• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.813-823
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• 2020

Purpose: In order to maximize the stability and productivity of the work through simulation prior to high-risk facilities and high-cost work such as dismantling the facilities inside the reactor, we intend to use digital twin technology that can be closely controlled by simulating the specifications of the actual control equipment. Motion control errors, which can be caused by the time gap between precision control equipment and simulation in applying digital twin technology, can cause hazards such as collisions between hazardous facilities and control equipment. In order to eliminate and control these situations, prior research is needed. Method: Unity 3D is currently the most popular engine used to develop simulations. However, there are control errors that can be caused by time correction within Unity 3D engines. The error is expected in many environments and may vary depending on the development environment, such as system specifications. To demonstrate this, we develop crash simulations using Unity 3D engines, which conduct collision experiments under various conditions, organize and analyze the resulting results, and derive tolerances for precision control equipment based on them. Result: In experiments with collision experiment simulation, the time correction in 1/1000 seconds of an engine internal function call results in a unit-hour distance error in the movement control of the collision objects and the distance error is proportional to the velocity of the collision. Conclusion: Remote decomposition simulators using digital twin technology are considered to require limitations of the speed of movement according to the required precision of the precision control devices in the hardware and software environment and manual control. In addition, the size of modeling data such as system development environment, hardware specifications and simulations imitated control equipment and facilities must also be taken into account, available and acceptable errors of operational control equipment and the speed required of work.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• KSBB Journal
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• v.29 no.4
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• pp.285-296
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• 2014

Although the problems of the algal blooms have been world-widely observed in freshwater, estuary, and marine throughout the year, it is not yet certain what are the basic causes of such blooms. Consequently, it is very difficult to predict when and where algal blooms occur. The constituents of the Asian dust are in a good agreement with the elements required for the algal growth, which suggests some possible relationship between the algal blooms and the Asian dust. There have been frequently algal blooms in drinking water from rivers or lakes. However, there is no any algal blooms in upwelling waters where the Asian dust cannot penetrate into the soil due to its relatively weak settling velocity (size of particles, $4.5{\pm}1.5{\mu}m$), which implies the possible close relationship of the Asian dust with algal blooms. The present initiative study is thus intended firstly in Korea to illustrate such a relationship by reviewing typical previous studies along with 12 years of weekly iron profiles (2001~2012) and two slant culture experiments with the dissolved Asian dust. The result showed bacterial suspected colonies in the slant culture experiment that are qualitatively in a good agreement with the recent Japanese studies. Since the diatoms require cheap energy (8%) compared to other phytoplankton (100%) to synthesize their cell walls by silicate, the present results can be used to predict algal blooms by diatoms if the concentrations of iron and silicate are available during spring and fall. It can be postulated that the algal blooms occur only if the environmental factors such as light, nutrients, calm water surface layer, temperature, and pH are simultaneously satisfied with the requirements of the micronutrients of mineral ions supplied by the Asian dust as enzymatic cofactors for the rapid bio-synthesis of the macromolecules during algal blooms. Simple eco-friendly methods to regulate the algal blooms are suggested for the initial stage of blooming with limited area: 1) to cover up the water surface with black curtain and inhibit photosynthesis during the day time, 2) to blow air (20.9%) or pure oxygen into the bottom of the water and inhibit rubisco for carbon uptake and nitrate reductase for nitrogen uptake activities in algal growth during the night, 3) to eliminate the resting spores or cysts by suction of bottom sediments as deep as 5 cm to prevent the next year germinations.

