• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.732-743
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• 2008

In H.264/AVC, 4$\times$4 block transform is used for intra and inter prediction instead of 8$\times$8 block transform. Using small block size coding, H.264/AVC obtains high temporal prediction efficiency, however, it has limitation in utilizing spatial redundancy. Motivated on these points, we propose a multi-dimensional transform which achieves both the accuracy of temporal prediction as well as effective use of spatial redundancy. From preliminary experiments, the proposed multi-dimensional transform achieves higher energy compaction than 2-D DCT used in H.264. We designed an integer-based transform and quantization coder for multi-dimensional coder. Moreover, several additional methods for multi-dimensional coder are proposed, which are cube forming, scan order, mode decision and updating parameters. The Context-based Adaptive Variable-Length Coding (CAVLC) used in H.264 was employed for the entropy coder. Simulation results show that the performance of the multi-dimensional codec appears similar to that of H.264 in lower bit rates although the rate-distortion curves of the multi-dimensional DCT measured by entropy and the number of non-zero coefficients show remarkably higher performance than those of H.264/AVC. This implies that more efficient entropy coder optimized to the statistics of multi-dimensional DCT coefficients and rate-distortion operation are needed to take full advantage of the multi-dimensional DCT. There remains many issues and future works about multi-dimensional coder to improve coding efficiency over H.264/AVC.

• Journal of the Korea Concrete Institute
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• v.23 no.3
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• pp.311-320
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• 2011

Fly ash (FA), byproduct from power plant has been actively used as mineral admixture for concrete. However, since bottom ash (BA) is usually used for land reclaim or subbase material, more active reuse plan is needed. Pond ash (PA) obtained from reclaimed land is mixed with both FA and BA. In this study, 6 PA from different domestic power plant are prepared and 5 different replacement ratios (10%, 20%, 30%, 50%, and 70%) for fine aggregate substitutes are considered to evaluate engineering properties of PA as fine aggregate and durability performance of PA concrete. Tests for fine aggregate of PA for fineness modulus, density and absorption, soundness, chloride and toxicity content, and alkali aggregate reaction are performed. For PA concrete, durability tests for compressive strength, drying shrinkage, chloride penetration/diffusion, accelerated carbonation, and freezing/thawing are performed. Also, basic tests for fresh concrete like slump and air content are performed. Although PA has lower density and higher absorption, its potential as a replacement material for fine aggregate is promising. PA concrete shows a reasonable durability performance with higher strength with higher replacement ratio. Finally, best PA among 6 samples is selected through quantitative classification, and limitation of PA concrete application is understood based on the test results. Various tests for engineering properties of PA and PA concrete are discussed in this paper to evaluate its application to concrete structure.

• Journal of Energy Engineering
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• v.26 no.4
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• pp.29-34
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• 2017

Factors of Zero-Energy Building are divided into active and passive factor. Passive factor means insulation, heat bridge of building like insulation, windows and doors, awning, outside etc. and active factor means energy output and efficiency coefficient. Energy output of active factor is achieved by new generating energy. This study anticipated how many effects will be produced when not new generating energy but Vehicle to Building; V2B, bi-directional charging and discharging technology, is applied to Zero-Energy Building. In new generating energy, power generation will be anticipated by geography and climate, but in V2B, several input variable like user's discharging intention and number of usable charger etc. should be considered. We can check how much V2B contribute to the Zero-Energy Building by anticipated results, and that results should be anticipated by using probabilitic method because there is few statistical data. This study anticipate change of charging and discharging pattern, based by Demand Response slot, by using monte carlo method among the probabilitic methods.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Korean Journal of Agricultural Science
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• v.12 no.1
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• pp.101-107
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• 1985

