bass model which is one of conventional diffusion modeling methods, the proposed method shows the 16% higher performance with the Korean patent filing data on the five selected technology areas.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.37-48
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• 2005

The efficiency of power supply circuits such as DC-DC converters and batteries varies on the trend of the power consumption because their efficiencies are not fixed. To analyze the efficiency of power supply circuits, we need the temporal behavior of the power consumption of the loads, which is dependent on the activity factors of the devices during the operation. Since it is not easy to model every detail of those factors, one of the most accurate power consumption analyses of power supply circuits is measurement of a real system, which is expensive and time consuming. In this paper, we introduce an active load emulator for embedded systems which is capable of power measurement, logging, replaying and synthesis. We adopt a pattern recognition technique for data compression in that long-term behaviors of power consumption consist of numbers of repetitions of short-term behaviors, and the number of short-term behaviors is generally limited to a small number. We also devise a heterogeneous structure of active load elements so that low-speed, high-current active load elements and high-speed, low-current active load elements may emulate large amount and fast changing power consumption of digital systems. For the performance evaluation of our load emulator, we demonstrate power measurement and emulation of a hard drive. As an application of our load emulator, it is used for the analysis of a DC-DC converter efficiency and for the verification of a low-power frequency scaling policy for a real-time task.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.11-18
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• 2020

This study aims to complement the poor performance of existing context-aware authentication techniques in the mobile environment. The data used are GPS, Call Detail Record(CDR) and app usage. locational classification according to GPS density was implemented in order to distinguish other people in populated areas in the processing of GPS. It also handles missing values that may occur in data collection. The authentication model consists of two long-short term memory(LSTM) and one Artificial Neural Network(ANN) that aggregates the results, which produces authentication scores. In this paper, we compare the accuracy of this technique with that of other studies. Then compare the number of authentication attempts required to detect someone else's authentication. As a result, we achieved an average 11.6% improvement in accuracy and faster detection of approximately 60% of the experimental data.

• Tunnel and Underground Space
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• v.28 no.3
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• pp.277-291
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• 2018

LSTM (Long Short-Term Memory) algorithm which is a kind of recurrent neural network was used to establish a model to predict the stress-strain curve of an material under uniaxial compression. The model was established from the stress-strain data from uniaxial compression tests of silica-gypsum specimens. After training the model, it can predict the behavior of the material up to the failure state by using an early stage of stress-strain curve whose stress is very low. Because the LSTM neural network predict a value by using the previous state of data and proceed forward step by step, a higher error was found at the prediction of higher stress state due to the accumulation of error. However, this model generally predict the stress-strain curve with high accuracy. The accuracy of both LSTM and tangential prediction models increased with increased length of input data, while a difference in performance between them decreased as the amount of input data increased. LSTM model showed relatively superior performance to the tangential prediction when only few input data was given, which enhanced the necessity for application of the model.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2655-2661
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• 2012

Validation of a food frequency questionnaire (FFQ) utilising a short-term measurement method is challenging when the reference method does not accurately reflect the usual food intake. In addition, food group intake that is not consumed on daily basis is more critical when episodically consumed foods are related and compared. To overcome these challenges, several statistical approaches have been developed to determine usual food intake distributions. The Multiple Source Method (MSM) can calculate the usual food intake by combining the frequency questions of an FFQ with the short-term food intake amount data. In this study, we applied the MSM to estimate the usual food group intake and evaluate the validity of an FFQ with a group of 333 Korean children (aged 3-6 y) who completed two 24-hour recalls (24HR) and one FFQ in 2010. After adjusting the data using the MSM procedure, the true rate of non-consumption for all food groups was less than 1% except for the beans group. The median Spearman correlation coefficients against FFQ of the mean of 2-d 24HRs data and the MSM-adjusted data were 0.20 (range: 0.11 to 0.40) and 0.35 (range: 0.14 to 0.60), respectively. The weighted kappa values against FFQ ranged from 0.08 to 0.25 for the mean of 2-d 24HRs data and from 0.10 to 0.41 for the MSM-adjusted data. For most food groups, the MSM-adjusted data showed relatively stronger correlations against FFQ than raw 2-d 24HRs data, from 0.03 (beverages) to 0.34 (mushrooms). The results of this study indicated that the application of the MSM, which was a better estimate of the usual intake, could be worth considering in FFQ validation studies among Korean children.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1429-1435
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• 2020

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.

• Journal of Microbiology and Biotechnology
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• v.30 no.11
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• pp.1688-1696
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• 2020

Soil physical and chemical characteristics, soil potential denitrification rates (PDR), community composition and nirK-, nirS- and nosZ-encoding denitrifiers were studied by using MiSeq sequencing, quantitative polymerase chain reaction (qPCR), and terminal restriction fragment polymorphism (T-RFLP) technologies base on short-term (5-year) tillage field experiment. The experiment included four tillage treatments: conventional tillage with crop residue incorporation (CT), rotary tillage with crop residue incorporation (RT), no-tillage with crop residue retention (NT), and rotary tillage with crop residue removed as control (RTO). The results indicated that soil organic carbon, total nitrogen and NH₄⁺-N contents were increased with CT, RT and NT treatments. Compared with RTO treatment, the copies number of nirK, nirS and nosZ in paddy soil with CT, RT and NT treatments were significantly increased. The principal coordinate analysis indicated that tillage management and crop residue returning management were the most and the second important factors for the change of denitrifying bacteria community, respectively. Meanwhile, this study indicated that activity and community composition of denitrifiers with CT, RT and NT treatments were increased, compared with RTO treatment. This result showed that nirK, nirS and nosZ-type denitrifiers communities in crop residue applied soil had higher species diversity compared with crop residue removed soil, and denitrifying bacteria community composition were dominated by Gammaproteobacteria, Deltaproteobacteria, and Betaproteobacteria. Therefore, it is a beneficial practice to increase soil PDR level, abundance and community composition of nitrogen-functional soil microorganism by combined application of tillage with crop residue management.