• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.153-164
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• 2010

A program consumes energy by executing its instructions. The amount of cosumed power is mainly proportional to algorithm complexity and it can be calculated by using complexity information. Generally, the complexity of a S/W is estimated by the microprocessor simulator. But, the simulation takes long time why the simulator is a software modeled the hardware and it only provides the information about computational complexity quantitatively. In this paper, we propose a complexity estimation method of analysis of S/W on source code level and produce the complexity metric mathematically. The function-wise complexity metrics give the detailed information about the calculation-concentrated location in function. The performance of the proposed method is compared with the result of the gate-level microprocessor simulator 'SimpleScalar'. The used softwares for performance test are $4{\times}4$ integer transform, intra-prediction and motion estimation in the latest video codec, H.264/AVC. The number of executed instructions are used to estimate quantitatively and it appears about 11.6%, 9.6% and 3.5% of error respectively in contradistinction to the result of SimpleScalar.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Journal of Energy Engineering
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• v.28 no.2
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• pp.18-35
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• 2019

The Korea District Heating Corporation operates a gas engine generator with a capacity of $4500m^3 /day$ of biogas generated from the sewage treatment plant of the Nanji Water Recycling Center and 1,500 kW. However, the actual operation experience of the biogas power plant is insufficient, and due to lack of accumulated technology and know-how, frequent breakdown and stoppage of the gas engine causes a lot of economic loss. Therefore, it is necessary to prepare technical fundamental measures for stable operation of the power plant In this study, a series of process problems of the gas engine plant using the biogas generated in the sewage treatment plant of the Nanji Water Recovery Center were identified and the optimization of the actual operation was made by minimizing the problems in each step. In order to purify the gas, which is the main cause of the failure stop, the conditions for establishing the quality standard of the adsorption capacity of the activated carbon were established through the analysis of the components and the adsorption test for the active carbon being used at present. In addition, the system was applied to actual operation by applying standards for replacement cycle of activated carbon to minimize impurities, strengthening measurement period of hydrogen sulfide, localization of activated carbon, and strengthening and improving the operation standards of the plant. As a result, the operating performance of gas engine # 1 was increased by 530% and the operation of the second engine was increased by 250%. In addition, improvement of vent line equipment has reduced work process and increased normal operation time and operation rate. In terms of economic efficiency, it also showed a sales increase of KRW 77,000 / year. By applying the strengthening and improvement measures of operating standards, it is possible to reduce the stoppage of the biogas plant, increase the utilization rate, It is judged to be an operational plan.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Progress in Medical Physics
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• v.23 no.3
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• pp.162-168
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• 2012

In proton therapy, in vivo dose verification is one of the most important parts to fully utilize characteristics of proton dose distribution concentrating high dose with steep gradient and guarantee the patient safety. Currently, in order to image the proton dose distribution, a prompt gamma distribution detection system, which consists of an array of multiple CsI(Tl) scintillation detectors in the vertical direction, a collimator, and a multi-channel DAQ system is under development. In the present study, the optimal design of prompt gamma distribution detection system was studied by Monte Carlo simulations using the MCNPX code. For effective measurement of high-energy prompt gammas with enough imaging resolution, the dimensions of the CsI(Tl) scintillator was determined to be $6{\times}6{\times}50mm^3$. In order to maximize the detection efficiency for prompt gammas while minimizing the contribution of background gammas generated by neutron captures, the hole size and the length of the collimator were optimized as $6{\times}6mm^2$ and 150 mm, respectively. Finally, the performance of the detection system optimized in the present study was predicted by Monte Carlo simulations for a 150 MeV proton beam. Our result shows that the detection system in the optimal dimensions can effectively measure the 2D prompt gamma distribution and determine the beam range within 1 mm errors for 150 MeV proton beam.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.2
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• pp.229-234
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• 2007

In the present work, the sequencing batch reactor process of two-sludge type was optimized. The effects of solid retention time, hydraulic retention time, length of biosorption phase and temperature variation were investigated, respectively. In the T-N removal, the long solid retention time was favored. It was speculated that SCOD biosorption efficiency was higher in long solid retention time than in short solid retention time. In the comparison of hydraulic retention time, the removal efficiency of $NH_4^+-N$ and T-N were almost same in all applied hydraulic retention times which were 8 hr, 10 hr and 15 hr. It was observed that there was no need to have the hydraulic retention time more than 20 min in biosorption phase for enhancement of T-N removal efficiency. An experimental comparison of removal efficiencies with different temperature conditions was carried out. Decrease of temperature didn't affect the performance of the process, however, phosphorus removal efficiency was a little higher at low temperature than high temperature. Consequently, the process developed in this study was much amenable to wastewater treatment which was conducted in the low temperature and high loading rate.