Since most farmers breeding livestocks in Korea is depended on imported feeds, the rate of self-supplying feeds is very important for a stable development of farmers. Therefore, it is considered necessary to increase the rate of self-supplying feeds. In this study, performance tests were carried out with barley and forage to find the design's parameters of hammer for a small size hammer mill which can be driven by 3.7-7.5 kW power tiller being used by most farmers. The revolution speed of hammer mill was 3000 rpm, widths of hammer were 20mm, 30mm, 40mm, and the levels of thickness of hammer were 2mm, 4mm and 6mm. Experimental materials used were barley and forage and screen openings for barley was 4.76mm, and 3.18mm for forage. The study results can be summarized as follows; 1. Results of grinding tests showed that particle sizes were 478-774 microns for barley and 350-434 microns for forage. They were decreased according to the increasing thickness and width of hammer. 2. Fineness modulus of grinded materials were 3.07-3.62 for barley and 2.69-2.93 for forage. They were inversely proportional to thickness and width of hammer. 3. The required power for grinding was 3.8-5.0 kW for barley and 0.9-1.4 kW for forage. The thickness of hammer was more important for less power requirement than width of hammer. 4. Grinding performance of a small size hammer mill was 99-170kg/kWh for barley and 11-21 kg/kWh for forage. The thickness of hammer was an important factor for grinding performance, and inversely proportional to grinding performance. For about 3.2 of fineness modulus, 4 mm thickness was the best, and an optimum width of hammer was 30mm for a small size hammer mill.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.3
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• pp.243-248
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• 2013

Internal combustion engines typically expel 30%-40% of the energy supplied by fuel to the environment through their exhaust system. Therefore, further significant improvements in the thermal efficiency of IC engines are possible by recovering the waste heat from the engine exhaust gas. With this fact in mind, a numerical simulation was carried out to investigate the potential of using thermoelectric generation with an internal combustion engine for waste heat recovery. Physical parameters such as the exhaust temperature and mass flow rate were evaluated in the exhaust system, and the optimum location for inserting a thermoelectric generator (TEG) into the system was determined. The TEG will be located in the exhaust system and will use the energy flow between the warmer exhaust gas and the external environment. The optimum position of the temperature distribution and the TEG performance were predicted through numerical analysis. The experimental results obtained showed that the power output significantly increases with the temperature difference between the cold and hot sides of the TEG.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.11
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• pp.727-736
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• 2016

In this study, a high-pressure multistage pump used in the combined cycle power plants is analyzed. The pump performance characteristics (differential head and efficiency) are numerically analyzed for different shapes of the radial diffuser. The design variables selected for the radial diffuser are, number of vanes, diameter ratio ($D_4/D_3$), return channel outlet angle(${\alpha}_6$), and pressure recovery factor ($C_p$). The numerical analysis results showed that the differential head and efficiency are the highest when the diameter ratio is the highest. Further, it was observed that the differential head was lower when the return channel outlet angle was $60^{\circ}$ than when it was $90^{\circ}$, because of pre-swirl at the diffuser outlet.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.125-138
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• 2006

The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in data acquisition system andinformation processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, damage detection of bridge structures using neural networks technique based on the modal properties is presented, which can effectively consider the modeling uncertainty in the analysis model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness and applicability of the proposed method.

• Journal of the Earthquake Engineering Society of Korea
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• v.24 no.3
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• pp.123-128
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• 2020

Analysis of the 2016 Gyeongju earthquake and the 2017 Pohang earthquake showed the characteristics of a typical high-frequency earthquake with many high-frequency components, short time strong motion duration, and large peak ground acceleration relative to the magnitude of the earthquake. Domestic nuclear power plants were designed and evaluated based on NRC's Regulatory Guide 1.60 design response spectrum, which had a great deal of energy in the low-frequency range. Therefore, nuclear power plants should carry out seismic verification and seismic performance evaluation of systems, structures, and components by reflecting the domestic characteristics of earthquakes. In this study, high-frequency amplification factors that can be used for seismic verification and seismic performance evaluation of nuclear power plant systems, structures, and equipment were analyzed. In order to analyze the high-frequency amplification factor, five sets of seismic time history were generated, which were matched with the uniform hazard response spectrum to reflect the characteristics of domestic earthquake motion. The nuclear power plant was subjected to seismic analysis for the construction of the Korean standard nuclear power plant, OPR1000, which is a reactor building, an auxiliary building assembly, a component cooling water heat exchanger building, and an essential service water building. Based on the results of the seismic analysis, a high-frequency amplification factor was derived upon the calculation of the floor response spectrum of the important locations of nuclear power plants. The high-frequency amplification factor can be effectively used for the seismic verification and seismic performance evaluation of electric equipment which are sensitive to high-frequency earthquakes.