• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.461-466
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• 2024

Korean local governments operates the participatory budgeting system autonomously. This study is to classify these entities into clusters. Among the diverse machine learning methodologies(Neural Network, Rule Induction(CN2), KNN, Decision Tree, Random Forest, Gradient Boosting, SVM, Naïve Bayes), the Support Vector Machine technique emerged as the most efficacious in the analysis of 2022 Korean municipalities data. The first cluster C1 is characterized by minimal committee activity but a substantial allocation of participatory budgeting; another cluster C3 comprises cities that exhibit a passive stance. The majority of cities falls into the final cluster C2 which is noted for its proactive engagement in. Overall, most Korean local government operates the participatory busgeting system in good shape. Only a small number of cities is less active in this system. We anticipate that analyzing time-series data from the past decade in follow-up studies will further enhance the reliability of classifying local government types regarding participatory budgeting.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.1
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• pp.58-71
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• 2015

Objectives: The aims of this study were to investigate the classification system of chemical substances in the Occupational Safety and Health Act(OSHA) and Chemical Substances Control Act(CSCA) and to compare several health indices (i.e., Time Weighted Average (TWA), Lethal Dose ($LD_{50}$), and Lethal Concentration ($LC_{50}$) of chemical substances by categories in each law. Methods: The chemicals regulated by each law were classified by the specific categories provided in the respective law; seven categories for OSHA (chemicals with OELs, chemicals prohibited from manufacturing, etc., chemicals requiring approval, chemicals kept below permissible limits, chemicals requiring workplace monitoring, chemicals requiring special management, and chemicals requiring special heath diagnosis) and five categories from the CSCA(poisonous substances, permitted substances, restricted substances, prohibited substances, and substances requiring preparation for accidents). Information on physicochemical properties, health indices including CMR characteristics, $LD_{50}$ and $LD_{50}$ were searched from the homepages of the Korean Occupational and Safety Agency and the National Institute of Environmental Research, etc. Statistical analysis was conducted for comparison between TWA and health index for each category. Results: The number of chemicals based on CAS numbers was different from the numbers of series of chemicals listed in each law because of repeat listings due to different names (e.g., glycol monoethylether vs. 2-ethoxy ethanol) and grouping of different chemicals under the same serial number(i.e., five different benzidine-related chemicals were categorized under one serial number(06-4-13) as prohibited substances under the CSCA). A total of 722 chemicals and 995 chemicals were listed at the OSHA and its sub-regulations and CSCA and its sub-regulations, respectively. Among these, 36.8% based on OSHA chemicals and 26.7% based on CSCA chemicals were regulated simultaneously through both laws. The correlation coefficients between TWA and $LC_{50}$ and between TWA and $LD_{50}$, were 0.641 and 0.506, respectively. The geometric mean values of TWA calculated by each category in both laws have no tendency according to category. The patterns of cumulative graph for TWA, $LD_{50}$, $LC_{50}$ were similar to the chemicals regulated by OHSA and CCSA, but their median values were lower for CCSA regulated chemicals than OSHA regulated chemicals. The GM of carcinogenic chemicals under the OSHA was significantly lower than non-CMR chemicals($2.21mg/m^3$ vs $5.69mg/m^3$, p=0.006), while there was no significant difference in CSCA chemicals($0.85mg/m^3$ vs $1.04mg/m^3$, p=0.448). $LC_{50}$ showed no significant difference between carcinogens, mutagens, reproductive toxic chemicals and non-CMR chemicals in both laws' regulated chemicals, while there was a difference between carcinogens and non-CMR chemicals in $LD_{50}$ of the CSCA. Conclusions: This study found that there was no specific tendency or significant difference in health indicessuch TWA, $LD_{50}$ and $LC_{50}$ in subcategories of chemicals as classified by the Ministry of Labor and Employment and the Ministry of Environment. Considering the background and the purpose of each law, collaboration for harmonization in chemical categorizing and regulation is necessary.

• Journal of Hospice and Palliative Care
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• v.21 no.1
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• pp.23-32
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• 2018

Purpose: Little is known regarding the extent to which dying patients with chronic obstructive pulmonary disease (COPD) receive life-sustaining procedures and palliative care in U.S. hospitals. We examine hospital cost trends and the impact of palliative care utilization on the use of life-sustaining procedures in this population. Methods: Retrospective nationwide cohort analysis was performed using National Inpatient Sample (NIS) data from 2005 and 2014. We examined the receipt of both palliative care and intensive medical procedures, defined as systemic procedures, pulmonary procedures, or surgeries using the International Classification of Diseases, 9th revision (ICD-9-CM). Results: We used compound annual growth rates (CAGR) to determine temporal trends and multilevel multivariate regressions to identify factors associated with hospital cost. Among 77,394,755 hospitalizations, 79,314 patients were examined. The CAGR of hospital cost was 5.83% (P<0.001). The CAGRs of systemic procedures and palliative care were 5.98% and 19.89% respectively (each P<0.001). Systemic procedures, pulmonary procedures, and surgeries were associated with increased hospital cost by 59.04%, 72.00%, 55.26%, respectively (each P<0.001). Palliative care was associated with decreased hospital cost by 28.71% (P<0.001). Conclusion: The volume of systemic procedures is the biggest driver of cost increase although there is a cost-saving effect from greater palliative care utilization.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.67-88
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• 2019

