• 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.

• Journal of the Korean Wood Science and Technology
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• v.43 no.6
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• pp.851-860
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• 2015

The objective of this study was to manufacture the wood based roughage using lumber from thinning of oak and pitch pine (Pinus rigida). And the study also aimed to investigate a feed value evaluation of wood based roughages. To investigate the optimization condition of steam-digestion treatment for roughage, the wood chips of oak and pitch pine were steam-digestion treated at $160^{\circ}C$ under pressure 6 atm depending on treatment times (60 min, 90 min and 120 min) followed by the content of essential oils analyzed. The essential oil content of steam-digestion treated roughages for 90 min and 120 min were under 0.1 mL/kg. The evaluation of feed value was carried out from steam-digestion treated roughages for 90 min through feed chemical composition analysis, NRC (National research Council) modeling, ruminal degradability analysis and relative economic value analysis. The feed chemical compositions including DM (dry mater), CP (crude protein), EE (ether extract), NDF (neutral detergent fiber), ADF (acid detergent fiber), ADL (acid detergent lignin), NFC (nonfiber carbohydrate) in oak roughage were 95.4, 1.36, 3.11, 90.05, 83.85, 17.33, 6.50%, respectively, and in pitch pine roughage were 94.37, 1.33, 5.48, 87.89, 86.88, 30.56, 6.32%, respectively. Both roughages showed low level of protein and very high level of NDF. The TDN (total digestible nutrient) levels using NRC (2001) model in oak and pitch pine roughages were 40.55, 31.22%, respectively. The ruminal in situ dry matter degradability was higher in oak roughage (23.84%) than in pitch pine roughage (10.02%). The economic values of oak and pitch pine rough-ages were 235, and 210 \, respectively.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.257-264
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• 2006

This study investigated the effects of hybrid process(chemical coagulation, Fenton oxidation and ceramic UF(ultrafiltration)) on COD and color removals of commercial reactive dyestuffs. In the case of chemical coagulation, the optimal concentrations of $Fe^{3+}$ coagulant for COD and color removals of RB49(reactive blue 49) and RY84(reactive yellow 84) were determined according to the different coagulant dose at the optimal pH. They were 2.78 mM(pH 7) in RB49 and 1.85 mM(pH 6) in RY84, respectively. In the case of Fenton oxidation, the optimal concentrations of $Fe^{3+}\;and\;H_2O_2$ were obtained. Optimal $[Fe^{2+}]:[H_2O_2]$ molar ratio of COD and color removals of RB49 and RY84 were 4.41:5.73 mM and 1.15:0.81 mM, respectively. In the case of ceramic UF, the flux and rejection of supernatant after Fenton oxidation were investigated. After ceramic UF for 9 hr, the average flux of RB49 and RY84 solutions were $53.4L/m^2hr\;and\;67.4L/m^2hr$ at 1 bar, respectively. In addition, the permeate flux increased and the average flux recovery were 98.5-99.9%(RB49) and 91.0-97.3%(RY84) according to adopting off-line cleaning(5% $H_2SO_4$). Finally, COD and color removals were 91.6-95.7% and 99.8% by hybrid process, respectively.

• Journal of the Korean Electrochemical Society
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• v.26 no.1
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• pp.1-10
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• 2023

The cathode, which is one of the four major components of a lithium secondary battery, is an important component responsible for the energy density of the battery. The mixing process of active material, conductive material, and polymer binder is very essential in the commonly used wet manufacturing process of the cathode. However, in the case of mixing conditions of the cathode, since there is no systematic method, in most cases, differences in performance occur depending on the manufacturer. Therefore, LiMn₂O₄ (LMO) cathodes were prepared using a commonly used THINKY mixer and homogenizer to optimize the mixing method in the cathode slurry preparation step, and their characteristics were compared. Each mixing condition was performed at 2000 RPM and 7 min, and to determine only the difference in the mixing method during the manufacture of the cathode other experiment conditions (mixing time, material input order, etc.) were kept constant. Among the manufactured THINKY mixer LMO (TLMO) and homogenizer LMO (HLMO), HLMO has more uniform particle dispersion than TLMO, and thus shows higher adhesive strength. Also, the result of the electrochemical evaluation reveals that HLMO cathode showed improved performance with a more stable life cycle compared to TLMO. The initial discharge capacity retention rate of HLMO at 69 cycles was 88%, which is about 4.4 times higher than that of TLMO, and in the case of rate capability, HLMO exhibited a better capacity retention even at high C-rates of 10, 15, and 20 C and the capacity recovery at 1 C was higher than that of TLMO. It's postulated that the use of a homogenizer improves the characteristics of the slurry containing the active material, the conductive material, and the polymer binder creating an electrically conductive network formed by uniformly dispersing the conductive material suppressing its strong electrostatic properties thus avoiding aggregation. As a result, surface contact between the active material and the conductive material increases, electrons move more smoothly, changes in lattice volume during charging and discharging are more reversible and contact resistance between the active material and the conductive material is suppressed.