• Journal of Information Technology and Architecture
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• v.9 no.1
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• pp.57-66
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• 2012

Recently, with a rapidly growing of the mobile content market, a variety of mobile-based applications are being launched. But mobile devices, compared to the average computer, take a lot of effort and time to get the final contents you want to use due to the restrictions such as screen size and input methods. To solve this inconvenience, a recommender system is required, which provides customized information that users prefer by filtering and forecasting the information.In this study, an tailored multi-information recommendation system utilizing a Personalized information recommendation system on smartphone is proposed. Filtering of information is to predict and recommend the information the individual would prefer to by using the user-based collaborative filtering. At this time, the degree of similarity used for the user-based collaborative filtering process is Euclidean distance method using the Pearson's correlation coefficient as weight value.As a real applying case to evaluate the performance of the recommender system, the scenarios showing the usefulness of recommendation service for the actual restaurant is shown. Through the comparison experiment the augmented reality based multi-recommendation services to the existing single recommendation service, the usefulness of the recommendation services in this study is verified.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.389-395
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• 2007

Collaborative filtering is a popular technique for information filtering to reduce information overload and widely used in application such as recommender system in the E-commerce domain. Collaborative filtering systems collect human ratings and provide Predictions based on the ratings of other people who share the same tastes. The quality of predictions depends on the number of items which are commonly rated by people. Therefore, it is difficult to apply pure collaborative filtering algorithm directly to dynamic collections where items are constantly added or removed. In this paper we suggest a method for managing dynamic collections. It creates taste space for items using a technique called Singular Vector Decomposition (SVD) and maintains clusters of core items on the space to estimate relevance of past and future items. To evaluate the proposed method, we divide database of user ratings into those of old and new items and analyze predicted ratings of the latter. And we experimentally show our method is efficiently applied to dynamic collections.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.42-47
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• 2017

In the distribution of digital image, the median filtering is used for a forgery. This paper proposed the algorithm of a image forensics detection for the classification of median filtering. For the solution of this grave problem, the feature vector is composed of 42-Dim. The detected quantity 32, 64 and 128 of forgery image edges, respectively, which are processed by the Hough transform, then it extracted from the start-end point coordinates of the Hough Lines. Also, the Hough Peaks of the Angle-Distance plane are extracted. Subsequently, both of the feature vectors are composed of the proposed scheme. The defined 42-Dim. feature vector is trained in SVM (Support Vector Machine) classifier for the MF classification of the forged images. The experimental results of the proposed MF detection algorithm is compared between the 10-Dim. MFR and the 686-Dim. SPAM. It confirmed that the MF forensic classification ratio of the evaluated performance is 99% above with the whole test image types: the unaltered, the average filtering ($3{\times}3$), the JPEG (QF=90 and 70)) compression, the Gaussian filtered ($3{\times}3$ and $5{\times}5$) images, respectively.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.218-225
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• 2007

Recommendation system is one way of implementing personalized service. The collaborative filtering is one of the major techniques that have been employed for recommendation systems. It has proven its effectiveness in the recommendation systems for such domain as motion picture or music. However, it has some limitations, i.e., sparsity and scalability. In this research, as one way of overcoming such limitations, we proposed the stepwise collaborative filtering method. To show the practicality of our proposed method, we designed and implemented a movie recommendation system which we shall call Step_CF, and its performance was evaluated using MovieLens data. The performance of Step_CF was better than that of Basic_CF that was implemented using the original collaborative filtering method.

• Proceedings of the KSRS Conference
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• v.2
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.11-17
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• 2018

In this paper, we propose how to improve the lack of diversity in collaborative filtering, using tag scores contained in items rather than ratings of items. Collaborative filtering has excellent performance among recommendation system, but it is evaluated as lacking diversity. In order to solve this problem, this paper proposes a method for supplementing diversity lacking in collaborative filtering by using tags. By using tags that can be used universally without using the characteristics of specific articles in a recommendation system, The proposed method can be used.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1159-1162
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• 1999

The ECG signal is very important information for diagnosis of patient and a cardiac disorder. It is hard to remove the noise because that is mixed with a lot of noise, and the error of the filtering will distort the ECG signal. The existing method for the filtering of the ECG signal has structure that has many steps for filtering, so that structure is complex and the processing speed is slow. For the improvement of that problem, we propose the method of filtering that has simple structure using the RBF neural networks and have good results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1031-1043
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• 1990

A sequential recursive Kalman filtering algorithm, using causal image model, which is designed to operate in real time in the scanning mode is developed to enhance quality of 64Kbps videocodec images via function of suppression of various noises and optimum restoration. In order to improve its performance, adapted an averaging of pixel values between processing lines and adaptive filtering strategy based on the local spatial variance. Effecttiveness of the Kalman filtering algorithm proposed has been proved in the processed test kalman filtering algorithm proposed has been proved in the processed test images and the NMSE, LOGMSE measured, therefore, it may proposes possibility of the usage in videocodec for pre- and post- processing.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.1-7
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• 2017

In this paper, we introduce three monitoring filtering techniques which reduce the overheads of dynamic data race detection. It is well known that detecting data races dynamically in multi-threaded programs is quite hard and troublesome task, because the dynamic detection techniques need to monitor all execution of a multi-threaded program and to analyse every conflicting memory and thread operations in the program. Thus, the main drawback of the dynamic analysis for detecting data races is the heavy additional time and space overheads for running the program. For the practicality, we also empirically compare the efficiency of three monitoring filtering techniques. The results using OpenMP benchmarks show that the filtering techniques are practical for dynamic data race detection, since they reduce the average runtime overhead to under 10% of that of the pure detection.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.602-609
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• 2007

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input when fed into content space let us improve and include new items in the recommendation. We present experimental results on movielens dataset that shows how our approach performs better than simple content-based system and naive hybrid system