• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.476-486
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• 2019

Objective : The functional information of $^{11}C$-methionine positron emission tomography (MET-PET) images can be applied for Gamma knife radiosurgery (GKR) and its image quality may affect defining the tumor. This study conducted the phantom-based evaluation for geometric accuracy and functional characteristic of diagnostic MET-PET image co-registered with stereotactic image in Leksell $GammaPlan^{(R)}$ (LGP) and also investigated clinical application of these images in metastatic brain tumors. Methods : Two types of cylindrical acrylic phantoms fabricated in-house were used for this study : the phantom with an array-shaped axial rod insert and the phantom with different sized tube indicators. The phantoms were mounted on the stereotactic frame and scanned using computed tomography (CT), magnetic resonance imaging (MRI), and PET system. Three-dimensional coordinate values on co-registered MET-PET images were compared with those on stereotactic CT image in LGP. MET uptake values of different sized indicators inside phantom were evaluated. We also evaluated the CT and MRI co-registered stereotactic MET-PET images with MR-enhancing volume and PET-metabolic tumor volume (MTV) in 14 metastatic brain tumors. Results : Imaging distortion of MET-PET was maintained stable at less than approximately 3% on mean value. There was no statistical difference in the geometric accuracy according to co-registered reference stereotactic images. In functional characteristic study for MET-PET image, the indicator on the lateral side of the phantom exhibited higher uptake than that on the medial side. This effect decreased as the size of the object increased. In 14 metastatic tumors, the median matching percentage between MR-enhancing volume and PET-MTV was 36.8% on PET/MR fusion images and 39.9% on PET/CT fusion images. Conclusion : The geometric accuracy of the diagnostic MET-PET co-registered with stereotactic MR in LGP is acceptable on phantom-based study. However, the MET-PET images could the limitations in providing exact stereotactic information in clinical study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.533-536
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• 2019

Recombinant baculoviruses are widely used to express heterologous genes in cultured insect cells. Recombinant baculoviruses can serve as gene-transfer vectors for expression of recombinant proteins in a wide range of mammalian cell types. Baculovirus system has significant benefits in view of safety, large-scale, and high level of gene expression. In this study, baculoviral vectors which were reconstructed from pOPINEneo-3C-GFP vector, were recombined with cytomegalovirus (CMV) promoter, green fluorescent protein (GFP), and p53 with NcoI and XhoI. These recombinant vectors were infected with various cells and cell lines. The baculovirus vector thus developed was analyzed by comparing the metastasis and expression of the recombinant genes with conventional vectors. These results suggest that the baculovirus vector has higher efficiency in metastasis and expression than the control vector. This work was supported by a grant from Mid-Career Researcher Program(NRF-2016R1A2B4016552) through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(MSIP).

• Journal of Nutrition and Health
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• v.54 no.5
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• pp.515-533
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• 2021

Purpose: A systematic review and meta-analysis of nutrition educational intervention studies was performed to assess the association between nutrition education intervention and fruit & vegetable (F&V) preferences and nutrition knowledge in preschool children. Methods: The relevant studies of nutrition education intervention and F&V preferences and nutrition knowledge published from January 2000 to June 2020 were located using PubMed, Web of Science, Cochrane Library, Research Information Sharing Service, Korean Studies Information Service System databases, and lists of references. A random-effects meta-analysis was conducted to estimate the standardized mean difference with a 95% confidence interval (CI). Subgroup analyses were performed to identify the association between nutrition education and F&V preferences and nutrition knowledge. Results: The results show that the effect sizes (ES) of F&V preferences and nutrition knowledge of preschool children were 0.31(95% CI, 0.23, 0.39), and 1.69(95% CI, 1.27, 2.12), respectively. The result of subgroup analysis, nutrition education focused on F&V (F&V preferences, ES: 0.32; nutrition knowledge, ES: 2.09) presented a slightly larger effect than general nutrition education (F&V preferences, ES: 0.26; nutrition knowledge, ES: 1.62). As for the type of exposure to F&V, direct exposure education (F&V preferences, ES: 0.40) had a greater effect than indirect exposure (F&V preferences, ES: 0.26). This meta-analysis showed that nutrition education intervention had positive effects on the F&V preferences and nutrition knowledge in preschool children. Conclusion: In conclusion, from the meta-analysis and subsequent subgroup analysis, we found that varied types of nutrition education intervention had varying effects on F&V preferences and nutrition knowledge in preschool children.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.1-27
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• 2021

