• Asia-Pacific Journal of Business
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• v.12 no.4
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• pp.289-297
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• 2021

Purpose - This study analyzed the correlation between economic liberalization and foreign direct investment. The purpose of this study is to seek ways to attract foreign direct investment from developing countries. Design/methodology/approach - This study analysed with observations of 19 from 2000 to 2018 using a fixed effect model, a random effect model, and a two-way fixed effect model. Findings - First, it was found that economic liberalization had a positive effect on attracting foreign direct investment in the early stages of economic liberalization. Second, it was found that economic liberalization in the deepening stage of economic liberalization had a negative effect on attracting foreign direct investment. In general, it was found that the higher the level of economic liberalization in developing countries is not accompanied by innovative changes in the industrial structure, the higher the level of economic liberalization is likely to decrease the inducement of foreign direct investment due to negative factors such as an increase in labor costs. Overall, this study approved that Economic liberalization have a non-linear (inverted U-shape) relationship with the inflow of foreign direct investment. Research implications or Originality - First, this study attempted to expand the variables for the determinants of FDI by analyzing economic factors which is a determinent of FDI. Second, economic liberalization generally has a positive effect on foreign direct investment, but it proved that it does not have only positive effects as a factor of attracting foreign direct investment in developing countries. The advantage of low wages in ASEAN countries acts as a factor for foreign direct investment, but as the degree of economic liberalization increases, the environment such as government size, guarantee of property rights, international trade freedom, fiscal soundness, and regulations change positively. On the other hand, it can be suggested that if the industrial level is less, it may lead to a loss of comparative advantage and a decrease in investment.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of the Korean Society of Safety
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• v.37 no.3
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• pp.52-59
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• 2022

Conventionally, all the unsafe acts by human beings in relation to industrial accidents have been regarded as unintentional human errors. Exceptionally, however, in the cases with fatalities, seriously injured workers, and/or losses that evoked social issues, attention was paid to violating related laws and regulations for finding out some people to be prosecuted and given judicial punishments. As Heinrich stated, injury or loss in an accident is quite a random variable, so it can be unfair to utilize it as a criterion for prosecution or punishment. The present study was conducted to comprehend how categorizing intentional violations in unsafe acts might disrupt conventional conclusions about the industrial accident process. It was also intended to seek out the right direction for countermeasures by examining unsafe acts comprehensively rather than limiting the analysis to human errors only. In an analysis of 150 industrial accident cases that caused fatalities and featured relatively clear accident scenarios, the results showed that only 36.0% (54 cases) of the workers recognized the situation they confronted as risky, out of which 29.6% (16 cases) thought of the risk as trivial. In addition, even when the risks were recognized, most workers attempted to solve the hazardous situations in ways that violated rules or regulations. If analyzed with a focus on human errors, accidents can be attributed to personal deviations. However, if considered with an emphasis on safety rules or regulations, the focus will naturally move to the question of whether the workers intentionally violated them or not. As a consequence, failure of managerial efforts may be highlighted. Therefore, it was concluded that management should consider unsafe acts comprehensively, with violations included in principle, during accident investigations and the development of countermeasures to prevent future accidents.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.318-323
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• 2022

Barium titanate (BaTiO₃) is considered to be a beneficial ceramic material for multilayer ceramic capacitor (MLCC) applications because of its high dielectric constant and low dielectric loss. Numerous attempts have been made to improve the physical properties of BaTiO₃ in response to recent market trends by employing multicomponent alloying strategies. However, owing to its significant number of atomic combinations and unpredictable physical properties, finding a traditional experimental approach to develop multicomponent systems is difficult; the development of such systems is also time-consuming. In this study, 168 new structures were fabricated using special quasi-random structures (SQSs) of Ba_1-xCa_xTi_1-yZryO₃, and 1680 physical properties were extracted from first-principles calculations. In addition, we built an integrated database to manage the computational results, and will provide big data solutions by performing data analysis combined with AI modeling. We believe that our research will enable the global materials market to realize digital transformation through datalization and intelligence of the material development process.

• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.450-450
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• 2021

