• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.11
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• pp.705-715
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• 2016

In this study, the cycle performance of an air conditioner with multi-indoor units is analyzed and simulated. The cycle performance could be predicted through the integration of mathematical formulation for these devices. The condenser pressure is obtained by an iteration process to match the mass flow rates of the compressor and the expansion valve and the evaporator pressure is determined by an iteration process, in which the suction super heat is tracing the targeted super heat. The required software was developed by system programming. the software algorithm is extended to predict the cycle performance of an air conditioner system with multi-indoor units, and then the numerical results are compared with experimental results. This mathematical model is validated from the result of experiments conducted on 8.3kW air conditioner. The errors in capacity, electronic power, and COP are found to be within 10% in general.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.1-5
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• 2010

This paper deals with a method to measure the thickness of scale-layer, iron oxide formed on the surface of the rolling steel, using a dielectric lens antenna. The dielectric lens antenna has an independent characteristic with the frequency in the X-band and changes the spherical wave radiated from a horn antenna into a plane wave at the focusing point. Using this concept, we regard a scale-layer on the rolling steel as a dielectric-PEC(Perfect Electric Conductor) layer and apply a theoretical analysis of the normal-incident plane wave. To reduce the phase error arising from the use of the dielectric lens antenna, this paper utilizes a regression process algorithm. In comparison with the conventional iteration algorithm, the present algorithm led to a unique solution for the thickness of the scale-layer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.673-676
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• 2015

Digital Pre-Distortion (DPD) is a linearization technique for nonlinear power amplifiers (PAs) by implementing inverse function of the PA at baseband digital stage. To obtain proper DPD parameters, a feedback path is required to convert the PA output to a baseband signal, and a memory is also needed to store the feedback signals. DPD parameters are usually found by an adaptive algorithm from the feedback samples. However, for the adaptive algorithm to converge to a reliable solution, long feedback samples are required, which increases convergence time and hardware complexity. In this paper, we propose a DPD technique that requires relatively short feedback samples. From the observation that the convergence time of the adaptive algorithm highly depends on the initial condition, this paper iteratively utilizes the feedback samples while keeping and using the converged DPD parameters at the former iteration as the initial condition at the current iteration. Computer simulation results show that the proposed method performs better than the conventional technique while the former requires much shorter feedback samples than the latter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.721-728
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• 2010

CAMC (constant amplitude multi-code) has a better performance of error correction in iterative decoding than SPCPC (single parity check product code). CAMC benefits from a processing gain since it belongs to a spread spectrum signal. We show that the processing gain enhances the performance of CAMC. Additional correction of bit errors is achieved in the de-spreading of iteratively decoded signal. If the number of errors which survived the iterative decoding is less than or equal to ($\sqrt{N}/2-1$), all of the bit errors are removed after the de-spreading. We also propose a stopping criterion in the iterative decoding, which is based on the histogram of EI (extrinsic information). The initial values of EI are randomly distributed, and then they converge to ($-E_{max}$) or ($+E_{max}$) over the iterations. The strength of the convergence reflects how successfully error correction process is performed. Experimental results show that the proposed method achieves a gain of 0.2 dB in Eb/No.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.11
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• pp.1248-1255
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• 2012

LDPC decoder architectures are generally classified into serial, parallel and partially parallel architectures. Conventional method of LDPC decoding in general give rise to a large number of computation operations, mass power consumption, and decoding delay. It is necessary to reduce the iteration numbers and computation operations without performance degradation. This paper studies horizontal shuffle scheduling(HSS) algorithm and self-correction normalized min-sum(SC-NMS) algorithm. In the result, number of iteration is half than conventional algorithm and performance is almost same between sum-product(SP) and SC-NMS. Finally, This paper implements high-speed LDPC decoder based on FPGA. Decoding throughput is 816 Mbps.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.296-306
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• 2003

In this paper, algorithm for non-iterative decoding method is proposed and fractal image decoder based on non-iterative fractal decoding algorithm used general purpose digital signal processors is designed and implemented. The algorithm is showed that the attractor image can be obtained analytically whe n the image is encoded using the fractal algorithm proposed by Monro and Dudbridge, in which the corresponding domain block for a range block is fifed. Using the analytical formulas, we can obtain the attractor image without iteration procedure. And we get general formulas of obtained analytical formulas. Computer simulation results for various test images show that we can increase the image decoding speed by more than five times when we use the analytical formulas compared to the previous iteration methods. The fractal image decoder contains two ADSP2181's and perform image decoding by three stage pipeline structure. The performance tests of the implemented decoder is elapsed 31.2ms/frame decoding speed for QCIF data when all the frames are decoded. The results enable us to process the real-time fractal decoding over 30 frames/sec.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.443-448
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• 2013

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.10
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• pp.67-74
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• 2007

Turbo code, a kind of channel coding technique, has ben used in the field of digital mobile communication system if the number of iterations is increased in the several channel environments, my further iteration results in very little improvement, and requires much delay and computation in proportion to the number of iterations. In this paper, it proposes an efficient stopping rules for the iteration process in turbo decoding. By using absolute mean values for the LLR difference values between the first and second decoder in the present decoding process, the proposed algorithm can largely reduce the average number of iterations without BER performance degradation in all SNR regions. As a result of simulation, the average number of iterations of proposed algorithm is reduced by about $18.25%{\sim}20.58%$ compared to SDR algerian in the lower SNR region, and is reduced by about $22.96%{\sim}28.74%$ compared to method using variance values of extrinsic information in the upper SNR region.

• Journal of Radiation Protection and Research
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• v.42 no.4
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• pp.212-221
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• 2017

Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.