• 한국IT서비스학회지
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• 제19권2호
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• pp.93-107
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• 2020

Recently, artificial intelligence (AI) technologies has been widely used in various fields such as finance, and distribution. Accordingly, Korea has also announced its AI R&D strategy for the realization of i-Korea 4.0 in May 2018. However, Korea's AI technology is inferior to major competitors such as the US, Canada, and Japan Therefore, in order to cope with the 4th industrial revolution, it is necessary to allocate AI R&D budgets efficiently through selection and concentration so as to gain competitive advantage under a limited budget. In this study, the importance of each AI technology was evaluated in multi-dimensional way through the questionnaire of expert group using the evaluation index derived from the literature review From the results of this study, we draw the following implication. In order to successfully establish the AI technology development strategies, it is necessary to prioritize the cognitive computing technology that has great market growth potential, ripple effect of technology development, and the urgency of technology development according to the principle of selection and concentration. To this end, it is necessary to find creative ideas, manage assessments, converge multidisciplinary systems and strengthen core competencies. In addition, since AI technology has a large impact on socioeconomic development, it is necessary to comprehensively grasp and manage scientific and technological regulations in order to systematically promote AI technology development.

• Journal of Information Processing Systems
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• 제14권5호
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• pp.1102-1113
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• 2018

Vibrotactile feedback is widely used in designing non-visual interactions on mobile phones, such as message notification, non-visual reading, and blind use. In this work, novel vibrotactile codes are presented to implement a non-visual text reading system for mobile phones. The 26 letters of the English alphabet are formed in an index table with four rows and seven columns, and each letter is mapped using the codes of vibrations. Two kinds of vibrotactile codes are designed with the actuator's on and off states and with specific lengths (short and long) assigned to each state. To improve the efficiency of tactile perception and user satisfaction, three user experiments are conducted. The first experiment explores the maximum number of continuous vibrations and minimum vibration time of the actuator's on and off states that the human can perceive. The second experiment determines the minimum interval between continuous vibrations. The vibrotactile reading system is designed and evaluated in the third experiment according to the results of the two preceding experiments. Results show that the character reading accuracy reaches 91.7% and the character reading speed is approximately 617.8 ms. Our method has better reading efficiency and is easier to learn than the traditional Braille coding method.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.93-100
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• 1988

The Auto Regressive Parameter Estimation and Pattern Classification of EKG Signal for Automatic Diagnosis. This paper presents the results from pattern discriminant analysis of an AR (auto regressive) model parameter group, which represents the HRV (heart rate variability) that is being considered as time series data. HRV data was extracted using the correct R-point of the EKG wave that was A/D converted from the I/O port both by hardware and software functions. Data number (N) and optimal (P), which were used for analysis, were determined by using Burg's maximum entropy method and Akaike's Information Criteria test. The representative values were extracted from the distribution of the results. In turn, these values were used as the index for determining the range o( pattern discriminant analysis. By carrying out pattern discriminant analysis, the performance of clustering was checked, creating the text pattern, where the clustering was optimum. The analysis results showed first that the HRV data were considered sufficient to ensure the stationarity of the data; next, that the patern discrimimant analysis was able to discriminate even though the optimal order of each syndrome was dissimilar.

• 보건의료산업학회지
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• 제14권1호
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• pp.195-209
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• 2020

Objectives: This study aimed to analyze the papers in the Korean Journal of Health Service Management (KJHSM) (2007-2018) in order to identify the research trends and aid the future development of healthcare-related research. Methods: Data collection was conducted from September 1-30, 2019. The KSHSM website and Lorea Citatin Index (KCI) electronic database provided 605 copies of original text. Results: Of these, 538 studies are original articles and 7 studies are review articles; 23.7% of the studies presented conceptual framework, 58.4% implemented convenience extraction, and 64.7% collected data using questionnaires. 29.3% of key words were included in the healthcare service, and 48.5% were excluded from the submission field. Conclusions: For the qualitative improvement and development of the journal, it is necessary to consider the relevance of refinement of the methodological approach, segmentation in the field of submission, and selection of keywords.

• 한국항행학회논문지
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• 제22권6호
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• pp.670-674
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• 2018

최근 댓글 / 텍스트, 이미지, 비디오, 블로그 및 사용자 경험을 포함한 소셜네트워크서비스(SNS) 데이터에는 다양한 고객의 추천 시스템을 구축하고 비즈니스 분석가에게 통찰력 있는 데이터 / 결과를 제공하는데 사용할 수 있는 많은 정보가 포함되어 있다. 멀티미디어 데이터, 특히 이미지 및 비디오와 같은 시각적 데이터는 SNS 데이터 중에서도 특정(문화권) 지역을 반영할 수 있는 가장 풍부한 데이터이며, 문화적 가치 및 관심사는 전반적으로 데이터의 많은 부분을 차지하고 있다. 이러한 방대한 데이터로부터 원하는 데이터를 지능적으로 추출하고, 엄청난 양의 데이터를 마이닝 하려면 보다 효율적이고 지능적인 데이터 분석 방법이 필요하다. 따라서 본 논문의 목적은 이러한 데이터를 모델링하고, 색인하고, 검색하는 방법에 대해 제안하고자 한다.