• Journal of the Korean Society of Marine Environment & Safety
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• v.16 no.2
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• pp.175-183
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• 2010

To understand the variation of macro benthos community according to the installation of structure and topographic placement in the shellfish farm on tidal flat, the practical example of the tidal shellfish growing area at Namsung-ri Goheung was observed. The results of the research for the field observation were summarized as follows. (1) The ground gradient of the shellfish farm was very flat below about $1^{\circ}$. The shellfish farm ground took the shape of $\sqcup$ from the shoreline to the place of 150 m seawards, and the shape of $\sqcap$ from there to the low tide line. During ebb tide, the $\sqcup$ shape ground stored the sea water, and the $\sqcap$ shape ground was supposed to act as the effect factor to leak slowly or to prevent the outflow. (2) The oyster shell bag or the type of riprap wall as the boundary in the shellfish farm was classified into five types. The air exposure time and flooding time were 181 and 434 minutes, respectively. (3) In the numerical experiment, the deep-sea water wave coming in the study area had 0.5 m of maximum wave height to show the very stable conditions and the wave direction pattern of S-direction was dominant at Naro great ridge, and SE, SSW and S-direction were distributed strongly around the shellfish farm. (4) By the grain size analysis, the sediment around tidal flat consisted of gravel 0.00~5.81(average 1.70)%, sand 14.15~18.39(average 13.23)%, silt 27.59~47.15(average 30.84)% and clay 35.79~55.73(average 36.19)%, and the sediment type was divided into (g)M(lightly gravelly mud), sM(sandy mud) and gM(gravelly mud) by Folk's diagram. (5) The macro benthos community survey conducted in this site in January, 2010 showed that 1 species of Mollusca, 8 species of Polychaeta and 2 species of Crustacea appeared, and 11 species occupying over 1% of total abundance were dominant.

• Journal of Life Science
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• v.22 no.4
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• pp.516-523
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• 2012

The purpose of this study was to investigate the effect of exercise and/or L-arginine on abdominal fat, IGF-1 on GH/IGF-1 axis, fibrinogen, and PAI-1 in aged and obese rats. Male Sprague-Dawley rats were treated with a D-galactose aging inducing agent (50 mg/kg) given intraperitoneally for 12 weeks. Thirty-two male Sprague-Dawley rats were treated and divided into four groups: aging-high fat diet group (AG+HF), AG+HF with L-arginine intake group (AG+LA), AG+HF with exercise group (AG+EX), and AG+EX with L-arginine intake group (AG+LA+EX). The experimental rats underwent treadmill training (60 min/day, 6 days/week at 0% gradient) for 12 weeks. L-arginine was given orally (150 mg/kg/day) for 12 weeks. After the experiment, blood was collected from the left ventricle and abdominal fat was extracted. The results showed that GH was significantly increased in AG+EX and AG+AL+EX. IGF-1 was significantly increased in both the AG+AL+EX and AG+EX group ($p$<0.05), while fibrinogen and PAI-1 were not significantly different among the groups. Abdominal fat was significantly decreased in the AG+LA, AG+EX, and AG+LA+EX groups ($p$<0.05) compared with the AG+HF group. In conclusion, this study suggests that exercise alone or L-arginine alone or a combination not only increases the GH and IGF-1 concentration, but also decreases the abdominal fat mass.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.217-229
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• 2015

In this study, we explored the development of scientific model through the social-construction process on "combustion." Students were 8th graders from one middle school class. Each student engaged in small group discussions three times and made a group model on combustion. Discourses between peers and teacher were videotaped, audiotaped, and transcribed. The results show that the small groups constructed an initial concept: 'Conditions of combustion', which they then evaluated and revised the initial concept through combustion experiment. Following the discussions, some small groups evaluated their model and made a revised model. Then, the small groups compared various models and constructed a scientific model through consensus within the small group and as a whole class. Finally, students kept revising their model to 'Burning needs oxygen.' This tells us that the social construction process of scientific model made a meaningful role to build scientific model through diverse discussion between the students and their teacher, although they have had some difficult process to reach the final consensus. The data also showed some group features: the members were open to other's ideas. They analyzed the differences between their own ideas from others and revised their model after the whole class discussion. Lastly, they showed the tendency to make a good use of teacher's guidance. This study implies the importance of having social interaction process for students to understand the scientific model and learn the nature of scientific inquiry in class.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.224-236
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• 2002

