• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.83-88
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• 2016

The use of consistent coastlines is an important element for the systematic management of maritime boundaries and the interests of local governments. The Hydrographic and Oceanographic Agency conducted a preliminary survey for consistent coastline production, since 2001. As a result, the length of coastline was different by year. Because of the lack of systematic management, the use of incorrect data, etc. We also changed the coastline on the sea chart to show on a digital map for realization of terrain expression method. However, there was a variation in shoreline length due to various surveying techniques and shoreline extraction methods. In this paper, the characteristics of Jeju-do coastline were analysed by using a modified divider method of fractal dimension. The accuracy of the vectorization was determined by converting the actual distance in the Public Survey Amendment for proper divider use. With 1:5,000 and 1:25,000 digital maps of Jeju-si and Seogwipo-si each fractal dimensions were calculated. Jeju-si=1.14 and Seogwipo-si=1.12 in 1: 5,000. Jeju-si=1.13 and Seogwipo-si=1.10 in 1: 25,000. Calculated fractal dimension were correlated to data from digital maps. It was considered that complexity and scale of coastlines affected. In the future coastline length statistics and minimum ratio of calculated coastline length to original length need to be determined for consistency of coastline length statistics.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.135-146
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• 2004

For improving the effectiveness of travel information, some rational paths are needed to provide them to users driving in real road network. To meet it, k-shortest path algorithms have been used in general. Although the k-shortest path algorithm can provide several alternative paths, it has inherent limit of heavy overlapping among derived paths, which nay lead to incorrect travel information to the users. In case of considering the network consisting of several turn prohibitions popularly adopted in real world network, it makes difficult for the traditional network optimization technique to deal with. Banned and penalized turns are not described appropriately for in the standard node/link method of network definition with intersections represented by nodes only. Such problem could be solved by expansion technique adding extra links and nodes to the network for describing turn penalties, but this method could not apply to large networks as well as dynamic case due to its overwhelming additional works. This paper proposes a link-based shortest path algorithm for the travel information in real road network where exists turn prohibitions. It enables to provide efficient alternative paths under consideration of overlaps among paths. The algorithm builds each path based on the degree of overlapping between each path and stops building new path when the degree of overlapping ratio exceeds its criterion. Because proposed algorithm builds the shortest path based on the link-end cost instead or node cost and constructs path between origin and destination by link connection, the network expansion does not require. Thus it is possible to save the time or network modification and of computer running. Some numerical examples are used for test of the model proposed in the paper.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.169-177
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• 2009

In this paper, we propose a new shot boundary detection method which is optimized for news video story parsing. This new news shot boundary detection method was designed to satisfy all the following requirements: 1) minimizing the incorrect data in data set for anchor shot detection by improving the recall ratio 2) detecting abrupt cuts and gradual transitions with one single algorithm so as to divide news video into shots with one scan of data set; 3) classifying shots into static or dynamic, therefore, reducing the search space for the subsequent stage of anchor shot detection. The proposed method, based on singular value decomposition with incremental clustering and mercer kernel, has additional desirable features. Applying singular value decomposition, the noise or trivial variations in the video sequence are removed. Therefore, the separability is improved. Mercer kernel improves the possibility of detection of shots which is not separable in input space by mapping data to high dimensional feature space. The experimental results illustrated the superiority of the proposed method with respect to recall criteria and search space reduction for anchor shot detection.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.9-18
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• 2018

Recently, the use of image stitching technology has been increasing as the number of contents based on virtual reality increases. Image Stitching is a method for matching multiple images to produce a high resolution image and a wide field of view image. The image stitching is used in various fields beyond the limitation of images generated from one camera. Image Stitching detects feature points and corresponding points to match multiple images, and calculates the homography among images using the RANSAC algorithm. Generally, corresponding points are needed for calculating conversion relation. However, the corresponding points include various types of noise that can be caused by false assumptions or errors about the conversion relationship. This noise is an obstacle to accurately predict the conversion relation. Therefore, RANSAC algorithm is used to construct an accurate conversion relationship from the outliers that interfere with the prediction of the model parameters because matching methods can usually occur incorrect correspondence points. In this paper, we propose an algorithm that extracts more accurate inliers and computes accurate transformation relations by using correspondence point relation information used in RANSAC algorithm. The correspondence point relation information uses distance ratio between corresponding points used in image matching. This paper aims to reduce the processing time while maintaining the same performance as RANSAC.

• Journal of Science Education
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• v.45 no.2
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• pp.247-256
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• 2021

Visual representation has been a useful tool in mathematical problem solving because it vividly express and structure the variables in the problem. But its effects may vary according to the types of problems. So, this study analyzes the survey results on the 5^th graders' visual representations using questionnaire consisting of the routine problems and the non-routine problems. The results are follows: The rate of correct answers in routine problems was higher than that of the non-routine problems. Even though the subjects were asked to solve the problem using visual representations, the ratio of solving the problem using the numerical expression was high in the routine problems. On the other hand, the rate of solving the problem using visual representation was high in the non-routine problems. The number of respondents who used visual representation in the non-routine problems was twice as many as that of the routine problems. But, among the subjects who used visual representation in the non-routine problems, the proportion of incorrect answers was also high, which resulted in using visual pictures. So, it is necessary to provide an experience that can use various types of the visual representations for problem solving and pay attention to the process of converting problems into visual representations.

