• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.27-33
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• 2023

Demands for eco-friendly food materials are increasing rapidly because of increased interest in well-being and health care, deterioration of air quality due to fine dust, and various soil and water pollution. Aquaponics is a system that can solve various problems such as economic activities, environmental problems, and safe food provision of the elderly population. However, techniques for deriving the optimal growth environment should be preceded. In this paper, we intend to design an intelligent plant growth measurement system that considers the characteristics of existing aquaponics. In particular, we would like to propose a module configuration plan for learning data and judgment systems when providing a uniform growth environment, focusing on designing systems suitable for production sites that do not have high-performance processing resources among intelligent aquaponics production management modules. It is believed that the proposed system can effectively perform deep learning with small analysis resources.

• Membrane Journal
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• v.33 no.4
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• pp.201-210
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• 2023

Dust filter membranes play a crucial role in human life and various industries, as they contribute to several important aspects of human health, safety, and environmental protection. This study presents the development of a polysulfone@polyphenylene sulfide/polytetrafluoroethylene (PSf@PPS/ePTFE) composite dust filter membrane with excellent thermal stability and adhesion properties for high-temperature conditions. FT-IR analysis confirms successful impregnation of PSf adhesive onto PPS fabric and interaction with ePTFE support. FE-SEM images reveal improved fiber interconnection and adhesion with increased PSf concentration. PSf@PPS/ePTFE-5 exhibits the most suitable porous structure. The composite membrane demonstrates exceptional thermal stability up to 400℃. Peel resistance tests show sufficient adhesion for dust filtration, ensuring reliable performance under tough, high-temperature conditions without compromising air permeability. This membrane offers promising potential for industrial applications. Further optimizations and applications can be explored.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.157-165
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• 2009

In this paper, we propose a distributed model that recognize ADLs of human can be occurred in daily living places. We collect and analyze user's environmental, location or activity information by simple sensor attached home devices or utensils. Based on these information, we provide a lifecare services by inferring the user's life pattern and health condition. But in order to provide a lifecare services well-refined activity recognition data are required and without enough inferred information it is very hard to build an ADL activity recognition model for high-level situation awareness. The sequence that generated by sensors are very helpful to infer the activities so we utilize the sequence to analyze an activity pattern and propose a distributed linear time inference algorithm. This algorithm is appropriate to recognize activities in small area like home, office or hospital. For performance evaluation, we test with an open data from MIT Media Lab and the recognition result shows over 75% accuracy.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.401-406
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• 2023

With the development of industry, fine dust is causing difficulties in various fields such as environment, health, and life, and a large amount of pollutants generated from human social activities are emerging as a serious environmental problem due to air pollution. Therefore, in this study, activated carbon was added to remove fine dust and volatile organic compounds by spinning cellulose acetate polymer fibers on a silicon support using the electrospinning method. By varying the activated carbon ratio and electrospinning time, the fine dust blocking effect and toluene adsorption performance were confirmed according to the activated carbon ratio and filter thickness. As a result, it was shown that the particles were effectively blocked with the increase in the electrospinning time due to the filter thickness increase. Adsorbed amount of toluene was increased with increase in activated carbon amount. Light transmittance was decrease with increase in electrospinning time, showing that there were light transmittance in filters electrospun for 20~30 minutes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.357-359
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• 2022

The market for eco-friendly food materials by online distribution is rapidly growing due to major environmental pollution such as air, soil, and water quality, and radical changes in living patterns caused by COVID-19. In addition, because of the aging population and the decrease in agricultural-related population due to social structural changes, aquaponics is emerging as a system that can solve problems such as independence of old economic activities, environmental protection, and securing healthy and safe food. This paper aims to design an intelligent plant growth measurement system among intelligent aquaponics production management modules for optimal growth environment derivation and quantitative production prediction by converging various ICT technologies into existing aquaponics systems. In particular, the focus is on designing systems suitable for production sites that do not have high-performance processing resources, and we propose a module configuration plan for production environments and training data and prediction systems.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.1-7
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• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.989-998
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• 2023

As diseases are already increasing due to aging population and changes in eating habits, interest in digital healthcare is increasing. According to the 2020 Elderly Status Survey, 84% of South Koreans aged 65 and older suffer from at least one chronic disease, and the number and duration of drugs taken by the elderly are also increasing. Effective management that enhances compliance of chronic disease patients can prevent the onset of complications, thereby averting progression to severe illnesses. Thus, a proper medication-taking habit is crucial. This paper proposes a medication information retrieval system using OCR technology. By leveraging Google Cloud Vision API, the system detects and recognizes the names of medicines. Once recognized, the medication name is searched in a database to provide users with medication information and medication schedule management services. By providing accurate medication information through the search, it is possible to induce changes in medication methods and habits. By eliminating the inconvenience of direct input through OCR technology, we anticipate enhancing user convenience by promptly delivering information.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.139-155
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• 2024

This study performed a multi-classification task to classify mesial temporal lobe epilepsy with left hippocampal sclerosis patients (left mTLE), mesial temporal lobe epilepsy with right hippocampal sclerosis (right mTLE), and healthy controls (HC) using magnetoencephalography (MEG) data. We applied various artificial neural networks and compared the results. As a result of modeling with convolutional neural networks (CNN), recurrent neural networks (RNN), and graph neural networks (GNN), the average k-fold accuracy was excellent in the order of CNN-based model, GNN-based model, and RNN-based model. The wall time was excellent in the order of RNN-based model, GNN-based model, and CNN-based model. The graph neural network, which shows good figures in accuracy, performance, and time, and has excellent scalability of network data, is the most suitable model for brain research in the future.

• Smart Media Journal
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• v.13 no.10
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• pp.35-42
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• 2024

This study aims to develop a system that predicts the health status of cattle based on behavior patterns and environmental data within a smart barn using an autonomous driving system. Maintaining a unique ID for each cow using only a camera, without external devices (such as RFID tags), is essential. This enables the tracking of behavior patterns such as standing, sitting, and lying for each cow over time. Additionally, environmental data such as temperature and humidity are integrated to comprehensively assess the cows' health conditions. To achieve this, we propose a unique ID retention algorithm that combines object detection using YOLO, tracking with Deep SORT, and re-identification (ReID). Experimental results show that the YOLO + Deep SORT + ReID algorithm delivers the best performance in maintaining unique IDs, and the LSTM-based behavior analysis model demonstrates high accuracy in predicting behavior patterns. This system can serve as an effective tool for real-time prediction of livestock health conditions, such as disease or stress, through comprehensive analysis of environmental data and behavior patterns inside the barn.

• Smart Media Journal
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• v.13 no.8
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• pp.58-66
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• 2024

Nitrogen oxides(NOx) in coal-fired power plants are significant contributors to air pollution, influencing the formation of ozone and fine particulate matter, thereby adversely affecting health. Therefore, accurate prediction of NOx emissions is essential. Existing researches have mainly performed based on off-line learning methods, leading to poor prediction performance with the limited training dataset. This paper proposes the online learning model of online support vector regression to predict NOx emissions from coal-fired power plants. Online learning model, which updates a model whenever new observations come out, demonstrates high prediction accuracy even when initial data is scarce. The experimental results showed that the performance of online learning prediction was better than existing off-line learning methods. The results indicated online learning method is a valuable tool for predicting NOx emissions, especially in situations where initial data is limited and data is continuously updated in real-time.