• Journal of Korea Multimedia Society
• /
• v.17 no.10
• /
• pp.1141-1149
• /
• 2014

Recently, various methods to effectively eliminate the noise are researched in image processing techniques. However, the conventional noise filtering techniques, which remove most of the noise, are less efficient for remained noise detection after filtering due to exploiting no face feature information. In this paper, we proposed a feature vector generation technique in the mouth region by distinguishing and revising the remained noise through gradient correction, when the outline is extracted after performing noise filtering.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2006.06a
• /
• pp.51-59
• /
• 2006

The surface wave data obtained in a tidal flat located in the sw coast of the Korean Peninsula were used to analyse the shear wave velocity structure of the area. First, the phase velocity dispersion curves were obtained by the tau-p stacking method and the group velocity dispersion curves by a wavelet transform method and the Multiple Filtering Technique by Dziewonski. The phase velocity dispersion curves exhibited bigger errors than the group velocity curves. The results showed that the wavelet transform method was more effective in separating the fundamental and the 1st higher mode group velocity curves than the Multiple Filtering Technique. Combined use of the fundamental and the 1st higher mode group velocity dispersion curves in the inversion for the shear wave velocity structure gave better spatial resolution compared when the fundamental mode group velocity was used alone. This study indicates that the group velocity dispersion curves can be used in the inversion of Rayleigh waves for the shear wave velocity structure, especially effectively with the higher mode group velocity curves together.

• Journal of Information Processing Systems
• /
• v.9 no.1
• /
• pp.157-172
• /
• 2013

There are many recommendation systems available to provide users with personalized services. Among them, the most frequently used in electronic commerce is 'collaborative filtering', which is a technique that provides a process of filtering customer information for the preparation of profiles and making recommendations of products that are expected to be preferred by other users, based on such information profiles. Collaborative filtering systems, however, have in their nature both technical issues such as sparsity, scalability, and transparency, as well as security issues in the collection of the information that becomes the basis for preparation of the profiles. In this paper, we suggest a movie recommendation system, based on the selection of optimal personal propensity variables and the utilization of a secure collaborating filtering system, in order to provide a solution to such sparsity and scalability issues. At the same time, we adopt 'push attack' principles to deal with the security vulnerability of collaborative filtering systems. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the selection of optimal personalization factors and the embodiment of a safe collaborative filtering system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2008.04a
• /
• pp.432-434
• /
• 2008

This paper deals with the DTW algorithm for the filtering contents, in order to improve the filtering performance rate. Contents filtering is the technology that confirm the identification of contents by using the feature of voice. Such technique is classified into general contents and adults contents. This proposed method extracts the information of voice contribute to improvement of filtering contents. In other words, We proposed filtering identification rate can be improved by using DTW algorithm. As a result, the proposed method is utilized improvement of filtering contents. Finally, we provide contents examples to test the accuracy of the proposed feature. Consequently, We know that the difference of characteristic between general contents and adults contents. In the future, We utilize this to improve filtering performance rate.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 2001.11a
• /
• pp.464-468
• /
• 2001

In this paper, we propose a robust watermarking technique that accepts time scaling, pitch shift, add noise and a lot of lossy compression such as MP3, AAC, WMA. The technique is developed based on digital filtering. Being designed according to critical band of HAS (human auditory system), the digital filters nearly affect audio quality. Furthermore, before implementing digital filtering, wavelet transform decomposes the audio signal into several signals that is composed of specific frequencies. Designed digital filters scan the decomposed signal. The designed digital filter, band-stop filter, distorts and eliminates specific frequencies of audio signals. Watermarking detection can be accomplished by FFT (Fast Fourier Transform). Firstly, segments of audio signal are transformed by FFT. Then, the obtained amplitude spectrum by FFT is summed repeatedly. Finally the watermark detector can find filters used to watermark encoding based on eliminating frequencies. The suggested technique can embed 4bits/s in a robust manner.

• Journal of Mechanical Science and Technology
• /
• v.18 no.2
• /
• pp.253-261
• /
• 2004

A simple and effective filtering method to control the member size of an optimized structure is proposed for topology optimization. In the present approach, the original objective sensitivities are replaced with their relative values evaluated within a filtering area. By adjusting the size of the filtering area, the member size of an optimized structure or the level of its topological complexity can be controlled even within a given finite element mesh. In contrast to the checkerboard-free filter, the present filter focuses on high-frequency components of the sensitivities. Since the present filtering method does not add a penalty term to the objective function nor require additional constraints, it is not only efficient but also simple to implement. Mean compliance minimization and eigenfrequency maximization problems are considered to verify the effectiveness of the present approach.

• Management Science and Financial Engineering
• /
• v.12 no.2
• /
• pp.71-85
• /
• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• KIEE International Transaction on Systems and Control
• /
• v.2D no.2
• /
• pp.115-119
• /
• 2002

In this paper we present a generalized directional morphological filtering algorithm for the removal of impulse noise, which is based on a combination of impulse noise detection and a weighted rank-order morphological filtering technique. For salt (or pepper) noise suppression, the generalized directional opening (or closing) filtering of the input signal is selectively used. The detection of impulse noise can be done by the geometrical difference of opening and closing filtering. Simulations show that this new filter has better detail feature preservation with effective noise reduction compared to other nonlinear filtering techniques.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.6B
• /
• pp.585-594
• /
• 2009

Efficient filtering is an important factor in RFID system performance. Because of huge volume of tag data in future ubiquitous environment, if RFID readers transmit tag data without filtering to upper-layer applications, which results in a significant system performance degradation. In this paper, we provide an efficient filtering technique which operates on RFID air protocol. RFID air protocol filtering between tags and a reader has some advantages over filtering in readers and middleware, because air protocol filtering reduces the volume of filtering work before readers and middleware start filtering. Exploiting the air protocol filtering advantage, we introduce a geometrical algorithm for generating air protocol filters and verify their performance through simulation with analytical time models. Results of dense RFID reader environment show that air protocol filtering algorithms reduce almost a half of the total filtering time when compared to the results of linear search.