• Korea Trade Review
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• v.41 no.3
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• pp.135-156
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• 2016

This study, with reference to data on economic conditions in Shandong Province, China, looked into trade and investment activities in Korea and major cities of Shandong - Qingdao, Yantai, Weihai and Jinan - and investigated claim cases between the two countries by type. In addition, we investigated the matter empirically by conducting a survey administered to 300 Korean companies investing in Shandong Province and, based on the data, tested hypotheses for inferential analysis. The findings are as follows: i) while hypotheses in which the size of a firm, represented by import and export volume, has a positive relation with the frequency of trade claim filings (H1) and with the financial value of the trade claims (H2) were quoted, company size proved to have a significantly negative relation with the time required to obtain a claim decision, which rejects the third hypothesis (H3) in which the relation was thought to be positive: ii) while products, as represented by the type of business, showed a clearly significant difference with the frequency of trade claim filings (H4) and with methods of preventing and responding to claims (H6), they did not show a significant link to the type of trade claim (H5). This study is a theoretical and empirical overview of Korean companies based in Shandong Province of China, and can be used to address the practical needs of the Korean companies looking to start business in Shandong Province.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.5
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• pp.407-416
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• 2018

The development of modern technology has rapidly grown the field of wearable devices. Wearable equipments should satisfy low power consumption and small/lightweight because of characteristics of body wearing. In this paper, an overview of wearable equipments is explained, and wearable device market is investigated. In addition, we investigate developed technology of wearable components, which is divided into component and communication technology. Meanwhile, a smart HSE system is required to meet the demand of the society for the serious industrial accident. To address this issue, we propose an interactive method between the wearable component and the HSE system, which are expected to be effective in safety management. As a detailed case study, a risk scenario is made with risk factors in welding workshop, and then we propose an interactive method between a wearable component and an HSE system that can reduce the risk. This proposed method is useful to achieve high level of worker's safety.

• Design Convergence Study
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• v.16 no.6
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• pp.169-184
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• 2017

There is a lot of needs that are not expressed as much as the expressed needs in familiar products and services that are used in daily life such as a smartphone. Finding the 'Inconveniences in familiar use' make it possible to create opportunities for value expanding in the existing products and service area. There are a lot of related works, which have studied the definition of hidden needs and the methods to find it. But, they are making it difficult to address the hidden needs in the cases of familiar use due to focus on the new product or service developing typically. In this study, we try to redefine the hidden needs in the daily familiarity and approach it in the new way to find out. Because of the users' unability to express what they want and the complexity of needs which can not be explained clearly, we can not approach it as the quantitative issue. For this reason, the basic data type selected as the user behavior data excluding all description is the screen-shot of the smartphone. We try to apply the integrated rules and patterns to the individual data using the qualitative coding techniques to overcome the limitations of qualitative analysis based on unstructured data. From this process, We can not only extract meaningful clues which can make to understand the hidden needs but also identify the possibility as a way to discover hidden needs through the review of relevance to actual market trends. The process of finding hidden needs is not easy to systemize in itself, but we expect the possibility to be conducted a reference frame for finding hidden needs of other further studies.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.853-861
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• 2018

This paper proposes the application of harmonic constraints to address the problems caused by abnormal voltage increases when electric railway vehicles are running. The AC line that supplies the train with power during operation is used to provide electricity of 25kV/60 Hz, but gradually the size and frequency of harmonics involved in the line are varied with the technological evolution of the railroad vehicle electrical equipment. An increase in heat losses due to the failure of the instrument transformer (PT), the main circuit device, which is a serious problem with the recent train safety operation, or to the main displacement voltage. When high frequency components are introduced through low frequency Transformers of the main circuit device, the high intensity of the components is caused by the high intensity of the core and the current flow of the parasitic core is increased, thus generating heat. To solve this problem, the recent adjustment of the sequence has applied artificial NOTCH OFF of the power converter. However, the method of receiving and controlling the OFF signal operates by interaction between the ground and the vehicle's devices, thus it is invalid in the event of failure, and an actual accident is occurring. Therefore, the harmonic currents were required to prevent possible flow of harmonics, and conducted a study to prevent accidental occurrence of train accidents and to verify feasibility of the device through the simulations of the train's experimental analysis and the simulations of the train for safe operation.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.129-138
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• 2022

In the context of the increasing applications of voice assistants in vehicles, we focused on the association between the visual appeal of the cars and the acoustic characteristics of the voice assistants. This study aimed to investigate the relationship between the visual appeal of the vehicle and the voice assistant based on their emotional characteristics. A total of 15 adjectives were used to assess the emotional characteristics of 12 types of cars and six types of voices. An online interview was carried out, instructing participants to match three adjectives with the presented car images or voices. This was followed with a brief interview to allow the participants to reflect on the adjective matches. Based on the assessments, we performed principal component analysis (PCA) to determine factors. We aimed to deploy the cars and voices and analyze the patterns of clustering. The PCA analysis revealed two factors profiled as "Light-Heavy" and "Comfortable-Radical." Both car and voice stimuli were deployed in a two-dimensional space showing the internal relationship within and between the two substances. Based on the coordination data, a hierarchical cluster grouped the 18 stimuli into four groups labeled as challenge, elegance, majesty, and vigor. This study identified two latent factors describing the emotional characteristics of both car images and voice types clustered into four groups based on their emotional characteristics. The coherent matches between car style and voice type are expected to address the design concept more successfully.

