• Magazine of the Korean Society of Agricultural Engineers
• /
• v.39 no.1
• /
• pp.41-47
• /
• 1997

A geographical resource analysis support system (GRASS) was incorporated to an input and output processor for the agricultural nonpoint source pollution (AGNPS) model. The resulting interface system, GIS-AGNPS was a user-friendly, menu-driven system. GIS-AGNPS was developed to automatically process the input and output data from GIS-based data using GRASS and Motif routines. GIS-AGNPS was consisted of GISAGIN which was an input processor for the AGNPS model, GISAGOUT a output processor for the AGNPS and management submodel. The system defines an input data set for AGNPS from attributes of basic and thematic maps. It also provides with editing modes so that users can adjust and detail the values for selected input parameters, if needed. The post-processor at the system displays graphically the outputs from AGNPS, which may he used to identify areas significantly contributing nonpoint source pollution loads.

• Journal of Hydrogen and New Energy
• /
• v.29 no.4
• /
• pp.339-356
• /
• 2018

Although fuel cell systems have advantages in terms of electric efficiency and environmental impact compared with conventional power systems, fuel cell systems have not been deployed widely due to their low reliability and high price. In order to guarantee the lifetime of 10 years, which is the commercialization goal of Polymer electrolyte fuel cells (PEFCs), it is necessary to improve durability and reliability through optimized operation and maintenance technologies. Due to the complexity of components and their degradation phenomena, it's not easy to develop and apply the diagnose and prognostic methodologies for PEFCs. The purpose of the paper is to show the current state on PEFC prognostic technology for condition based maintenance. For the prognostic of PEFCs, the model driven method, the data-driven, and the hybrid method can be applied. The methods reviewed in this paper can contribute to the development of technologies to reduce the life cycle cost of fuel cells and increase the reliability through prognostics-based health management system.

• International journal of advanced smart convergence
• /
• v.11 no.2
• /
• pp.171-184
• /
• 2022

The purpose of this paper is output oriented, in order to maximize the output level of sustainable development efficiency of foreign trade in western China with limited input. This paper adopts the relevant input-output indicators of sustainable foreign trade development of 11 provinces and cities in western China from 2016 to 2020, and uses DEA model to measure their technical efficiency, pure technical efficiency and scale efficiency. Malmquist index was used to calculate the total factor productivity change index of each province in western China from 2016 to 2020. We found that, on the whole, the average values of technical efficiency, pure technical efficiency and scale efficiency of provinces and cities in western China from 2016 to 2020 are greater than 0.8, indicating that the western region has high technical efficiency, relatively high management and institutional level and high existing scale level. Scale efficiency is lower than pure technical efficiency on the whole, indicating that the current sustainable development efficiency of foreign trade in western China is mainly limited by its scale level. The technological progress index is higher than the technological efficiency change index, indicating that the total factor productivity of the sustainable development of foreign trade in western China is mainly driven by technological progress and more influenced by external factors. We think the conclusion of this study can provide important reference information for the sustainable development of foreign trade of provinces and cities in western China.

• Journal of Korea Water Resources Association
• /
• v.51 no.6
• /
• pp.503-514
• /
• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of the Korea Safety Management & Science
• /
• v.17 no.1
• /
• pp.337-348
• /
• 2015

This research introduces the Financial Effect Measurement (FEM) models which measures both the improvement and the innovation performance of Quality Control Circle (QCC) and activities of Six Sigma. Concepts and principle of Comprehensive Income Statement (CIS), Balanced Scorecard (BSC), Time-Driven Activity Based-Costing (TDABC) and Total Productive Maintenance (TPM) are applied in order to develop the 4 FEM models presented in this paper. First of all, FEM using CIS depicts the improvement effects of production capacity and yield using relationships between demand and supply, and line balancing efficiency between bottleneck process and non-bottleneck processes. Secondly, cause-and-effect relation of Key Performance Indicator (KPI) is used to present Critical Success Factor (CSF) effects for QC Story 15 steps of QCC and DMAIC (Define, Measure, Analyze, Improve, and Control) of Six Sigma. The next is FEM model for service management innovation activities that uses TDABC to calculate the time-driven effect for improving the indirect activities according to the cost object. Lastly, FEM model for TPM activities presents the interpretation of improvement effect model of TPM Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) maintenance using profit, cash and Economic Added Value (EVA) as metrics of enterprise values. To better understand and further investigate FEMs, recent cases on National Quality Circle Contest are used to evaluate new financial effect measurement developed in this paper.

