• 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.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Progress in Medical Physics
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• v.25 no.2
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• pp.79-88
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• 2014

The dose distributions within the real volumes of tumor targets and critical organs during internal target volume-based intensity-modulated radiation therapy (ITV-IMRT) for liver cancer were recalculated by applying the effects of actual respiratory organ motion, and the dosimetric features were analyzed through comparison with gating IMRT (Gate-IMRT) plan results. The ITV was created using MIM software, and a moving phantom was used to simulate respiratory motion. The doses were recalculated with a 3 dose-volume histogram (3DVH) program based on the per-field data measured with a MapCHECK2 2-dimensional diode detector array. Although a sufficient prescription dose covered the PTV during ITV-IMRT delivery, the dose homogeneity in the PTV was inferior to that with the Gate-IMRT plan. We confirmed that there were higher doses to the organs-at-risk (OARs) with ITV-IMRT, as expected when using an enlarged field, but the increased dose to the spinal cord was not significant and the increased doses to the liver and kidney could be considered as minor when the reinforced constraints were applied during IMRT plan optimization. Because the Gate-IMRT method also has disadvantages such as unsuspected dosimetric variations when applying the gating system and an increased treatment time, it is better to perform a prior analysis of the patient's respiratory condition and the importance and fulfillment of the IMRT plan dose constraints in order to select an optimal IMRT method with which to correct the respiratory organ motional effect.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.16-21
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• 2017

In this paper, a Small-sized and planer resonator design of Magnetic Resonance - Wireless Power Transfer(MR-WPT) were proposed for practical applications of mobile devices, such as a laptop, a smart-phone and a tablet pc. The proposed MR-WPT system were based on four coil MR-WPT and designed as a transmitter part (Tx) and a receiver part (Rx) both are the same shape with the same loop and resonator. There are four different spiral coil type of resonators with variable of line length, width, gap and turns in $50mm{\times}50mm$ size. The both of top and bottom side of substrate(acrylic; ${\varepsilon}_r=2.56$, tan ${\delta}=0.008$) ere used to generate high inductance and capacitance in limited small volume. Loops were designed on the same plane of resonator to reduce their volume, and there are three different size. The proposed MR-WPT system were fabricated with two acrylic substrate plane of Tx and Rx each, the Rx and Tx loops and resonators were fabricated of copper sheets. There are 12 combinations of 3 loops and 4 resonators, each combination were measured to calculate transfer efficiency and resonance frequency in transfer distance from 1cm to 5cm. The measured results, the highest transfer efficiency was about 70%, and average transfer efficiency was 40%, on the resonance frequency was about 6.78 MHz, which is standard band by A4WP. We proposed small-sized and planer resonator of MR-WPT and showed possibility of mobile applications for small devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.56-63
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• 2018

The management of Combined Sewer Overflows(CSOs) and Separated Sewer Overflows(SSOs) discharge directly to the effluent system in an untreated state, which occurs when the facility capacity is exceeded due to heavy rain, has become an important issue in recent years as the heavy rain becomes a regular phenomenon. Despite the continuous development of filtration technology, targeting densely populated urban areas, CSOs are rarely applied. Therefore, this study was carried out to optimize the process to apply CSOs in a pilot-scale horizontal flow filtration system with a rope-type synthetic fiber. The research was carried out in two steps: a preliminary study using artificial samples and a field study using sewage. In the preliminary study using an artificial sample, head loss of the filter media itself was analyzed to be approximately 1.1cm, and the head loss was increased by approximately 0.1cm as the linear velocity was increased by 10m/hr. In addition, the SS removal efficiency was stable at 81.4%, the filtration duration was maintained for more than 6 hours, and the average recovery rate of 98% was obtained by air backwashing only. In the on-site evaluation using sewage, the filtration duration was approximately 2 hours and the average removal efficiency of 83.9% was obtained when belt screen (over 450 mesh) was applied as a pre-treatment process to prevent the premature clogging of filter media. To apply the filtration process to CSOs and SSOs, it was concluded that the combination with the pre-treatment process was important to reinforce the hydraulic dimension for the stable maintain of operation period, rather than efficiency. Compared to the dry season, the quality of incoming sewage was lower in the rainy season, which was attributed to the characteristics of the drainage area with higher sanitary sewerage. In addition, the difference in removal efficiency according to the influent quality of the wet season and dry season was small.