A long-term growth simulation was performed at 496 land units in the western coastal plains (WCP) of North Korea to test the potential adaptability of each land unit for growing South Korean rice cultivars. The land units for rice cultivation (CZU), each of them represented by a geographically referenced 5 by 5 km grid tell, were identified by analyzing satellite remote sensing data. Surfaces of monthly climatic normals for daily maximum and minimum temperature, precipitation number of rain days and solar radiation were generated at a 1 by 1 km interval by spatial statistical methods using observed data at 51 synoptic weather stations in North and South Korea during 1981-2000. Grid cells felling within a same CZU and, at the same time, corresponding to the satellite data- identified rice growing pixels were extracted and aggregated to make a spatially explicit climatic normals relevant to the rice growing area of the CZU. Daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CERES-rice model suitable for 11 major South Korean cultivars were derived from long-term field observations. Eight treatments comprised of 2 transplanting dates $\times$ 2 cropping systems $\times$ 2 irrigation methods were assigned to each cultivar. Each treatment was simulated with the randomly generated 30 years' daily weather data (from planting to physiological maturity) for 496 land units in WCP to simulate the growth and yield responses to the interannual climate variation. The same model was run with the input data from the 3 major crop experiment stations in South Korea to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for comparison. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific South Korean cultivar. The results may be utilized as decision aids for agrotechnology transfer to North Korea, for example, germplasm evaluation, resource allocation and crop calendar preparation.

• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.7-12
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• 2013

This study presents the influence on growth properties determined using a novel predictive growth model of wild-type Salmonella spp. KSC 101 by variations in the temperature and time during cut packaging in cold, uncooked pork meat. The experiment performed for model development included an arrangement of different temperatures ($0^{\circ}C$, $5^{\circ}C$, $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$) and time durations (0, 1, 2, and 3 hours) that reflect actual pork-cut and packaging processes. No growth was observed at $0^{\circ}C$ and $5^{\circ}C$, whereas some growth was observed at $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$, with a mean increase of only 0.34 log CFU/g. The growth observed at $20^{\circ}C$ was more robust than that observed at $15^{\circ}C$, but the difference was not statistically significant (p > 0.05). However, compared with PMP (Pathogen Modeling Program), the wild-type Salmonella spp. KSC 101 showed a more rapid growth. We used the Gompertz 4 parameter equation as the primary model, and the exponential decay formula as the secondary model. The estimated $R^2$ values were 0.99 or higher. The developed model was evaluated by comparison of the experimental and predictive values, and the values were in agreement with the ${\pm}0.5$ log CFU/g, although the RMSE (Root mean square error) value was 0.103, which indicates a slight overestimation. Therefore, we suggest that the developed predictive growth model would be useful as a tool for evaluating sanitation criteria in pork cut-packaging processes.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.135-148
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• 2010

