• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.106-115
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• 2019

A sharing economy has emerged through today's trust-building mechanisms, and a sharing economy is called a future economic model through a positive future market prospect. In this context, while the overseas sharing economic business is becoming a global trend, the domestic sharing economic business is busy following the global trend. The purpose of this study is to investigate the development direction of sharing economic business in Korea. First, the sharing economic cases of 50 oversea and domestic businesses were analyzed by time series analysis. Next, a cross-country analysis to analyze the business distribution and KCERN's sharing economic model through sharing economic cube model was conducted. Finally, profit model analysis through business case study and the relationship between the derived factors were investigated. As a result of the analysis, this study found comparative trends between overseas and domestic including differences in cultural and institutional environments and profit models. This study suggested directions for domestic sharing economy business.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.87-94
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• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• The Journal of Information Systems
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• v.26 no.4
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• pp.327-353
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• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.

• Journal of Environmental Science International
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• v.11 no.10
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• pp.1061-1068
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• 2002

To identify possible associations with concentrations of ambient air pollutants and daily mortality in Busan, this study assessed the effects of air pollution for the time period 1999-2000. Poisson regression analysis by Generalized Additive Model were conducted considering trend, season, meteorology, and day-of-the-week as confounders in a nonparametric approach. Busan had a 10% increase in mortality in persons aged 65 and older(95% Cl : 1.01-1.10) in association with IQR in $NO_2$(lagged 2 days). An increase of $NO_2$(lagged 2days) was associated with a 4% increase in respiratory mortality(Cl : 1.02-1.11) and CO(lagged 1 day) showed a 3% increase(Cl : 1.00-1.07).

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.197-210
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• 2024

Purpose : This study aimed to analyze research trends in the field of integrative medicine through a bibliometric analysis of articles published in Integrative Medicine Research (IMR) journal from 2017 to 2022. Methods : Articles published in IMR journal between 2017 and 2022 were searched using the Web of Science database on August 22, 2023. The analysis was performed using the Bibliometrix and Biblioshiny tools in R (version 4.3.1) and VOSviewer (version 1.6.19). Results : The key findings were as follows: average citations per article (9.41), total authors (1,142), single-authored articles (12), average articles per author (0.27), average co-authors per article (5.27), and rate of international co-authorships (15.69 %). The most-cited article was on the cryopreservation of cells or tissues and their clinical applications. The top keyword analysis by author keywords showed that "acupuncture" was the most frequently used keyword (33 times). Co-occurrence network analysis showed 85 high-frequency keywords that appeared five or more times, and the top five keywords by total link strength were "acupuncture," "herbal medicine," "prevalence," "alternative medicine," and "complementary." The study found that, contrary to the trend in complementary and alternative medicine research in Korea, the IMR journal actively conducts intervention studies to provide clinical evidence. Conclusion : In the IMR journal, "acupuncture" was the most frequent of author keywords. The analysis of keyword trend topics over time showed that the keyword "systematic review" continued to appear from 2020 to 2022, and the keyword "clinical practice guideline" appeared for the first time in 2021. In particular, the co-occurrence network analysis highlighted keywords related to intervention research, in contrast to domestic research trends. While this study analyzed only one journal, future studies expanding the category of integrative medicine and increasing the number of journals analyzed may provide further insights.

• The Journal of Information Systems
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• v.31 no.3
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• pp.67-87
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• 2022

Purpose This study quantified companies' views on the COVID-19 pandemic with sentiment analysis of U.S. public companies' disclosures. It aims to provide timely insights to shareholders, investors, and consumers by analyzing and visualizing sentiment changes over time as well as similarities and differences by industry. Design/methodology/approach From more than fifty thousand Form 10-K and Form 10-Q published between 2020 and 2021, we extracted over one million texts related to the COVID-19 pandemic. Using the FinBERT language model fine-tuned in the finance domain, we conducted sentiment analysis of the texts, and we quantified and classified the data into positive, negative, and neutral. In addition, we illustrated the analysis results using various visualization techniques for easy understanding of information. Findings The analysis results indicated that U.S. public companies' overall sentiment changed over time as the COVID-19 pandemic progressed. Positive sentiment gradually increased, and negative sentiment tended to decrease over time, but there was no trend in neutral sentiment. When comparing sentiment by industry, the pattern of changes in the amount of positive and negative sentiment and time-series changes were similar in all industries, but differences among industries were shown in neutral sentiment.

• Journal of the Korean GEO-environmental Society
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• v.21 no.5
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• pp.5-11
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• 2020

Outliers detection and elimination included in field monitoring datum are essential for effective foundation of unusual movement, long and short range forecast of stability and future behavior to various structures. Integrated outlier analysis system for assessing long term time series data was developed in this study. Outlier analysis could be conducted in two step of primary analysis targeted at single dataset and second multi datasets analysis using synthesis value. Integrated outlier analysis system presents basic information for evaluating stability and predicting movement of structure combined with real-time safety management platform. Field application results showed increased correlation between synthesis value including similar sort of sensor showing constant trend and each single dataset. Various monitoring data in case of showing different trend can be used to analyse outlier through correlation-weighted value.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.83-95
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• 2021

In this study, major research trends at home and abroad were compared and analyzed in order to derive key research fields in the financial security field and to suggest directions. To this end, 689 domestic and 20,736 foreign data were collected from domestic and international academic journal DB, and major research fields related to financial security were extracted through LDA analysis. After that, hot & cold topics were derived through time series linear regression analysis. As a result of the analysis, studies related to government policy issues, personal information, and accredited certification were derived as promising research fields in Korea. In the case of foreign countries, related studies were drawn to develop advanced security systems such as cryptographic protocols and quantum security. Recently, it has become possible to apply various security technologies in Korea through the abolition of public certification. Accordingly, as changes in promising research fields are expected, the results of this study are expected to contribute to the establishment and development of a successful roadmap for domestic financial security.

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.402-408
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• 2010

Forecasting have qualitative and quantitative methods. Quantitative one analyze macro-economic factors such as the rate of exchange, oil price, interest rate and also predict the micro-economic factors such as sales and demands. Applying various statistical methods depends on the type of data. when data has seasonality and trend, Time Series analysis is proper but when it has casual relation, Regression analysis is good for this. Time Series and Regression can be used together. This study investigate artificial neural networks which is predictive technique for casual relation and try to compare the accuracy of forecasting between regression analysis and artificial neural network.