• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.25-35
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• 2008

Variable Message Sign(VMS) is one of the subsystem of Intelligent Transportation Systems (ITS), which is useful for providing real-time information on weather, traffic and highway conditions. However, there are various situations such as incidents/accidents, constructions, special events, etc., which would be occurred on segments, it is unable to control traffic with only the VMS. Thus, it is essential to use of PVMS(Portable Variable Message Signs), which can move to the location needed traffic control and provide more active traffic information than VMS. This study developed a legibility distance model for PVMS messages using in-vehicle Differential Global Positioning Data(DGPS). Traffic conditions, drivers' characteristics, weather conditions and characteristics of PVMS message were investigated for establishing the legibility model based on multiple linear regression analysis. The factors such as height of PVMS characters, spot speed, age, gender and day and night were identified as dominants affecting the variation of legibility distances. It is expected that the proposed model would play a significant role in designing PVMS messages for providing more effective real-time traffic information.

• Journal of Marine Life Science
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• v.6 no.1
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• pp.1-8
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• 2021

In marine ecosystems, the biosynthesis and catabolism of dimethylsulfoniopropionate (DMSP) by marine bacteria is critical to microbial survival and the ocean food chain. Furthermore, these processes also influence sulfur recycling and climate change. Recent studies using emerging genome sequencing data and extensive bioinformatics analysis have enabled us to identify new DMSP-related genes. Currently, seven bacterial DMSP lyases (DddD, DddP, DddY, DddK, DddL, DddQ and DddW), two acrylate degrading enzymes (DddA and DddC), and four demethylases (DmdA, DmdB, DmdC, and DmdD) have been identified and characterized in diverse marine bacteria. In this review, we focus on the biochemical properties of DMSP cleavage enzymes with special attention to DddD, DddA, and DddC pathways. These three enzymes function in the production of acetyl coenzyme A (CoA) and CO₂ from DMSP. DddD is a DMSP lyase that converts DMSP to 3-hydroxypropionate with the release of dimethylsulfide. 3-Hydroxypropionate is then converted to malonate semialdehyde by DddA, an alcohol dehydrogenase. Then, DddC transforms malonate semialdehyde to acetyl-CoA and CO₂ gas. DddC is a putative methylmalonate semialdehyde dehydrogenase that requires nicotinamide adenine dinucleotide and CoA cofactors. Here we review recent insights into the structural characteristics of these enzymes and the molecular events of DMSP degradation.

• Journal of the Korean earth science society
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• v.40 no.4
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• pp.353-381
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• 2019

The general world model for homogeneous and isotropic universe has been proposed. For this purpose, we introduce a global and fiducial system of reference (world reference frame) constructed on a (4+1)-dimensional space-time, and assume that the universe is spatially a 3-dimensional hypersurface embedded in the 4-dimensional space. The simultaneity for the entire universe has been specified by the global time coordinate. We define the line element as the separation between two neighboring events on the expanding universe that are distinct in space and time, as viewed in the world reference frame. The information that determines the kinematics of the geometry of the universe such as size and expansion rate has been included in the new metric. The Einstein's field equations with the new metric imply that closed, flat, and open universes are filled with positive, zero, and negative energy, respectively. The curvature of the universe is determined by the sign of mean energy density. We have demonstrated that the flat universe is empty and stationary, equivalent to the Minkowski space-time, and that the universe with positive energy density is always spatially closed and finite. In the closed universe, the proper time of a comoving observer does not elapse uniformly as judged in the world reference frame, in which both cosmic expansion and time-varying light speeds cannot exceed the limiting speed of the special relativity. We have also reconstructed cosmic evolution histories of the closed world models that are consistent with recent astronomical observations, and derived useful formulas such as energy-momentum relation of particles, redshift, total energy in the universe, cosmic distance and time scales, and so forth. The notable feature of the spatially closed universe is that the universe started from a non-singular point in the sense that physical quantities have finite values at the initial time as judged in the world reference frame. It has also been shown that the inflation with positive acceleration at the earliest epoch is improbable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.108-114
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• 2021

In recent years, human is experiencing an 'untact' era that has not been existed before. A remarkable features of an untact era is a limited or no interaction with other people and things in a daily life. In order to overcome this special situation, diverse state-of-the-art technologies like ICT technology are being used to give the effect of face-to-face and contact. In particular, Virtual Reality technology allows users to actively interact with virtual environments, objects, and other people, thereby providing effects that are as similar as possible to experience in real life. School freshmen are also experiencing various inconveniences because they are not experiencing the environment or academic management of newly admitted schools in the untact era. This research is a study and implementation of 'Virtual School Life Experience Content' which provides new students to tour campus, to utilize the school buildings facilities, to experience of academic schedules including course registration, and to participate in school events in an environment that is as close as possible to the real campus. Freshmen will be able to easily adapt to a new school through the implemented VR content.