• Progress in Medical Physics
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• v.23 no.1
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• pp.8-14
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• 2012

In order to develop a Patient respiratory management system includinga biofeedback function for4-dimentional radiation therapy, this study investigated anoptimal tracking algorithmfor moving target using IR (Infra-red) camera as well as commercial camera. A tracking system was developed by LabVIEW 2010. Motion phantom images were acquired using a camera (IR or commercial). After image process were conducted to convert acquired image to binary image by applying a threshold values, several edge enhance methods such as Sobel, Prewitt, Differentiation, Sigma, Gradient, Roberts, were applied. The targetpattern was defined in the images, and acquired image from a moving targetwas tracked by matching pre-defined tracking pattern. During the matching of imagee, thecoordinateof tracking point was recorded. In order to assess the performance of tracking algorithm, the value of score which represents theaccuracy of pattern matching was defined. To compare the algorithm objectively, we repeat experiments 3 times for 5 minuts for each algorithm. Average valueand standard deviations (SD) of score were automatically calculatedsaved as ASCII format. Score of threshold only was 706, and standard deviation was 84. The value of average and SD for other algorithms which combined edge detection method and thresholdwere 794, 64 in Sobel, 770, 101 in Differentiation, 754, 85 in Gradient, 763, 75 in Prewitt, 777, 93 in Roberts, and 822, 62 in Sigma, respectively. According to score analysis, the most efficient tracking algorithm is the Sigma method. Therefore, 4-dimentional radiation threapy is expected tobemore efficient if threshold and Sigma edge detection method are used together in target tracking.

• Journal of radiological science and technology
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• v.34 no.1
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• pp.51-58
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• 2011

The aim of the study is to analyze the dose delivery error depending on the depth variation according to target positions and arc trajectories by comparing the simulated treatment planning with the actual dose delivery in conformal arc therapy. We simulated the conformal arc treatment planning with the three target positions (center, 2.5 cm, and 5 cm in the phantom). For the experiments, IMRT body phantom (I’mRT Phantom, Wellhofer Dosimetry, Germany) was used for treatment planning with CT (Computed Tomography, Light speed 16, GE, USA). The simulated treatment plans were established by three different target positions using treatment planning system (Eclipse, ver. 6.5, VMS, Palo Alto, USA). The radiochromic film (Gafchromic EBT2, ISP, Wayne, USA) and dose analysis software (OmniPro-IMRT, ver. 1.4, Wellhofer Dosimetry, Germany) were used for the measurement of the planned arc delivery using 6 MV photon beam from linear accelerator (CL21EX, VMS, Palo Alto, USA). Gamma index (DD: 3%, DTA: 2 mm) histogram and dose profile were evaluated for a quantitative analysis. The dose distributions surrounded by targets were also compared with each plans and measurements by conformity index (CI), and homogeneity index (HI). The area covered by 100% isodose line was compared to the whole target area. The results for the 5 cm-shifted target plan show that 23.8%, 35.6%, and 37% for multiple conformal arc therapy (MCAT), single conformal arc therapy (SCAT), and multiple static beam therapy, respectively. In the 2.5 cm-shifted target plan, it was shown that 61%, 21.5%, and 14.2%, while in case of center-located target, 70.5%, 14.1%, and 36.3% for MCAT, SCAT, and multiple static beam therapy, respectively. The values were resulted by most superior in the MCAT, except the case of the 5 cm-shifted target. In the analysis of gamma index histogram, it was resulted of 37.1, 27.3, 29.2 in the SCAT, while 9.2, 8.4, 10.3 in the MCAT, for the target positions of center, shifted 2.5 cm and 5 cm, respectively. The fail proportions of the SCAT were 2.8 to 4 times as compared to those of the MCAT. In conclusion, dose delivery error could be occurred depending on the target positions and arc trajectories. Hence, if the target were located in the biased position, the accurate dose delivery could be performed through the optimization of depth according to arc trajectory.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.58-64
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• 2011

Objective: The 3-dimensional reconstruction method with resolution recovery modeling has advantages of high spatial resolution and contrast because of its precise modeling of spatial blurring according to the distance from detector plane. The aim of this study was to evaluate one of the resolution recovery reconstruction methods (Astonish, Philips Medical), compare it to other iterative reconstructions, and verify its clinical usefulness. Materials and Methods: NEMA IEC PET body phantom and Flanges Jaszczak ECT phantom (Data Spectrum Corp., USA) studies were performed using Skylight SPECT (Philips) system under four different conditions; short or long (2 times of short) radius, and half or full (40 kcts/frame) acquisition counts. Astonish reconstruction method was compared with two other iterative reconstructions; MLEM and 3D-OSEM which vendor supplied. For quantitative analysis, the contrast ratios obtained from IEC phantom test were compared. Reconstruction parameters were determined by optimization study using graph of contrast ratio versus background variability. The qualitative comparison was performed with Jaszczak ECT phantom and human myocardial data. Results: The overall contrast ratio was higher with Astonish than the others. For the largest hot sphere of 37 mm diameter, Astonish showed about 27.1% and 17.4% higher contrast ratio than MLEM and 3D-OSEM, in short radius study. For long radius, Astonish showed about 40.5% and 32.6% higher contrast ratio than MLEM and 3D-OSEM. The effect of acquired counts was insignificant. In the qualitative studies with Jaszczak phantom and human myocardial data, Astonish showed the best image quality. Conclusion: In this study, we have found out that Astonish can provide more reliable clinical results by better image quality compared to other iterative reconstruction methods. Although further clinical studies are required, Astonish would be used in clinics with confidence for enhancement of images.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.1
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• pp.91-98
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• 2014

