• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.625-632
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• 2004

Since the H.264 encoding adopts both forward prediction and hi-direction prediction modes and exploits Variable Length Coding(VLC), the amount of data generated from video encoder varies as Flaying time goes by. The fixed bit rate encoding system which has limited transmission channel capacity uses a buffer to control output bitstream It's necessary to control the bitstream to maintain within manageable range so as to protect buffer from overflow or underflow. With existing bit amount control algorithms, the $\lambda_{MODE}$ which is relationship between distortion value and quantization parameter often excesses normal value to end up with video error. This paper proposes an algorithm to protect buffer from overflow or underflow by introducing a new quantization parameter against distortion value of H.264 video data. The test results of 6 exemplary data show that the proposed algorithm has the same PSNR as and up to 8% reduced bit rate against existing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7C
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• pp.715-723
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• 2009

In this paper, we propose a new detection method called dual-cell combining (DCC) detection for the acquisition in time of spread spectrum codes in the presence of residual frequency offset (RFO). When the RFO exists, the correlation peak used for detection during the acquisition process is split into two neighboring peaks with smaller amplitudes, resulting in considerable degradation in the overall acquisition performance of conventional methods. In the DCC detection method, the decision variable for detection is formed by combining two consecutive correlator outputs so that the influence of the reduction in the correlation peak due to the RFO can be alleviated. Numerical results show that the DCC detection method can offer better mean-time-to-synchrouization performance than the conventional method based on the cell-by-cell detection.

• Journal of Oriental Neuropsychiatry
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• v.28 no.4
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• pp.401-408
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• 2017

Objectives: The purpose of this study is to obtain the differential variable and learning effect of the serial position effect, targeting individuals aged 60 years and over. A total of 89 individuals were categorized into the following groups: Alzheimer's group, vascular dementia group, elderly depression group, and normal group. Methods: Considering the size of the groups used for comparison, of the total 89 participants, 28 were in the Alzheimer's group, 24 in the vascular dementia group, 16 in the elderly depression group, and 21 were randomly chosen for the normal group. In the CERAD-K word list memory test, 10 words were shown to the subjects. We then asked them to freely recall the words. After changing the order of the words three times, the same process was followed. Results: First, a significant difference was observed among the groups in terms of word recalling rate. The analysis conducted afterward showed that, as predicted, the depressed group showed significantly better performance in recalling the words. Second, the vascular dementia patients and the Alzheimer's patients showed, as predicted, better recall of the largest recency region of words shown. Third, the normal group and the elderly depressed group showed a high-performance rate, proving that the reiterative method can contribute to the recalling process. Thus, we demonstrated that the Alzheimer's group showed the deterioration of short memory recalling skills and the elderly depressed showed deteriorated output performance skill.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.258-264
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• 2018

This paper presents application of a differential search (DS) meta-heuristic optimization algorithm for optimal operation of a micro grid system. The DS algorithm simulates the Brownian-like random-walk movement used by an organism to migrate. The micro grid system consists of a wind turbine, a diesel generator, a fuel cell, and a photovoltaic system. The wind turbine generator is modeled by considering the characteristics of variable output. Optimization is aimed at minimizing the cost function of the system, including fuel costs and maximizing fuel efficiency to generate electric power. The simulation was applied to a micro grid system only. This study applies the DS algorithm with excellence and efficiency in terms of coding simplicity, fast convergence speed, and accuracy in the optimal operation of micro grids based on renewable energy resources, and we compared its optimum value to other algorithms to prove its superiority.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.476-484
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• 2010

Recently, as the piezoelectric transformer technology develops, piezoelectric transformer may become a variable alternative to magnetic transformers in various applications. Because it was have to favorable characteristics such as electromagnetic-noise free, compact size, higher efficiency, and superior power density, linkage flux, noiseless, etc. its resonance frequency was used to output waveform of a sine wave. In this paper, the switching mode power supply of about 87.2[KHz] is driven by the multilayer thickness vibration mode piezoelectric transformer and the DC to DC converter drive circuit using an electrical equivalent circuit is proposed. Also, it was possible to drive power source device of the high-luminance LED by propose circuits.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5460-5466
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• 2014

In this study, mainly for kitchen knives and small swords, cutlery, etc. STS420J2 used material used for the experiments. In order to cure the surface of the test piece after the rough grinding and fine grinding was performed in order polishing. Perform the surface hardening of STS420J2 local area by using a diode laser. The output of the laser diode and the feed rate to the process variable. Micro-hardness testing, microstructure testing, scanning electron microscope testing(SEM), the heat input to the analysis. After analyzing the experiment to compare the mechanical properties of the material. When using a diode laser to assess the soundness of the surface hardening. Accordingly, the process for deriving the optimum demonstrate the feasibility.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1620-1625
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• 2015

In this study, a variety of industrial gears, shafts, chains, rollers, mold, etc. are widely used inautomotive steel carbon steel for machine structural SM45C typical material used for the experiments. In order to cure the surface of the test piece after the rough grinding and fine grinding was performed in order polishing. Perform the surface hardening of SM45C lacal area by using a diode laser. The output of the laser diode and the feed rate to the process variable. Micro-hardness testing, microstructure testing, scanning electron microscope testing(SEM), the heat input to the analysis. After analyzing the experiment to compare the mechanical properties of the material. When using a diode laser to assess the soundness of the surface hardening. Accordingly, the process for deriving the optimum demonstrate the feasibility.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• Safety and Health at Work
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• v.11 no.3
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• pp.322-334
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• 2020

Background: Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage. Method: Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O₂, N₂, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB. Results: The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure. Conclusion: The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.1 s.343
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• pp.1-6
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• 2006

This paper proposes substrate-bias voltage generator to reduce leakage power consumption of digital logic circuits operating at supply voltage of 0.5V. Proposed substrate-bias voltage generator is composed of VSS and VBB generator. The former circuit produces negative voltage and supplies its output voltage for VBB generator. As a result VBB generator develops much lower negative voltage than that of conventional one. Proposed circuit is fabricated using 0.18um 1Poly-6Metal CMOS process and measurement result demonstrated stable operation with substrate-bias voltage of -0.95V.