• Journal of Korea Trade
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• 제23권7호
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• pp.96-112
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• 2019

Purpose - "Trade Facilitation" aims the easier flow of trade across borders, driven not only by effective customs administration, the efficiency of appropriate authorities, but also by telecommunications, the quality of infrastructures and competent logistics. Facilitating trade will help lower trade development costs as well as improve economic development and enhance economic benefits for emerging economies at a time when imports and exports are sent in and out across borders several times in the form of intermediate and final products. Not only that, globalization is being accelerated, which in turn increases competitiveness and this makes logistics one of the key factors when it comes to international trade. Highly efficient logistics services promote product movement, ensure product safety and delivery speed, and reduce trade costs between countries. The purpose of this study is, by using the LPI indices based on gravity model estimates, to analyze the impact of each LPI component on trade with the 20 biggest exporting countries of Northeast Asian countries-Korea, Japan, and China-which account for 19.05% of global exports. Design/methodology - Also, this study statistically analyzes the impact of trade on Northeast Asian countries' top 20 exporting countries, using the LPI indices relevant to Trade Facilitation based on the gravity model estimates. Findings - As a result, it was turned out that the distance, GDP, and the LPI components have relevant impact on the trade exports of all three countries but demonstrated little relation to the demographic perspective. Originality/value - The study also found we can increase the trade volume by improving three countries' trade partners' LPI indices since Korea, Japan, and China share most of their 20 biggest trade partners.

• Asia Pacific Journal of Business Review
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• 제6권2호
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• pp.27-60
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• 2022

Previous studies stated that low carbon innovation performance could be influenced by government regulations and the green market, which is the new trend of consumer consumption in the present time, mainly focusing on external factors. Before study augured that low carbon innovation performance could be driven by internal and external factors of cooperation such as institutional pressure, stakeholder pressure, and innovation resources. However, the study of green transformational leadership and organizational environmental culture on low carbon innovation performance is rare, especially in Chinese manufacturing, as well as the effect of influencing factors of TPB model: environmental attitude, subjective norm, and perceived behavior capability on low carbon innovation performance. Previous studies mostly used the TPB model for predicting individual behavior. This study established a theoretical model combining the TPB model with green transformational leadership and organizational environmental culture of Chinese automobile manufacturing on low carbon innovation performance. This study consists of two sections of research methodology: section 1 related to questionnaire design and data collection. We established a questionnaire and distributed it online, targeting responses from the managerial level working in Chinese automobile manufacturing. Eventually, 155 valid questionnaires were used for analysis. Section 2 involved data analysis using statistical software. Reliability and data validity was examined by reliability analysis and factor analysis. Correlations and convergent validity analyses were applied, and structural equation modeling was conducted to test the proposed hypotheses. The findings indicated that green transformational leadership, organizational environmental culture, and essential factors of TPB model; environmental attitude, subjective norm and perceived behavior capability positively affect low carbon innovation performance. In addition, the indirect effect of green transformational leadership was tested and found that organizational environmental culture and TPB factors mediated the relationship between transformational leadership and low carbon innovation performance.

• 시스템엔지니어링학술지
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• 제18권2호
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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.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Structural Engineering and Mechanics
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• 제83권4호
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.331-337
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• 2022

Smart learning is augmented with digital, context-aware, and adaptable technologies to encourage students to learn better and faster. To ensure that digital learning is successful and that implementation is efficient, it is critical that the dimensions of digital learning are arranged correctly and that interactions between the various elements are merged in an efficient and optimal manner. This paper builds and discusses a basic framework for smart learning in the digital age, aimed to improve students' abilities and performance in learning. The proposed framework consists of five dimensions: Teacher, Technology, Learner, Digital content, and Evaluation. The Teacher and Learner dimensions operate on two levels: (a) an abstract level to fit in knowledge and skills or interpersonal characteristics and (b) a concrete level in the form of digital devices used by teachers and learners. Moreover, this paper proposes asynchronous online course delivery model. An Arabic smart learning platform has been developed, based on these smart learning core dimensions and the asynchronous online course delivery model, because despite the official status of this language in many countries, there is a lack of Arabic platforms to teach Arabic. Moreover, many non-native Arabic speakers around the world have expressed an interest in learning it. The Arabic digital platform consists of over 70 lessons classified into three competence levels: beginner, intermediate, and advanced, delivered by Arabic experts and Arabic linguists from various Arab countries. The five dimensions are described for the Arabic platform in this paper. Learner dimension is the Arabic and non-Arabic speakers, Teacher dimension is Arabic experts and Arabic linguistics, Technology dimension consists of technology for Arabic platform that includes web design, cloud computing, big data, etc. The digital contents dimension consists of web-based video, records, etc. The evaluation dimension consists of Teachers rating, comments, and surveys.

