• Journal of the Korean Operations Research and Management Science Society
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• 제38권4호
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• pp.35-52
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• 2013

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

• Korean Journal of Applied Biomechanics
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• 제27권2호
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• pp.117-124
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• 2017

Objective: The purpose of this study was to examine the effect of changes in degrees of freedom of the fingers (i.e., the number of the fingers involved in tasks) on the task performance during force production and releasing task. Method: Eight right-handed young men (age: $29.63{\pm}3.02yr$, height: $1.73{\pm}0.04m$, weight: $70.25{\pm}9.05kg$) participated in this study. The subjects were required to press the transducers with three combinations of fingers, including the index-middle (IM), index-middle-ring (IMR), and index-middle-ring-little (IMRL). During the trials, they were instructed to maintain a steady-state level of both normal and tangential forces within the first 5 sec. After the first 5 sec, the subjects were instructed to release the fingers on the transducers as quickly as possible at a self-selected manner within the next 5 sec, resulting in zero force at the end. Customized MATLAB codes (MathWorks Inc., Natick, MA, USA) were written for data analysis. The following variables were quantified: 1) finger force sharing pattern, 2) root mean square error (RMSE) of force to the target force in three axes at the aiming phase, 3) the time duration of the release phase (release time), and 4) the accuracy and precision indexes of the virtual firing position. Results: The RMSE was decreased with the number of fingers increased in both normal and tangential forces at the steady-state phase. The precision index was smaller (more precise) in the IMR condition than in the IM condition, while no significant difference in the accuracy index was observed between the conditions. In addition, no significant difference in release time was found between the conditions. Conclusion: The study provides evidence that the increased number of fingers resulted in better error compensation at the aiming phase and performed a more constant shooting (i.e., smaller precision index). However, the increased number of fingers did not affect the release time, which may influence the consistency of terminal performance. Thus, the number of fingers led to positive results for the current task.

• Korean Journal of Applied Biomechanics
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• 제29권3호
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• pp.157-166
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• 2019

Objective: This study examined the effects of stimulating fingertip temperature on the patterns of force sharing and stability properties during multi-finger force production tasks. Method: 9 adult subjects (male: 3, female: 6, age: $26.11{\pm}4.01yrs$, height: $169.22{\pm}5.97cm$, weight: $61.44{\pm}11.27kg$) participated in this study. The experiment consisted of three blocks: 1) maximal voluntary contraction (MVC) task, 2) single-finger ramp task to quantify enslaving (i.e., unintended force production by non-task fingers), and 3) 12 trials of multi-finger steady-state force production task at 20% MVC. There were three temperature conditions including body-temperature (i.e., control condition), $40^{\circ}C$, and $43^{\circ}C$, and the stimulation was given to the index finger only for all experimental conditions. Results: There were no significant differences in the MVC forces, enslaving, and the accuracy of performance during the steady-state task between the conditions. However, the share of stimulated index finger force increased with the index fingertip temperature, while the share of middle finger force decreased. Also, the coefficient of variation of both index and middle finger forces over repetitive trials increased with the index fingertip temperature. Under the framework of the uncontrolled manifold (UCM) hypothesis used to quantify indices of multi-finger synergies (i.e., stability property) stabilizing total force during the steady-state task, the two variance components within the UCM analysis increased together with the fingertip temperature, while no changes in the synergy indices between the conditions. Conclusion: The current results showed that fingertip temperature stimulation only to index finger does not affect to muscle force production capability of multi-finger, independence of individual fingers, and force production accuracy by the involvement of all four fingers. The effect of fingertip temperature on the sharing pattern and force variation may be due to diffuse reflex effects of the induced afferent activity on alpha-motoneuronal pools. However, the unchanged stability properties may be the reflection of the active error compensation strategies by non-stimulated finger actions.

• Korean Journal of Remote Sensing
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• 제38권6_1호
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Journal of the Institute of Electronics and Information Engineers
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• 제50권6호
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• pp.276-286
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• 2013

SVD-based deconvolution algorithm has been known as the most effective technique for CT perfusion image analysis. In this algorithm, in order to reduce noise effects, SVD coefficients smaller than a certain threshold are removed. As the truncation threshold, either a fixed value or a variable threshold yielding a predetermined OI (oscillation index) is frequently employed. Each of these two thresholding methods has an advantage to the other either in accuracy or efficiency. In this paper, we propose a Monte Carlo simulation method to evaluate the accuracy of the two methods. An extension of the proposed method is presented as well to measure the effects of image smoothing on the accuracy of the thresholding methods. In this paper, after the simulation method is described, experimental results are presented using both simulated data and real CT images.

• Journal of the Korean Society of Environmental Restoration Technology
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• 제4권2호
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• pp.36-44
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• 2001

The main purpose of this study was to classify forest vegetation effectively using Landsat Thematic Mapper data(June, 1994) in mountainous region. The research area was the Mt. Wolak Experimental Forest of Chungbuk National University, near Chungju and Jecheon city, Chungcheongbuk-do. To classify forest vegetation effectively, Normalized Difference Vegetation Index(NDVI) was used to reduce topographic effects. This NDVI was modified and transformed to the value of 0 to 255, and then the modified values were combined with other Landsat Thematic Mapper bands. To classify forest and land cover types, unsupervised classification method was used. The results of this study are summarized as follows. 1. Combinations of band "3, 5, NDVI" in Landsat Thematic Mapper data showed a good separation with high accuracy. The expected classification accuracy was 95.1% in Landsat Thematic Mapper data. 2. The Land Cover types were classified into six groups : coniferous forest, deciduous forest, mixed forest, paddy and grass, non-forest, and other undetectable areas. As these classified results were compared with the reconnaissance survey and aerial black and white infrared photographs, the overall classification accuracy was 76.5% in Landsat Thematic Mapper data. 3. The portion of non-forest in Mt. Wolak area was 1.9%. The percentages of coniferous, deciduous and mixed forests were 30.9%, 35.7% and 26.4%, respectively. 4. As these classified results were compared with other reference data, the percentages of coniferous, deciduous and mixed forests increased, but the portion of non-forest was exceedingly diminished. These differences are thought to be from the different research method and the different season of received Landsat Thematic Mapper data.

