• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.135-140
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• 2024

It is said that PV power generation systems have a long lifespan compared to other renewable energy sources and require little maintenance. However, there are cases where the performance expected during initial design is not achieved due to shading, temperature rise, mismatch, contamination/deterioration of PV modules, failure of inverter, leakage current, and arc generation. Therefore, in order to solve the problems of these systems, the power generation amount and operation status are investigated qualitatively, or the performance is comparatively analyzed based on the performance ratio (PR), which is the performance index of the solar power generation system. However, because it includes large losses, it is difficult to accurately determine whether there are any abnormalities such as performance degradation, failure, or defects in the PV power generation system using only the performance coefficient. In this paper, we studied a status diagnosis algorithm for shading, inverter failure, leakage, and arcing of PV modules to optimize the power generation of PV power generation systems according to changes in the surrounding environment. In addition, using the studied algorithm, we examined the results of an empirical test on condition diagnosis for each area and the resulting optimized operation of power generation.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.8
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• pp.449-455
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• 2017

Process input data including material and energy, process output data including product, co-product and its environmental emissions of the reference and target processes were collected and analyzed to evaluate the process performance. Environmentally problematic input/environmental emissions of the manufacturing processes were identified using these data. Significant process inputs contributing to each of the environmental emissions were identified using multiple regression analysis between the process inputs and environmental emissions. Optimum combination of the end-of-pipe technologies for treating the environmental emissions considering economic aspects was made using the linear programming technique. The cement manufacturing processes in Korea and the EU producing same type of cement were chosen for the case study. Environmentally problematic input/environmental emissions of the domestic cement manufacturing processes include coal, dust, and $SO_x$. Multiple regression analysis among the process inputs and environmental emissions revealed that $CO_2$ emission was influenced most by coal, followed by the input raw materials and gypsum. $SO_x$ emission was influenced by coal, and dust emission by gypsum followed by raw material. Optimization of the end-of-pipe technologies treating dust showed that a combination of 100% of the electro precipitator and 2.4% of the fiber filter gives the lowest cost. The $SO_x$ case showed that a combination of 100% of the dry addition process and 25.88% of the wet scrubber gives the lowest cost. Salient feature of this research is that it proposed a method for identifying environmentally problematic input/environmental emissions of the manufacturing processes, in particular, cement manufacturing process. Another feature is that it showed a method for selecting the optimum combination of the end-of-pipe treatment technologies.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.3
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• pp.187-197
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• 2010

Carbon dioxide Capture and Storage(CCS) is regarded as one of the most promising options to response climate change. CCS is a three-stage process consisting of the capture of carbon dioxide($CO_2$), the transport of $CO_2$ to a storage location, and the long term isolation of $CO_2$ from the atmosphere for the purpose of carbon emission mitigation. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the $CO_2$ mixture captured from the power plants and steel making plants contains many impurities such as $N_2$, $O_2$, Ar, $H_2O$, $SO_2$, $H_2S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification, transport and injection processes. In order to design a reliable $CO_2$ marine geological storage system, it is necessary to analyze the impact of these impurities on the whole CCS process at initial design stage. The purpose of the present paper is to compare and analyse the relevant physical property models including BWRS, PR, PRBM, RKS and SRK equations of state, and NRTL-RK model which are crucial numerical process simulation tools. To evaluate the predictive accuracy of the equation of the state for $CO_2-SO_2$ mixture, we compared numerical calculation results with reference experimental data. In addition, optimum binary parameter to consider the interaction of $CO_2$ and $SO_2$ molecules was suggested based on the mean absolute percent error. In conclusion, we suggest the most reliable physical property model with optimized binary parameter in designing the $CO_2-SO_2$ mixture marine geological storage process.

• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.3
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• pp.217-226
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• 2009

Marine geological storage of $CO_2$ is regarded as one of the most promising options to response climate change. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources, to transport to the storage sites and to store $CO_2$ into the marine geological structure such as deep sea saline aquifer. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the captured $CO_2$ mixture contains many impurities such as $N_2$, $O_2$, Ar, $H_2O$, $SO_x$, $H_2S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification and transport processes. In order to design a reliable $CO_2$ marine geological storage system, it is necessary to perform numerical process simulation using thermodynamic equation of state. The purpose of the present paper is to compare and analyse the relevant equations of state including PR, PRBM, RKS and SRK equation of state for $CO_2-N_2$ mixture. To evaluate the predictive accuracy of the equation of the state, we compared numerical calculation results with reference experimental data. In addition, optimum binary parameter to consider the interaction of $CO_2$ and $N_2$ molecules was suggested based on the mean absolute percent error. In conclusion, we suggest the most reliable equation of state and relevant binary parameter in designing the $CO_2-N_2$ mixture marine geological storage process.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.9
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• pp.955-960
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• 2008

We investigated and estimated at the characteristics of decomposition and mineralization of benomyl using a design of experiment(DOE) based on the general factorial design in an E-beam process, and also the main factors(variables) with benomyl concentration(X$_1$) and E-beam irradiation(X$_2$) which consisted of 5 levels in each factor was set up to estimate the prediction model and the optimization conditions. At frist, the benomyl in all treatment combinations except 17 and 18 trials was almost degraded and the difference in the decomposition of benomyl in the 3 blocks was not significant(p > 0.05, one-way ANOVA). However, the % of benomyl mineralization was 46%(block 1), 36.7%(block 2) and 22%(block 3) and showed the significant difference of the % that between each block(p < 0.05). The linear regression equations of benomyl mineralization in each block were also estimated as followed; block 1(Y$_1$ = 0.024X$_1$ + 34.1(R$^2$ = 0.929)), block 2(Y$_2$ = 0.026X$_2$ + 23.1(R$^2$ = 0.976)) and block 3(Y$_3$ = 0.034X$_3$ + 6.2(R$^2$ = 0.98)). The normality of benomyl mineralization obtained from Anderson-Darling test in all treatment conditions was satisfied(p > 0.05). The results of prediction model and optimization point using the canonical analysis in order to obtain the optimal operation conditions were Y = 39.96 - 9.36X$_1$ + 0.03X$_2$ - 10.67X$_1{^2}$ - 0.001X$_2{^2}$ + 0.011X$_1$X$_2$(R$^2$ = 96.3%, Adjusted R$^2$ = 94.8%) and 57.3% at 0.55 mg/L and 950 Gy, respectively. A Microtox test using V. fischeri showed that the toxicity, expressed as the inhibition(%), was reduced almost completely after an E-beam irradiation, whereas the inhibition(%) for 0.5 mg/L, 1 mg/L and 1.5 mg/L was 10.25%, 20.14% and 26.2% in the initial reactions in the absence of an E-beam illumination.

• 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.

• Microbiology and Biotechnology Letters
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• v.17 no.4
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• pp.392-396
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• 1989

Methylobacterium organophilum, a facultative methylotroph was cultivated on a methanol as a sole carbon and energy source. The cell growth was affected by the various components of minimal synthetic medium and the medium composition was optimized with 0.5% (v/v) methanol at pH 6.8 and at 3$0^{\circ}C$. The maximum specific growth rate of M. organophilum was achieved to 0.26 hr$^{-1}$ in the optimized medium which has following composition: Methanol, 0.5% (v/v):(NH$_4$)$_2$SO$_4$, 1.0g/l:KH$_2$PO$_4$, 2.13g/l:KH$_2$PO$_4$, 1.305g/ι:MgSO$_4$.7$H_2O$. 45g/l and trace elements (CaCl$_2$.2$H_2O$, 3.3mg:FeSO$_4$.7$H_2O$, 1.3mg:MnSO$_4$.4$H_2O$, 130$\mu\textrm{g}$:ZnSO$_4$.5$H_2O$, 40$\mu\textrm{g}$:Na$_2$MoO$_4$.2$H_2O$, 40$\mu\textrm{g}$:CoCl$_2$.6$H_2O$, 40$\mu\textrm{g}$:H$_3$BO$_3$, 30$\mu\textrm{g}$ per liter). By the limitation of nitrogen and deficiency of Mn$^{+2}$ or Fe$^{+2}$, the cell growth was significantly repressed. Methanol greatly repressed the cell growth and the complete inhibition was observed at concentration above 4% (v/v). In order to overcome the methanol inhibition and to prevent the methanol limitation, intermittent feeding of methanol was conducted by a D.O.-stat technique. PHB production by M. organophilum was stimulated by deficiency of nutrients such as NH$_{4}^{+}$, SO$_{4}^{-2}$, $Mg^{+2}$, $K^{+}$, or PO$_{4}^{-3}$ in the medium. The maximum PHB content was obtained as 58% of dry cell weight under deficiency of potassium ion in the optimized synthetic medium.

