• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3050-3063
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• 2023

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.170-183
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• 2007

This paper presents a delay-dependent approach to robust filtering for linear parameter-varying (LPV) systems with discrete and distributed time-invariant delays in the states and outputs. It is assumed that the state-space matrices affinely depend on parameters that are measurable in real-time. Some new parameter-dependent delay-dependent stability conditions are established in terms of linear matrix inequalities (LMIs) such that the filtering process remains asymptotically stable and satisfies a prescribed $H_{\infty}$ performance level. Using polynomially parameter-dependent quadratic (PPDQ) functions and some Lagrange multiplier matrices, we establish the parameter-independent delay-dependent conditions with high precision under which the desired robust $H_{\infty}$ filters exist and derive the explicit expression of these filters. A numerical example is provided to demonstrate the validity of the proposed design approach.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1796-1801
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• 2009

This paper is concerned with the problem of delay-dependent robust $H_{\infty}$ filtering for uncertain descriptor systems with time-varying delay. The considering uncertainty is convex compact set of polytoic type. The purpose is the design of a linear filter such that the resulting filtering error descriptor system is regular, impulse-free, and asymptotically stable with $H_{\infty}$ norm bound. By establishing a finite sum inequality based on quadratic terms, a new delay-dependent bounded real lemma (BRL) for delayed descriptor systems is derived. Based on the derived BRL, a robust $H_{\infty}$ filter is designed in terms of linear matrix inequaltity (LMI). Numerical examples are given to illustrate the effectiveness of the proposed method.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.1
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• pp.56-61
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• 2000

We present a fast inspection method of machine vision for in-line quality assurance of liquid crystal displays(LCD) and plasma display panels(PDP). The method incorporates an optical spatial filter in the Fourier plane of the imaging optics to block the normal periodic pattern, extracting only defects real time without relying on intensive software image process. Special emphasis is on designing a collimated white light source to provide high degree of spatial coherence for effective real time Fourier transform. At the same time, a low level of temporal coherence is attained to improve defect detection capabilities by avoiding undesirable coherent noises. Experimental results show that the proposed inspection method offers a detection accuracy of 15% tolerance, which is sufficient for industrial applications.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2301-2306
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• 2005

Even if there are lots of researches on localization using 2D range finder in static environment, very few researches have been reported for robust real-time localization of mobile robot in uncertain and dynamic environment. In this paper, we present a new localization method based on ICP(Iterative Closest Point) algorithm for navigation of mobile robot under dynamic or uncertain environment. The ICP method is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. We use the method to align global map with 2D scanned data from range finder. The proposed algorithm accelerates the processing time by uniformly sampling the line fitted data from world map of mobile robot. A data filtering method is also used for threshold of occluded data from the range finder sensor. The effectiveness of the proposed method has been demonstrated through computer simulation and experiment in an office environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.158-166
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• 2005

Even though post-processing methods based on projections onto convex sets (POCS) have shown good performance for blocking artifact reduction, it is infeasible to implement POCS for real-time practical applications. This paper proposes DCT domain post-processing method based on POCS. The proposed method shows very similar performance compared to the conventional POCS method, while it reduces tremendously the computational complexity. DCT domain POCS performs the lowpass filtering in the DCT domain, and it removes the inverse DCT and forward DCT modules. Through the investigation of lowpass filtering in the iterative POCS method, we define kth order lowpass filtering which is equivalent to the lowpass filtering in the kth iteration, and the corresponding kth order DCT domain POCS. Simulation results show that the kth order DCT domain POCS without iteration gives very similar performance compared to the conventional POCS with k iterations, while it requires much less computations. Hence the proposed DCT domain POCS method can be used efficiently in the practical post-processing applications with real-time constraints.

• Convergence Security Journal
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• v.21 no.1
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• pp.115-120
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• 2021

An O2O-based real estate brokerage web sites or apps are increasing explosively. As a result, the environment has been changed from the existing offline-based real estate brokerage environment to the online-based environment, and consumers are getting very good feelings in terms of time, cost, and convenience. However, behind the convenience of online-based real estate brokerage services, users often suffer time and money damage due to false information or malicious false information. Therefore, in this study, in order to reduce the damage to consumers that may occur in the O2O-based real estate brokerage service, we designed a false property information filtering system that can determine the authenticity of registered property information using artificial intelligence technology. Through the proposed research method, it was shown that not only the authenticity of the property information registered in the online real estate service can be determined, but also the temporal and financial damage of consumers can be reduced.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5S
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• pp.1599-1608
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• 2000

Automated collaborative filtering is on the verge of becoming a popular technique to reduce overloaded information as well as to solve the problems that content-based information filtering systems cannot handle. In this paper, we describe three different algorithms that perform collaborative filtering: GroupLens that is th traditional technique; Best N, the modified one; and an algorithm that uses clustering. Based on the exeprimental results using real data, the algorithm using clustering is compared with the existing representative collaborative filtering agent algorithms such as GroupLens and Best N. The experimental results indicate that the algorithms using clustering is similar to Best N and better than GroupLens for prediction accuracy. The results also demonstrate that the algorithm using clustering produces the best performance according to the standard deviation of error rate. This means that the algorithm using clustering gives the most stable and the best uniform recommendation. In addition, the algorithm using clustering reduces the time of recommendation.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.3
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• pp.128-134
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• 2014

A real-time, continuous-scale image interpolation method is proposed based on a bilinear interpolation with directionally adaptive low-pass filtering. The proposed algorithm was optimized for hardware implementation. The ordinary bi-linear interpolation method has blocking artifacts. The proposed algorithm solves this problem using directionally adaptive low-pass filtering. The algorithm can also solve the severe blurring problem by selectively choosing low-pass filter coefficients. Therefore, the proposed interpolation algorithm can realize a high-quality image scaler for a range of imaging systems, such as digital cameras, CCTV and digital flat panel displays.