• Journal of People, Plants, and Environment
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• v.24 no.4
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• pp.355-363
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• 2021

Background and objective: This study was conducted on Forsythia velutina, a special plant, in Gyeongsangnam-do Arboretum under the Gyeongsangnam-do Forest Environment Research Institute, which is located in the southern part of Korea. Methods: The research aimed to analyze the flowering characteristics of the plant by calculating the optimal temperature and humidity according to the flowering time and flowering period for 8 years from 2010 to 2017 in order to provide basic data for bioclimate studies of endemic plants. Results: It was observed that the Forsythia velutina showed a life cycle from mid-March and to mid-November. Average growth period was 243 (± 6.5) days. In testing the reliability of a single variable according to the meteorological factors, the Cronbach's Alpha was 0.701, which indicates that the findings were relatively reliable. The average date of flowering was March 16 (SD = 5.8) and the average date on which blossoms fall was March 29 (SD = 5.2). A substantial difference in flowering period was observed from year to year 11 to 23 days, with an average of 16 days (± 4.7). The temperature and humidity in February to March, which affect the flowering, were 2.9-5.5℃, and 66.5-73.0%, respectively, and showed differences every year. Conclusion: The correlation between flowering time and meteorological factors was positive, and the highest daily temperature and average daily temperature had the highest significance. When establishing basic data on plant species for the conservation of endemic plants, the changes in life cycle events and weather conditions are identified. It is believed that it will be helpful in establishing a conservation strategy for the plant species in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Earthquakes and Structures
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• v.25 no.1
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• pp.1-13
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• 2023

Reinforced concrete (RC) moment frames are used as lateral seismic load resisting systems in mid- and high-rise buildings in different regions of the world. Based on the seismic design provisions and construction details presented in design codes, RC frames with different levels of ductility (ordinary, intermediate, and special) can be designed and constructed. In Iran, there are RC buildings with various uses which have been constructed based on different editions of design codes. The seismic performance of RC structures (particularly moment frames) in real seismic events is of great importance. In this paper, the observations made on damaged RC moment frames after the destructive Sarpol-e Zahab earthquake with a moment magnitude of 7.3 are reported. Different levels of damage from the development of cracks in the structural and non-structural elements to the total collapse of buildings were observed. Furthermore, undesirable failure modes which are not expected in ductile seismic-resistant buildings were frequently observed in the damaged buildings. The RC moment frames built based on the previous editions of the design codes showed partial or total collapse in this seismic event. The extensive destruction of RC moment frames compared with the other structural systems (such as braced steel frames and confined masonry buildings) was attributed not only to the deficiencies in the construction practice of these buildings but also to the design procedure. In addition, the failure and collapse of masonry infills in RC moment frames were frequent modes of failure in this seismic event. In this paper, the main reasons related to design practice which led to extensive damage in the RC moment frames and their collapse are addressed.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.12
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• pp.1794-1800
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• 2009

This research was performed to acquire dietitians' attitudes toward events marketing at school foodservice operations in the Busan area. A total of 359 questionnaires were distributed to dietitians employed at school foodservice operations in Busan from July 1 to 31, 2006 (response rate: 93%). All dietitians assessed the importance and performance of event marketing at 3.39/5.00 and 2.78/5.00. The elementary and high school had significantly (p<0.01) higher average scores of performance of event marketing than those of the middle school. The contract managed foodservices had significantly (p<0.01) higher average scores of performance of event marketing than those of the independent managed foodservices. In the Importance-Performance Analysis (IPA), high importance and high performance (B area: doing great) were seasonal event, traditional festival day event, subdivisions of the seasonal event, environment event, school event, the day event and high importance whereas low performance (A area: focus here) was health event. Event marketing increased customer satisfaction and confidence. Therefore, these results suggest that there may be a need to implement special events at school foodservice in order to increase students' satisfaction.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.1-27
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• 2012

Many companies have recently become interested in using social networking sites such as Twitter and Facebook as a new channel to communicate with their customers. For example, companies often offer "special deals" (e.g., coupons, discounts, free samples, etc.) to their customers who participate in promotions or events on social networking sites. Companies often make important announcements on their products or services on social networking sites. By doing so, customers are encouraged to continue to have relationships with companies on social networking sites and to recommend the companies' presence on social networking sites to other potential customers. Moreover, customers who keep close relationships with companies on social networking sites often provide the companies with valuable suggestions and feedback. For instance, Starbucks has more than 2 million followers on Twitter, and often receive suggestions and feedback for their product offerings and services from the followers on Twitter. Although companies realize potential benefits of using social networking sites as a channel to communicate with their customers, it appears that many companies have difficulty forging long-lasting relationships with customers on social networking sites. It is often reported that many customers who had followed companies on Twitter later stopped following them for various reasons. Therefore, it is an important issue to understand what motivates customers to continue to keep relationships with companies on social networking sites. Nonetheless, due attention has yet paid to this issue until recently. This study intends to contribute to our understanding on customers' intention to continue to follow companies on Twitter and to spread positive word-of-mouth about companies on Twitter. Specifically, we identify seven potential factors that customers perceive as important in evaluating their experience with companies on Twitter. The seven factors include similarity, receptivity, interactivity, ubiquitous connectivity, enjoyment, usefulness and transparency. We posit that the seven perception factors can affect the two types of satisfaction, emotional and cognitive, which can in turn influence on customers' intention to follow companies on Twitter and to spread positive word-of-mouth about companies on Twitter. Research hypotheses formulated in this study were tested with data collected from a questionnaire survey administered to customers who had been following companies on Twitter. The data was analyzed with the partial least square (PLS) approach to structural equation modeling. The results of data analysis based on 177 usable responses were generally supportive of our predictions for the effects of the seven factors identified and the two types of satisfaction. In particular, out results suggest that emotional satisfaction was strongly influenced by perceived similarity, perceived receptivity, perceived enjoyment, and perceived transparency. Cognitive satisfaction was significantly influenced by perceived similarity, perceived interactivity, perceived enjoyment, and perceived transparency. While cognitive satisfaction was found to have significant and positive effects on both continued following and word-of-mouth intentions, emotional satisfaction had a significant and positive effect only on word-of-mouth intention.