Arab boycotts of Danish products, Australian boycotts of French products and Chinese consumer aversion toward Japanese products are all examples of how adverse actions at the country level might impact consumers' behavior. The animosity literature has examined how consumers react to the adverse actions of other countries, and how such animosity impacts consumers' attitudes and preferences for products from the transgressing country. For example, Chinese consumers are less likely to buy Japanese products because of Japanese atrocities during World War II and the unjust economic dealings of the Japanese (Klein, Ettenson and Morris 1998). The marketing literature, however, has not examined how consumers react to adverse actions committed by their own country against other countries, and whether such actions affect their attitudes towards purchasing products that originated from the adversely affected country. The social psychology literature argues that consumers will experience a feeling called collective guilt, in response to such adverse actions. Collective guilt stems from the distress experienced by group members when they accept that their group is responsible for actions that have harmed another group (Branscombe, Slugoski, and Kappenn 2004). Examples include Americans feeling guilty about the atrocities committed by the U.S. military at Abu Ghraib prison (Iyer, Schamder and Lickel 2007), and the Dutch about their occupation of Indonesia in the past (Doosje et al. 1998). The primary aim of this study is to examine consumers' perceptions of adverse actions by members of one's own country against another country and whether such perceptions affected their attitudes towards products originating from the country transgressed against. More specifically, one objective of this study is to examine the perceptual antecedents of collective guilt, an emotional reaction to adverse actions performed by members of one's country against another country. Another objective is to examine the impact of collective guilt on consumers' perceptions of, and preference for, products originating from the country transgressed against by the consumers' own country. If collective guilt emerges as a significant predictor, companies originating from countries that have been transgressed against might be able to capitalize on such unfortunate events. This research utilizes the animosity model introduced by Klein, Ettenson and Morris (1998) and later expanded on by Klein (2002). Klein finds that U.S. consumers harbor animosity toward the Japanese. This animosity is experienced in response to events that occurred during World War II (i.e., the bombing of Pearl Harbor) and more recently the perceived economic threat from Japan. Thus this study argues that the events of Word War II (i.e., bombing of Hiroshima and Nagasaki) might lead U.S. consumers to experience collective guilt. A series of three hypotheses were introduced. The first hypothesis deals with the antecedents of collective guilt. Previous research argues that collective guilt is experienced when consumers perceive that the harm following a transgression is illegitimate and that the country from which the transgressors originate should be responsible for the adverse actions. (Wohl, Branscombe, and Klar 2006). Therefore the following hypothesis was offered: H1a. Higher levels of perceived illegitimacy for the harm committed will result in higher levels of collective guilt. H1b. Higher levels of responsibility will be positively associated with higher levels of collective guilt. The second and third hypotheses deal with the impact of collective guilt on the preferences for Japanese products. Klein (2002) found that higher levels of animosity toward Japan resulted in a lower preference for a Japanese product relative to a South Korean product but not a lower preference for a Japanese product relative to a U.S. product. These results therefore indicate that the experience of collective guilt will lead to a higher preference for a Japanese product if consumers are contemplating a choice that inv olves a decision to buy Japanese versus South Korean product but not if the choice involves a decision to buy a Japanese versus a U.S. product. H2. Collective guilt will be positively related to the preference for a Japanese product over a South Korean product, but will not be related to the preference for a Japanese product over a U.S. product. H3. Collective guilt will be positively related to the preference for a Japanese product over a South Korean product, holding constant product judgments and animosity. An experiment was conducted to test the hypotheses. The illegitimacy of the harm and responsibility were manipulated by exposing respondents to a description of adverse events occurring during World War II. Data were collected using an online consumer panel in the United States. Subjects were randomly assigned to either the low levels of responsibility and illegitimacy condition (n=259) or the high levels of responsibility and illigitemacy (n=268) condition. Latent Variable Structural Equation Modeling (LVSEM) was used to test the hypothesized relationships. The first hypothesis is supported as both the illegitimacy of the harm and responsibility assigned to the Americans for the harm committed against the Japanese during WWII have a positive impact on collective guilt. The second hypothesis is also supported as collective guilt is positively related to preference for a Japanese product over a South Korean product but is not related to preference for a Japanese product over a U.S. product. Finally there is support for the third hypothesis, since collective guilt is positively related to the preference for a Japanese product over a South Korean product while controlling for the effect of product judgments about Japanese products and animosity. The results of these studies lead to several conclusions. First, the illegitimacy of harm and responsibility can be manipulated and that they are antecedents of collective guilt. Second, collective guilt has an impact on a consumers' decision when they face a choice set that includes a product from the country that was the target of the adverse action and a product from another foreign country. This impact however disappears from a consumers' decision when they face a choice set that includes a product from the country that was the target of the adverse action and a domestic product. This result suggests that collective guilt might be a viable factor for company originating from the country transgressed against if its competitors are foreign but not if they are local.