• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.407-417
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• 2024

The objective of this study is to predict and reduce potential damage caused by chlorine gas leaks, a hazardous material, when vehicles transporting it overturn due to accidents or other incidents. The goal is to forecast the anticipated damages caused by chlorine toxicity levels (ppm) and to design effective response strategies for mitigating them. To predict potential damages, we conducted quantitative assessments using the ALOHA program to calculate the toxic effects (ppm) and damage distances resulting from chlorine leaks, taking into account potential negligence of drivers during transportation. The extent of damage from toxic gas leaks is influenced by various factors, including the amount of the leaked hazardous material and the meteorological conditions at the time of the leak. Therefore, a comprehensive analysis of damage distances was conducted by examining various scenarios that involved variations in the amount of leakage and weather conditions. Under intermediate conditions (leakage quantity: 5 tons, wind speed: 3 m/s, atmospheric stability: D), the estimated distance for exceeding the AEGL-2 level of 2 ppm was calculated to be 9 km. This concentration poses a high risk of respiratory disturbance and potential human casualties, comparable to the toxicity of hydrogen chloride. In particular, leaks in urban areas can lead to significant loss of life. In the event of a leakage incident, we proposed a plan to minimize damage by implementing appropriate response strategies based on the location and amount of the leak when an accident occurs.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.

• Journal of Technology Innovation
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• v.26 no.3
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• pp.37-68
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• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.4
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• pp.407-414
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• 2010

Until now, the pore size distribution, PSD, of soil profile has been calculated from soil moisture characteristic data by water release method or mercury porosimetry using the capillary rise equation. But the current methods are often difficult to use and time consuming. Thus, in this work, theoretical framework for an easy and fast technique was suggested to estimate the PSD from unsaturated hydraulic conductivity data in an undisturbed field soil profile. In this study, unsaturated hydraulic conductivity data were collected and simulated by the variation of soil parameters in the given boundary conditions (Brooks and Corey soil parameters, ${\alpha}_{BC}=1-5L^{-1}$, b = 1 - 10; van Genuchten soil parameters, ${\alpha}_{VG}=0.001-1.0L^{-1}$, m = 0.1 - 0.9). Then, $K_s$ (1.0 cm $h^{-1})$ was used as the fixed input parameter for the simulation of each models. The PSDs were estimated from the collected K(h) data by model simulation. In the simulation of Brooks-Corey parameter, the saturated hydraulic conductivity, $K_s$, played a role of scaling factor for unsaturated hydraulic conductivity, K(h) Changes of parameter b explained the shape of PSD curve of soil intimately, and a ${\alpha}_{BC}$ affected on the sensitivity of PSD curve. In the case of van Genuchten model, $K_s$ and ${\alpha}_{VG}$ played the role of scaling factor for a vertical axis and a horizontal axis, respectively. Parameter m described the shape of PSD curve and K(h) systematically. This study suggests that the new theoretical technique can be applied to the in situ prediction of PSD in undisturbed field soil.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Research in Community and Public Health Nursing
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• v.5 no.2
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• pp.109-129
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• 1994

Chronic health problems has become a major concern and challenge to the health care professionals today. Especially hypertension, one of the leading primary cause of death in Korea, is a typical chronic disease requiring adequate and continuous management. Though these hypertensives need to maintain desirable health practice by themselves for their life time, many previous studies indicated that most of the essential hypertensives have no specific symptoms and thus, reluctant to follow appropriate medical regimens causing the condition further aggravated and complicated. Self-care is an essential factor that keeps chronic patients in control of their health and wellness. Thus this study was conducted to identify the effect of the comprehensive self-regulation program as a nursing intervention on the promotion self-care performance and improvement in physical parameters of hypertensives. For this purpose, a one group quasi-experimental research with pre and post test design was used. The subjects of the study was consisted of thirty persons with mild or moderate essential hypertension from two companies in Cheong-ju city. The whole program was carried out from October, 1993 to February, 1994. The self-regulation program was consisted with group education on hypertension and self-care, self-regulation including the blood pressure self-monitoring and recording, recording of daily self-care activities, and encouraging and reinforcing self-efficacy through verbal persuation and enactive attainment. The subjects were asked to measure their own blood pressure by themselves twice per day and to record blood pressure and the daily self-care performance according to the instructions provided during the whole period of 9 weeks. The instruments used for data collection in this study were as follows : 1) Instruments used for measuring the knowledge about hypertension, multiple health locus of control, and perceived benifits and barriers were adapted from previous studies and modified by author to be fit for the subjects. 2) Self-efficacy scale and self-care performance record were developed by the author. 3) Physiological parameters included systolic / diastolic blood pressure, body weight, level of blood cholesterol, and 24hour ambulatory blood pressure. The post-experimental Cronbach's Alpha as the reliability test of scales were 0.703-0.897, an appropriate level of confidence. The effect of the program was analyzed by experimental stages ; the first week, the fifth week, and the ninth week since the experimental imput began. Data were analyzed by the SPSS PC+ program with paired t-test and t-test, repeated measure ANOVA, and pearson's correlation to de termine the effect of program. The results were as follows : 1) After the self-regulation program, scores on knowledge(t=-2.41, p=.011), perceived self-efficacy (F=5.60, p=.001), self-care performance(F=22.31, p=.0001) were significantly higher than those before the program. 2) After the program, both systolic and diastolic blood pressure were significantly lower than those before the program(F=10.89 -13.11, p=.0001). However in 24hour ambulatory blood pressure, systolic mean pressure was nearly significantly lower, but not in diastolic mean pressure. 3) After the program, the body weight was significant decresed(t=5.53, p=.0001), but the blood cholesterol level was not decreased significantly except in those cases with higher cholesterol level. 4) There were significant relationships between changes in self-care performance and diastolic pressure at 1st week (r=.3389, p=.033) and changes in self-care performance and systolic pressure at 9th week(r=.3651, p=.024). 5) There were significant relationship between perceived self-efficacy and self-care performance at 5th week(r=.5313, p=.001) and 9th week (r=.3026, p=.052). 6) After the program, internal health locus of control and perceived benefits did not show significant change, but perceived barriers was significantly lower than those before the program (t=3.57, p=.0001). From the above results, it can be concluded that 1) The self-regulation program is an effective nursing strategy to promote self-care performance of hypertensives and to lower the blood pressure. Thus this program can be recommended in the management of the hypertensives in workplaces and community settings. 2) The synthesis of Orem's self-care theory and Bandura's self-regulation & self-efficacy theory in this study was proved to enhance explanation and prediction of the change of self-care behavior. Thus the result of the study would contribute in development of the self-care theory and an expansion of practice-theory.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.343-349
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• 2013

