• The Transactions of the Korea Information Processing Society
• /
• v.7 no.5S
• /
• pp.1666-1675
• /
• 2000

This paper proposes a RTWM (Real-Time Web Middleware) framework for real-time EC(Electronic Commerce) systems. RTWM system is extended the existing COS( CORBA Object Service) model added to the event monitoring, real-time scheduler, real-time event filtering for supporting real-time concept of EC systems. Especially, this paper is concentrated on providing suitable event filtering function for EC system in order to meed various user time requirements under distributed system environment. It stores time constraint requirements an interesting event information input from users into QoS repository, then processes the data through appropriate RTFA(Real-Time Filtering Agent) module when real-time events occur. From this method, users can get the filtered event result reflected their requirements about real-time filtering. It means this system provides thigh QoS to users. In addition, it results in decreasing network traffic as unnecessary event information is filtered from network.

• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.336-343
• /
• 2017

We propose a real time personalized recommendation algorithm on the mobile device. We use a unified collaborative filtering with reduced data. We use Fuzzy C-means clustering to obtain the reduced data and Konohen SOM is applied to get initial values of the cluster centers. The proposed algorithm overcomes data sparsity since it extends data to the similar users and similar items. Also, it enables real time service on the mobile device since it reduces computing time by data clustering. Applying the suggested algorithm to the MovieLens data, we show that the suggested algorithm has reasonable performance in comparison with collaborative filtering. We developed Android-based smart-phone application, which recommends restaurants with coupons and restaurant information.

• Journal of Korea Multimedia Society
• /
• v.22 no.6
• /
• pp.695-705
• /
• 2019

The level of psychological damage caused by verbal abuse among cyberbully victims is very serious. It is going to introduce a system that determines the level of sanctions against chatting in real time using the automatic prohibited words filtering based on artificial neural network. In this paper, we propose a keyword filtering method that detects the modified prohibited words and determines whether the corresponding chat should be sanctioned in real time, and a real-time chatting screening system using it. The accuracy of filtering through machine learning was improved by processing data in advance through coding techniques that express consonants and vowels of similar pronunciation at close distances. After comparing and analyzing Mahalanobis-based clustering algorithms and artificial neural network-based algorithms, algorithms that utilize artificial neural networks showed high performance. If it is applied to Internet chatting, comments or online games, it is expected that it will be able to filter more effectively than the existing filtering method and that this will ease communication inconvenience due to existing indiscriminate filtering methods.

• ETRI Journal
• /
• v.28 no.4
• /
• pp.509-512
• /
• 2006

In a real environment, sound recordings are commonly distorted by channel and background noise, and the performance of audio identification is mainly degraded by them. Recently, Philips introduced a robust and efficient audio fingerprinting scheme applying a differential (high-pass filtering) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the robustness of the audio fingerprinting scheme is still important in a real environment. In this letter, we introduce alternatives to the frequency-temporal filtering combination for an extension method of Philips' audio fingerprinting scheme to achieve robustness to channel and background noise under the conditions of a real situation. Our experimental results show that the proposed filtering combination improves noise robustness in audio identification.

• International Journal of Quality Innovation
• /
• v.2 no.1
• /
• pp.30-49
• /
• 2001

This paper considers a recent environment in the manufacturing process in which data in large amounts can be obtained on-line in real-time. Under this environment an on-line real-time Statistical Process Control (SPC) scheme equipped with detection of a process change, change-point estimation, and recognition of the change pattern is proposed. The proposed SPC scheme is composed of a Cusum chart, filtering methods and Akaike Information Criterion (AIC). We examine the performance of this scheme by Monte Carlo simulation and show its usefulness.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.4
• /
• pp.488-493
• /
• 2007

In general, recommendation systems quantify real-time context information obtained in the stage of collaborative filtering and use quantified context information in order to recommend services. But the recommendation systems can have problems of recommending inaccurate information because of lack of context information or classifying users into inaccurate groups because of simple classification works in the stage of quantification. In this paper, we solved the problems of lack of context information obtained in real-time by combining users' profile information used in the contents-based filtering and context information obtained in real-time. In addition, we tried collaborative filtering at the quantification stage by improving absolute classification methods to relative ones. As the result of experiments, this method improved prediction preference by 5.8% than real-time recommendation systems using context information in pure P2P environment.

• MALSORI
• /
• no.51
• /
• pp.85-98
• /
• 2004

It is almost impossible to generate the sound field effect in real time with the time-domain linear convolution because of its large multiplication operation requirement. To solve this, three methods are introduced to reduce the number of multiplication operations in this paper. Firstly, the time-domain linear convolution is replaced with the frequency-domain circular convolution. In other words, the linear convolution result can be derived from that of the circular convolution. This technique reduces the number of multiplication operations remarkably, Secondly, a subframe concept is introduced, i.e., one original frame is divided into several subframes. Then the FFT is executed for each subframe and, as a result, the number of multiplication operations can be reduced. Finally, the QFT is used in stead of the FFT. By combining all the above three methods into our final the SFE generation algorithm, the number of computations are reduced sufficiently and the real-time SFE generation becomes possible with a general PC.

• Journal of Korean Society of Transportation
• /
• v.14 no.3
• /
• pp.7-26
• /
• 1996

The estimation of real-time Origin-Destination(O-D) parameters, which gives travel demand between combinations of origin and destination points on a urban freeway network, from on-line surveillance traffic data is essential in developing an efficient ATMS strategy. On this need a real-time O-D parameter estimation model is formulated as a parameter adaptive filtering model based on the extended Kalman Filter. A Monte Carlo test have shown that the estimation of time-varying O-D parameter is possible using only traffic counts. Tests with field data produced the interesting finding that off-ramp volume predictions generated using a constant freeway O-D matrix was replaced by real-time estimates generated using the parameter adaptive filter.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.4
• /
• pp.456-465
• /
• 2006

The problem of recursive filtering far linear discrete-time systems with uncertainties is considered. A new suboptimal filtering algorithm is herein proposed. It is based on the fusion formula, which represents an optimal mean-square linear combination of local Kalman estimates with weights depending on cross-covariances between local filtering errors. In contrast to the optimal weights, the suboptimal weights do not depend on current measurements, and thus the proposed algorithm can easily be implemented in real-time. High accuracy and efficiency of the suboptimal filtering algorithm are demonstrated on the following examples: damper harmonic oscillator motion and vehicle motion constrained to a plane.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.4
• /
• pp.515-519
• /
• 2003

This paper proposes a new image sequence stabilization (ISS) scheme based on filtering of absolute frame positions. The proposed ISS scheme removes undesired motion effects in real-time, while preserving desired gross camera displacements. The well-known finite impulse response (FIR) filter is adopted for filtering. The proposed ISS scheme provides a filtered position and velocity with fine inherent properties. It is demonstrated that the filtered position is not affected by the constant velocity. It is also shown that the filtered velocity is separated from the position. Via numerical simulations, the performance of the proposed scheme is shown to be superior to the existing Kalman filtering scheme.