• Journal of Nutrition and Health
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• v.54 no.6
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• pp.584-593
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• 2021

In the 2020 Dietary Reference Intakes for Koreans, an acceptable macronutrient distribution range (AMDR), similar to the one established in 2015, was determined for carbohydrates. AMDR is the ratio that signifies energy intake from carbohydrates to the total energy intake, and is a reference that indicates a decreasing risk of chronic diseases. The AMDR of carbohydrate was determined to be optimal at 55-65% for all ages above 1 year. For the first time, in the year 2020, the estimated average requirement (EAR) and recommended nutrient intake (RNI) for carbohydrates were established. The EAR was based on the amount of glucose used per day in the brain, and was set at 100 g/day for all ages above 1 year. The RNI was set at 130 g/day, by adding a double coefficient of variation using a 15% coefficient of variation, for all ages above 1 year. In pregnant women, the amount of glucose utilized by the fetus brain was considered additionally, and for lactating women the amount of lactose secreted into maternal milk was additionally taken into consideration. Since the EAR of carbohydrate indicates the minimum amount of glucose required by the brain and is not an appropriate intake amount as an energy source, it is incorrect to compare the carbohydrate intake with the EAR or RNI. To evaluate the nutritional status of carbohydrate, it is appropriate to use the AMDR. Carbohydrate intakes within the AMDR range has the possibility in reducing the risk of chronic diseases. Hence, it is important to consider the quality as well as quantity of carbohydrates consumed.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.871-877
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• 2022

This study was conducted to improve the problems of exposure dose and image reading applied to patients due to the incorrect use of AEC during chest radiography. Images were acquired by dividing the case where AEC was used as the test condition and the case where AEC was not used. As a result of the study, the dose was reduced by 1.17% in 110 kVp without AEC than with AEC, 17.2% decrease at 100 kVp, 30.19% decrease at 90 kVp, and 46.45% decrease at 80 kVp. There was a significant difference in the statistical values according to the tube voltage change in the lung, trachea, and heart SNR average values with AEC and without AEC 110 kVp, but the difference in image quality was insignificant in actual images. When AEC was not applied at the same tube voltage, the dose could be reduced by 17.2% while maintaining the image quality similar to that of with AEC at 100 kVp without AEC. Therefore, rather than relying on AE conditions during chest radiographic examination, it is considered that the conditions should be considered for the examination while lowering the dose by selecting an appropriate tube voltage.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.111-120
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• 2024

Cloud coverage is a key factor in determining whether to proceed with observations. In the past, human judgment played an important role in weather evaluation for observations. However, the development of remote and robotic observation has diminished the role of human judgment. Moreover, it is not easy to evaluate weather conditions automatically because of the diverse cloud shapes and their rapid movement. In this paper, we present the development of a cloud monitoring program by applying a machine learning-based Python module "cloudynight" on all-sky camera images obtained at Miryang Arirang Astronomical Observatory (MAAO). The machine learning model was built by training 39,996 subregions divided from 1,212 images with altitude/azimuth angles and extracting 16 feature spaces. For our training model, the F1-score from the validation samples was 0.97, indicating good performance in identifying clouds in the all-sky image. As a result, this program calculates "Cloudiness" as the ratio of the number of total subregions to the number of subregions predicted to be covered by clouds. In the robotic observation, we set a policy that allows the telescope system to halt the observation when the "Cloudiness" exceeds 0.6 during the last 30 minutes. Following this policy, we found that there were no improper halts in the telescope system due to incorrect program decisions. We expect that robotic observation with the 0.7 m telescope at MAAO can be successfully operated using the cloud monitoring program.

• Korean Journal of Radiology
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• v.24 no.12
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• pp.1284-1292
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• 2023

Objective: We investigated the impacts of computed tomography (CT) added to ultrasound (US) for preoperative evaluation of patients with papillary thyroid carcinoma (PTC) on staging, surgical extent, and postsurgical survival. Materials and Methods: Consecutive patients who underwent surgery for PTC between January 2015 and December 2015 were retrospectively identified. Of them, 584 had undergone preoperative additional thyroid CT imaging (CT + US group), and 859 had not (US group). Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were used to adjust for 14 variables and balance the two groups. Changes in nodal staging and surgical extent caused by CT were recorded. The recurrence-free survival and distant metastasis-free survival after surgery were compared between the two groups. Results: In the CT + US group, discordant nodal staging results between CT and US were observed in 94 of 584 patients (16.1%). Of them, CT accurately diagnosed nodal staging in 54 patients (57.4%), while the US provided incorrect nodal staging. Ten patients (1.7%) had a change in the extent of surgery based on CT findings. Postsurgical recurrence developed in 3.6% (31 of 859) of the CT + US group and 2.9% (17 of 584) of the US group during the median follow-up of 59 months. After adjustment using IPTW (580 vs. 861 patients), the CT + US group showed significantly higher recurrence-free survival rates than the US group (hazard ratio [HR], 0.52 [95% confidence interval {CI}, 0.29-0.96]; P = 0.037). PSM analysis (535 patients in each group) showed similar HR without statistical significance (HR, 0.60 [95% CI, 0.31-1.17]; P = 0.134). For distant metastasis-free survival, HRs after IPTW and PSM were 0.75 (95% CI, 0.17-3.36; P = 0.71) and 0.87 (95% CI, 0.20-3.80; P = 0.851), respectively. Conclusion: The addition of CT imaging for preoperative evaluation changed nodal staging and surgical extent and might improve recurrence-free survival in patients with PTC.