• Journal of Environmental Health Sciences
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• v.45 no.2
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• pp.154-163
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• 2019

Objectives: The hazardous heavy metal contents of cosmetics were investigated and the resulting values were compared by type of cosmetics: skin care preparations (SCP), hair preparations (HP), makeup preparations (MP), and eye makeup preparations (EMP). Methods: The hazardous heavy metal contents (Pb, As, Cd, Sb, Ni and Hg) were analyzed for 358 cosmetics products (187 SCP, 82 HP, 56 MP, and 33 EMP). Hg was measured by the amalgamation method, and other hazardous heavy metals were measured by inductively coupled plasma optical emission spectrometer (ICP-OES) after decomposition using the microwave method. Results: The mean contents of Pb, As, Cd, Sb, Ni, and Hg in cosmetics were 0.424, 0.068, 0.024, 0.398, $0.567{\mu}/g$, and Not Detected, respectively. All of the hazardous heavy metals were detected in most products, but below the recommended maximums of the Ministry of Food and Drug Safety. The level of Cd was the lowest at 14.8%, and Sb was the highest at 41.2%. Pb, Sb and Ni showed the highest mean value and detection rate in EMP. As, Cd, and Hg showed the highest in SCP, HP, and MP, respectively. Conclusion: Hazardous heavy metals were detected in most products. In particular, Pb, Sb, and Ni were broadly detected in EMP, meaning more stringent quality control is required.

• Current Optics and Photonics
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• v.3 no.3
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• pp.256-262
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• 2019

Extensive tumor resection accompanied by radiotherapy and chemotherapy is the standard of care for malignant gliomas. However, there is a significant obstacle to the complete resection of the tumor due to the difficulty of distinguishing tumor and normal brain tissue with a conventional surgical microscope. Recently, multiple studies have shown the possibility of fluorescence-guided surgery in malignant gliomas. The most used fluorescence dyes for brain tumor surgery are 5-aminolevulinic acid (5-ALA) and indocyanine green (ICG). In this paper, a new fluorescence guided operation system, which can detect both 5-ALA and ICG fluorescent images simultaneously, is presented. This operation system consists of light emitting diodes (LEDs) which emits 410 nm and 740 nm wavelengths. We have performed experiments on rats in order to verify the operation of the newly developed operation system. Oral administration and imaging were performed to observe the fluorescence of 5-ALA and ICG fluorescence in rats. When LEDs at wavelengths of 410 nm and 740 nm were irradiated on rats, 628 nm wavelength with a violet fluorescence color and 825 nm wavelength with a red fluorescence color were expressed in 5-ALA and ICG fluorescent material, respectively, thus we were able to distinguish the tumor tissues easily. Previously, due to the poor resolution of the conventional surgical microscope and the fact that the color of the vein is similar to that of the tumor, the tumor resection margin was not easy to observe, thus increasing the likelihood for cancer recurrence. However, when the tumor is observed through the fluorescence guided operation system, it is possible to easily distinguish the color with the naked eye and it can be completely removed. Therefore, it is expected that surgical removal of cancerous tumors will be possible and surgical applications and surgical microscopes for cancer tumor removal surgery will be promising in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.584-590
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• 2020

Long-term use of digital screens in daily life can lead to computer vision syndrome including symptoms such as eye strain, dry eyes, and headaches. To prevent computer vision syndrome, it is important to limit screen usage time and take frequent breaks. There are a variety of applications that can help users know the screen usage time. However, these apps are limited because users see various screens such as desktops, laptops, and tablets as well as smartphone screens. In this paper, we propose and evaluate machine learning-based models that detect the screen device in use using color, IMU and lidar sensor data. Our evaluation shows that neural network-based models show relatively high F1 scores compared to traditional machine learning models. Among neural network-based models, the MLP and CNN-based models have higher scores than the LSTM-based model. The RF model shows the best result among the traditional machine learning models, followed by the SVM model.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.54-63
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• 2021

