• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.179-185
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• 2019

A core technology of social robot is voice recognition and dialogue engine technology, but too much money is needed for development and an implementation of robot's conversation function is difficult resulting from insufficiency of performance. Dialogue function's implementation between human and robot can be possible due to advance of cloud AI technology and several company's supply of their open API. In this paper, current intelligent social robot technology trend is investigated and effective social robot system architecture is designed. Also an effective analysis and design method of social robot system will be presented by showing user requirement analysis using object-oriented method, flowchart and screen design.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.159-165
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• 2018

In this study, it is analyzed that keywords of the interest in the 4th industrial revolution for science and Technician K women's university students, areas to prioritize in the strategy of 4th industry revolution, to research compare analyze the recognition of science technology such as the most necessary education, human resource development of universities and companies in Korea and abroad according to the technology trend required in the 4th Industrial revolution era and which area to prepare for the 4th industrial revolution. The survey result shows different thoughts of science and Technician(KOFST) and the women university students. In the 1) 4th industrial revolution, the 96% of former are interested, while 60% of latter are interested. And in the most used keywords, the former group used AI(24%), Fusion new industry(21%) the most, while the latter group used AI(34%), Robot(18%). And, 3) in the strategic priority, the science technology experts are interested in education, R&D system innovation(27%), IoT, Information and Communication(26%) and the university students are interested in IoT, Information and Communication(31%), AI(28%). Finally, 4) the science technology experts thought of Autonomous Vehicle(20%), 3D Printer(7%), AI(16%) important, while the women university students thought of AI(27%), VR/Augmented Reality(17%), and Autonomous Vehicle(16%) the most necessary education. In the 4th industrial revolution, we need people with ability to solve complicated problems with creativity based on understanding and absorbing new knowledge and thinking of converged idea.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.3
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• pp.145-156
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• 2024

This study explores the integration of cutting-edge technology with records management, aiming to create new value not only in work processes but also in record information services. The research focuses on the case of constructing an "AI-based cultural heritage research record learning data and search system," carried out by the National Research Institute of Cultural Heritage (NRICH) Archives, and analyzes user satisfaction results. "Discovering Cultural Heritage with ZOOM" is a system designed to proactively predict research data demand by constructing big data (learning data) from images (675,338 items) contained in 1,421 volumes of publications in the cultural heritage field, spanning from 1973 to the present, and simultaneously presenting 50 similar images. This initiative aims to foster change and development in the field of records management and cultural heritage in response to the Fourth Industrial Revolution's advanced technologies. It is expected to provide valuable information to researchers, practitioners, and the general public alike.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.273-282
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• 2023

Recently, various technologies such as logistics automation and port operations automation with ICT technology are being developed to build smart ports. However, there is a lack of technology development for port safety and safety accident prevention. This paper proposes an AI-based shipping container loading safety management system for the prevention of safety accidents at container loading fields in ports. The system consists of an AI-based shipping container safety accident risk classification and storage function and a real-time safety accident monitoring function. The system monitors the accident risk at the site in real-time and can prevent container collapse accidents. The proposed system is developed as a prototype, and the system is ecaluated by direct application in a port.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.642-648
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• 2019

Vessels sail according to the COLREG to prevent a collision. However, it is difficult to apply COLREG under special situation as heavy traffic, at this time personal skills of the operator are required. In this case, traffic control is required through the maritime traffic monitoring system. Therefore, maritime traffic management is globally implemented by VTS. In this system, VTS of icer uses the VTS system to assess risks and recommends possible safety operation to vessels with radio systems. This study considers that the risk analysis method with AI (Artificial Intelligence) technology from the operator's aspect. In addition, the research explains the Maritime Traffic Safety Monitoring System, Including AR (Augmented Reality) technology to increase vessel control efficiency. This system is able to predict hazards and risk priorities, and it leads to sequential elimination of dangerous situations. Especially, the hazard situations can be analyzed from operator's perspective of each vessel instead of the VTS officer's aspect, which is more practical than the conventional method. Furthermore, the result of analysis enables to comprehend quantitative hazardous areas and support recommended routes to avoid a collision. As a result, I firmly believe that the system will support to prevent a collision in complex traffic waters. In particular, it could be adopted as a collision prevention system for Maritime Autonomous Surface Ship, which occupies a significant proportion in Maritime 4th industrial revolution.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.230-235
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• 2022

This study is to select an optimal object detection algorithm for designing a self-checkout counter to improve the inconvenience of payment systems for products without existing barcodes. To this end, a performance comparison analysis of YOLO v2, Tiny YOLO v2, and the latest YOLO v5 among deep learning-based object detection algorithms was performed to derive results. In this paper, performance comparison was conducted by forming learning data as an example of 'donut' in a bakery store, and the performance result of YOLO v5 was the highest at 96.9% of mAP. Therefore, YOLO v5 was selected as the artificial intelligence object detection algorithm to be applied in this paper. As a result of performance analysis, when the optimal threshold was set for each donut, the precision and reproduction rate of all donuts exceeded 0.85, and the majority of donuts showed excellent recognition performance of 0.90 or more. We expect that the results of this paper will be helpful as the fundamental data for the development of an automatic payment system using AI self-service technology that is highly usable in the non-face-to-face era.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.92-97
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• 2022

Plastics are classified into 7 types such as PET (PETE), HDPE, PVC, LDPE, PP, PS, and Other for separation and recycling. Recently, large corporations advocating ESG management are replacing them with bioplastics. Incineration and landfill of disposal of plastic waste are responsible for air pollution and destruction of the ecosystem. Because it is not easy to accurately classify plastic materials with the naked eye, automated system-based screening studies using various sensor technologies and AI-based software technologies have been conducted. In this paper, NIR scanning devices considering the NIR wavelength characteristics that appear differently for each plastic material and a system that can identify the type of plastic by learning the NIR spectrum data collected through it. The accuracy of plastic material identification was evaluated through a decision tree-based SVM model for multiclass classification on NIR spectral datasets for 8 types of plastic samples including biodegradable plastic.

• The Research Journal of the Costume Culture
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• v.31 no.5
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• pp.651-668
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• 2023

This study aims to help companies with efficient investment and marketing strategies by empirically verifying the impact on satisfaction and purchase intention for artificial intelligence-based digital technology supported shopping assistants introduced in e-commerce. Frequency, factor, SEM, and multiple group analysises were conducted using SPSS 26.0 and Amos 26.0. As a result, first, motivated consumer innovativeness elements of AI shopping assistant were derived into a total of four categories: functional, hedonic, rational, and reliable. Second, in the order of hedonic and rational, satisfaction with the AI shopping assistant was significantly affected, and in the order of rational and functional, purchase intention was significantly affected. The satisfaction with the AI shopping assistant did not affect the purchase intention. Third, in the case of hedonic, the AI-preferred group had a more significant effect on satisfaction than the human-preferred group, and in the case of rational, there was no difference by group in purchase intention. Thus, it was found that consumers prefer AI shopping helpers for e-commerce because they can shop reasonably and are functionally convenient. Therefore, when introducing AI shopping assistants, it is essential to include content that can compare and analyze fundamental information, such as product prices, as well as search functions and payment system compatibility that facilitate shopping.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.