• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.141-152
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• 1993

This study aims to generate digital elevation model from digital satellite imagery. Digital elevation model is being increasingly used for geo-spatial information system database development and for digital map production. Image matching technique was applied to acquire conjugate image coordinates and the algorithm for digital elevation model generation is presented in this study The exterior orientation parameters of the satellite imagery is determined by bundle adjustment and standard correlation was applied for image matching conjugate of image points. The window as well as the searching area have to be defined in image matching. Different sizes of searching area were tested to study the appropriate size of the searching area. Various coordinate transformation methods were applied to improve the computation speed as well as the geometric accuracy. The results were then statistically analysed after which the searching area is determined with the safety factor. To evaluate the accuracy of digital elevation model, 3-D coordinates were extracted from 1/5000 scale topographic map and this was compared to the digital elevation model generated from satellite imagery. The algorithm for generation of digital elevation model generated from satellite imagery is presented in this study which will prove effective in the database development of geo-spatial information system and in digital elevation modelling of large areas.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.141-153
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• 2010

The study is about the development of operating speed prediction models aimed for an evaluation of design consistency of four lane rural roads. The main differences of this study relative to previous research are the method of data collection and classification of road alignments. The previous studies collected speed data at several points in the horizontal curve and approaching tangent. This method of collection is based on the assumption that acceleration and deceleration only occurs at horizontal tangents and the speed is kept constant at horizontal curves. However, this assumption leads to an unreliable speed estimation, so drivers' behavior is not well represented. Contrary to the previous approach, speed data were collected with one and data analysis using a speed profile is made for data selection before building final models. A total of six speed prediction models were made according to the combination of horizontal and vertical alignments. The study predicts that the speed data analysis and selection for model building employed in this study can improve the prediction accuracy of models and be useful to analyze drivers' speed behavior in a more detailed way. Furthermore, it is expected that the operating speed prediction models can help complement the current design-speed-based guidelines, so more benefits to drivers as real road users, rather than engineers or decision makers, can be achieved.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.67-73
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• 2021

The purpose of this study is to obtain a safe area for a passing vessel between anchored vessels by developing a model to predict the collision risk, frequent collisions occur between the anchored vessel and the passing vessel through the anchorage. For this, this study selected the southern anchorage of Busan port, which is the largest harbor in Korea, as the target area and extracted and analyzed VTS (Vessel Traffic Service) data during the period in which anchored vessels were the most waited. The ratio of D/L for each bearing was obtained to determine the safe distance (D) passes based on the length (L) of the passing vessel between anchored vessels. Based on the average domain of the D/L ratio distribution, the percentage of anchored vessels within the scope of the pre-studied ship's domain was analyzed to obtain a domain reflecting the degree of risk of VTSOs. Further research will evaluate and analyze the collision risk of a passing vessel using Domain-watch, the minimum safe distance between anchored vessels, and the safe domain of a passing vessel through anchorage, to develop a model for VTS to manage anchorages more efficiently and safely.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1901-1910
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• 2022

Arriving in the new space age, securing technology for fusion application of KOMPSAT-3·3A and global satellite images is becoming more important. In general, multi-sensor satellite images have relative geometric errors due to various external factors at the time of acquisition, degrading the quality of the satellite image outputs. Therefore, we propose a fine-image registration methodology to minimize the relative geometric error between KOMPSAT-3·3A and global satellite images. After selecting the overlapping area between the KOMPSAT-3·3A and foreign satellite images, the spatial resolution between the two images is unified. Subsequently, tie-points are extracted using a hybrid matching method in which feature- and area-based matching methods are combined. Then, fine-image registration is performed through iterative registration based on pyramid images. To evaluate the performance and accuracy of the proposed method, we used KOMPSAT-3·3A, Sentinel-2A, and PlanetScope satellite images acquired over Daejeon city, South Korea. As a result, the average RMSE of the accuracy of the proposed method was derived as 1.2 and 3.59 pixels in Sentinel-2A and PlanetScope images, respectively. Consequently, it is considered that fine-image registration between multi-sensor satellite images can be effectively performed using the proposed method.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.29-35
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• 2022

UV rays have beneficial or harmful effects on the human body depending on the degree of exposure. An accurate UV information is required for proper exposure to UV rays per individual. The UV rays' information is provided by the Korea Meteorological Administration as one component of daily weather information in Korea. However, it does not provide an accurate UVI at the user's location based on the region's Ultraviolet index. Some operate measuring instrument to obtain an accurate UVI, but it would be costly and inconvenient. Studies which assumed the UVI through environmental factors such as solar radiation and amount of cloud have been introduced, but those studies also could not provide service to individual. Therefore, this paper proposes a deep learning model to calculate UVI using solar object information and sunlight characteristics to provide an accurate UVI at individual location. After selecting the factors, which were considered as highly correlated with UVI such as location and size and illuminance of sun and which were obtained through the analysis of sky images and solar characteristics data, a data set for DNN model was constructed. A DNN model that calculates the UVI was finally realized by entering the solar object information and sunlight characteristics extracted through Mask R-CNN. In consideration of the domestic UVI recommendation standards, it was possible to accurately calculate UVI within the range of MAE 0.26 compared to the standard equipment in the performance evaluation for days with UVI above and below 8.