Purpose : To compare the dosimetry for the left breast cancer treatment between three dimensional conformal radiation radiotherapy (3D-CRT) and Hybrid planning and to estimate usefulness of Hybrid planning Materials and Methods : Five patients with left breast cancer were included in the study. They were planned using several different radiotherapy techniques including: 1)open rectangular field, 2)tangential wedge-based field 3)field in field, 4)hybrid planning(energy, wedge combine). For each patient planning was using Light Speed RT-16 CT and PINNACLE planning system-ver.9.2. Hybrid plan was made using same system and using the same targets and optimization goals. We comparing the Homogeneity Index(HI), normal organs at the does-volume histogram(DVH) Results : In all plans, the Homogeneity Index(HI) of Hybrid planning was significantly better than other. Dose comparison of HI= 2D-RT:38.32, TW:38.32, FIF:29.22, HYBRID:30.57. 2D-RT, TW, FIF Hybrid$V_{75_-lung}$=112.33, 125.14, 121.3, 123.78. $V_{50_-lung}$=155.43, 159.62, 157.96, 159.06. $V_{25_-lung}$=199.86, 200.22, 198.65, 200.31. $V_{50_-heart}$=26.07, 27.1, 26.85, 27.17 $V_{30_-heart}$=33.71, 34.37, 34.15, 34.65 Conclusion : In summary, 3D-CRT, Hybrid planning techniques were found to have acceptableCTV coverage in our study. However the Hybrid planning increased radiation dose exposure to normal tissue. If you apply for treatment of inhomogeneity areas like lung, For best results will be achieved.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.12
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• pp.715-732
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• 2009

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2008. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) Research trends in thermal and fluid engineering have been surveyed in the categories of general fluid flow, fluid machinery and piping, new and renewable energy, and fire. Well-developed CFD technologies were widely applied in developing facilities and their systems. New research topics include fire, fuel cell, and solar energy. Research was mainly focused on flow distribution and optimization in the fields of fluid machinery and piping. Topics related to the development of fans and compressors had been popular, but were no longer investigated widely. Research papers on micro heat exchangers using nanofluids and micro pumps were also not presented during this period. There were some studies on thermal reliability and performance in the fields of new and renewable energy. Numerical simulations of smoke ventilation and the spread of fire were the main topics in the field of fire. (2) Research works on heat transfer presented in 2008 have been reviewed in the categories of heat transfer characteristics, industrial heat exchangers, and ground heat exchangers. Research on heat transfer characteristics included thermal transport in cryogenic vessels, dish solar collectors, radiative thermal reflectors, variable conductance heat pipes, and flow condensation and evaporation of refrigerants. In the area of industrial heat exchangers, examined are research on micro-channel plate heat exchangers, liquid cooled cold plates, fin-tube heat exchangers, and frost behavior of heat exchanger fins. Measurements on ground thermal conductivity and on the thermal diffusion characteristics of ground heat exchangers were reported. (3) In the field of refrigeration, many studies were presented on simultaneous heating and cooling heat pump systems. Switching between various operation modes and optimizing the refrigerant charge were considered in this research. Studies of heat pump systems using unutilized energy sources such as sewage water and river water were reported. Evaporative cooling was studied both theoretically and experimentally as a potential alternative to the conventional methods. (4) Research papers on building facilities have been reviewed and divided into studies on heat and cold sources, air conditioning and air cleaning, ventilation, automatic control of heat sources with piping systems, and sound reduction in hydraulic turbine dynamo rooms. In particular, considered were efficient and effective uses of energy resulting in reduced environmental pollution and operating costs. (5) In the field of building environments, many studies focused on health and comfort. Ventilation. system performance was considered to be important in improving indoor air conditions. Due to high oil prices, various tests were planned to examine building energy consumption and to cut life cycle costs.