• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Korean Security Journal
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• no.43
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• pp.67-97
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• 2015

In Korea, a single emergency number, such as 911 of the USA and 999 of the UK, does not exist. This issue became highly controversial, when the Sewol Ferry Sinking disaster occurred last year. So, the Korean government has planned to adopt a single emergency number, integrating 112 of the Police, 119 of the Fire and Ambulance, 122 of the Korean Coast Guard, and many other emergency numbers. However, the integration plan recently proposed by the Ministry of Public Safety Security seems to be, what is called, a "partial integration model" which repeals the 122 number, but still maintains 112, 119, and 110 respectively. In this context, the study looks into USA's (diverse) 911 operating system, and subsequently tries to draw general features or characteristics. Further, the research attempts to derive policy implication from the general features. If the proposed partial integration model reflects the policy implications, the model can virtually operate like the 911 system -i.e. a single emergency number system - creating inter-operability between responding agencies such as police, fire, and ambulance, even though it is not a perfect integration model. The features drawn are (1) integration of emergency call-taking, (2) functional separation of call-taking and dispatching, (3) integration of physical facilities for call-taking and dispatching, and (4) professional call-takers and dispatchers. Moreover, the policy implications derived from the characteristics are (1) a user-friendly system - fast but accurate responses, (2) integrated responses to accidents, (3) professional call-taking and dispatching & objective and comprehensive risk assessment, and finally (4) active organizational learning in emergency call centers. Considering the policy implications, the following suggestions need to be applied to the current proposed plan: 1. Emergency services' systems should be tightly linked and connected in a systemic way so that they can communicate and exchange intelligence with one another. 2. Public safety answering points (call centers) of each emergency service should share their education and training modules, manuals, etc. Common training and manuals are also needed for inter-operability. 3. Personal management to enable-long term service in public safety answering points (call centers) should be established as one of the ways to promote professionalism.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.83-105
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• 2016

Measuring an individual's subjective wellbeing in an accurate, unobtrusive, and cost-effective manner is a core success factor of the wellbeing support system, which is a type of medical IT service. However, measurements with a self-report questionnaire and wearable sensors are cost-intensive and obtrusive when the wellbeing support system should be running in real-time, despite being very accurate. Recently, reasoning the state of subjective wellbeing with conventional sentiment analysis and unstructured data has been proposed as an alternative to resolve the drawbacks of the self-report questionnaire and wearable sensors. However, this approach does not consider contextual polarity, which results in lower measurement accuracy. Moreover, there is no sentimental word net or ontology for the subjective wellbeing area. Hence, this paper proposes a method to extract keywords and their contextual polarity representing the subjective wellbeing state from the unstructured text in online websites in order to improve the reasoning accuracy of the sentiment analysis. The proposed method is as follows. First, a set of general sentimental words is proposed. SentiWordNet was adopted; this is the most widely used dictionary and contains about 100,000 words such as nouns, verbs, adjectives, and adverbs with polarities from -1.0 (extremely negative) to 1.0 (extremely positive). Second, corpora on subjective wellbeing (SWB corpora) were obtained by crawling online text. A survey was conducted to prepare a learning dataset that includes an individual's opinion and the level of self-report wellness, such as stress and depression. The participants were asked to respond with their feelings about online news on two topics. Next, three data sources were extracted from the SWB corpora: demographic information, psychographic information, and the structural characteristics of the text (e.g., the number of words used in the text, simple statistics on the special characters used). These were considered to adjust the level of a specific SWB. Finally, a set of reasoning rules was generated for each wellbeing factor to estimate the SWB of an individual based on the text written by the individual. The experimental results suggested that using contextual polarity for each SWB factor (e.g., stress, depression) significantly improved the estimation accuracy compared to conventional sentiment analysis methods incorporating SentiWordNet. Even though literature is available on Korean sentiment analysis, such studies only used only a limited set of sentimental words. Due to the small number of words, many sentences are overlooked and ignored when estimating the level of sentiment. However, the proposed method can identify multiple sentiment-neutral words as sentiment words in the context of a specific SWB factor. The results also suggest that a specific type of senti-word dictionary containing contextual polarity needs to be constructed along with a dictionary based on common sense such as SenticNet. These efforts will enrich and enlarge the application area of sentic computing. The study is helpful to practitioners and managers of wellness services in that a couple of characteristics of unstructured text have been identified for improving SWB. Consistent with the literature, the results showed that the gender and age affect the SWB state when the individual is exposed to an identical queue from the online text. In addition, the length of the textual response and usage pattern of special characters were found to indicate the individual's SWB. These imply that better SWB measurement should involve collecting the textual structure and the individual's demographic conditions. In the future, the proposed method should be improved by automated identification of the contextual polarity in order to enlarge the vocabulary in a cost-effective manner.

