• Journal of agriculture & life science
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• v.50 no.5
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• pp.81-93
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• 2016

Rehmannia glutinosa(Gaertn.) DC. is a herbaceous perennial plant and belonging to the Scrophulariaceae and used as roots for medicinal part and purpose. R. glutinosa is and usually used for fresh rehmannia root or prepared rehmannia root. However, it is very difficult to propagate using the seeds because of lack germination so it is propagated using the vegetative method as the rootstock. Currently, propagation and harvesting using the rootstock of R. glutinosa has difficulties about production of the high quality and quantity in R. glutinosa because of root rot disease. To optimize in vitro cultures and to improve the rootstock and seedling of R. glutinosa after morphological and genetical determination, 5 plant culture media (MS, DJ, LS, QL, and WPM) were used in this study then WPM was selected for better growth, for multiplication condition(WPM + IAA 1.0 mg/L + IBA 0.5 mg/L), and for root enlargement condition(WPM + NAA 0.1 mg/L) of R. glutinosa. Based on these results, in vitro seedlings of R. glutinosa were transferred to soil for acclimation with environment adaptation and shown the positive effects about root enlargement and root formation. Therefore, it can be used for high quality of R. glutinosa production and production of the rootstock based on propagation using in vitro seedlings of R. glutinosa.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.4
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• pp.341-350
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• 2023

The templated grain growth (TGG) method has gained significant attention for its ability to produce highly textured piezoelectric ceramics with significantly enhanced performance, making it a promising method for transducer and actuator applications. However, the texturing process using the TGG method requires the optimization of multiple steps, which can be challenging for beginners in this field. Therefore, in this tutorial, we provide an overview of the TGG method mainly based on our previous published works, including its various processing steps such as synthesizing anisotropic-shaped templates with size and size distribution control using the molten salt synthesis technique, tape casting, and identifying key factors for proper alignment of the templates in the target matrix system. Our goal is to provide a resource that can serve as a basic reference for researchers and engineers looking to improve their understanding and utilization of the TGG method for producing textured piezoelectric ceramics.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.307-319
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• 2023

This study aims to explain the key concepts and principles of text-based generative artificial intelligence (AI) that has been receiving increasing interest and utilization, focusing on its application in science education. It also highlights the potential and limitations of utilizing generative AI in science education, providing insights for its implementation and research aspects. Recent advancements in generative AI, predominantly based on transformer models consisting of encoders and decoders, have shown remarkable progress through optimization of reinforcement learning and reward models using human feedback, as well as understanding context. Particularly, it can perform various functions such as writing, summarizing, keyword extraction, evaluation, and feedback based on the ability to understand various user questions and intents. It also offers practical utility in diagnosing learners and structuring educational content based on provided examples by educators. However, it is necessary to examine the concerns regarding the limitations of generative AI, including the potential for conveying inaccurate facts or knowledge, bias resulting from overconfidence, and uncertainties regarding its impact on user attitudes or emotions. Moreover, the responses provided by generative AI are probabilistic based on response data from many individuals, which raises concerns about limiting insightful and innovative thinking that may offer different perspectives or ideas. In light of these considerations, this study provides practical suggestions for the positive utilization of AI in science education.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.388-393
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• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.401-410
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• 2023

In this paper, we propose optimization methods for implementing SPARKLE, one of the NIST LWC finalists, on a 64-bit ARMv8 processor. The proposed methods consist of two approaches: an implementation using ARM A64 instructions and another using NEON ASIMD instructions. The A64-based implementation is optimized by performing register scheduling to efficiently utilize the available registers on the ARMv8 architecture. By utilizing the optimized A64-based implementation, we can achieve speeds that are 1.69 to 1.81 times faster than the C reference implementation on a Raspberry Pi 4B. The ASIMD-based implementation, on the other hand, optimizes data by parallelizing the ARX-boxes to perform more than three of them concurrently through a single vector instruction. While the general speed of the optimized ASIMD-based implementation is lower than that of the A64-based implementation, it only slows down by 1.2 times compared to the 2.1 times slowdown observed in the A64-based implementation as the block size increases from SPARKLE256 to SPARKLE512. This is an advantage of the ASIMD-based implementation. Therefore, the ASIMD-based implementation is more efficient for SPARKLE variant block cipher or permutation designs with larger block sizes than the original SPARKLE, making it a useful resource.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.395-401
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• 2009

One of the key design factors for the ventilation and safety system at extra long tunnel is the airflow velocity induced by the natural ventilation force. Despite of the importance, it has not been widely studied due to the complicated influencing variables and the relationship among them is difficult to quantify. At this moment none of the countries in the world defines its specific value on verified ground. It is also the case in Korea. The recent worldwide disasters by tunnel fires and demands for better air quality inside tunnel by users require the optimization of the tunnel ventilation system. This indicates why the natural ventilation force is necessary to be thoroughly studied. This paper aims at predicting the natural ventilation force at a 11 km-long tunnel which is in the stage of detailed design and will be the longest vehicle tunnel in Korea. The concept of barometric barrier which can provide the maximum possible natural ventilation force generated by the topographic effect on the external wind is applied to estimate the effect of wind pressure and the chimney effect caused by the in and outside temperature difference is also analyzed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.17-23
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• 2009

Due to the rapid increase of vehicles on the street, Korean society is facing worsening traffic congestions and air pollutions. Also, the oil price pickup has led to increasing need for the use of public transportation. In particular, transfering among public transportation may be a main factor for riders who are commuting for a long distance journey. In order to ensure such connectivity, transfer stations have been actively built in Korea. However, it would be necessary to shift those vehicles, from cars to public transportations by enhancing the users' satisfaction with public transportation through strategies for minimizing the users' waiting cost by building an efficient connective system between transportation modes as well as the preparation of aforementioned transfer stations. Therefore, this study aimed to develop an algorithm for minimizing transferring passengers' waiting costs based on service intervals of linked buses within the transfer facilities. In order to adjust the service interval, we calculated the total costs, involving the wait cost of transfer passengers and bus operation costs, and produced an allocation interval, that would minimize the costs. We selected a KTX departing from Seoul station, and a No. 6014 bus route in Gwangmyeong city where it starts from the Gwangmyeong station in order to for verifying the model. Then, the transfer passengers' total waitting cost was reduced equivalent to the maximum of 212 minutes, and it revealed that the model performed very effectively.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.85-92
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• 2023

Recently, interest in artificial intelligence has increased due to the development of artificial intelligence fields such as ChatGPT and self-driving cars. However, there are still many unknown elements in training process of artificial intelligence, so that optimizing the model requires more time and effort than it needs. Therefore, there is a need for a tool or methodology that can analyze the weight changes during the training process of artificial intelligence and help out understatnding those changes. In this research, I propose a visualization system which helps people to understand the accumulated weight changes. The system calculates the weights for each training period to accumulates weight changes and stores accumulated weight changes to plot them in 3D space. This research will allow us to explore different aspect of artificial intelligence learning process, such as understanding how the model get trained and providing us an indicator on which hyperparameters should be changed for better performance. These attempts are expected to explore better in artificial intelligence learning process that is still considered as unknown and contribute to the development and application of artificial intelligence models.