• Environmental and Resource Economics Review
• /
• v.28 no.2
• /
• pp.277-306
• /
• 2019

Mackerel is one of the most widely consumed aquatic products in Korea. Concerns about the depletion of stocks have also arisen as the catch has decreased. The primary purpose of this study is to estimate the mackerel stock and derive the optimal level of catch in Korea. We apply a generalized maximum entropy econometric method to estimate the mackerel growth function, which does not require the steady state assumption. We incorporate a bootstrapping approach to derive the significance levels of parameter estimates. We found that the average ratio of catch to the estimated total stock was less than 30% before the 1990s but exceeded 40% in the 1990s. After 2000, it dropped back to about 36%. This finding indicates that mackerel may have been over-fished in the 1990s, but the government regulations introduced in the 2000s alleviated over-fishing problems. Nevertheless, our dynamic optimization analysis suggests that the total allowable catch may need to be carefully controlled to achieve socially optimal management of resources.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.5
• /
• pp.64-74
• /
• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Journal of Communication Design
• /
• v.65
• /
• pp.530-538
• /
• 2018

Recently, museums are attracting the attention of visitors and society. However, research on museum interpretation conducted at domestic and foreign is relatively limited. The plan design of the museum commentary system was mainly based on the perspective of the experts, and most of them do not reflect the desire of the visitors. This study focused on visitors' experience of using museum Interpretation system. Through analyzing the effect of the Interpretation system of the museum that the visitor feels, we examined whether the Interpretation system of the museum meets the cultural needs of the visitors and identified the problems and deficiencies in the system. After that, the problems were rearranged and suggested the main elements of the commentary system of the museum finally, so as to help improve the museum commentary system and the educational function.In the first part of the study, the four major explanatory methods existing in the museum were confirmed. After that, we conducted in - depth interviews on four types of commentary methods and collected existing problems and deficiencies. The results of the type analysis were grouped into 14 types and the questionnaire was used to conduct a general survey on 14 problems. In this study, the user 's discomfort and problems were identified in the museum Interpretation system, and based on this, five key elements necessary for the museum Interpretation system were derived. Among them, inhalation is the weakest element in the museum commentary system, and future research will be conducted on how to apply each factor. I hope that you will be a reference material when you carry out research on the optimization of the Interpretation system of the museum and the improvement of the Interpretation experience.

• Korean Journal of Optics and Photonics
• /
• v.31 no.6
• /
• pp.281-289
• /
• 2020

This paper reports a slim mobile lens design using a hybrid refractive/diffractive optical element. Conventionally a wide field of view (FOV) camera-lens design adopts a retrofocus type having a negative (-) lens at the forefront, so that it improves in imaging performance over the wide FOV, but with the sacrifice of longer total track length (TTL). However, we chose a telephoto type as a baseline design layout having a positive (+) lens at the forefront, to achieving slimness, based on the specification analysis of 23 reported optical designs. Following preliminary optimization of a baseline design and aberration analysis based on Zernike-polynomial decomposition, we applied a hybrid refractive/diffractive element to effectively reduce the residual chromatic spherical aberration. The optimized optical design consists of 6 optical elements, including one hybrid element. It results in a very slim telephoto ratio of 1.7, having an f-number of 2.0, FOV of 90°, effective focal length of 2.23 mm, and TTL of 3.7 mm. Compared to a comparable conventional lens design with no hybrid elements, the hybrid design improved the value of the modulation transfer function (MTF) at a spatial frequency of 180 cycles/mm from 63% to 71-73% at zero field (0 F), and about 2-3% at 0.5, 0.7, and 0.9 fields. It was also found that a design with a hybrid lens with only two diffraction zones at the stop achieved the same performance improvement.

• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.46 no.4
• /
• pp.349-360
• /
• 2020

Bio-conversion manufacturing technology has been developed to produce ginsenoside Rd which is increasingly in demand as a cosmetic material due to various possibilities related to improving skin function. In order to convert ginsenoside Rb1 which is contained in red ginseng saponin (RGS) into Rd, several commercial enzymes were tested. Viscoflow MG was found to be the most efficient. In order to optimize the conversion of RGS to ginsenoside Rd by enzymatic transition was carried out using response surface methodology (RSM) based on Box-Behnken design (BBD). The main independent variables were RGS concentration, enzyme concentration, and reaction time. Conversion of ginsenoside Rd was performed under 17 conditions selected according to BBD model and optimization conditions were analyzed. The concentration of the converted ginsenoside Rd ranged from 0.3113 g/L to 0.5277 g/L, and the highest production volume was obtained under condition of reacting 2% RGS and 1.25% enzyme for 13.5 hours. Consequently, RGS concentration, enzyme concentration which is 0.05 less than p-value and among the interactions between the independent variables, the interaction between enzyme concentration and reaction time was confirmed to be the most influential.

• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.46 no.4
• /
• pp.371-382
• /
• 2020

There have been continuous attempts to quantify sensory attributes of cosmetic products by measuring relevant physical properties. The most representative method to evaluate stickiness is to measure axial force using texture analyzer. Stickiness is known to correlate with AUC which abbreviates area under curve in the obtained axial force curve as a function of time. Recently, Normandie University research group developed in vivo stickiness evaluation method considering the characteristics of skin along with established evaluation method[8]. Based on the study, we tried to optimize in vivo stickiness evaluation method especially for cosmetic creams. The experiment was carried out on 5 different facial creams products by changing the amount and the times of rolling of creams, and the shape and material of probes. Based on the results of the sensory evaluation, the most consistent conditions were established as the optimal evaluation method. As a result, applying 70 μL of cream and rubbing 10 times for 7 s inside the 3.4 cm circle were judged to be suitable. As for the probes, spherical metallic probe was more proper due to its reproducibility. We conducted the settled method on 10 subjects to check its validity. Although the absolute values of AUC differed depending on the individuals, the AUC values were all ranked the same. Finally, for the standardization of stickiness of AUC, polyvinylpyrrolidone (PVP) was set as a reference material and we measured AUC of its aqueous solution by changing concentration. Then, the degree of stickiness recognition for 5 different creams was surveyed to check the correlation between AUC and stickiness.

• Journal of Korea Water Resources Association
• /
• v.54 no.11
• /
• pp.955-968
• /
• 2021

A water resource plan is routinely based on a natural flow and can be estimated using observed streamflow data or a long-term continuous rainfall-runoff model. However, the watershed with the natural flow is very limited to the upstream area of the dam. In particular, for the ungauged watershed, a rainfall-runoff model is established for the gauged watershed, and the model is then applied to the ungauged watershed by transferring the associated parameters. In this study, the GR4J rainfall-runoff model is mainly used to regionalize the parameters that are estimated from the 14 dam watershed via an optimization process. In terms of optimizing the parameters, the Bayesian approach was applied to consider the uncertainty of parameters quantitatively, and a number of parameter samples obtained from the posterior distribution were used for the regionalization. Here, the relationship between the estimated parameters and the topographical factors was first identified, and the dependencies between them are effectively modeled by a Copula function approach to obtain the regionalized parameters. The predicted streamflow with the use of regionalized parameters showed a good agreement with that of the observed with a correlation of about 0.8. It was found that the proposed regionalized framework is able to effectively simulate streamflow for the ungauged watersheds by the use of the regionalized parameters, along with the associated uncertainty, informed by the basin characteristics.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.3
• /
• pp.155-163
• /
• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Journal of Mushroom
• /
• v.20 no.3
• /
• pp.178-182
• /
• 2022

Despite the long history of mushroom use, studies examining the genetic function of mushrooms and the development of new varieties via bio-molecular methods are significantly lacking compared to those examining other organisms. However, owing to recent developments, attempts have been made to use a novel gene-editing technique involving CRISPR/Cas9 technology and genetic scissors in mushroom studies. In particular, research is actively being conducted to utilize ribonucleoprotein particles (RNPs) that can be genetically edited with high efficiency without foreign gene insertion for ease of selection. However, RNPs are too large for Cas9 protein to pass through the cell membrane of the protoplasmic reticulum. Furthermore, guide RNA is unstable and can be easily decomposed, which remarkably affects gene editing efficiency. In this study, nanoparticles were used to mitigate the shortcomings of RNP-based gene editing techniques and to obtain transformants stably. We used Lentinula edodes (shiitake mushroom) Sanjo705-13 monokaryon strain, which has been successfully used in previous genome editing experiments. To identify a suitable osmotic buffer for the isolation of protoplast, 0.6 M and 1.2 M sucrose, mannitol, sorbitol, and KCl were treated, respectively. In addition, with various nanoparticle-forming materials, experiments were conducted to confirm genome editing efficiency via the formation of nanoparticles with calcium phosphate (CaP), which can be bound to Cas9 protein without any additional amino acid modification. RNPs/NP complex was successfully formed and protected nuclease activity with nucleotide sequence specificity.

• Smart Media Journal
• /
• v.11 no.10
• /
• pp.65-75
• /
• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.