기후변화 보고서에 따르면 집중 호우의 강도 및 빈도 증가가 향후 몇 년동안 지속될 것이라 제시하였다. 이러한 집중호우가 빈번히 발생하게 된다면 강우 침식성이 증가하여 표토 침식에 더 취약하게 발생된다. Universal Soil Loss Equation (USLE) 입력 매개 변수 중 하나인 강우침식능인자는 토양 유실을 예측할때 강우 강도의 미치는 영향을 제시하는 인자이다. 선행 연구에서 USLE 방법을 사용하여 강우침식능인자를 산정하였지만, 60분 단위 강우자료를 이용하였기 때문에 정확한 30분 최대 강우강도 산정을 고려하지 못하는 한계점이 있다. 본 연구의 목적은 강우침식능인자를 이전의 진행된 방법보다 더 빠르고 정확하게 예측하는 머신러닝 모델을 개발하며, 총 월별 강우량, 최대 일 강우량 및 최대 시간별 강우량 데이터만 있어도 산정이 가능하도록 하였다. 이를 위해 본 연구에서는 강우침식능인자의 산정 값의 정확도를 높이기 위해 1분 간격 강우 데이터를 사용하며, 최근 강우 패턴을 반영하기 위해서 2013-2019년 자료로 이용했다. 우선, 월별 특성을 파악하기 위해 USLE 계산 방법을 사용하여 월별 강우침식능인자를 산정하였고, 국내 50개 지점을 대상으로 계산된 월별 강우침식능인자를 실측 값으로 정하여, 머신러닝 모델을 통하여 강우침식능인자 예측하도록 학습시켜 분석하였다. 이 연구에 사용된 머신러닝 모델들은 Decision Tree, Random Forest, K-Nearest Neighbors, Gradient Boosting, eXtreme Gradient Boost 및 Deep Neural Network을 이용하였다. 또한, 교차 검증을 통해서 모델 중 Deep Neural Network이 강우침식능인자 예측 정확도가 가장 높게 산정하였다. Deep Neural Network은 Nash-Sutcliffe Efficiency (NSE) 와 Coefficient of determination (R²)의 결과값이 0.87로서 모델의 예측성을 입증하였으며, 검증 모델을 테스트 하기 위해 국내 6개 지점을 무작위로 선별하여 강우침식능인자를 분석하였다. 본 연구 결과에서 나온 Deep Neural Network을 이용하면, 훨씬 적은 노력과 시간으로 원하는 지점에서 월별 강우침식능인자를 예측할 수 있으며, 한국 강우 패턴을 효율적으로 분석 할 수 있을 것이라 판단된다. 이를 통해 향후 토양 침식 위험을 지표화하는 것뿐만 아니라 토양 보전 계획을 수립할 수 있으며, 위험 지역을 우선적으로 선별하고 제시하는데 유용하게 사용 될 것이라 사료된다.

• Animal Bioscience
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• v.37 no.6
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• pp.1065-1076
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• 2024

Objective: This study aimed to assess the effects of dietary mulberry leaves on the growth, production performance, gut microbiota, and immunological parameters of poultry and livestock. Methods: The PubMed, Embase, and Scopus databases were systematically analyzed to identify pertinent studies up to December 2022. The effects of mulberry leaf diet was assessed using the weighted mean difference, and the 95% confidence interval was calculated using a random-effects model. Results: In total, 18 studies that sampled 2,335 poultry and livestock were selected for analysis. Mulberry leaves improved the average daily gain and reduced the feed/meat ratio in finishing pigs, and the average daily gain and average daily feed intake in chicken. In production performance, mulberry leaves lowered the half carcass weight, slaughter rate, and loin eye area in pigs, and the slaughter rate in chickens. Regarding meat quality in pigs, mulberry leaves reduced the cooked meat percentage, shear force, crude protein, and crude ash, and increased the 24 h pH and water content. In chickens, it increased the drip loss, shear force, 45 min and 24 h pH, crude protein, and crude ash. Mulberry leaves also affect the abundances of gut microbiota, including Bacteroides, Prevotella, Megamonas, Escherichia-Shigella, Butyricicoccus, unclassified Ruminococcaceae, Bifidobacterium, Lactobacillus, and Escherichia coli in poultry and livestock. Mulberry leaves at different doses were associated with changes in antioxidant capacity in chickens, and immune organ indexes in pigs. With respect to egg quality, mulberry leaves at different doses improved the shell strength, yolk color, eggshell thickness, and eggshell weight. However, moderate doses diminished the egg yolk ratio and the egg yolk moisture content. Conclusion: In general, dietary mulberry leaves improved the growth, production performance, and immunological parameters in poultry and livestock, although the effects varied at different doses.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.27 no.1
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• pp.6-19
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• 2023

A systematic review was conducted in compliance with PRISMA statement standards to identify all studies reporting outcomes of laparoscopic resection of benign or malignant lesions located in caudate lobe of liver. Pooled outcome data were calculated using random-effects models. A total of 196 patients from 12 studies were included. Mean operative time, volume of intraoperative blood loss, and length of hospital stay were 225 minutes (95% confidence interval [CI], 181-269 minutes), 134 mL (95% CI, 85-184 mL), and 7 days (95% CI, 5-9 days), respectively. The pooled risk of need for intraoperative transfusion was 2% (95% CI, 0%-5%). It was 3% (95% CI, 1%-6%) for conversion to open surgery, 6% (95% CI, 0%-19%) for need for intra-abdominal drain, 1% (95% CI, 0%-3%) for postoperative mortality, 2% (95% CI, 0%-4%) for biliary leakage, 2% (95% CI, 0%-4%) for intra-abdominal abscess, 1% (95% CI, 0%-4%) for biliary stenosis, 1% (95% CI, 0%-3%) for postoperative bleeding, 1% (95% CI, 0%-4%) for pancreatic fistula, 2% (95% CI, 1%-5%) for pulmonary complications, 1% (95% CI, 0%-4%) for paralytic ileus, and 1% (95% CI, 0%-4%) for need for reoperation. Although the available evidence is limited, the findings of the current study might be utilized for hypothesis synthesis in future studies. They can be used to inform surgeons and patients about estimated risks of perioperative complications until a higher level of evidence is available.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.