• Journal of Climate Change Research
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• v.5 no.1
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• pp.1-12
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• 2014

With continued economic growth, Korea has seen an increase in the nighttime activities of its citizens as hours of activity have extended into night. There is an increasing trend in energy consumption related to citizens' nighttime activities. In order to analyze ideas for an efficient replacement of the power consumption of streetlights and for profit generation by applying grid-type solar systems, this study used an RETScreen model. Through energy analysis and cost analysis, the application benefit and viability of grid-type solar street light systems were analyzed. With analysis result of a total weekly power generation of 114 kWh via a grid-connected solar streetlight system, it was shown that the net present value of a grid-connected solar street light system is 155,362 KRW, which would mean a payback period of about 5.2 years, and as such, it was shown that profit could be generated after about 6 years. In addition, if the grid-connected solar power generation system proposed by this study is to be applied, it was shown that 401,935 KRW in profit could be generated after the 20-year useful life set for the solar system. In addition, the sensitivity analysis was performed taking into account the price fluctuations of SMP, maintenance. As a result, a payback period has increased by 1~2 years, and there were no significant differences. Because the most important factor that affect the economic analysis is the cost of supply certification of renewable energy, a stable sales and acquisition of this certification are very important. the Seoul-type Feed in Tariff(FIT) connected to other institutions will enable steady sales by confirming to purchase the certification for 12 years. Therefore, if those issues mentioned above are properly reflected, Central-grid PV system project will be able to perform well in the face of unfavorable condition of solar PV installation.

• Analytical Science and Technology
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• v.32 no.1
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• pp.17-23
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• 2019

Sources of NOx are both anthropogenic (e.g. fossil fuel combustion, vehicles, and other industrial processes) and natural (e.g. lightning, biogenic soil processes, and wildfires). The nitrogen stable isotope ratio of NOx has been proposed as an indicator for NOx source partitioning, which would help identify the contributions of various NOx sources. In this study, the ${\delta}^{15}N-NO_2$ values of vehicle emissions were measured in an urban region, to understand the sources and processes that influence the isotopic composition of NOx emissions. The Ogawa passive air sampler was used to determine the isotopic composition of $NO_2$(g). In urban tunnels, the observed $NO_2$ concentration and ${\delta}^{15}N-NO_2$ values averaged $3809{\pm}2656ppbv$ and $7.7{\pm}1.8$‰, respectively. The observed ${\delta}^{15}N-NO_2$ values are associated with slight regional variations in the vehicular $NO_2$ source. Both $NO_2$ concentration and ${\delta}^{15}N-NO_2$ values were significantly higher near the expressway ($965{\pm}125ppbv$ and $5.9{\pm}1.4$‰) than at 1.1 km from the expressway ($372{\pm}96ppbv$ and $-11.5{\pm}2.9$‰), indicating a high proportion of vehicle emissions. Ambient ${\delta}^{15}N-NO_2$ values were used in a binary mixing model to estimate the percentage of the ${\delta}^{15}N-NO_2$ value contributed by vehicular NOx emissions. The calculated percentage of the ${\delta}^{15}N-NO_2$ contribution by vehicles was significantly higher close to the highway, as observed for the $NO_2$ concentration and ${\delta}^{15}N-NO_2$.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• 한국사회정책
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• v.24 no.1
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• pp.33-66
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• 2017

Based on the premise that the systematic career process of workers in the general labor market was one of core elements of successful achievements and their establishment both at the individual and organizational level, this study set out to conduct empirical analysis of factors influencing the subjective career success of disabled workers in competitive employment at the multi-dimensional levels of individuals and organizations(corporations) and thus provide practical implications for the career management directionality of their successful vocational life with data based on practical and statistical accuracy. For those purposes, the investigator administered a structured questionnaire to 126 disabled workers at 48 companies in Seoul, Gyeonggi, Chungcheong, and Gangwon and collected data about the individual and organizational characteristics. Then the influential factors were analyzed with the multilevel analysis technique by taking into consideration the organizational effects. The analysis results show that organizational characteristics explained 32.1% of total variance of subjective career success, which confirms practical implications for the importance of organizational variables and the legitimacy of applying the multilevel model. The significant influential factors include the degree of disability, desire for growth, self-initiating career attitude and value-oriented career attitude at the individual level and the provision of disability-related convenience, career support, personnel support, and interpersonal support at the organizational level. The latter turned out to have significant moderating effects on the influences of subjective career success on the characteristic variables at the individual level. Those findings call for plans to increase subjective career success through the activation of individual factors based on organizational effects. The study thus proposed and discussed integrated individual-corporate practice strategies including setting up a convenience support system by reflecting the disability characteristics, applying a worker support program, establishing a frontier career development support system, and providing assistance for a human network.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.57-70
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• 2021