• Journal of Information Science Theory and Practice
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• 제10권4호
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• pp.3-22
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• 2022

This paper provides an overview of the emergence of resource discovery systems and services, along with their advantages, best practices, and current landscapes. It outlines some of the key services and functionalities of a comprehensive discovery model suitable for academic libraries in developing countries. The proposed model (VuFind as a discovery tool) performs like other existing web-scale resource discovery systems, both commercial and open-source, and is capable of providing information resources from different sources in a single-window search interface. The objective of the paper is to provide seamless access to globally distributed subscribed as well as open access resources through its discovery interface, based on a unified index. This model uses Koha, DSpace, and Greenstone as back-ends and VuFind as a discovery layer in the front-end and has also integrated many enhanced search features like Bento-box search, Geodetic search, and full-text search (using Apache Tika). The goal of this paper is to provide the academic community with a one-stop shop for better utilising and integrating heterogeneous bibliographic data sources with VuFind (https://vufind.org/vufind).

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.113-124
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• 2021

The study investigates how financial markets respond to a shock to tone and semantic similarity of the Bank of Thailand press releases. The techniques in natural language processing are employed to quantify the tone and the semantic similarity of 69 press releases from 2010 to 2018. The corpus of the press releases is accessible to the general public. Stock market returns and bond yields are measured by logged return on SET50 and short-term and long-term government bonds, respectively. Data are daily from January 4, 2010, to August 8, 2019. The study uses the Structural Vector Auto Regressive model (SVAR) to analyze the effects of unanticipated and temporary shocks to the tone and the semantic similarity on bond yields and stock market returns. Impulse response functions are also constructed for the analysis. The results show that 1-month, 3-month, 6-month and 1-year bond yields significantly increase in response to a positive shock to the tone of press releases and 1-month, 3-month, 6-month, 1-year and 25-year bond yields significantly increase in response to a positive shock to the semantic similarity. Interestingly, stock market returns obtained from the SET50 index insignificantly respond to the shocks from the tone and the semantic similarity of the press releases.

• Child Health Nursing Research
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• 제27권1호
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• pp.13-23
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• 2021

Purpose: This study aimed to develop a mobile-based self-management health alarm (MSHA) program for modifying obese children's lifestyle based on the information-motivation-behavioral skills (IMB) model and to test its feasibility. Methods: A methodological study for the development of the MSHA program and pilot study with a one-group pretest-posttest design for feasibility testing was conducted. The MSHA program was designed to provide obesity-related information (I), monitor daily diet and exercise, provide motivational text messages (M), and enhance healthy diet and exercise skills (B) via a mobile-based web platform. In the feasibility test, six obese children participated in the 4-week program, and the number of days per week that they achieved their goals and differences in metabolic components were assessed. Data were analyzed using descriptive statistics and the Wilcoxon signed-rank test. Results: Participants successfully achieved their diet and exercise goals≥5 days per week. Body mass index (z=-1.99, p=.046), waist circumference (z=-2.20, p=.028), and triglyceride levels (z=-2.21, p=.027) significantly decreased. Conclusion: The MSHA program showed positive effects on health behaviors and metabolic syndrome risk. The program may be effective in improving metabolic syndrome in obese children by promoting self-health management behaviors.

• 융합신호처리학회논문지
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• 제23권3호
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• pp.143-149
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• 2022

This excerpt delineates a quantitative measure of relationship between a research title and its respective abstract extracted from different journal articles documented through a Korean Citation Index (KCI) database published through various journals. In this paper, we propose a machine learning-based similarity metric that does not assume normality on dataset, realizes the imbalanced dataset problem, and zero-variance problem that affects most of the rule-based algorithms. The advantage of using this algorithm is that, it eliminates the limitations experienced by Pearson correlation coefficient (r) and additionally, it solves imbalanced dataset problem. A total of 107 journal articles collected from the database were used to develop a corpus with authors, year of publication, title, and an abstract per each. Based on the experimental results, the proposed algorithm achieved high correlation coefficient values compared to others which are cosine similarity, euclidean, and pearson correlation coefficients by scoring a maximum correlation of 1, whereas others had obtained non-a-number value to some experiments. With these results, we found that an effective title must have high correlation coefficient with the respective abstract.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1599-1618
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• 2024

Access control has always been one of the effective methods to protect data security. However, in new computing environments such as big data, data resources have the characteristics of distributed cross-domain sharing, massive and dynamic. Traditional access control mechanisms are difficult to meet the security needs. This paper proposes CACM-MMSR to solve distributed cross-domain access control problem for massive resources. The method uses blockchain and smart contracts as a link between different security domains. A permission decision model migration method based on access control logs is designed. It can realize the migration of historical policy to solve the problems of access control heterogeneity among different security domains and the updating of the old and new policies in the same security domain. Meanwhile, a semantic reasoning-based permission decision method for unstructured text data is designed. It can achieve a flexible permission decision by similarity thresholding. Experimental results show that the proposed method can reduce the decision time cost of distributed access control to less than 28.7% of a single node. The permission decision model migration method has a high decision accuracy of 97.4%. The semantic reasoning-based permission decision method is optimal to other reference methods in vectorization and index time cost.