• Journal of the Society of Cosmetic Scientists of Korea
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• v.47 no.2
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• pp.163-170
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• 2021

This study was conducted to create a technology to remove acne bacteria with human-friendly materials. First, the Cutibacterium acnes (C. acnes) were adsorbed to the mica disc to grow, and then the biofilm was checked through an atomic microscope to see if the biofilm had grown. Based on the topographic image, the shape changed round, the size was 17% longer on average, and the phase value of the resonance frequency separating materials was observed as a single value, the biofilm grown by covering the extracellular polymeric substrate (EPS). As a result of processing 50 mM of amino acids in the matured biofilm, the concentration of C. acnes decreased when valine, serine, arginine and leucine were treated. Scanning with nanoindentation and AFM contact modes confirmed that the hardness of biofilms treated with Valine (Val) increased. This indicates that an AFM tip measured cell which may have more solidity than that of EPS. The experiment of fluorescent tagged to EPS displays an existence of EPS at the condition of 10 mM Val, but an inhibition of growth of EPS at the 50 mM Val. Number of C. acnes was also reduced above 10 mM of Val. Weak adhesion of biofilm generated from an inhibition of EPS formation seems to induce decrease of C. acnes. Accordingly, we elucidated that Val has an efficiency which eliminates C. acnes by approach of an inhibition of EPS.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.8
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• pp.671-680
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• 2021

This paper deals with the contents of designing and producing reconnaissance hardware and software, and verifying the functions after being installed on the CanSat platform and ground stations. The main reconnaissance mission is largely composed of two things: terrain search that renders the surrounding terrain in 3D using radar, GPS, and IMU sensors, and real-time detection of major objects through optical camera image analysis. In addition, data analysis efficiency was improved through GUI software to enhance the completeness of the CanSat system. Specifically, software that can check terrain information and object detection information in real time at the ground station was produced, and mission failure was prevented through abnormal packet exception processing and system initialization functions. Communication through LTE and AWS server was used as the main channel, and ZigBee was used as the auxiliary channel. The completed CanSat was tested for air fall using a rocket launch method and a drone mount method. In experimental results, the terrain search and object detection performance was excellent, and all the results were processed in real-time and then successfully displayed on the ground station software.

• Journal of Electrochemical Science and Technology
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• v.13 no.1
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• pp.148-158
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• 2022

The electrochemical CO₂ reduction (ECR) to produce value-added fuels and chemicals using clean energy sources (like solar and wind) is a promising technology to neutralize the carbon cycle and reproduce the fuels. Presently, the ECR has been the most attractive route to produce carbon-building blocks that have growing global production and high market demand. The electrochemical CO₂ reduction could be extensively implemented if it produces valuable products at those costs which are financially competitive with the present market prices. Herein, the electrochemical conversion of CO₂ obtained from flue gases of a power plant to produce diesel and formic acid using a consistent techno-economic approach is presented. The first scenario analyzed the production of diesel fuel which was formed through Fischer-Tropsch processing of CO (obtained through electroreduction of CO₂) and hydrogen, while in the second scenario, direct electrochemical CO₂ reduction to formic acid was considered. As per the base case assumptions extracted from the previous outstanding research studies, both processes weren't competitive with the existing fuel prices, indicating that high electrochemical (EC) cell capital cost was the main limiting component. The diesel fuel production was predicted as the best route for the cost-effective production of fuels under conceivable optimistic case assumptions, and the formic acid was found to be costly in terms of stored energy contents and has a facile production mechanism at those costs which are financially competitive with its bulk market price. In both processes, the liquid product cost was greatly affected by the parameters affecting the EC cell capital expenses, such as cost concerning the electrode area, faradaic efficiency, and current density.

