• Tunnel and Underground Space
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• v.34 no.3
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• pp.208-217
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• 2024

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.2
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• pp.103-110
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• 2024

Lipid nanoparticles (LNPs) are a stable and an effective system that protects cell-impermeable biologically active compounds such as nucleic acids, proteins, and peptides against degradation caused by subtle environmental changes. This study focuses on developing LNPs encapsulating gallic acid (GA), an antioxidant, to effectively prolong the half-life of tetrahexyldecyl ascorbate (THDC), a oil-soluble vitamin C derivative. These LNPs were synthesized in small, uniform sizes at room temperature and pressure conditions using a microfluidics chip. Compared to liposomes manufactured under high pressure and high temperature conditions through conventional microfluidizers, LNPs manufactured through microfluidics chips had excellent dispersion and temperature stability, and improved skin absorption as well as improved oxidative stability of fat-soluble vitamin C derivatives. Future studies will focus on ex vivo and in vivo evaluations to study skin improvement to further validate these results.

• Food Science and Preservation
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• v.30 no.2
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• pp.334-346
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• 2023

The antioxidant ability of 80% ethanolic extract of nutmeg seed (NM80) was evaluated using in vitro assays and bulk oil and oil-in-water (O/W) emulsion matrices. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) cation radical scavenging, and oxygen radical antioxidant capacity (ORAC) in vitro assays were used to evaluate the antioxidant ability of the extract. The DPPH radical scavenging activities of 25, 50, 100, and 200 ㎍/mL NM80 were 12.5, 20.9, 35.1, and 62.8%, respectively, while the ABTS cation radical scavenging activities were 2.7, 6.5, 30.5, and 29.8%, respectively, demonstrating a dose-dependent effect. The ORAC value was significantly higher at an NM80 concentration of 25 ㎍/mL than the positive control (p<0.05). The conjugated dienoic acid (CDA), ρ-anisidine, and tertiary butyl alcohol values in 90-min-heated corn oil containing 200 ppm of NM80 were significantly reduced by 3.26, 16.94, and 17.34%, respectively, compared to those for the sample without NM80 (p<0.05). However, the headspace oxygen content and CDA value in the O/W emulsion containing 200 ppm of NM80 at 60℃ had 6.29 and 82.85% lower values, respectively, than those for the sample without NM80 (p<0.05). The major volatile compounds of NM80 were allyl phenoxyacetate, eugenol acetate, and eugenol. NM80 could be an effective natural antioxidant in lipid-rich foods in bulk oil or O/W emulsion matrix.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.139-145
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• 2024

This paper presents the design of a voltage controlled oscillator (VCO) which is one of the key building blocks in modern wireless communication systems with small VCO gain (K_vco) variation. To compensate conventional large K_vco variation, a series varactor bank has been added to the conventional LC-tank with parallel capacitor bank array. And also, in order to achieve excellent phase noise performance while maintaining wide tuning range, a mixed coarse/fine tuning scheme(series varactor array and parallel capacitor array) is chosen. The switched varactor array bank is controlled by the same digital code for switched capacitor array without additional digital circuits. For use at a low voltage of 1.2V, the proposed current reference circuit in this paper used a current reference circuit for safety with the common gate removed more safely. Implemented in a TSMC 0.13㎛ CMOS RF technology, the proposed VCO can be tuned from 4.4GH to 5.3GHz with the K_vco (VCO gain ) variation of less than 9.6%. While consuming 3.1mA from a 1.2V supply, the VCO has -120dBc/Hz phase noise at 1MHz offset from the carrier of the 5.3 GHz.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.394-403
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• 2023

In the pursuit of sustainable and environmentally-friendly agricultural practices, we conducted an extensive study on the rhizosphere bacteria inhabiting soils that have been devoid of chemical fertilizers for an extended period exceeding 40 years. Through this investigation, we isolated a total of 80 species of plant growth-promoting rhizosphere bacteria and assessed their potential to enhance plant growth. Among these isolates, Burkholderia cepacia CD2 displayed remarkable plant growth-promoting activity, making it an optimal candidate for further analysis. Burkholderia cepacia CD2 exhibited a range of beneficial characteristics conducive to plant growth, including phosphate solubilization, siderophore production, denitrification, nitrate utilization, and urease activity. These attributes are well-known to positively influence the growth and development of plants. To validate the taxonomic classification of the strain, 16S rRNA gene sequencing confirmed its placement within the Burkholderia genus, providing further insights into its phylogenetic relationship. To delve deeper into the potential mechanisms underlying its plant growth-promoting properties, we sought to confirm the presence of specific genes associated with plant growth promotion in CD2. To achieve this, whole genome sequencing (WGS) was performed by Plasmidsaurus Inc. (USA) utilizing Oxford Nanopore technology (Abingdon, UK). The WGS analysis of the genome of CD2 revealed the existence of a subsystem function, which is thought to be a pivotal factor contributing to improved plant growth. Based on these findings, it can be concluded that Burkholderia cepacia CD2 has the potential to serve as a microbial fertilizer, offering a sustainable alternative to chemical fertilizers.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.303-320
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• 2024

This study presents the development and performance evaluation of a custom GPT-based chatbot tailored to provide solutions following Polya's problem-solving stages. A beta version of the chatbot was initially deployed to assess its mathematical capabilities, followed by iterative error identification and correction, leading to the final version. The completed chatbot demonstrated an accuracy rate of approximately 89.0%, correctly solving an average of 57.8 out of 65 image-based problems from a 6th-grade elementary mathematics textbook, reflecting a 4 percentage point improvement over the beta version. For a subset of 50 problems, where images were not critical for problem resolution, the chatbot achieved an accuracy rate of approximately 91.0%, solving an average of 45.5 problems correctly. Predominant errors included problem recognition issues, particularly with complex or poorly recognizable images, along with concept confusion and comprehension errors. The custom chatbot exhibited superior mathematical performance compared to the general-purpose ChatGPT. Additionally, its solution process can be adapted to various grade levels, facilitating personalized student instruction. The ease of chatbot creation and customization underscores its potential for diverse applications in mathematics education, such as individualized teacher support and personalized student guidance.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Journal of Family Resource Management and Policy Review
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• v.28 no.2
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• pp.27-38
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• 2024

The purpose of this study was to identify issues in the family support center evaluation project by analyzing the differences in perception between evaluators and the family Support center using a meta-evaluation analysis model and seeking improvement alternatives. The results revealed a significant difference in group average: the evaluator group scored 4.21 out of 5 points, and the family center group scored 3.20 points. The improvement alternatives for each meta-evaluation item are as follows. In the evaluation environment, it is necessary to specify the purpose and utilization of evaluation within the guidelines of the Ministry of Gender Equality and Family. Evaluation input required the establishment of an evaluation support organization within the Korean Institute for Healthy Family. During the evaluation process, it was necessary to improve the use of the integrated family support information system and diversify communication channels. The evaluation results required the strengthening of follow-up education for family centers. In terms of evaluation utilization, it was necessary to strengthen support for various incentives and subcenters. This study provides implications for improving the evaluation system for various policy service delivery systems.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.