• Korean Journal of Environment and Ecology
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• v.30 no.6
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• pp.1047-1056
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• 2016

This study aims to provide managerial implications for provincial parks through an analysis on visitors' characteristics and motivational factors. The information was collected by surveying 290 visitors. The survey questionnaire consisted of questions regarding visitors' socioeconomic characteristics, characteristics of their travel behavior, visitors' motivation to visit the park, and the degree of satisfaction derived from visiting the park. Results show that most respondents appeared not to collect any information on the park prior to their visit. It was also seen that most visitors do not visit other tourist sites nearby, and are not aware of the Gochang UNESCO biosphere, which indicates that Gochang-gun, which is responsible for park management, needs to make more efforts to promote the park. A factor analysis on the visitors' motivation to visit the park extracted three factors to visit the Seonunsan Provincial Park: 'to hike,' 'to experience and observe nature,' i.e., nature learning field trip and camping, and 'to build and nurture bonding with family and friends.' To examine the effect of these various motivational factors had on the visitors' satisfaction level upon visiting the park, we conducted a multiple regression analysis with the three extracted factors to visit the park and the respondents' socioeconomic characteristics as independent variables, and the degree of recommendation of visiting the park as a dependent variable. The result shows found that, of the three travel factors, only the 'hiking' factor statistically significantly affected the degree of recommendation of visiting the park. This result suggests that the Seonunsan Provincial Park only satisfied hikers and failed to meet the demands for nature experience and observation. It is therefore suggested that the park managers develop new experience-based tourism programs, such as guided tours conducted by professional eco-interpreters.

• Journal of The Korean Association of Information Education
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• v.1 no.1
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• pp.38-48
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• 1997

This study is an attempt to present a usage of the Web as CAI in the classroom and to give a direction to the future education in the face of information age. Characteristcs of information society, current curriculum, educational and teacher education are first analyzed in this article. The features of internet and 'Web are then summarized to present benefits of usage in the classroom as a CAI tool. The literature shows several characteristics of information society as follows : a technological computer, a provision and sharing of information, multi functional society, a participative democracy', an autonomy, a time value..A problem solving and 4 Cs(e.g., cooperation, copying, communication, creativity) are newly needed in this learning environment. The Internet is a large collection of networks that are tied together so that users can share their vast resources, a wealth of information, and give a key to a successful, efficient. individual study over a time and space. The 'Web increases an academic achievement, a creativity, a problem solving, a cognitive thinking, and a learner's motivation through an easy access to : documents available on the Internet, files containing programs, pictures, movies, and sounds from an FTP site, Usenet newsgroups, WAIS seraches, computers accessible through telnet, hypertext document, Java applets and other multimedia browser enhancements, and much more, In the Web browser will be our primary tool in searching for information on the Internet in this information age.

• Journal of the Korean earth science society
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• v.32 no.2
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• pp.212-224
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• 2011

In school science class, actual geological field trips tend to be restricted due to a number of problems including travel distance, cost, safety, and so on. Therefore, alternative way should be sought to provide students with the benefits of actual field trip. The purpose of this study is to develop web-based virtual field trip (VFT) about Jeju island in Korea by using flash panorama, and to explore a variety of ways to utilize the VFT. The characteristics of Jeju VFT are as follows: it provides virtual space for secondary school students to learn about volcanic topography and geology; students can access contents in a non-sequential order by virtue of web-based system, and students can control learning pace according to their ability; it is possible to investigate the same field site repeatedly, not limited by time and space; it presents differentiated worksheets for different school grade; it provides diverse complementary web contents, e. g., closeup features, thin sections, inquiry questions, and explanations of outcrops. We proposed several ways with instructional models to utilize Jeju VFT in science class and extra-school curricular as well.

• Communications of Mathematical Education
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• v.29 no.3
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• pp.391-421
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• 2015

The scientific technology developed rapidly and the internet became more popular, also, the world became interactive with one another and the word 'Global' became popular and built a new paradigm. As the development of the society, the ideal criteria for the competent student changed. Consequently, the attention for the globalized education increased. From the points of view of mathematical education, it became a important task to be prepared for international competitiveness for korean talented students. For theses reasons, this article analyzes the characteristics of IBDP and its textbook, which is an international official curriculum and one of the actualizing method for internalization Korean high school curriculum and text book, specifically, focusing on algebra part. Especially, Korean curriculum textbooks and the Mathematical Higher Level textbooks of IBDP was compared and analyzed. As a result, the depth and range of the content, standard level of the question, methods being used to explain the concept, type of questions as well as teaching - learning method were analyzed and in each chapter of the algebra we give meaningful result and proposed discussion.

• Journal of Broadcast Engineering
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• v.11 no.2 s.31
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• pp.207-221
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• 2006

A fully automatic and computationally efficient method is proposed for intelligent display of soccer video on small multimedia mobile devices. The rapid progress of the multimedia signal processing has contributed to the extensive use of multimedia devices with a small LCD panel. With these emerging small mobile devices, the video sequences captured for standard- or HDTV broadcasting may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera technique, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Thus, an intelligent display technique is needed for small-display-viewers. To this end, one of the key technologies is to determine region of interest (ROI), which is a part of the scene that viewers pay more attention to than other regions. In this paper, the focus is on soccer video display for mobile devices. Instead of taking visual saliency into account, we take domain-specific approach to exploit the characteristics of the soccer video. The proposed scheme includes three modules; ground color learning, shot classification, and ROI determination. The experimental results show the propose scheme is capable of intelligent video display on mobile devices.