• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.65-77
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• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• Korean small business review
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• v.41 no.3
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• pp.151-172
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• 2019

The changes in the 4th industrial revolution era are not limited to specific sectors, but affect all sectors of industry. Thus all companies are required to respond effectively to changes. Some companies response by adopting cutting-edge ICT and some companies improve the organizational structure, or enhance the competence of individual employees. This study is based on the assumption that the responses to the change in the 4th industrial revolution era should not be uniform, and that the response strategies and priorities should vary according to the characteristics of the companies. The purpose of this study is to suggest both different response strategies and the priority of the responding factors(areas) to small and medium-sized enterprises. Data were collected through the semi-Delphi method. As a result of data analysis, the priorities of the medium-sized enterprises were as follows: introduction of IT-strengthening the competence of the individuals - establishing technology infrastructure-improving organizational structure - efficiency of work - improving organizational culture. While the priorities of the response factors(area) of the small-sized companies were as follows: strengthening the competence of the individuals - efficiency of work - introduction of IT - establishing technology infrastructure - improving organizational structure - improving organizational culture.

• Korean Association of Arts Management
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• no.52
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• pp.39-73
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• 2019

The purpose of this study is to explore the issues and alternatives in the promotion of space and exhibition support projects in visual arts, and to suggest an applicable direction of the government grant program. For this study, FGI and in-depth interviews were conducted in the direct beneficiaries of the grant program, the staff of the grant program, and the group of visual arts expert. As a result, the deliberation and selection of the support project should be carried out with sufficient time in consideration of the specificity of the support project. And the project requires to divide into two kinds of the projects based on the understanding of the social role and differentiation of non-profit exhibition spaces and private art museums. For the grant application and assessments, a long-term support is necessary to bring the capability and issues in efficient allocation of the budget, flexibility of budget item, and the budget shortfalls. Furthermore, the reliability between the grant program and beneficiaries needs to form a healthy partnership, and the evaluation criteria and eNARADOEUM system should be more practical and rationally established. Through this analysis, the implications of understanding the specificity of visual arts support projects, establishment of sustainable visual arts creation policies, and budget utilization were derived. Consequently, cultural and artistic support projects were directed to place more emphasis on efficiency than control, direction considering the position of consumers than suppliers, and long-term business planning rather than short-term perspective.

• Korea Trade Review
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• v.42 no.1
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• pp.69-97
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• 2017

This study purpose to examine the importer's risks that may arise from origin investigation by Customs authorities. We have drawn the important factors affecting the application of FTA preferential tariffs and divided the stages from the conclusion of the contract for the importer to the undergoing origin investigation. In addition, we demonstrate empirically that the risks that arise in areas where importers are difficult to control exist. As a management method of the uncontrollable risk from the importer, we have provided the methods that the seller stipulated the seller's responsibility in the trade contract, prepared for situations in which no one was responsible, and formulated a friendly and cooperative supply chain. Even if the seller's liability is clarified in the contract for sale, the risk of the investigation into the origin of the imported goods is not completely eliminated. This is because, under the current agreement and system, there is no way for the customs authority of the contracting party of the FTA to claim compensation for damages incurred by importers due to breach of agreement such as not returning the result of the origin verification. Importers are subject to customs duties, but there may actually be situations in which no one is responsible for them.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.23-34
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• 2023

Wearable technology for military applications has received considerable attention as a means of personal status check and monitoring. Among many, an implementation to recognize specific motion states of a human is promising in that allows active management of troops by immediately collecting the operational status and movement status of individual soldiers. In this study, as an extension of military wearable application research, a new ankle wearable device is proposed that can glean the information of a soldier on the battlefield on which action he/she takes in which environment. Presuming a virtual situation, the soldier's upper limbs are easily exposed to uncertainties about circumstances. Therefore, a sensing module is attached to the ankle of the soldier that may always interact with the ground. The obtained data comprises 3-axis accelerations and 3-axis rotational velocities, which cannot be interpreted by hand-made algorithms. In this study, to discern the behavioral characteristics of a human using these dynamic data, a data-driven model is introduced; four features extracted from sliced data (minimum, maximum, mean, and standard deviation) are utilized as an input of the model to learn and classify eight primary military movements (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). As a result, the proposed device could recognize a movement status of a solider with 95.16% accuracy in an arbitrary test situation. This research is meaningful since an effective way of motion recognition has been introduced that can be furtherly extended to various military applications by incorporating wearable technology and artificial intelligence.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.37-46
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• 2023

As real estate values rise, interest in cadastral resurvey is increasing. Accordingly, a cadastral resurvey project is actively underway for drone operation through securing work efficiency and improving accuracy. The need for utilization and management of cadastral resurvey results (drone images) is being raised, and through this study, a 3D spatial information platform was developed to solve the existing drone image management and utilization limitations and to provide drone image-based 3D cadastral information. It is proposed to build and use. The study area was selected as a district that completed the latest cadastral resurvey project in which the study was organized in February 2023. Afterwards, a web-based 3D platform was applied to the study to solve the user's spatial limitations, and the platform was designed and implemented based on drone images, spatial information, and attribute information. Major functions such as visualization of cadastral resurvey results based on 3D information and comparison of performance between previous cadastral maps and final cadastral maps were implemented. Through the open platform established in this study, anyone can easily utilize the cadastral resurvey results, and it is expected to utilize and share systematic cadastral resurvey results based on 3-dimensional information that reflects the actual business district. In addition, a continuous management plan was proposed by integrating the distributed results into one platform. It is expected that the usability of the 3D platform will be further improved if a platform is established for the whole country in the future and a service linked to the cadastral resurvey administrative system is established.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.135-143
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• 2009

Integrating the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor technologies using the precise GPS Carrier phase measurements is a methodology that has been widely applied in those application fields requiring accurate and reliable positioning and attitude determination; ranging from 'kinematic geodesy', to mobile mapping and imaging, to precise navigation. However, such integrated system may not fulfil the demanding performance requirements when the baseline length between reference and mobil user GPS receiver is grater than a few tens of kilometers. This is because their positioning/attitude determination is still very dependent on the errors of the GPS observations, so-called "baseline dependent errors". This limitation can be remedied by the integration of GPS and INS sensors, using multiple reference stations. Hence, in order to derive the GPS distance dependent errors, this research proposes measurement processing algorithms for multiple reference stations, such as a reference station ambiguity resolution procedure using linear combination techniques, a error estimation based on Kalman filter and a error interpolation. In addition, all the algorithms are evaluated by processing real observations and results are summarized in this paper.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.