• Korean Journal of Environment and Ecology
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• v.32 no.2
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• pp.231-243
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• 2018

This study was conducted to determine and compare the perceived number of users (PNU) and evaluative standards for each type of visitors to "Healing Field" of Jangseong Pyunbaek Healing Forest (JPHF). Data were collected from 158 samples selected by convenient sampling method from the users of "Healing Field" in JPHF during September 2017. Visitors to "Healing Field" consisted of 49.3% for recreation (VR), 15.3% for healing (VH), and 35.3% for both recreation and healing (VRH). Respondents' average PNU was 23.8 (median: 20.0), and the range of average PNU by visitor type was 21.1-27.8 (median: 20.0). Respondents' PNU showed low correlations with perceived crowding (PC) (r=0.23) and perceived interference of healing experience (PIHE) (r=0.20). There were no differences in the optimal number of users (ONU) of "Healing Field" among the types of visitors. The overall average ONU was 28.9(median: 25.0), and the range of average ONU by types of visitors was 25.4-31.4 (median: 25.0-27.5). The overall maximum acceptable number of users (MANU) was 38.1, and MANU for VR, VH, and VRH was 36.9, 32.0, and 38.0, respectively. Respondents' mean PC was 2.73 (SD: 3.26, 9-point scale) while mean PIHE was 3.26 (SD: 1.77, 9-point scale). The VRH group showed a higher PC and PIHE than the VR group. The group with greater PNU than ONU showed higher PC and PIHE than the other groups. Based on the results, this paper presented some managerial implications, discussed the limitations of the study, and suggested the further studies.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Korea Port Economic Association
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• v.35 no.4
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• pp.1-16
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• 2019

The purpose of this study is to Analyze the problems that container shipping companies exist through the commercialization of container shipping for Non-Arctic countries and the opportunity factors for the transport of the Arctic shipping to improve cooperation cross-border relation Arctic policy and the use of transport. In order to design a hierarchy analysis method study model, four high and 17 low factors were extracted by designing a hierarchy analysis method study model based on results by prior study and in-depth interview. The first of the higher factors is the internal strength of assessing the value of the Arctic, the will and capabilities of the shipping companies in creating new markets with the vision and goals of the shipping companies. Second, the internal constraints associated with the shipping companies advance to the NSR mean the negative factors for the entry into the NSR and the internal weaknesses that cause the shipping companies capacity limitations. Third, the economic benefits from the use of NSR are external factor for shipping companies in cooperation with the future economic value of the Arctic and with respect to Arctic sea and Arctic advance and development from Arctic coastal countries. Finally, external pre-emptive tasks means to respond to use NSR by external restrictions on transport to prepare the possibility of severe weather conditions, the customs policy change of coastal countries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7978-7989
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• 2015

Dyslipidemia is a major risk factor for cardio-cerebrovascular disease. Although the prevalence among Korean adults is very high, its management is known to be poor. The aim of this study was to access the prevalence, awareness, treatment, and control rates of dyslipidemia according to treatment guideline rather than diagnostic criteria. The risk factors for cardio-cerebrovascular disease were evaluated to apply the appropriate risk-based threshold of the lipid treatment targets according to risk category. Analysis was done using nationally representative data (n = 16,263) collected from adults aged 20 years and older participating the Korea National Health and Nutrition Examination Survey (KNHANS) 2010-2012. The age-standardized prevalence rate of dyslipidemia according to treatment criteria was 34.1%. Of these prevalent cases, however, only 19.2% were aware; 9.5% treated; and 8.7% controlled. The age-standardized control rate among treated persons was 47.5%. Men had a significantly higher prevalence than women (39.7% vs. 28.8%), but a significantly lower rate of awareness, treatment, and control (16.0% vs. 22.3%, 7.7% vs. 11.3%, and 6.1% vs. 11.2%, respectively). As the higher risk category, the prevalence rate was higher but the control rate was lower. The prevalence of patients with diabetes was 82.5% when applying the treatment criteria (LDL-cholesterol level of ${\geq}100mg/dL$ and triglyceride level of ${\geq}200mg/dL$). However, only 11.9 % of these were controlled, whose LDL-cholesterol and triglycerides were lower than the treatment goals. Our findings suggest that effective strategies are required to decrease the gap between the prevalence of dyslipidemia and the following treatment. It would be worthwhile to strengthen the follow-up management of patients with dyslipidemia in the National Health Screening Program, especially in the high risk group of cardio-cerebrovascular disease.