• Annual Conference of KIPS
• /
• 2011.04a
• /
• pp.1371-1374
• /
• 2011

MDA(Model Driven Architecture)는 추상적인 모델 계층을 사용하기 때문에 다양한 플랫폼에 적용가능하고, 각 모델 계층과 코드 생성의 자동화를 통해 개발의 효율성을 극대화한다. 본 연구에서는 XML 형태로 저장된 설계정보를 분석하여 MDA 기반 컴포넌트 설계 정보를 관리하는 도구를 개발하였다. 이 도구는 UML로 작성된 설계모델를 XMI(XML Metadata Interchage) 형태로 저장하여 각종 설계도구에서 Java, C++과 같은 언어에 대한 실제 프로그램 골격코드가 자동으로 생성되도록 하였다. 역으로 골격코드를 기반으로 구현된 콤포넌트의 원시코드를 수집하여 다시 컴포넌트 설계모델 정보를 추출하는 기능을 구현하였고, 이를 다시 시각적 정보로 재구성 하였다. 이러한 기능들은 기존의 단방향적 개발 구조 방식에서 벗어나 이미 개발되거나 개발 중인 프로그램에 대한 분석 및 평가 등을 통해서 재사용성을 높여주는 순환적인 개발 구조 방식을 제공한다.

• Annual Conference of KIPS
• /
• 2024.10a
• /
• pp.553-556
• /
• 2024

Talking Head Generation is a highly practical task that is closely tied to current technology and has a wide range of applications in everyday life. This technology will be of great help in the fields of photography, online conversation as well as in education and medicine. In this paper, the authors proposed a novel approach for Individual Audio-Driven Talking Head Generation by leveraging a sequence of landmarks and employing a diffusion model for image reconstruction. Building upon previous landmark-based methods and advancements in generative models, the authors introduce an optimized noise addition technique designed to enhance the model's ability to learn temporal information from input data. The proposed method outperforms recent approaches in metrics such as Landmark Distance (LD) and Structural Similarity Index Measure (SSIM), demonstrating the effectiveness of the diffusion model in this domain. However, there are still challenges in optimization. The paper conducts ablation studies to identify these issues and outlines directions for future development.

• Journal of The Korean Association For Science Education
• /
• v.40 no.6
• /
• pp.657-670
• /
• 2020

The knowledge-information-processing competency is the most essential competency in a knowledge-information-based society and is the most fundamental competency in the new problem-solving ability. Data-driven science inquiry, which emphasizes how to find and solve problems using vast amounts of data and information, is a way to cultivate the problem-solving ability in a knowledge-information-based society. Therefore, this study aims to develop a teaching-learning model and strategy for data-driven science inquiry and to verify the validity of the model in terms of knowledge information processing competency. This study is developmental research. Based on literature, the initial model and strategy were developed, and the final model and teaching strategy were completed by securing external validity through on-site application and internal validity through expert advice. The development principle of the inquiry model is the literature study on science inquiry, data science, and a statistical problem-solving model based on resource-based learning theory, which is known to be effective for the knowledge-information-processing competency and critical thinking. This model is titled "Exploratory Scientific Data Analysis" The model consisted of selecting tools, collecting and analyzing data, finding problems and exploring problems. The teaching strategy is composed of seven principles necessary for each stage of the model, and is divided into instructional strategies and guidelines for environment composition. The development of the ESDA inquiry model and teaching strategy is not easy to generalize to the whole school level because the sample was not large, and research was qualitative. While this study has a limitation that a quantitative study over large number of students could not be carried out, it has significance that practical model and strategy was developed by approaching the knowledge-information-processing competency with respect of science inquiry.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.5
• /
• pp.109-116
• /
• 2022

Recently, a model-based development method centered on highly readable and standardized UML (Unified Modeling Language) models has been applied to solve unclear communications in large-scale software development. However, it is difficult to apply consistent UML models depending on software developers' proficiency, understanding of models and modeling tools. In this paper, we propose a method for developing a Model Verification System to apply an consistent UML model to software development. Then, the developed Model Verification System is partially applied to the Naval Combat System Software development to prove its function. The Model Verification System provides automatic verification of models created by developers according to domain characteristics. If the Model Verification System proposed in this paper is used, It has the advantage of being able to apply the consistent UML model more easily to Naval Combat System Software Development.

• The Korean Journal of Food & Health Convergence
• /
• v.9 no.4
• /
• pp.19-23
• /
• 2023

The integration of ChatGPT, an AI-powered language model, is causing a profound transformation within the food industry, impacting various domains. It offers novel capabilities in recipe creation, personalized dining, menu development, food safety, customer service, and culinary education. ChatGPT's vast culinary dataset analysis aids chefs in pushing flavor boundaries through innovative ingredient combinations. Its personalization potential caters to dietary preferences and cultural nuances, democratizing culinary knowledge. It functions as a virtual mentor, empowering enthusiasts to experiment creatively. For personalized dining, ChatGPT's language understanding enables customer interaction, dish recommendations based on preferences. In menu development, data-driven insights identify culinary trends, guiding chefs in crafting menus aligned with evolving tastes. It suggests inventive ingredient pairings, fostering innovation and inclusivity. AI-driven data analysis contributes to quality control, ensuring consistent taste and texture. Food writing and marketing benefit from ChatGPT's content generation, adapting to diverse strategies and consumer preferences. AI-powered chatbots revolutionize customer service, improving ordering experiences, and post-purchase engagement. In culinary education, ChatGPT acts as a virtual mentor, guiding learners through techniques and history. In food safety, data analysis prevents contamination and ensures compliance. Overall, ChatGPT reshapes the industry by uniting AI's analytics with culinary expertise, enhancing innovation, inclusivity, and efficiency in gastronomy.