The entire tourism industry is being hit hard by the COVID-19 as a global pandemic. Accommodation sharing services such as Airbnb, which have recently expanded due to the spread of the sharing economy, are particularly affected by the pandemic because transactions are made based on trust and communication between consumer and supplier. As the pandemic situation changes individuals' perceptions and behavior of travel, strategies for the recovery of the tourism industry have been discussed. However, since most studies present macro strategies in terms of traditional lodging providers and the government, there is a significant lack of discussion on differentiated pandemic response strategies considering the peculiarity of the sharing economy centered on peer-to-peer transactions. This study discusses the marketing strategy for individual hosts of Airbnb during COVID-19. We empirically analyze the effect of changes in listing descriptions posted by the Airbnb hosts on listing performance after COVID-19 was outbroken. We extract nine aspects described in the listing descriptions using the Attention-Based Aspect Extraction model, which is a deep learning-based aspect extraction method. We model the effect of aspect changes on listing performance after the COVID-19 by observing the frequency of each aspect appeared in the text. In addition, we compare those effects across the types of Airbnb listing. Through this, this study presents an idea for a pandemic crisis response strategy that individual service providers of accommodation sharing services can take depending on the listing type.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.556-561
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• 2020

The aging and declining agricultural population in the modern society requires improvement of the agricultural environment and is one of the representative problems. And since most of the work systems always require a transport work, the ratio of labor consumed in the transport work is very high. Accordingly, many types of transport vehicles are being developed and sold, and in the early days, most of them are powered transport vehicles using fossil fuels. However, it is paying attention to next-generation eco-friendly energy such as hydrogen, fuel cells, solar power, and bio due to the strengthening of international environmental regulations such as global warming and the Convention on Climate Change and the depletion of fossil fuels. Therefore, in this study, the ultimate goal is to develop an eco-friendly, easy-to-operate, safe agricultural electric vehicle that replaces fossil fuels. It was designed with a focus on controlling a wide range of vehicle speeds and securing stability of electric agricultural vehicles. Considering the performance and design, it is composed of a frame, a driving part, a steering part, and a controller system, and we are going to review and manufacture each part. It is believed that the manufactured electric vehicle for agriculture can be easily and conveniently operated in an agricultural society where young manpower is scarce, and can be helpful to the agricultural society through high efficiency.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.133-142
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• 2023

The office environment is changing according to work types, Information Technology (IT) advancements, and the Coronavirus disease (COVID)-19 situation. In order for office space users to perform their tasks comfortably and efficiently, it is necessary to secure individual privacy as well as easy communication among members. In Korea, the demand for improving the acoustic performance of office spaces is also increasing, but the related performance criteria and guidelines have not been established. In this study, standardization of office space acoustic performance measurement and evaluation methods and European countries' acoustic performance criteria were compared and reviewed. It is proposed to comprehensively review international standardization trends and acoustic performance standards in each country and to establish and utilize criteria for evaluating the acoustic performance and satisfaction of office spaces in Korea through our survey. Considering the international standardization direction and compatibility with communication and Public Address (PA) systems, it is appropriate to establish criteria using the speech transmission index or Speech Transmission Index (STI) application index. This criterion will be highly utilizable and compatible. In addition, since the office furniture industry is interested in improving the acoustic performance of office space, it is necessary to establish a labelling system for speech level reduction of office furniture.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.147-153
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• 2023

As it spreads to all industries of artificial intelligence technology, AIaaS equipped with artificial intelligence services is emerging. In particular, non-IT companies are suffering from the absence of software experts, difficulties in training big data models, and difficulties in collecting and analyzing various types of data. AIaaS makes it easier and more economical for users to build a system by providing various IT resources necessary for artificial intelligence software development as well as functions necessary for artificial intelligence software in the form of a service. Therefore, the supply and demand for such cloud-based AIaaS services will increase rapidly. However, the quality of services provided by AIaaS becomes an important factor in what is required as the supply and demand for AIaaS increases. However, research on a comprehensive and practical quality evaluation metric to measure this is currently insufficient. Therefore, in this paper, we develop and propose a usability, replacement, scalability, and publicity metric, which are the four metrics necessary for measuring reusability, based on implementation, convenience, efficiency, and accessibility, which are characteristics of AIaaS, for reusability evaluation among the service quality measurement factors of AIaaS. The proposed metrics can be used as a tool to predict how much services provided by AIaaS can be reused for potential users in the future.