• The Journal of Sustainable Design and Educational Environment Research
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• v.21 no.4
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• pp.21-28
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• 2022

This study analyzes the reality of the overall operating system, such as recognition and satisfaction with school field support work system of the Education Facilities Management Center, recognition level of restructuring of work areas, and the direction of improvement for school facility maintenance support in various types of future learning environments. To analyze the problem of this study, a survey was conducted on 290 education administrative officials in Gyeonggi-do. First, school site awareness and work performance satisfaction of the Educational Facilities Management Center were evaluated as "below average," and it is necessary to improve the qualitative work area that is practically helpful to schools. Second, in the area of organizational operation, it is desirable to avoid simple tasks with a low evaluation of "below average" and to switch to an operating system that improves efficiency. Third, the need for the facility environment area (professionalism, safety) was the highest, but the center's ability and work processing level were evaluated very low as "below average," so it is urgent to improve the center's capacity. Fourth, in the area of social and educational policy change, the center received a high score for the need for access from the perspective of a learning environment linked to future education. Therefore, a policy review on the restructuring and improvement of work areas suitable for this is necessary.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.77-89
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• 2022

It has been proven through many previous researches that the pretrained language model with a large corpus helps improve performance in various natural language processing tasks. However, there is a limit to building a large-capacity corpus for training in a language environment where resources are scarce. Using the Cross-lingual Post-Training (XPT) method, we analyze the method's efficiency in Korean, which is a low resource language. XPT selectively reuses the English pretrained language model parameters, which is a high resource and uses an adaptation layer to learn the relationship between the two languages. This confirmed that only a small amount of the target language dataset in the relationship extraction shows better performance than the target pretrained language model. In addition, we analyze the characteristics of each model on the Korean language model and the Korean multilingual model disclosed by domestic and foreign researchers and companies.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.33-39
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• 2023

As the number of people with chronic diseases increases due to an aging society, it is urgent to prevent and manage their diseases. Although biometric authentication methods and Telemedicine Systems have been introduced to solve these problems, it is difficult to solve the security problem of medical information and personal authentication. Since smart healthcare includes personal medical information of subjects, the security of personal information is the most important field. Therefore, in this paper, we tried to propose a Telemedicine System using a smart wearable device ECG in the form of a wristband and face personal authentication in a private blockchain environment. This system targets various medical personnel and patients with chronic diseases in all regions, and uses a private blockchain that can increase data integrity and transparency, ECG and face authentication that are difficult to forge and alter and have high personal identification to provide a system with high security and reliability. composed. Through this, it is intended to contribute to increasing the efficiency of chronic disease management by focusing on disease prevention and health management for patients with chronic diseases at home.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.43-53
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• 2023

In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.27-30
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• 2022

The plasma process, which has many advantages in terms of efficiency and environment compared to conventional process methods, is widely used in semiconductor manufacturing. Plasma Sheath is a dark region observed between the plasma bulk and the chamber wall surrounding it or the electrode. The Plasma Sheath Monitoring Sensor (PSMS) measures the difference in voltage between the plasma and the electrode and the RF power applied to the electrode in real time. The PSMS data, therefore, are expected to have a high correlation with the state of plasma in the plasma chamber. In this study, a model for predicting the state of nitrogen ions in the plasma chamber is training by a deep learning machine learning techniques using PSMS data. For the data used in the study, PSMS data measured in an experiment with different power and pressure settings were used as training data, and the ratio, flux, and density of nitrogen ions measured in plasma bulk and Si substrate were used as labels. The results of this study are expected to be the basis of artificial intelligence technology for the optimization of plasma processes and real-time precise control in the future.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.415-422
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• 2021

Cotton is an important fiber crop, and its seeds are used as feed for dairy cattle. Crop biotechnology has been used to improve agronomic traits and quality in the agricultural industry. The frequent unintentional release of LM cotton into the environment in South Korea is attributed to the increased application of living modified (LM) cotton in food, feed, and processing industries. To identify and monitor the LM cotton, a method for detecting the approved LM cotton in South Korea is required. In this study, we developed a method for the simultaneous detection of four LM cotton varieties, MON757, MON88702, COT67B, and GHB811. The genetic information of each LM event was obtained from the European Commission-Joint Research Centre and Animal and Plant Quarantine Agency. We designed event-specific primers to develop a multiplex PCR method for LM cotton and confirmed the specific amplification. Using specificity assay, random reference material(RM) mixture analysis and limit of detection(LOD), we verified the accuracy and specificity of the multiplex PCR method. Our results demonstrate that the method enabled the detection of each event and validation of the specificity using other LM RMs. The efficiency of multiplex PCR was further verified using a random RM mixture. Based on the LOD, the method identified 25 ng of template DNA in a single reaction. In summary, we developed a multiplex PCR method for simultaneous detection of four LM cotton varieties, for possible application in LM volunteer analysis.