• Asian-Australasian Journal of Animal Sciences
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• 제17권2호
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• pp.159-163
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• 2004

Data pertaining to first lactation records of 316 Murrah buffaloes, progeny of 47 sires, maintained at NDRI Farm for a period of 18 years were analysed to construct selection indices and to examine their robustness by changing the relative economic values of different economic traits. A total of 120 selection indices were constructed for three sets of relative economic values ( 40 for each set) considering different combinations of seven first lactation traits viz. age at first calving (AFC), first lactation 305 day or less milk yield (FLMY), first lactation length (FLL), first calving interval (FCI), milk yield per day of first lactation length (MY/FLL), milk yield per day of first calving interval (MY/FCI) and milk yield per day age at second calving (MY/ASC). The three sets of relative economic values were based on economic values of different traits, 1% standard deviation of different traits and regression of different traits on FLMY. The 'optimum' indices for the first two sets had five traits each namely AFC, FLMY, FLL, FCI and MY/ASC giving improvement in aggregate genotype of Rupees 269.11 and Rs. 174.88, respectively. The accuracy of selection from both indices was 70.79 and 69.39%, respectively. The 'best' selection index from the third set of data again had five traits (AFC, FLMY, FLL, FCI and MY/FLL) giving genetic gain of Rs. 124.16 and accuracy of selection of 71.81%. The critcal levels or break-even points for FLMY for varying levels of AFC and FCI estimated from the "optimum index" suggested the need of enhancement of present production level of the herd or reduction of AFC or FCI. It was concluded that economic values of various first lactation traits were the most appropriate to construct selection indices as compared to other criteria of assigning relative economic weights in Murrah buffaloes.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제44권3호
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• pp.385-396
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• 2024

To enhance the accuracy of sinkhole detection using GPR, this study derived a convolutional neural network that can optimally extract sinkhole characteristics from GPR B-scan grayscale images. The pre-trained convolutional neural network is evaluated to be more than twice as effective as the vanilla convolutional neural network. In pre-trained convolutional neural networks, fast feature extraction is found to cause less overfitting than feature extraction. It is analyzed that the top-1 verification accuracy and computation time are different depending on the type of architecture and simulation conditions. Among the pre-trained convolutional neural networks, InceptionV3 are evaluated as most robust for sinkhole detection in GPR B-scan grayscale images. When considering both top-1 verification accuracy and architecture efficiency index, VGG19 and VGG16 are analyzed to have high efficiency as the backbone for extracting sinkhole feature from GPR B-scan grayscale images. MobileNetV3-Large backbone is found to be suitable when mounted on GPR equipment to extract sinkhole feature in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제20권6호
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• pp.1189-1194
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• 2016

While increasing the convenience of life is a high population BMI (Body Mass Index) is increasing rapidly. Accordingly, the development of the monitoring system to manage and prevent obesity is the time that is required. But most of the monitoring system, the less information it receives management and show to have only simple information calculated this was a low efficiency problem. Also Users with normal and disease Management accuracy is low. In this paper shows the user in a graph of Body Mass Index, BMR (Basal metabolic rate) divided by grade increased accuracy for users to manage their own. Also represented by recovery with exercise machines you used, select a balanced movement mechanism, expressed as a Kcal consumption. If the graph recent data show only increased the visibility. We developed an efficient web-based monitoring system for design a exercise plan.

• Journal of the Korean Society of Environmental Restoration Technology
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• 제19권1호
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• pp.73-83
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• 2016

Forest degradation reduces ecosystem services provided by forest and could lead to change in composition of species. In North Korea, there has been significant forest degradation due to conversion of forest into terrace fields for food production and cut-down of forest for fuel woods. This study analyzed the phenological changes in North Korea, in terms of vegetation and moisture in soil and vegetation, from March to Octorber 2013, using MODIS (MODerate resolution Imaging Spectroradiometer) images and indexes including NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), and NDWI (Normalized Difference Water Index). In addition, marginal farmland was derived using elevation data. Lastly, degraded terrace fields of 16 degree was analyzed using NDVI, NDSI, and NDWI indexes, and marginal farmland characteristics with slope variable. The accuracy value of land cover classification, which shows the difference between the observation and analyzed value, was 84.9% and Kappa value was 0.82. The highest accuracy value was from agricultural (paddy, field) and forest area. Terrace fields were easily identified using slope data form agricultural field. Use of NDVI, NDSI, and NDWI is more effective in distinguishing deforested terrace field from agricultural area. NDVI only shows vegetation difference whereas NDSI classifies soil moisture values and NDWI classifies abandoned agricultural fields based on moisture values. The method used in this study allowed more effective identification of deforested terrace fields, which visually illustrates forest degradation problem in North Korea.