• Journal of Information Technology Services
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• v.17 no.4
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• pp.85-100
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• 2018

In this study, we formulated rational criteria to efficiently provide traffic information services via a crafted approach. By utilizing this, we presented a detailed traffic information service providing method that can overcome the limitations of existing link unit information provision system. Three methodologies such as user survey, data mining, and KHCM (Korea Highway Capacity Manual) utilization method were applied to formulate a rational expression standard for traffic information service. Each method was designed to establish a quantitative criterion for various traffic conditions and to enable user-oriented traffic information service in consideration of the traffic principal/compatibility. Considering the results of each methodological analysis in a comprehensive manner, the basic expression standards for traffic information service was formulated. Then we presented improvements such as traffic condition step by road, speed range of traffic condition, expression term of traffic condition and so on. In order to complement the problems of the information provision system of the existing link unit based on the derived improvement criterion, we presented the detailed traffic information service provision method by using the traffic speed data of the second order. And we applied this to the two links of Daegu city. The method presented in this research can improve the quality of traffic information service. Not only it can be used for various fields such as optimal route search, traffic safety service and so on.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Fashion & Textile Research Journal
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• v.18 no.2
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• pp.225-234
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• 2016

An online suit-customizing system for the special accessibility needs of wheelchair users should be developed because the demand for business suits by wheelchair users involved in economic activities has increased. This study develops a user interface an online customizing system for men's suits specialized for wheelchair users. This study used a five-step approach: (1) search for online men's suits customizing system in web porter sites, (2) select three sites based on three terms, (3) heuristic testing with five web specialists, (4) development of a system user interface based on suggestions for improvement from the heuristic test, (5) usability testing of the user interface prototype by 10 disabled men in wheelchairs. The interface of Company S had high ratings on interactivity, accessibility, informativeness, and consistency in the heuristic test results; subsequently, a user interface was developed based on suggestions for improvement from the heuristic test. This online user interface for customizing men's suits provides better usability to wheelchair users than existing online interfaces aimed at the non-disabled and disabled; consequently, this study contributes to the commercialization of an online customizing system for men's suits specializing in serving wheelchair users.

• Geophysics and Geophysical Exploration
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• v.10 no.1
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• pp.1-18
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• 2007

Non-linear elastic wavefield inversion is a powerful method for estimating elastic parameters for physical constraints that determine subsurface rock and properties. Here, I introduce six elastic-wave velocity models by reconstructing elastic-wave velocity variations from real data and a 2D elastic-wave velocity model. Reflection seismic data information is often decoupled into short and long wavelength components. The local search method has difficulty in estimating the longer wavelength velocity if the starting model is far from the true model, and source frequencies are then changed from lower to higher bands (as in the 'frequency-cascade scheme') to estimate model elastic parameters. Elastic parameters are inverted at each inversion step ('simultaneous mode') with a starting model of linear P- and S-wave velocity trends with depth. Elastic parameters are also derived by inversion in three other modes - using a P- and S-wave velocity basis $('V_P\;V_S\;mode')$; P-impedance and Poisson's ratio basis $('I_P\;Poisson\;mode')$; and P- and S-impedance $('I_P\;I_S\;mode')$. Density values are updated at each elastic inversion step under three assumptions in each mode. By evaluating the accuracy of the inversion for each parameter set for elastic models, it can be concluded that there is no specific difference between the inversion results for the $V_P\;V_S$ mode and the $I_P$ Poisson mode. The same conclusion is expected for the $I_P\;I_S$ mode, too. This gives us a sound basis for full wavelength elastic wavefield inversion.