Data including 1,372,050 milk records pertaining to 438,019 cows from 1983 to 2011 collected during performance tests conducted by the National Livestock Cooperative Dairy Improvement Center were used to calculate milk income and profit of individuals and investigate the effects of production levels of early lactation (parity 1 and 2, respectively). Individuals with a moderate level of early lactation stayed longer in herds. Among parity 1, the 9,000 kg or higher group had a lower mean number of lactations than the overall mean of 3.13. The 7,000 kg or lower and 10,000 kg or higher groups had lower mean life time milking days than the overall mean of 1,076.8 days. Standard deviations of lifetime traits tended to decrease as production levels increased. For parity 2, the 11,000 kg or higher group had a lower mean number of lactation than the overall mean of 3.43. The lifetime milking days was highest in the 12,000 kg group (1,212.0 days), and generally smaller in the lower groups. Profit increased as the production level of groups increased for both parity 1 and 2. In groups with low production levels, profit of parity 1 was higher than that of parity 2, while the reverse was true in groups with high production levels. These results suggest that individuals in the low production groups had a greater likelihood to be culled due to reproductive or other problems. Furthermore, the accuracy of the prediction of lifetime profit of individuals with a milk yield of 305 days seems to be higher for parity 2 than parity 1; therefore, it is desirable to predict lifetime profit using the 305d milk yield of parity 2. In conclusion, breeding goals are based on many factors in functions for the estimation of profit; however, production levels during early lactation (parity 1 and 2) can be used as indicators of profit to extend profitability.

• Journal of the Korean earth science society
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• v.38 no.1
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• pp.24-34
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• 2017

The westerly waves generation is described in the advanced earth science textbook used at high school as follows: as westerly wind approaches and blows over large mountains, the air flow shows wave motions in downwind side, which can be explained by the conservation of potential vorticity. However, there has been no case study showing the phenomena of the mesoscale westerly waves with observational data in the area of small mountains in Korea. And thus the wind speed and time persistency of westerly winds along with the width and length of mountains have never been studied to explain the generation of the westerly waves. As a first step, we assured the westerly waves generated in the downwind side of Sobaek mountains based on surface station wind data nearby. Furthermore, the critical or minimum wind velocity of the westerly wind over Sobaek mountains to generate the downwind wave were derived and calcuated tobe about $0.6m\;s^{-1}$ for Sobaek mountains, which means that the westerly waves could be generated in most cases of westerly blowing over the mountains. Using surface station data and 4-dimensional assimilation data of RDAPS (Regional Data Assimilation and Prediction System) provided by Korea Meteorological Agency, we also analyzed cases of westerly waves occurrence and life cycle in the downwind side of Sobaek mountains for a year of 2014. The westerly waves occurred in meso-${\beta}$ or -${\gamma}$ scales. The westerly waves generated by the mountains disappeared gradually with wind speed decreasing. The occurrence frequency of the vorticity with meso-${\beta}$ scale got to be higher when the stronger westerly wind blew. When we extended the spatial range of the analysis, phenomena of westerly waves were also observed in the downwind side of Yensan mountains in Northeastern China. Our current work will be a study material to help students understand the atmospheric phenomena perturbed by mountains.