• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.1
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• pp.8-14
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• 2007

X-ray digital tomosynthesis is widely used in the nondestructive testing and evaluation, especially for the printed circuit boards (PCBs). In this study, we propose a simple method to reduce the blur artefact, frequently claimed in the conventional digital tomosynthesis based on SAA (shift-and-add) algorithm, and thus restore the image sharpness. The proposed method is basically based on the SAA, but has a correction procedure by finding blur artefacts from the forward-and back-projection for the firstly obtained, manipulated backprojection data. The manipulation is the replacement of the original data at the POI (plane-of-interest) by zeros. This method has been compared with the conventional SAA algorithm using the experimental measurements and Monte Carlo simulation for the designed PCB phantom. The comparison showed a much enhancement of sharpness in the images obtained from the proposed method.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.277-287
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• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.89-96
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• 2015

Location-based social media data have high potential to be used in various area such as big data, location based services and so on. In this study, we applied a series of analysis methodology to figure out how the important keywords in location-based social media are spatially distributed by analyzing text information. For this purpose, we collected tweet data with geo-tag in Gangnam district and its environs in Seoul for a month of August 2013. From this tweet data, principle keywords are extracted. Among these, keywords of three categories such as food, entertainment and work and study are selected and classified by category. The spatial clustering is conducted to the tweet data which contains keywords in each category. Clusters of each category are compared with buildings and benchmark POIs in the same position. As a result of comparison, clusters of food category showed high consistency with commercial areas of large scale. Clusters of entertainment category corresponded with theaters and sports complex. Clusters of work and study showed high consistency with areas where private institutes and office buildings are concentrated.

• Ocean and Polar Research
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• v.31 no.1
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• pp.111-119
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• 2009

Biogenic surface films, which are often present in coastal areas, may enhance the signatures of hydrodynamic processes in microwave, optical, and infrared imagery. We analyzed ERS-1/2 Synthetic Aperture Radar (SAR) and Envisat Advanced Synthetic Aperture Radar (ASAR) images taken over the Japan/East Sea (JES). We focused on the appearance of the contrast SAR signatures, particularly the dark features of different scales caused by various oceanic and atmospheric phenomena. Spiral eddies of different scales were detected through surface film patterns both near the coast and in the open regions of the JES in warm and cold seasons. During field experiments carried out at the Pacific Oceanological Institute (POI) Marine Station 'Cape Shults' in Peter the Great Bay, the sea surface roughness characteristics were measured during the day and night using a developed polarization spectrophotometer and various digital cameras and systems of floats. The velocity of natural and artificial slicks was estimated using video and ADCP time series of tracers deployed on the sea surface. The slopes of gravity-capillary wave power spectra varied between .4 and .5. Surface currents in the natural and artificial slicks increased with the distance from the coast, varying between 4 and 40 cm/s. The contrast of biogenic and anthropogenic slicks detected on vertical and horizontal polarization images against the background varied over a wide range. SAR images and ancillary satellite and field data were processed and analyzed using specialized GIS for marine coastal areas.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.615-620
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• 2013

With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user's log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.2
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• pp.29-41
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• 2013

Purpose: Today, travelers are provided large amount information which includes Web sites and tourist magazines about introduction of tourist spot. Many approaches have been proposed to analyze the large amount of available information with the aim of discovering the most popular Points of Tourist Interest and routes. However, it is not easy for users to process the information in a short time. Therefore travelers prefer to receive pertinent information easier and have that information presented in a clear and concise manner. Research Design, Data and Methodology: Whether you are looking for banks by company, foreign exchange services, free wireless hotspots, touristic attractions, campsites, supermarkets, restaurants, cinemas, The aim of POI Tourism Services is to enable tourists to find spots that only the locals know, giving the tourists opportunity to the tourists to explore new areas of the place like never before. This paper proposes find the scope for a personalized service for tourist "Special Interest Tourism" recommendation for tourists who travel within India & for the benefit of Foreign Nationals who visit the country. Results: The major focus of the study is to understand the demand for such a service being integrated into the conventional tour package. The major findings made during the course of the show that the market for "Special Interest Tourism" based services stands at approximately 63%. Travel today is mainly for the people from the middle income group having a fixed budget while traveling and would like economic travel solutions that fit their budget. Conclusion: This accounts for a major part of the market for the service. Most tourist prefer to go on week end getaways or trips that last more than a week, this means that a specialized trip plan based on the travelers interests is feasible with these type of travelers. Maximum demand for "Special Interest Tourism" based services would be during the festive seasons.

• Phonetics and Speech Sciences
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• v.7 no.3
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• pp.139-149
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• 2015

This paper aims to analyze pronunciation variations of loanwords produced by Korean and improve the performance of pronunciation modeling of loanwords in Korean by using syllable-based segmentation and phonological knowledge. The loanword text corpus used for our experiment consists of 14.5k words extracted from the frequently used words in set-top box, music, and point-of-interest (POI) domains. At first, pronunciations of loanwords in Korean are obtained by manual transcriptions, which are used as target pronunciations. The target pronunciations are compared with the standard pronunciation using confusion matrices for analysis of pronunciation variation patterns of loanwords. Based on the confusion matrices, three salient pronunciation variations of loanwords are identified such as tensification of fricative [s] and derounding of rounded vowel [ɥi] and [$w{\varepsilon}$]. In addition, a syllable-based segmentation method considering phonological knowledge is proposed for loanword pronunciation modeling. Performance of the baseline and the proposed method is measured using phone error rate (PER)/word error rate (WER) and F-score at various context spans. Experimental results show that the proposed method outperforms the baseline. We also observe that performance degrades when training and test sets come from different domains, which implies that loanword pronunciations are influenced by data domains. It is noteworthy that pronunciation modeling for loanwords is enhanced by reflecting phonological knowledge. The loanword pronunciation modeling in Korean proposed in this paper can be used for automatic speech recognition of application interface such as navigation systems and set-top boxes and for computer-assisted pronunciation training for Korean learners of English.

• Steel and Composite Structures
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• v.33 no.3
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• pp.319-332
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• 2019

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.321-328
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• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.