• Animal Bioscience
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• 제35권12호
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• pp.1817-1826
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• 2022

Public concern on the methods of raising food-producing animals has increased, especially in the last two decades, leading to voluntary and mandated changes in the animal production methods. The primary objective of these changes is to improve the welfare of farm animals. The use of gestational stalls is currently a major welfare issue in swine production. Several studies assessed the welfare of alternative housing systems for gestating sows. A comparative study was performed with gestating sows housed in either individual stalls or in groups in a pen with an electronic sow feeder. This review assessed the welfare of each housing system using physiological, behavioral, and reproductive performance criteria. The current review identified clear advantages and disadvantages of each housing system. Individual stall housing allowed each sow to be given an individually tailored diet without competition, but the sows had behavioral restrictions and showed stereotypical behaviors (e.g., bar biting, nosing, palate grinding, etc.). Group-housed sows had increased opportunities to display such behavior (e.g., ability to move around and social interactions); however, a higher prevalence of aggressive behavior, especially first mixing in static group type, caused a negative impact on longevity (more body lesions, scratch and bite injuries, and lameness, especially in subordinate sows). Conclusively, a more segmented and diversified welfare assessment could be beneficial for a precise evaluation of each housing system for sows. Further efforts should be made to reduce aggression-driven injuries and design housing systems (feeding regimen, floor, bedding, etc.) to improve the welfare of group-housed sows.

• 우주기술과 응용
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• 제2권3호
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• pp.187-194
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• 2022

안테나의 고속 정밀 지향성 확보를 위해 동작되는 모터의 구동에 의한 외란의 크기는 극히 미소함에도 불구하고, 정밀 지향 성능이 요구되는 관측 위성의 영상 품질을 저해하는 주요 원인으로 작용하게 된다. 고해상도 관측위성의 지향 성능 향상을 통한 고해상도 영상정보 획득을 위해서는 지향성 안테나의 모터 구동시 발생하는 미소진동(Jitter)이 주요 임무 장비에 전달되지 않도록 적절한 진동절연 및 저감 설계가 요구된다. 본 논문에서는 실제 위성에 적용된 지향성 안테나 진동저감 장치에 대해 개발과정과 적용 전후의 미소 진동 저감 효과에 대해 살펴보고자 한다. 이 장치는 별다른 인터페이스 추가 장비 없이, 지향성 안테나 구동부 내에 있는 기어 하나만을 스프링 댐퍼 기어 형태로 교체함으로서, 발생되는 지터문제를 획기적으로 개선할 수 있는 방법으로 고안되었으며, 이 방식은 2015년에 발사된 서브미터급 고해상도 지구관측 위성에 처음으로 적용되어 발사되었으며, 현재까지 성공적으로 운용 중에 있다.

• 한국컴퓨터그래픽스학회논문지
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• 제29권4호
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• pp.7-16
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• 2023

캐릭터 애니메이션 분야에서 목표 지향적 이동을 위해 원하는 궤적을 재현하는 것은 항상 어려운 과제이다. 생성 모델을 사용하는 데이터 기반 방법은 명시적인 조건 없이 긴 동작 시퀀스를 예측하는 효율적인 방법 중 하나이다. 이러한 방법은 고품질의 결과물을 생성해내지만, 멀리 있는 목표물을 무작위로 타격하는 것처럼 더 어려운 상황의 모션을 합성(synthesis)에 있어서는 제한될 수 있다. 하지만 이는 모션 데이터 클립을 모방하는 GAN Discriminator 를 사용하고 강화학습을 통해 해결할 수 있다. 본 연구는 캐릭터들이 GAN 기반 접근법과 리워드 설계를 통해 복싱을 구현하는 것을 목표로 한다. 논문에서 사용된 두 가지의 최신 연구인 Adversarial Motion Prior 와 Adversarial Skill Embedding 에 대해 비교실험하며, 또한 복싱을 경쟁 스포츠에 적용하기 위하여 멀티 에이전트 강화 학습을 위한 대규모 self-play 프레임워크인 TimeChamber 를 활용한다.

• Journal of Korea Trade
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• 제26권3호
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• pp.45-62
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• 2022

Purpose - Using unexpected changes in geopolitical tensions on the Korean peninsula as a quasi-natural experimental setting, we examine whether and how geopolitical risks travel across borders through firm-level imports and exports linkages. We also test whether the effects are driven by either imports or exports and assess whether firms can effectively hedge themselves against geopolitical risks. Design/methodology - We focus on a series of unanticipated geopolitical events taken place in Korea in 2018. Making use of the shocks to geopolitical climate, we identify five milestone events toward peace talks. We employ the event studies methodology. We examine heterogenous firm-level stock price reactions around key event dates depending on firms' exposure to geopolitical risks. As a measure of firms' exposure to geopolitical risks in Korea, we utilize a text-based measure of firm-level trade links. When a firm announces and discusses its purchase of inputs from Korea or sales of outputs to Korea in their annual disclosure filings, we define a firm to have a trade relationship with Korea and have exposure to Korean geopolitical risks. Similarly, we use a measure of a firm's hedging policies based on a firm's textual mention of the use of foreign exchange derivatives in their annual disclosure. Findings - We find that U.S. firms that have direct trade links to Korea gained significantly more value when the intensity of geopolitical risks drops compared to firms without such trade links to Korea. The effects are pronounced for firms purchasing inputs from or selling outputs to Korea. We find that the effectiveness of foreign exchange hedging against geopolitical risks is limited. Originality/value - We document the international transmission of geopolitical uncertainty through trade linkages. Export links as well as import links serve as important nexus of transmission of geopolitical risks across borders. Hedging strategies involving foreign-exchanges derivatives do not seem to insulate firms again geopolitical risks. With the recent movements of localization and reshuffling of the global value chain, our results suggest a significant impact of geopolitical risks in Korea on the construction of the global value chain.