• International Journal of Oral Biology
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• v.48 no.2
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• pp.9-18
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• 2023

The rapid detection of bacteria in the oral cavity, its species identification, and bacterial count determination are important to diagnose oral diseases caused by pathogenic bacteria. The existing clinical microbial diagnosis methods are time-consuming as they involve observing patients' samples under a microscope or culturing and confirming bacteria using polymerase chain reaction (PCR) kits, making the process complex. Therefore, it is required to analyze the development status of substances and systems that can rapidly detect and analyze pathogenic microorganisms in the oral cavity. With research advancements, a close relationship between oral and systemic diseases has been identified, making it crucial to identify the changes in the oral cavity bacterial composition. Additionally, an early and accurate diagnosis is essential for better prognosis in periodontal disease. However, most periodontal disease-causing pathogens are anaerobic bacteria, which are difficult to identify using conventional bacterial culture methods. Further, the existing PCR method takes a long time to detect and involves complicated stages. Therefore, to address these challenges, the concept of point-of-care (PoC) has emerged, leading to the study and implementation of various chair-side test methods. This study aims to investigate the different PoC diagnostic methods introduced thus far for identifying pathogenic microorganisms in the oral cavity. These are classified into three categories: 1) microbiological tests, 2) microchemical tests, and 3) genetic tests. The microbiological tests are used to determine the presence or absence of representative causative bacteria of periodontal diseases, such as A. actinomycetemcomitans, P. gingivalis, P. intermedia, and T. denticola. However, the quantitative analysis remains impossible, and detecting pathogens other than the specific ones is challenging. The microchemical tests determine the activity of inflammation or disease by measuring the levels of biomarkers present in the oral cavity. Although this diagnostic method is based on increase in the specific biomarkers proportional to inflammation or disease progression in the oral cavity, its commercialization is limited due to low sensitivity and specificity. The genetic tests are based on the concept that differences in disease vulnerability and treatment response are caused by the patient's DNA predisposition. Specifically, the IL-1 gene is used in such tests. PoC diagnostic methods developed to date serve as supplementary diagnostic methods and tools for patient education, in addition to existing diagnostic methods, although they have limitations in diagnosing oral diseases alone. Research on various PoC test methods that can analyze and manage the oral cavity bacterial composition is expected to become more active, aligning with the shift from treatment-oriented to prevention-oriented approaches in healthcare.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.35-56
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• 2023

With the development of information and communication technology, numerous reviews are continuously posted on websites, which causes information overload problems. Therefore, users face difficulty in exploring reviews for their decision-making. To solve such a problem, many studies on review helpfulness prediction have been actively conducted to provide users with helpful and reliable reviews. Existing studies predict review helpfulness mainly based on the features included in the review. However, such studies disable providing the reason why predicted reviews are helpful. Therefore, this study aims to propose a methodology for applying eXplainable Artificial Intelligence (XAI) techniques in review helpfulness prediction to address such a limitation. This study uses restaurant reviews collected from Yelp.com to compare the prediction performance of six models widely used in previous studies. Next, we propose an explainable review helpfulness prediction model by applying the XAI technique to the model with the best prediction performance. Therefore, the methodology proposed in this study can recommend helpful reviews in the user's purchasing decision-making process and provide the interpretation of why such predicted reviews are helpful.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.471-485
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• 2023

Recently, due to the nature of blockchain that guarantees users' anonymity, more and more cases are being exploited for crimes such as illegal transactions. However, cryptocurrency is protected in cryptocurrency wallets, making it difficult to recover criminal funds. Therefore, this study acquires artifacts from the data and memory area of a local PC based on user behavior from four browser extension wallets (Metamask, Binance, Phantom, and Kaikas) to track and retrieve cryptocurrencies used in crime, and analyzes how to use them from a digital forensics perspective. As a result of the analysis, the type of wallet and cryptocurrency used by the suspect was confirmed through the API name obtained from the browser's cache data, and the URL and wallet address used for the remittance transaction were obtained. We also identified Client IDs that could identify devices used in cookie data, and confirmed that mnemonic code could be obtained from memory. Additionally, we propose an algorithm to measure the persistence of obtainable mnemonic code and automate acquisition.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.83-105
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• 2023

A robot-friendly building certification system was established in 2022 to accommodate the growing number of service robots introduced into buildings. However, this system primarily targeted office buildings, with limitations in applying other functional architectures. To address this problem, we developed a certification model of a robot-friendly environment to extend the existing system to apartment complexes. Using focus group interviews and the analytic hierarchy process, we established 28 evaluating items categorized as (a) architecture and facility design, (b) networks and systems, (c) building operations management, and (d) support for robot activity and other services. These indicators were weighted based on their relative importance within and between categories, resulting in scores ranging from 1 to 18 points and a total of 176 points. According to evaluations with the 28 items, each apartment complex could be graded as "best," "excellent," or "general" based on its total achieved scores. This study is significant, as we present the world's first certification model of a robot-friendly environment for apartment complexes that considers human-robot interactions