• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.93-99
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• 2014

This paper deals with the FC-MMA (Fast Convergence-Multi Modulus Algorithm) which is improving the convergence characteristics of the MMA (Multiple Modulus Algorithm) adaptive equalization algorithm that is used for the minimization of the intersymbol interference which occurs in the time dispersive communication channel. In the time varying charateristics and the abnormal situation like as outage of the communication channel, the adaptive equalizer needs to adapting the new environment more rapidly. For this problem, the residual isi and the maximum distortion performance index which are meaning the convergence characteristics are widely adapted in the adaptive equalizer. The 16-QAM signal is transmitted and it was confirmed that the proposed algorithm, the FC-MMA has the fast convergence performance such as in the 1.75 times fast in residual isi and 2.5 times fast in the maximum distortion in order to reaching the steady state compare to the MMA algorithm in the same channel environment by the computer simulation.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.301-316
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• 2020

Algorithm journalism refers to the practices of automated news generation using algorithms that generate human sounding narratives. Algorithm journalism is known to have strengths in automating repetitive tasks through rapid and accurate analysis of data, and has been actively used in news domains such as sports and finance. In this paper, we propose an interactive card news system that generates personalized local election articles in 2018. The system consists of modules that collects and analyzes election data, generates texts and images, and allows users to specify their interests in the local elections. When a user selects interested regions, election types, candidate names, and political parties, the system generates card news according to their interest. In the study, we examined how personalized card news are evaluated in comparison with text and card news articles by human journalists, and derived implications on the potential use of algorithm in reporting political events.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.189-193
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• 2003

A new algorithm is presented for adaptive notch filtering of narrow band or sine signals for embedded among broad band noise. The notch filter is implemented as a constrained infinite impulse response filter with a minimal number of parameters, Based on the recursive prediction error (RPE) method, the algorithm has the advantages of the fast convergence, accurate results and initial estimate of filter coefficient and its covariance is revealed. A convergence criterion is also developed. By using the information of the noise-to-signal power, the algorithm can self-adjust its initial filter coefficient estimate and its covariance to ensure convergence.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.165-166
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• 2007

This paper proposes an algorithm to improve the anti-theft function of the CAR_AV using the CAN(Controller Area Network) communication between CAR AV and cluster. Compare to the typical anti-theft algorithms like security code entering, the proposed one is more secure in most cases. The proposed algorithm is suitable for the recent automobile that employ the CAN network as the communication channel between their devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1296-1309
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• 2023

In this paper, based on the soft cancellation (SCAN) bit-flipping (SCAN-BF) algorithm, a generalized SCAN bit-flipping (GSCAN-BF-Ω) decoding algorithm is carried out, where Ω represents the number of bits flipped or corrected at the same time. GSCAN-BF-Ω algorithm corrects the prior information of the code bits and flips the prior information of the unreliable information bits simultaneously to improve the block error rate (BLER) performance. Then, a joint threshold scheme for the GSCAN-BF-2 decoding algorithm is proposed to reduce the average decoding complexity by considering both the bit channel quality and the reliability of the coded bits. Simulation results show that the GSCAN-BF-Ω decoding algorithm reduces the average decoding latency while getting performance gains compared to the common multiple SCAN bit-flipping decoding algorithm. And the GSCAN-BF-2 decoding algorithm with the joint threshold reduces the average decoding latency further by approximately 50% with only a slight performance loss compared to the GSCAN-BF-2 decoding algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.326-354
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• 2014

In this paper, a new green clustering algorithm is proposed to be as a first approach in the framework of an energy efficient strategy for centralized enterprise high-density WLANs. Traditionally, in order to maintain the network coverage, all the APs within the WLAN have to be powered-on. Nevertheless, the new algorithm can power-off a large proportion of APs while the coverage is maintained as its always-on counterpart. The two main components of the new approach are the faster procedure based on K-means and the more accurate procedure based on Evolutionary Algorithm (EA), respectively. The two procedures are processes in parallel for different designed requirements and there is information interaction in between. In order to implement the new algorithm, EA is applied to handle the optimization of multiple objectives. Moreover, we adapt the method for selection and recombination, and then introduce a new operator for mutation. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% to 90% of energy consumption can be saved while it is able to maintain the original network coverage during periods when few users are online or the traffic load is low.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.564-582
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• 2015

This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step for the sake of driving the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. Numerical results show that the proposed QBFO has more powerful properties in terms of convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. Furthermore, we examine the employment of our proposed QBFO for cognitive radio spectrum allocation. The results indicate that the proposed QBFO based spectrum allocation scheme achieves high efficiency of spectrum usage and improves the transmission performance of secondary users, as compared to color sensitive graph coloring algorithm and quantum genetic algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10B
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• pp.1210-1215
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• 2011

In this paper, a supervised adaptive equalization algorithm based on probability density function (PDF) matching method is introduced and its decision-feedback version is proposed for underwater communication channels with strong impulsive noise and severe multipath characteristics. The conventional least mean square (LMS) algorithm based on mean squared error (MSE) criterion has shown to be incapable of coping with impulsive noise and multipath effects commonly shown in underwater communications. The linear PDF matching algorithm, which shows immunity to impulsive noise, however, has revealed to yield unsatisfying performance under severe multipath environments with impulsive noise. On the other hand, the proposed nonlinear PDF matching algorithm with decision feedback proves in the simulation to possess superior robustness against impulsive noise and multipath characteristics of underwater communication channels.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.156-157
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• 2011

In this work, we study the checkpoint/restart problem for message-passing parallel applications running on cloud computing environment. This is a new direction which arises from the trend of enabling the applications to run on the cloud computing environment. The main objective is to propose an efficient checkpoint algorithm for message-passing parallel applications considering communications with external systems. We further implement the novel algorithm by modifying gSOAP and OpenMPI (the open source libraries) which support service calls and checkpoint message-passing parallel programs, especially. The simulation showed that additional costs to the executing and checkpointing application of the algorithm are negligible. Ultimately, the algorithm supports efficiently the checkpoint/restart service for message-passing parallel applications, that send requests to external services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4934-4949
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• 2015

The unstable characteristic values of communication signals along with the varying SNR (Signal Noise Ratio) environment make it difficult to identify the modulations of signals. Most of relevant literature revolves around signal recognition under stable SNR, and not applicable for signal recognition at varying SNR. To solve the problem, this research developed a novel communication signal recognition algorithm based on Holder coefficient and cloud theory. In this algorithm, the two-dimensional (2D) Holder coefficient characteristics of communication signals were firstly calculated, and then according to the distribution characteristics of Holder coefficient under varying SNR environment, the digital characteristics of cloud model such as expectation, entropy, and hyper entropy are calculated to constitute the three-dimensional (3D) digital cloud characteristics of Holder coefficient value, which aims to improve the recognition rate of the communication signals. Compared with traditional algorithms, the developed algorithm can describe the signals' features more accurately under varying SNR environment. The results from the numerical simulation show that the developed 3D feature extraction algorithm based on Holder coefficient cloud features performs better anti-noise ability, and the classifier based on interval gray relation theory can achieve a recognition rate up to 84.0%, even when the SNR varies from -17dB to -12dB.