• Journal of Life Science
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• v.18 no.10
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• pp.1377-1383
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• 2008

A bacterium producing non- or partially digestible dextran was isolated from kimchi broth by enrichment culture technique. The bacterium was identified tentatively as Leuconostoc sp. strain SKY. We established the response surface methodology (Box-Behnken design) to optimize the principle parameters such as culture pH, temperature, and yeast extract concentration for maximizing production of dextran. The ranges of parameters were determined based on prior screening works done at our laboratory and accordingly chosen as 5.5, 6.5, and 7.5 for pH, 25, 30, and $35^{\circ}C$ for temperature, and 1, 5, and 9 g/l yeast extract. Initial concentration of sucrose was 100 g/l. The mineral medium consisted of 3.0 g $KH_2PO_4$, 0.01 g $FeSO_4{\cdot}H_2O$, 0.01 g $MnSO_4{\cdot}4H_2O$, 0.2 g $MgSO_4{\cdot}7H_2O$, 0.01 g NaCl, and 0.05 g $CaCO_3$ per 1 liter deionized water. The optimum values of pH and temperature, and yeast extract concentration were obtained at pH (around 7.0), temperature (27 to $28^{\circ}C$), and yeast extract (6 to 7 g/l). The best dextran yield was 60% (dextran/g sucrose). The best dextran productivity was 0.8 g/h-l.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.6
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• pp.307-317
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• 2002

In this paper we propose a new mesh reconstruction scheme that produces a displaced subdivision surface directly from unorganized points. The displaced subdivision surface is a new mesh representation that defines a detailed mesh with a displacement map over a smooth domain surface, but original displaced subdivision surface algorithm needs an explicit polygonal mesh since it is not a mesh reconstruction algorithm but a mesh conversion (remeshing) algorithm. The main idea of our approach is that we sample surface detail from unorganized points without any topological information. For this, we predict a virtual triangular face from unorganized points for each sampling ray from a parameteric domain surface. Direct displaced subdivision surface reconstruction from unorganized points has much importance since the output of this algorithm has several important properties: It has compact mesh representation since most vertices can be represented by only a scalar value. Underlying structure of it is piecewise regular so it ran be easily transformed into a multiresolution mesh. Smoothness after mesh deformation is automatically preserved. We avoid time-consuming global energy optimization by employing the input data dependant mesh smoothing, so we can get a good quality displaced subdivision surface quickly.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.1
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• pp.103-113
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• 2020

Jack-up drilling rigs are widely used in the offshore oil and gas exploration industry. Although originally designed for use in shallow waters, trends in the energy industry have led to a growing demand for their use in deep sea and harsh environmental conditions. To extend the operating range of jack-up units, their design must be based on reliable analysis while eliminating excessive conservatism. In current industrial practice, jack-up drilling rigs are designed using the working(or allowable) stress design (WSD) method. Recently, classifications have been developed for specific regulations based on the load and resistance factor design (LRFD) method, which emphasises the reliability of the methods. This statistical method utilises the concept of limit state design and uses factored loads and resistance factors to account for uncertainly in the loads and computed strength of the leg components in a jack-up drilling rig. The key differences between the LRFD method and the WSD method must be identified to enable appropriate use of the LRFD method for designing jack-up rigs. Therefore, the aim of this study is to compare and quantitatively investigate the differences between actual jack-up lattice leg structures, which are designed by the WSD and LRFD methods, and subject to different environmental load-to-dead-load ratios, thereby delineating the load-to-capacity ratios of rigs designed using theses methods under these different enviromental conditions. The comparative results are significantly advantageous in the leg design of jack-up rigs, and determine that the jack-up rigs designed using the WSD and LRFD methods with UC values differ by approximately 31 % with respect to the API-RP code basis. It can be observed that the LRFD design method is more advantageous to structure optimization compared to the WSD method.