• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.128-139
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• 2018

As The Fourth Industrial Revolution and Intelligent Information Age came into full-scale, the policy of open government data has become a hot topic for each country. The United States, the United Kingdom, and other countries are shifting policy direction to "creating value" of open government data. Also, in the age of the digital economy where the data market is soaring, open government data is gradually being recognized as a new raw material for new business and start-ups. In addition, Korea ranked first in the OECD open government data evaluation twice in a row, and was highly evaluated in the international evaluation. However, domestic firms are still lacking in qualitative openness of government data, data is dispersed among institutions, lack of public-private data linkage, and development of app-oriented development. This study attempts to analyze major national policies for the creation of a data ecosystem that considers data lifecycle, from production to storage, distribution and utilization of data. First, the target countries were the leading public data countries among the OGP member countries, the USA, the UK, Australia and Canada. The results of this study are as follows. As a result of analyzing the results and comparing Korea's policies, it was concluded that most of Korea is superior in open government data policy. However, improvement of data quality, development of open data portal as an open platform, support for finding various users including apps and web development companies, and cultivation of open government data utilizing personnel are analyzed as policy issues. In addition, the direction of policy for the balanced ecosystem of Korea is presented together.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.63-75
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• 2018

A Service Area plays an important role in preventing accidents in advance by creating a space for long distance drivers or drowsy drivers to rest. Therefore, proper positioning of the expressway service area is essential, and it is important to analyze accurate demand forecasting and user travel behavior. Thus, this study analysis travel behavior and developed odel of the probability of using the service area by using the DSRC data collected by the RSE on the highway. According to the analysis, the usage behavior of highway service areas was most frequently when travel time was 90 minutes or more on weekdays and 70 minutes or more on weekends. The utilization rate of the service area estimated from the probability model of use of the rest area in this study was 1 % to 2 % error. The results of this study are meaningful in analyzing the behavior of the use of rest areas using the structured data and can be used as a differentiated strategy for selecting the location of rest areas and enhancing the service level of users.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.4
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• pp.503-514
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• 1998

The purpose of this study was to develop a large representative data base for jeans marketing strategy This study was to survey brand features(launching year, launching company, design concept, sales volume, and price zone) in the current market, and was to examine brand awareness and it's relationship to segmented distribution regions, demo- graphic variables(sex, age, monthly household income, and seasonal clothing expenditure). This study was also to analyze brand image and consumer's seeking image. The 660 questionnaires were distributed and 618 reliable ones were used for statistical analysis. A SAS statistical package including frequency table, Chi-square test, factor analysis, analysis of variance(ANOVA), Duncan's multiple range test and paired-t test was used. The results are as follows: 1. Brand awareness involves "brand recall" based on asking a person to name the brand recalled first, and "brand recognition" based on asking to identify brand name from 30 given brands. The result of recall brand test indicated that Levi's was dominant brand. People recognized about 70.8% of brands on the average. Brand recognition was influenced by segmented distribution region and demographic variables. 2. There was significantly positive relationship between brand recognition and purchasing behavior. 3. National brands were more recognized than Licensed brands. 4. The result showed that "Nix" was best represented for sophisticated brand image, "Strom" for characteristic, "Jambangee" for resonable price, and "Levi's" for classic '||'&'||' comfortable brand image. 5. As a result of factor analysis on consumer's seeking image, six factors(characteristic, young, intelligent/sexy, comfortable, exotic and popular) were found. Several factors had a relationship with preferred design, demographic variables, fashion interest, and brand recognition. variables, fashion interest, and brand recognition.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.273-277
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• 1998

The purpose of this study was to develop a large representative data base for jeans marketing strategy. This study was to survey brand awareness and analyze brand image and consumer's seeking image. The 700 questionnaires were distributed and 656 reliable ones were used for statistical analysis. A SAS statistical package including frequency table, factor analysis, analysis of variance, Duncan's multiple range test, Peason's correlation test was used. The results are as follows: 1. Brand awareness involves "brand recall" based on asking a person to name recalled first, and "brand recognition" based on asking to identify brand name from 30 given brands. The result indicated that "Levi" was dominant for brand recall and Guess was dominant for brand recognition. 2. Regarding the brand image, the result showed that "Vov" was best represented for sophisticated 8t trendy brand images, "Storm" for sophisticated brand image, "Jambangee" for reasonable price & comfortable brand images, and "Levis" for classic & design/color brand images. 3. As a result of factor analysis on consumer's seeking image, six factors(characteristic/gay, intelligent/sexy, feminine/sophisticated, active/functional, cute/young, simple/comfortable) were found. Several factors had a relationship with demographic variables, preferred design, fashion interest.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.737-752
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• 2014

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.633-641
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• 2020

The development of intelligent information technology based on intelligence and data and network technology implemented by artificial intelligence has instigated innovation in society as a whole and has shown wide social and economic impact. Therefore, not only overseas but also in Korea, AI education is in a hurry to cultivate talents who will lead the upcoming society. Data is an important part of artificial intelligence, and data literacy, which can collect, process, and analyze data, to make data-based decisions, can be seen as an important competency to be developed along with AI literacy. Therefore, in this study, an AI data science education program that can increase data literacy of elementary school students was developed and applied to the experimental group, and its effectiveness was verified through a pre- and post response sample t-test. As a result, all of the four detailed competencies of data literacy, data understanding, collection, analysis, and expression, showed statistically significant improvement, indicating that the AI data science education program was effective in improving students' data literacy.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.385-391
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• 2013

Big Data is data of big size which is not processed, collected, stored, searched, analyzed by the existing database management system. The parallel genetic algorithm using the Hadoop for BigData technology is easily realized by implementing GA(Genetic Algorithm) using MapReduce in the Hadoop Distribution System. The previous study that the genetic algorithm using MapReduce is proposed suitable transforming for the GA by MapReduce. However, they did not show good performance because of frequently occurring data input and output. In this paper, we proposed the MRPGA(MapReduce Parallel Genetic Algorithm) using improvement Map and Reduce process and the parallel processing characteristic of MapReduce. The optimal solution can be found by using the topology, migration of parallel genetic algorithm and local search algorithm. The convergence speed of the proposal method is 1.5 times faster than that of the existing MapReduce SGA, and is the optimal solution can be found quickly by the number of sub-generation iteration. In addition, the MRPGA is able to improve the processing and analysis performance of Big Data technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.115-131
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• 2022

Carbon dioxide (CO₂) emissions from the transportation sector in South Korea accounts for 16.5% of all CO₂ emissions, and road transportation accounts for 96.5% of this sector's emissions in South Korea. Hence, constant research is being carried out on methods to reduce CO₂ emissions from this sector. With the emerging use of smart crossings, attempts to monitor individual vehicles are increasing. Moreover, the potential commercial deployment of autonomous vehicles increases the possibility of obtaining individual vehicle data. As such, CO₂ emission research was conducted at five signalized intersections in the Gangnam District, Seoul, using data such as vehicle type, speed, acceleration, etc., obtained from image detectors located at each intersection. The collected data were then applied to the MOtor Vehicle Emission Simulator (MOVES)-Matrix model-which was developed to obtain second-by-second vehicle activity data and analyze daily CO₂ emissions from the studied intersections. After analyzing two large and three small intersections, the results indicated that 3.1 metric tons of CO₂ were emitted per day at each intersection. This study reveals a new possibility of analyzing CO₂ emissions using actual individual vehicle data using an improved analysis model. This study also emphasizes the importance of more accurate CO₂ emission analyses.