• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.9-16
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• 2024

In this study, we delve into the effects of personalization algorithms on the creation of "filter bubbles," which can isolate individuals intellectually by reinforcing their pre-existing biases, particularly through personalized Google searches. By setting up accounts with distinct ideological learnings-progressive and conservative-and employing deep neural networks to simulate user interactions, we quantitatively confirmed the existence of filter bubbles. Our investigation extends to the deployment of an LSTM model designed to assess political orientation in text, enabling us to bias accounts deliberately and monitor their increasing ideological inclinations. We observed politically biased search results appearing over time in searches through biased accounts. Additionally, the political bias of the accounts continued to increase. These results provide numerical evidence for the existence of filter bubbles and demonstrate that these bubbles exert a greater influence on search results over time. Moreover, we explored potential solutions to mitigate the influence of filter bubbles, proposing methods to promote a more diverse and inclusive information ecosystem. Our findings underscore the significance of filter bubbles in shaping users' access to information and highlight the urgency of addressing this issue to prevent further political polarization and media habit entrenchment. Through this research, we contribute to a broader understanding of the challenges posed by personalized digital environments and offer insights into strategies that can help alleviate the risks of intellectual isolation caused by filter bubbles.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.595-602
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• 2000

This paper is aimed at developing an location tracking system of mobile station based on currently available mobile communication network or mobile Phone and PCS(Personal Communication System). When the location tracking of mobile stations is in services, Emergency-119, all of crime investigation, effective urban traffic management and the safety protection of Alzheimer＇s patients can be available. In order to track the location of the mobile and base station, assumption in this paper is to use the statistic characteristics of LOS when modeling the standard noise in case that radio path is LNOS environment. The standard variation of the standard noise is $\pm150$. First, location is estimated by the positioning algorithms of TOA and TDOA and compared each other. Second, after canceling the standard noise by Kalman filter, location is estimated by the above two positioning algorithms. Finally, the location by the Kalman filter and two positioning algorithms is estimated by smoothing method. As a result, 2 dimensional average location error is imvoved by 51.2m in TOA and 34.8m in TDOA when Kalman filer and two positioning algorithms are used, compared with the two positioning algorithm used. And there is 3 more meter improvement after smoothing than Kalman filer and two positioning algorithms used.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.32-43
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• 1994

This paper presents new QRD-based recursive least squares algorithms for bilinear lattice filter. Bilinear recursive least square lattice algorithms are derived by using the QR decomposition for minimization covariance matrix of predication error by applying Givens rotation to the bilinear recursive least squares lattics algorithms. The proposed algorithms are applied to the bilinear system identification to evaluate the performance of algoithms. Computer simulations show that the convergence properties of the proposed algorithms are superior to that of the algorithms proposed by Baik when signal includes the measurement noise.

• Journal of Astronomy and Space Sciences
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• v.35 no.3
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• pp.163-173
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• 2018

This paper presents relative navigation using intermittent laser-based measurement data for spacecraft flying formation that consist of two spacecrafts; namely, chief and deputy spacecrafts. The measurement data consists of the relative distance measured by a femtosecond laser, and the relative angles between the two spacecrafts. The filtering algorithms used for the relative navigation are the extended Kalman filter (EKF), unscented Kalman filter (UKF), and least squares recursive filter (LSRF). Numerical simulations reveal that the relative navigation performances of the EKF- and UKF-based relative navigation algorithms decrease in accuracy as the measurement outage period increases. However, the relative navigation performance of the UKF-based algorithm is 95 % more accurate than that of the EKF-based algorithm when the measurement outage period is 80 sec. Although the relative navigation performance of the LSRF-based relative navigation algorithm is 94 % and 370 % less accurate than those of the EKF- and UKF-based navigation algorithms, respectively, when the measurement outage period is 5 sec; the navigation error varies within a range of 4 %, even though the measurement outage period is increased. The results of this study can be applied to the design of a relative navigation strategy using the developed algorithms with laser-based measurements for spacecraft formation flying.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.219-228
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• 2007

In this paper, we present a novel blind adaptive multiuser detector structure and three robust recursive filters to improve the performance in CDMA environments: Sigma point kalman filter (SPKF), particle filter (PF), and Gaussian mixture sigma point particle filter (GMSPPF). Our proposed robust recursive filters have superior performance over a conventional extended Kalman filter (EKF). The proposed multiuser detector algorithms initially use Kalman prediction form to estimated channel parameters, and unknown data symbol be predicted. Second, based on this predicted data symbol, the robust recursive filters (e.g., GMSPPF) is a refined estimation of joint multipaths and time delays. With these estimated multipaths and time delays, data symbol detection is carried out (Kalman correction form). Computer simulations show that the proposed algorithms outperform the conventional blind multiuser detector with the EKF. Also we can see it provides a more viable means for tracking time-varying amplitudes and time delays in CDMA communication systems, compared to that of the EKF for near-far ratio of 20 dB. For this reason, it is believed that the proposed channel estimators can replace well-known filter such as the EKF.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.8
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• pp.375-377
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• 2006

An adaptive IIR filter in ANC(Active Noise Control) systems is more effective than an adaptive FIR filter when acoustic feedback exists, in which cause an order of an adaptive FIR filter must be very large if some of poles of the ideal control filter are near the unit circle. But the IIR filters may have stability problems especially when the adaptive algorithm for adaptive filters is not yet converged. In this paper, a stabilized multi-channel recursive LMS (MCRLMS) algorithm for an adaptive multi-channel IIR filter is presented. RLMS algorithms usually diverge before the algorithm is not yet converged. So, in the beginning of the ANC system, the stability of the RLMS algorithms could be improved by pulling the poles of the IIR filter to the center of the unit circle, and returning the poles to their original positions after the filter converges. Computer simulations and experiments for dipole ducts using a TMS320C32 digital signal processor have performed to show the effectiveness of a proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.917-924
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• 2003

Recurrent neural networks have been successfully applied to communications channel equalization. Major disadvantages of gradient-based learning algorithms commonly employed to train recurrent neural networks are slow convergence rates and long training sequences required for satisfactory performance. In a high-speed communications system, fast convergence speed and short training symbols are essential. We propose decision feedback equalizers using a recurrent neural network trained with Kalman filtering algorithms. The main features of the proposed recurrent neural equalizers, utilizing extended Kalman filter (EKF) and unscented Kalman filter (UKF), are fast convergence rates and good performance using relatively short training symbols. Experimental results for two time-varying channels are presented to evaluate the performance of the proposed approaches over a conventional recurrent neural equalizer.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.748-764
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• 2019

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.1
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• pp.86-91
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• 2012

To provide high voice quality of real-time voice communication based on telematics exposed to various noise environments, the noise reduction algorithm with low computing load is required to effectively remove the noise. In this paper, we propose a noise reduction algorithm based on Mel-Filter and illustrate the proposed algorithm comparing with conventional noise reduction algorithms. As a experimental result that evaluates the performance of the noise reduction algorithms under the car and babble noise environments, the proposed noise reduction algorithm has the lower computing load with the similar PESQ score compared to the conventional noise reduction algorithms. It proves that the proposed noise reduction algorithm can efficiently remove the noise in mobile telematics.