• Applied Chemistry for Engineering
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• v.31 no.3
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• pp.291-298
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• 2020

ZITO/Ag/ZITO multilayer transparent electrodes at room temperature on glass substrates were prepared using RF/DC magnetron sputtering. Transparent conductive films with a sheet resistance of 9.4 Ω/㎡ and a transmittance of 83.2% at 550 nm were obtained for the multilayer structure comprising ZITO/Ag/ZITO (100/8/42 nm). The sheet resistance and transmittance of ZITO/Ag/ZITO multilayer films meant that they would be highly applicable for use in polymer-dispersed liquid crystal (PDLC)-based smart windows due to the ability to effectively block infrared rays (heat rays) and thereby act as an energy-saving smart glass. Effects of the thickness of the PDLC layer and the intensity of ultraviolet light (UV) on electro-optical properties, photopolymerization kinetics, and morphologies of difunctional urethane acrylate-based PDLC systems were investigated using new transparent conducting electrodes. A PDLC cell photo-cured using UV at an intensity of 2.0 mW/c㎡ with a 15 ㎛-thick PDLC layer showed outstanding off-state opacity, good on-state transmittance, and favorable driving voltage. Also, the PDLC-based smart window optimized in this study formed liquid crystal droplets with a favorable microstructure, having an average size range of 2~5 ㎛ for scattering light efficiently, which could contribute to its superior final performance.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.417-423
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• 2007

Oxy-gasification or oxygen-blown gasification, enables a clean and efficient use of coal and opens a promising way to CO2 capture. The coal gasification process of a slurry feed type, entrained-flow coal gasifier was numerically predicted in this paper. The purposes of this study are to develop an evaluation technique for design and performance optimization of coal gasifiers using a numerical simulation technique, and to confirm the validity of the model. By dividing the complicated coal gasification process into several simplified stages such as slurry evaporation, coal devolatilization, mixture fraction model and two-phase reactions coupled with turbulent flow and two-phase heat transfer, a comprehensive numerical model was constructed to simulate the coal gasification process. The influence of turbulence on the gas properties was taken into account by the PDF (Probability Density Function) model. A numerical simulation with the coal gasification model is performed on the Conoco-Philips type gasifier for IGCC plant. Gas temperature distribution and product gas composition are also presented. Numerical computations were performed to assess the effect of variation in oxygen to coal ratio and steam to coal ratio on reactive flow field. The concentration of major products, CO and H2 were calculated with varying oxygen to coal ratio (0.2-1.5) and steam to coal ratio(0.3-0.7). To verify the validity of predictions, predicted values of CO and H2 concentrations at the exit of the gasifier were compared with previous work of the same geometry and operating points. Predictions showed that the CO and H2 concentration increased gradually to its maximum value with increasing oxygen-coal and hydrogen-coal ratio and decreased. When the oxygen-coal ratio was between 0.8 and 1.2, and the steam-coal ratio was between 0.4 and 0.5, high values of CO and H2 were obtained. This study also deals with the comparison of CFD (Computational Flow Dynamics) and STATNJAN results which consider the objective gasifier as chemical equilibrium to know the effect of flow on objective gasifier compared to equilibrium. This study makes objective gasifier divided into a few ranges to study the evolution of the gasification locally. By this method, we can find that there are characteristics in the each scope divided.

• Electronic Materials Letters
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• v.14 no.6
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• pp.689-699
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• 2018

Nanostructured Ni doped $Bi_2S_3$ ($Bi_{2-x}Ni_xS_3$, $0{\leq}x{\leq}0.07$) is explored as a candidate for telluride free thermoelectric material, through a combination process of mechanical alloying with subsequent consolidation by cold pressing followed with a sintering process. The cold pressing method was found to impact the thermoelectric properties in two ways: (1) introduction of the dopant atom in the interstitial sites of the crystal lattice which results in an increase in carrier concentration, and (2) introduction of a porous structure which reduces the thermal conductivity. The electrical resistivity of $Bi_2S_3$ was decreased by adding Ni atoms, which shows a minimum value of $2.35{\times}10^{-3}{\Omega}m$ at $300^{\circ}C$ for $Bi_{1.99}Ni_{0.01}S_3$ sample. The presence of porous structures gives a significant effect on reduction of thermal conductivity, by a reduction of ~ 59.6% compared to a high density $Bi_2S_3$. The thermal conductivity of $Bi_{2-x}Ni_xS_3$ ranges from 0.31 to 0.52 W/m K in the temperature range of $27^{\circ}C$ (RT) to $300^{\circ}C$ with the lowest ${\kappa}$ values of $Bi_2S_3$ compared to the previous works. A maximum ZT value of 0.13 at $300^{\circ}C$ was achieved for $Bi_{1.99}Ni_{0.01}S_3$ sample, which is about 2.6 times higher than (0.05) of $Bi_2S_3$ sample. This work show an optimization pathway to improve thermoelectric performance of $Bi_2S_3$ through Ni doping and introduction of porosity.

• Journal of Climate Change Research
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• v.2 no.3
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• pp.175-189
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• 2011

In this research, we describe the methodology and the quantification about GHG reduction effects, expected by optimization of operation mode according to establishing additional heat storage system of Bundang Combined Cycle Power Plant. As an intermediate form of General Combined Cycle Power Plant and Heat supply only district heating plant, Bundang Combined Cycle Power Plant(and Ilsan, Anyang, Bucheon) is possible to satisfy demand for the electrical load and thermal load capacity at the same time through changes to the operation mode itself. Therefore, through the operating transition of high-efficiency mode that the condenser cooling water is recovered and supplied to district heat and cooling, establishing additional heat storage system have flexible supply ability at the power and heat market. In this research, We calculated using the operating performance for the last three years(2008~2010) and efficiency of each mode-specific values. As a result, GHG reduction effects were calculated as $97.95kg_{-}CO_2/Gcal$ per heat energy 1 Gcal supplied at the heat storage system and we expected emmision reduction effect about $13,500Ton_{-}CO_2/yr$.