• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.265-288
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• 2023

Process mining has generally analyzed the default logs of Information Systems such as SAP ERP, but as the use of automation software called RPA expands, the logs by RPA bots can be utilized. In this study, the actual status of RPA automation in the field was identified by applying RPA bots to the work of three domestic manufacturing companies (cosmetic field) and analyzing them after leaving logs. Using Uipath and Python, we implemented RPA bots and wrote logs. We used Disco, a software dedicated to process mining to analyze the bot logs. As a result of log analysis in two aspects of bot utilization and performance through process mining, improvement requirements were found. In particular, we found that there was a point of improvement in all cases in that the utilization of the bot and errors or exceptions were found in many cases of process. Our approach is very scientific and empirical in that it analyzes the automation status and performance of bots using data rather than existing qualitative methods such as surveys or interviews. Furthermore, our study will be a meaningful basic step for bot behavior optimization, and can be seen as the foundation for ultimately performing process management.

• Journal of radiological science and technology
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• v.45 no.6
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• pp.523-529
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• 2022

Gamma spectroscopy analysis is widely used for radioactivity analysis, and various factors are required for radioactivity calculations. Among the factors, K3 for each sample significantly influences the results. The previous methods of correcting the self-absorption effect include a computational simulation method and a method that requires making a CRM(certified reference material) identical to the sample medium. However, the above methods have limitations when used in small institutions because they require specialized program utilization skills or high manufacturing costs and large facilities. The aim of this study is to develop a method that can be easily and rapidly applied to radioactivity analysis. After filling the beaker with water, we placed the radiation source in a uniform position and used the measured value as the benchmark. Next, a correction factor was derived based on the difference in the radiation source count of the benchmark and the identically measured sample. For the radiation source, Eu-152, which emits a broad range of energy within the measurement range of gamma rays, and Cs-134 and Cs-137, which are indicator nuclides in environmental radiation analysis, were used. The sample was selected within the density range of 0.26-2.11 g/cm³, and the correction factor was derived by calculating the count difference of each sample compared to the reference value of water. This study presents a faster and more convenient method than the existing research methods for determining the self-absorption effect correction, which has become increasingly necessary.

• Journal of Korean Society of Disaster and Security
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• v.16 no.1
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• pp.105-113
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• 2023

Jeollanam-do has 25 land-to-island and island-to-island bridges, the largest number in Korea. It is a local government rich in specialized marine and tourism resources centered on the archipelago and the sea bridges connecting them. However, in the case of sea-crossing bridges, when strong winds or typhoons occur, there is an issue that increases anxiety among users and local residents due to excessive vibration of the bridge, apart from structural safety of the bridge. In fact, in the case of Cheonsa Bridge in Shinan-gun, which was recently opened in 2019, vehicle traffic restrictions due to strong winds and excessive vibrations frequently occurred, resulting in complaints from local residents and drivers due to increased anxiety. Therefore, based on the data measured using IoT measurement technology, it is possible to relieve local residents' anxiety about the safety management of marine bridges by providing quantitative and accurate bridge vibration levels related to traffic and wind conditions of bridges in real time to local residents. This study uses the existing measurement system and IoT sensor to constantly observe the wind speed and vibration of the marine bridge, and transmits it to local residents and managers to relieve anxiety about the safety and traffic of the sea-crossing bridge, and strong winds and to develop technologies capable of preemptively responding to large-scale disasters.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.14-34
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• 2023

The purpose of this study was to calculate the appropriate yellow-signal time for intersections, to find out the relationship between the approach speed and intersection width when calculating the time, and to secure safety by minimizing conflicts and dilemma sections in intersections that change according to the signal operation. For this purpose, 6,824 data points from 5 intersections were collected and analyzed. The main results of the study are as follows. First, the approach speed of individual vehicles in different lanes was analyzed, and the width of an intersection was defined by considering the conflict in each direction. Second, we developed a multiple regression model based on the approach speed and conflict points, which compensated for the problems of an existing formula. Third, a standard table is presented for applying the appropriate yellow-signal time according to the approach speed and intersection width based on a development formula. A method is also presented to determine the safety of the length of the dilemma according to the change in the yellow-light time by presenting a calculation table that can cross-analyze the yellow-signal time and a dilemma section using the relationship.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.125-144
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• 2023

