• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.367-376
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• 2001

For the electronic map service by wireless, the existing map database cannot be used directly. This is because, the data volume of a map is too big to transfer by wireless and although the map is transferred successfully, the devices to display the map usually don’t have enough resources as the ones for desktop computers. It is also not acceptable to construct map database for the exclusive use of wireless service because of the vast cost. We propose new technique to generate a map for wireless service dynamically, from the existing map database. This technique includes the generalization method to reduce the map data volume and filtering method to guarantee that the data volume don’t exceed the limit of bandwidth. The generalization is performed in 3 steps :ㅁ step of merging the layers, a step of reducing the size of spatial objects, and a step of processing user interface. The filtering is performed by 2 module, counter and selector module. The counter module checks whether the data blume of generated map by generalization, exceeds the bandwidth limit. The selector module eliminates the excess objects and selects the rest, on the basis of distance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1612-1616
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• 2006

For map services in the mobile environment, it should be considered that resource restriction or the mobile device. on, if a map database dedicated to mobile services may not be developed, the spatial data extracted from general map databases should be simplified before transmitting. % is paper suggests the filtering methods to manipulate the spatial data, which are changed to be able to displayed on the mobile devices. The suggested methods are evaluated by experiments. This method is based on the map generalization operator 'selection' and is refined to adapt on mobile phone environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.331-333
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• 2015

Data is increasing explosively with the spread of networks and mobile devices and there are problems in effectively processing the rapidly increasing data using existing recommendation techniques. Therefore, researches are being conducted on how to solve the scalability problem of the collaborative filtering technique. In this paper applies MapReduce, which is a distributed parallel process framework, to the collaborative filtering technique to reduce the scalability problem and heighten accuracy. The proposed technique applies MapReduce and the index technique to a user-based collaborative filtering technique and as a method which improves neighbor numbers which are used in similarity calculations and neighbor suitability, scalability and accuracy improvement effects can be expected.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.24-29
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• 1999

In this paper, a simple contour-preserving filtering algorithm is proposed. The goal of the contour-preserving filtering method is to remove noise ad granularity as the preprocessing for the image segmentation procedure. Our method finds edge map and separates the image into the edge region and the non-edge region using this edge map. For the non-edge region, typical smoothing filters could be used to remove the noise and the small areas during the segmentation procedure. The result of simulation shows that our method is slightly better than the typical methods such as the median filtering and gradient inverse weighted filtering in the point of view of analysis of variance (ANOVA).

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.1
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• pp.102-110
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• 2000

This paper presents an algorithm of local map building for autonomous robot navigation using LASER range finder information. We develop a model of sensor output for a LASER range finder, and obtain an output data of the LASER range finder for a given environment. From the output data, a local map is obtained through the following procedures: (1) filtering of output data to remove noisy and unnecessary data, (2) comparison of filtered data with the original data to restore useful data, (3) thickening of the map obtained from the restored data, and (4) skeletonizing of the thickened map to get a final local map. Through some simulation studies, a map is obtained from the LASER range finder information for a given indoor environment, and is compared with the environment.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.29-36
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• 2012

In order to create a wind resource map, we need wind data, contour map and roughness map. Moving Filter technique was applied to Udo of Jeju Island to improve the accuracy and efficiency of creating roughness map based on the Land Cover Map of the Ministry of Environment. The Land Cover Map was simplified using moving filtering, and the roughness map was created with this Land Cover Map. The wind resource map was created using this roughness map. Finally, we verified the validity and application of moving filter technique for wind resource map. As a result, the wind map which was created using the roughness map with moving filtering showed bias values which were all negative. It means the wind map is underestimated to values of wind energy and RMSE values were also from 0.0237m/s to 0.0253m/s at 50m height. In other words, estimation of wind resource using image filtering provides reliable results at 80m height typically when the wind turbine is installed. Finally, we found that image filtering technique is very useful tool to make wind resource map.

• Korean Management Science Review
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• v.29 no.2
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• pp.79-90
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• 2012

Focusing on the rapid market penetration of smart phones, the importance of LBS (Location Based Service) is drastically increased. However, traditional GPS method has critical weakness caused by limited availability, such as indoor environment. WPS is newly attractive method as a widely applicable positioning method. In WPS, RSSI (Received Signal Strength Indication) data of all Wi-Fi APs (Access Point) are measured and stored into a huge database. The stored RSSI data in database make single radio fingerprint map. By the radio fingerprint map, we can estimate the actual position of target point. The essential factor of radio fingerprint database is data integrity of RSSI. Because of millions of APs in urban area, RSSI measurement data are seriously contaminated. Therefore, we present the unified filtering method for RSSI measurement data. As the results of filtering, we can show the effectiveness of suggested method in practical positioning system of mobile operator.

• Journal of Biomedical Engineering Research
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• v.32 no.3
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• pp.245-250
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• 2011

Parallel imaging technique can provide several advantages for a multitude of MRI applications. Especially, in SENSE technique, sensitivity maps were always required in order to determine the reconstruction matrix, therefore, a number of difference approaches using sensitivity information from coils have been demonstrated to improve of image quality. Moreover, many filtering methods were proposed such as adaptive matched filter and nonlinear diffusion technique to optimize the suppression of background noise and to improve of image quality. In this study, we performed SENSE reconstruction using computer simulations to confirm the most suitable method for the feasibility of filtering effect and according to changing order of polynomial fit that were applied on variation of spatial resolution of sensitivity map. The image was obtained at 0.32T(Magfinder II, Genpia, Korea) MRI system using spin-echo pulse sequence(TR/TE = 500/20 ms, FOV = 300 mm, matrix = $128{\times}128$, thickness = 8 mm). For the simulation, obtained image was multiplied with four linear-array coil sensitivities which were formed of 2D-gaussian distribution and the image was complex white gaussian noise was added. Image processing was separated to apply two methods which were polynomial fitting and filtering according to spatial resolution of sensitivity map and each coil image was subsampled corresponding to reduction factor(r-factor) of 2 and 4. The results were compared to mean value of geomety factor(g-factor) and artifact power(AP) according to r-factor 2 and 4. Our results were represented while changing of spatial resolution of sensitivity map and r-factor, polynomial fit methods were represented the better results compared with general filtering methods. Although our result had limitation of computer simulation study instead of applying to experiment and coil geometric array such as linear, our method may be useful for determination of optimal sensitivity map in a linear coil array.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1213-1226
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• 2008

A recommender system supports people in making recommendations finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task. We proposed new recommender system which combined SOM(Self-Organizing Map) neural networks with the Collaborative filtering which most recommender systems hat applied First, we segmented user groups according to demographic characteristics and then we trained the SOM with people's preferences as ito inputs. Finally we applied the classic collaborative filtering to the clustering with similarity in which an recommendation seeker belonged to, and therefore we didn't have to apply the collaborative filtering to the whose data set. Experiments were run for EachMovies data set. The results indicated that the predictive accuracy was increased in terms of MAE(Mean-Absolute-Error).