Webtoon is a Korean-style digital comics platform that distributes comics content produced using the characteristic elements of the Internet in a form that can be consumed online. With the recent rapid growth of the webtoon industry and the exponential increase in the supply of webtoon content, the need for effective webtoon content recommendation measures is growing. Webtoons are digital content products that combine pictorial, literary and digital elements. Therefore, webtoons stimulate consumer sentiment by making readers have fun and engaging and empathizing with the situations in which webtoons are produced. In this context, it can be expected that the sentiment that webtoons evoke to consumers will serve as an important criterion for consumers' choice of webtoons. However, there is a lack of research to improve webtoons' recommendation performance by utilizing consumer sentiment. This study is aimed at developing consumer sentiment pattern maps that can support effective recommendations of webtoon content, focusing on consumer sentiments that have not been fully discussed previously. Metadata and consumer sentiments data were collected for 200 works serviced on the Korean webtoon platform 'Naver Webtoon' to conduct this study. 488 sentiment terms were collected for 127 works, excluding those that did not meet the purpose of the analysis. Next, similar or duplicate terms were combined or abstracted in accordance with the bottom-up approach. As a result, we have built webtoons specialized sentiment-index, which are reduced to a total of 63 emotive adjectives. By performing exploratory factor analysis on the constructed sentiment-index, we have derived three important dimensions for classifying webtoon types. The exploratory factor analysis was performed through the Principal Component Analysis (PCA) using varimax factor rotation. The three dimensions were named 'Immersion', 'Touch' and 'Irritant' respectively. Based on this, K-Means clustering was performed and the entire webtoons were classified into four types. Each type was named 'Snack', 'Drama', 'Irritant', and 'Romance'. For each type of webtoon, we wrote webtoon-sentiment 2-Mode network graphs and looked at the characteristics of the sentiment pattern appearing for each type. In addition, through profiling analysis, we were able to derive meaningful strategic implications for each type of webtoon. First, The 'Snack' cluster is a collection of webtoons that are fast-paced and highly entertaining. Many consumers are interested in these webtoons, but they don't rate them well. Also, consumers mostly use simple expressions of sentiment when talking about these webtoons. Webtoons belonging to 'Snack' are expected to appeal to modern people who want to consume content easily and quickly during short travel time, such as commuting time. Secondly, webtoons belonging to 'Drama' are expected to evoke realistic and everyday sentiments rather than exaggerated and light comic ones. When consumers talk about webtoons belonging to a 'Drama' cluster in online, they are found to express a variety of sentiments. It is appropriate to establish an OSMU(One source multi-use) strategy to extend these webtoons to other content such as movies and TV series. Third, the sentiment pattern map of 'Irritant' shows the sentiments that discourage customer interest by stimulating discomfort. Webtoons that evoke these sentiments are hard to get public attention. Artists should pay attention to these sentiments that cause inconvenience to consumers in creating webtoons. Finally, Webtoons belonging to 'Romance' do not evoke a variety of consumer sentiments, but they are interpreted as touching consumers. They are expected to be consumed as 'healing content' targeted at consumers with high levels of stress or mental fatigue in their lives. The results of this study are meaningful in that it identifies the applicability of consumer sentiment in the areas of recommendation and classification of webtoons, and provides guidelines to help members of webtoons' ecosystem better understand consumers and formulate strategies.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of Distribution Research
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• v.17 no.3
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• pp.115-132
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• 2012

This study empirically examines the impact of SSM market entry on changes in market shares among retailing types. The data is monthly time-series data spanning over the period from January 2000 to December 2010, and the effect of SSM market entry on market shares of retailing types is analyzed by utilizing several key factors such as the number of new SSM monthly entrants, total number of SSMs, the proportion of new SSM entrant that is smaller than $165m^2$ to total new SSM entrants. According to the Korean Standard Industrial Classification codes, the retailing type is classified into 5 groups: department stores, retail sale in other non-specialized large stores(big marts), supermarkets, convenience stores, and retail sale in other non-specialized stores with food or beverages predominating (others). The market shares of retailing types are calculated by the ratio of each retailing type monthly sales to total monthly retailing sales in which total retailing sales is the sum of each retailing type sales. The empirical model controls for the size effects with the number of monthly employees for each retailing type and the macroeconomic effects with M2. The empirical model employed in this study is as follows; $$MS_i=f(NewSSM,\;CumSSM,\;employ_i,\;under165,\;M2)$$ where $MS_i$ is the market share of each retailing type (department stores, big marts), supermarkets, convenience stores, and others), NewSSM is the number of new SSM monthly entrants, CumSSM is total number of SSMs, $employ_i$ is the number of monthly employees for each retailing type, and under165 is the proportion of new SSM entrant that is smaller than $165m^2$ to total new SSM entrants. The correlation among these variables are reported in