As COVID-19 spreads, people's interest in smart learning that can do non-face-to-face learning is increasing nowadays. In this study, we aim to empirically analyze how users' thoughts on COVID-19 and the information quality and system quality of smart learning systems affect users' acceptance of smart learning and examine the effect of perceived sensitivity and severity of COVID-19 on the satisfaction and use of smart learning through concerns about the risk of transmission. In addition, we examined the influence of information quality composed of content quality and interaction quality and system quality composed of system accessibility and functionality on the use of smart learning through user satisfaction. To verify the validity of the proposed model, we conducted a survey on 334 users with experience in using smart learning, and performed the analysis using Smart PLS 3.0. According to the analysis results, among information quality and system quality, only functionality has a positive (+) effect on the satisfaction of smart learning, and satisfaction has a positive (+) effect on the usage behavior. However, it is found that accessibility among system quality do not affect satisfaction, and concern about the risk of transmission has a negative effect on satisfaction. This study can provide meaningful guidelines to researchers when researching smart learning to support students' learning in a pandemic situation of a new infectious disease, such as COVID-19. It will also be able to provide useful implications for educational institutions and companies related to smart learning.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean small business review
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• v.42 no.4
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• pp.69-102
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• 2020

Shedding new light on the research trend on entrepreneurial ecosystems in the 40-year history of the Asia Pacific Journal of Small Business, this study aims at exploring a potential measurement framework of ecological inputs and outputs in an entrepreneurial ecosystem that promotes entrepreneurship at geographical and spatial levels. As a result of the analysis of research on the entrepreneurial ecosystem in the journal, we found that prior studies emphasized the managerial importance of various ecological factors on the premise of possible causalities between the factors and entrepreneurship. However, empirical research to verify the premised causality has been underexplored yet. This literature gap may lead to unbalanced development of conceptual and case studies that identify requirements for successful entrepreneurial ecosystems based on experiential facts, thereby hindering the generalization of the research results for practical implications. In that there is a growing interest in creating and operating productive entrepreneurial ecosystems as an innovation engine that drives national and regional economic growth, it is necessary to explore and develop the measurement framework for ecological factors that can be used in future empirical research. Hereupon, we apply a conceptual model of 'input-output-outcome-impact' to categorize individual environmental factors identified in prior studies. Based on the model. We operationalize ecological input factors as the financial, intellectual, institutional, and social capitals, and ecological output factors as the establishment-based, innovation-based, and performance-based entrepreneurship. Also, we propose several longitudinal databases that future empirical research can use in analyzing the potential causality between the ecological input and output factors. The proposed framework of entrepreneurial ecosystems, which focuses on measuring ecological input and output factors, has a high application value for future research that analyzes the causality.

• Journal of the Korean Geotechnical Society
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• v.38 no.7
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• pp.49-62
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• 2022

The hollow modular concrete block reinforced foundation method is one of the ground reinforcement foundation methods that uses hexagonal honeycomb-shaped concrete blocks with mixed crushed rock to reinforce soft grounds. It then forms an artificial layered ground that increases bearing capacity and reduces settlement. The hollow modular honeycomb-shaped concrete block is a geometrically economical, stable structure that distributes forces in a balanced way. However, the behavioral characteristics of hollow modular concrete block reinforced foundations are not yet fully understood. In this study, a bearing capacity test is performed to analyze the reinforcement effectiveness of the hollow modular concrete block through the laboratory model tests. From the load-settlement curve, punching shear failure occurs under the unfilled sand condition (A-1-N). However, the filled sand condition (A-1-F) shows a linear curve without yielding, confirming the reinforcement effect is three times higher than that of unreinforced ground. The bearing capacity equation is proposed for the parts that have contact pressure under concrete, vertical stress of hollow blocks, and the inner skin friction force from horizontal stress by confining effect based on the schematic diagram of confining effect inside a hollow modular concrete block. As a result of calculating the bearing capacity, the percentage of load distribution for contact force on the area of concrete is about 65%, vertical force on the area of hollow is 16.5% and inner skin friction force of area of the inner wall is about 18.5%. When the surcharge load is applied to the concrete part, the vertical stress occurs on the area of the hollow part by confining effect first. Then, in the filled sand in the hollow where the horizontal direction is constrained, the inner skin friction force occurs by the horizontal stress on the inner wall of the hollow modular concrete block. The inner skin friction force suppresses the punching of the concrete part and reduces contact pressure.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.