Recently, the use of Large Language Models (LLMs) such as ChatGPT has been increasing in various fields such as interactive commerce and mobile financial services. However, LMMs, which are mainly created by learning existing documents, can also learn various human biases inherent in documents. Nevertheless, there have been few comparative studies on the aspects of bias and discrimination in LLMs. The purpose of this study is to examine the existence and extent of nine types of discrimination (Age, Disability status, Gender identity, Nationality, Physical appearance, Race ethnicity, Religion, Socio-economic status, Sexual orientation) in LLMs and suggest ways to improve them. For this purpose, we utilized BBQ (Bias Benchmark for QA), a tool for identifying discrimination, to compare three large-scale language models including ChatGPT, GPT-3, and Bing Chat. As a result of the evaluation, a large number of discriminatory responses were observed in the mega-language models, and the patterns differed depending on the mega-language model. In particular, problems were exposed in elder discrimination and disability discrimination, which are not traditional AI ethics issues such as sexism, racism, and economic inequality, and a new perspective on AI ethics was found. Based on the results of the comparison, this paper describes how to improve and develop large-scale language models in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.249-265
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• 2023

Recently, the satellite industry has been paying attention to the private-led 'New Space' paradigm, which is a departure from the traditional government-led industry. The space industry, which is considered to be the next food industry, is still receiving relatively little attention in Korea compared to the global market. Therefore, the purpose of this study is to explore future signals that can help determine the market entry strategies of private companies in the domestic satellite industry. To this end, this study utilizes the theoretical background of future signal theory and the Keyword Portfolio Map method to analyze keyword potential in patent document data based on keyword growth rate and keyword occurrence frequency. In addition, news data was collected to categorize future signals into first symptom and early information, respectively. This is utilized as an interpretive indicator of how the keywords reveal their actual potential outside of patent documents. This study describes the process of data collection and analysis to explore future signals and traces the evolution of each keyword in the collected documents from a weak signal to a strong signal by specifically visualizing how it can be used through the visualization of keyword maps. The process of this research can contribute to the methodological contribution and expansion of the scope of existing research on future signals, and the results can contribute to the establishment of new industry planning and research directions in the satellite industry.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.167-175
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• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.

• Journal of the Daesoon Academy of Sciences
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• v.44
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• pp.53-96
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• 2023

This article apprehends Yeongdae (靈臺), the most sacred shrine of Daesoon Jinrihoe which has earlier historical precedents, as an example of material religion. In East Asia, the first Yeongdae was a structure that King Wen of Zhou commissioned to be built. As the time passed by, the meaning of Yeongdae was changed to signify a mental yeongdae, the object and the aim of cultivation, and the notional mindset that appears in the Daoist meditation, Cunsi (存思, visualization). This implies that Yeongdae has signified both material and non-material objects. Throughout most of history, these two concepts had never been related to each other, but shifted in form and meaning depending on context. Daesoon Jinrihoe, which emerged in the modern era of Korea, used the concept of investiture of gods and combined the two into one. Accordingly, the Yeongdae, referred to by King Wen to indicate his shrine, was expressed as a spot wherein the gods were enshrined on the earth. As an innovation, Daesoon Jinrihoe argued that gods correspond to properly eligible human beings according to the degree of their cultivation and that sacred space was instead defined a spot within the human mind, a mental yeongdae, where the gods could be enshrined into humanity (神封於人). From the perspective of Lévi-Strauss, the factors that are discovered in the tradition of East Asia, namely, the Yeongdae of King Wen and the mental yeongdae of Zhuangzi can combined with consistency and established in the doctrinal system of Daesoon Jinrihoe. Such an attempt refers to bricolage which re-creates the traditional concepts of the past. In this regard, the concept of invented tradition coined by Eric Hobsbawm could also be used to conclude that the Yeongdae (the shrine of gods) of Daesoon Jinrihoe can be expressed as an invented Yeongdae, which transcends the existing categories of material yeongdae and non-material yeongdae.

• Journal of International Area Studies (JIAS)
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• v.15 no.1
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• pp.83-108
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• 2011

South America has vast energy resources with the renewable and non-renewable sources. However, many countries in the region are unable to guarantee adequate energy security both of energy supply and demand. Currently the possibility of energy security is high through regional energy integration based on the potential economic benefits. The difference of regulation system with the individual countries in the region impose strong barriers to integration process. Security of energy supply and its demand as well is fundamental issues in this region and regional energy cooperation is essential for getting rid of the insecurity of energy supplies. Despite of this problem, currently Latin American countries made a great effort to make multilateral energy security regime through projecting great energy infrastructure network(e.g. IIRSA) or mechanism especially in South America, which can give countries access to the region's reserve supplies by providing regulations and pricing mechanism with a shared energy market in this region. Brazil's active leading in the formulation of such movement toward energy security integration and participation of energy infrastructure network is good initiative to enforce this great energy security change. Politically and economically, Brazil's geographical position and the level of market size and oil and natural gas resources, in addition the leadership in renewable energy sources make it a sound candidate to take over the coordination of the secure integration of region's energy market. However, on the conditions of existing many obstacles such as, control of the output of the region's power plant, energy flows, the environmental matter within local community must be overcome to make more advance process and steps. Finally, to secure more institutional approach, this region must settle regional disputes resolution regime urgently.