• Journal of The Korean Association of Information Education
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• v.20 no.4
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• pp.333-340
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• 2016

When elementary students write their own sentences, it is often educationally beneficial to compare them with other people's similar sentences. However, it is impractical for use in most classrooms, because it is burdensome for teachers to look up all of the sentences written by students. To cope with this problem, we propose a novel approach for automatic selection of similar sentences based on a three-step process: 1) extracting the subword units from the word-level sentences, 2) training the model with the encoder-decoder architecture, and 3) using the approximate k-nearest neighbor search algorithm to find the similar sentences. Experimental results show that the proposed approach achieves the accuracy of 75% for our test data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.923-930
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• 2002

This study aims at developing dialog system to improve the quality of life of the elderly with a dementia. The proposed system mainly consists of three modules including speech recognition, automatic search of the time-sorted dialog database, and agreeable responses with the recorded voices of caregivers. For the first step, the dialog that dementia patients often utter at a nursing home is first investigated. Next, the system is organized to recognize the utterances in order to meet their requests or demands. The system is then responded with recorded voices of professional caregivers. For evaluation of the system, the comparison study was carried out when the system was introduced or not, respectively. The occupational therapists then evaluated a male subjects reaction to the system by photographing his behaviors. The evaluation results showed that the dialog system was more responsive in catering to the needs of dementia patient than professional caregivers. Moreover, the proposed system led the patient to talk more than caregivers did in mutual communication.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.388-396
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• 2009

This paper proposes a new fire-smoke detection method by using extracted features from camera images and pattern recognition technique. First, moving regions are detected by analyzing the frame difference between two consecutive images and generate candidate smoke regions by applying smoke color model. A smoke region generally has a few characteristics such as similar color, simple texture and upward motion. From these characteristics, we extract brightness, wavelet high frequency and motion vector as features. Also probability density functions of three features are generated using training data. Probabilistic models of smoke region are then applied to observation nodes of our proposed Dynamic Bayesian Networks (DBN) for considering time continuity. The proposed algorithm was successfully applied to various fire-smoke tasks not only forest smokes but also real-world smokes and showed better detection performance than previous method.

• Natural Product Sciences
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• v.21 no.3
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• pp.162-169
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• 2015

Hamamelis japonica (Hamamelidaceae), widely known as Japanese witch hazel, is a deciduous flowering shrub that produces compact clumps of yellow or orange-red flowers with long and thin petals. As a part of our ongoing search for phenolic constituents from this plant, eleven phenolic constituents including six flavonol glycosides, a chalcone glycoside, two coumaroyl flavonol glycosides and two galloylated compounds were isolated from the flowers. Their structures were elucidated as methyl gallate (1), myricitrin (2), hyperoside (3), isoquercitrin (4), quercitrin (5), spiraeoside (6), kaempferol 4'-O-β-glucopyranoside (7), chalcononaringenin 2'-O-β-glucopyranoside (8), trans-tiliroside (9), cis-tiliroside (10), and pentagalloyl-O-β-D-glucose (11), respectively. These structures of the compounds were identified on the basis of spectroscopic studies including the on-line LCNMR-MS and conventional NMR techniques. Particularly, directly coupled LC-NMR-MS afforded sufficient structural information rapidly to identify three flavonol glycosides (2 - 4) with the same molecular weight in an extract of Hamamelis japonica flowers without laborious fractionation and purification step. Cytotoxic effects of all the isolated phenolic compounds were evaluated on HCT116 human colon cancer cells, and pentagalloyl-O-β-D-glucose (11) was found to be significantly potent in inhibiting cancer cell growth.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.155-164
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• 2020

This study measured the effect of PPL(Product Placement: PPL) in OTT(Over The Top) to search a new advertising revenue model according to the change of viewers' video content consumption patterns. On the first, by two research steps, the experiment was carried out using an eye-tracker and then a survey as the second step was administered asking subjects about their attitude about advertising messages, attitude about brand, and intention to purchase the brands used in the experiments. Specifically, the PPL materials used in the experiments were classified with three parts. This study has the meaning as approaching to the PPL research with new methodology by quantitatively access through the eye tracking of the subjects beyond the conventional qualitative measure that depends only on the memory of them. This research aims to find the possibility of indirect advertising as a new revenue model in the OTT environment.