Since the recent COVID-19 pandemic, countries have been strengthening trade protection for their security, and the importance of securing strategic materials, such as food, is drawing attention. In addition to the cultural aspects, the global preference for food produced in Korea is increasing because of the Korean Wave. Thus, the Korean food industry can be developed into a high-value-added export food industry. Currently, Korea has a low self-sufficiency rate for foodstuffs apart from rice. Korea also suffers from problems arising from population decline, aging, rapid climate change, and various animal and plant diseases. It is necessary to develop technologies that can overcome the production structures highly dependent on the outside world of food and foster them into export-type system industries. The global agricultural industry-related technologies are actively being modified via data accumulation, e.g., environmental data, production information, and distribution and consumption information in climate and production facilities, and by actively expanding the introduction of the latest information and communication technologies such as big data and artificial intelligence. However, long-term research and investment should precede the field of living organisms. Compared to other industries, it is necessary to overcome poor production and labor environment investment efficiency in the food industry with respect to the production cost, equipment postmanagement, development tailored to the eye level of field workers, and service models suitable for production facilities of various sizes. This paper discusses the flow of domestic and international technologies that form the core issues of the site centered on the 4th Industrial Revolution in the field of agriculture, livestock, and fisheries. It also explains the environmental awareness production technologies centered on sustainable intelligence platforms that link climate change responses, optimization of energy costs, and mass production for unmanned production, distribution, and consumption using the unstructured data obtained based on detection and growth measurement data.

• Genomics & Informatics
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• v.20 no.3
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• pp.26.1-26.19
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• 2022

Diabetes and its related complications are associated with long term damage and failure of various organ systems. The microvascular complications of diabetes considered in this study are diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. The aim is to identify the weighted co-expressed and differentially expressed genes (DEGs), major pathways, and their miRNA, transcription factors (TFs) and drugs interacting in all the three conditions. The primary goal is to identify vital DEGs in all the three conditions. The overlapped five genes (AKT1, NFKB1, MAPK3, PDPK1, and TNF) from the DEGs and the co-expressed genes were defined as key genes, which differentially expressed in all the three cases. Then the protein-protein interaction network and gene set linkage analysis (GSLA) of key genes was performed. GSLA, gene ontology, and pathway enrichment analysis of the key genes elucidates nine major pathways in diabetes. Subsequently, we constructed the miRNA-gene and transcription factor-gene regulatory network of the five gene of interest in the nine major pathways were studied. hsa-mir-34a-5p, a major miRNA that interacted with all the five genes. RELA, FOXO3, PDX1, and SREBF1 were the TFs interacting with the major five gene of interest. Finally, drug-gene interaction network elucidates five potential drugs to treat the genes of interest. This research reveals biomarker genes, miRNA, TFs, and therapeutic drugs in the key signaling pathways, which may help us, understand the processes of all three secondary microvascular problems and aid in disease detection and management.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.4
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• pp.593-599
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• 2022

In this study, a crack monitoring system capable of detecting cracks based on image processing techniques was developed to effectively check cracks, which are the main damage of concrete structures, and a program capable of imaging and analyzing cracks was developed using machine vision. This system provides objective and quantitative data by replacing the appearance inspection that checks cracks with the naked eye. The verification of the development system was applied to the construction site of a self-healing concrete water tank to monitor the crack and the amount of change in the crack width according to age. In the case of crack width detected by image analysis, the difference from the measured value using a digital microscope was up to 0.036 mm, and the crack healing effect of self-healing concrete could be confirmed through the reduction of crack width.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1279-1292
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• 2022

This study proposes a non-contact input method based on face recognition and Pyautogui mouse control as a system that can help users who have difficulty using input devices such as conventional mouse due to physical discomfort. This study includes features that help web surfing more conveniently, especially screen zoom, scroll function, and also solves the problem of eye fatigue, which has been suggested as a limitation in existing non-contact input systems. In addition, various set values can be adjusted in consideration of individual physical differences and Internet usage habits. Furthermore, no high-performance CPU or GPU environment is required, and no separate tracker devices or high-performance cameras are required. Through these studies, we intended to contribute to the realization of barrier-free access by increasing the web accessibility of the disabled and the elderly who find it difficult to use web content.

• Analytical Science and Technology
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• v.36 no.3
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• pp.121-127
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• 2023

The detection of blood at a crime scene is an important process for identification and case reconstruction. However, blood may be difficult to observe with the naked eye on dark or multi-colored surfaces. Acidic hydrogen peroxide (AHP) is a recently reported blood enhancement reagent that can enhance blood with high sensitivity by increasing the exposure time of the camera. However, it has never been compared to previously known techniques on dark or multi-colored surfaces. For this purpose, the method of observation/photographing (UV and IR photography), alginate casting, leuco rhodamine 6G (LR6G), and AHP were applied to bloody impression on dark or multi-colored surfaces and the results were compared. As a result, blood treated with AHP had a higher contrast to the surfaces than UV and IR photography, and it was applicable on all surfaces, opposed to alginate casting. In addition, AHP successfully enhanced blood on dark or multi-colored surfaces, similar to LR6G.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.638-651
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• 2023

The objective of this study was to quantitatively estimate the level of grazing area damage in outdoor free-range pig production using a Unmanned Aerial Vehicles (UAV) with an RGB image sensor. Ten corn field images were captured by a UAV over approximately two weeks, during which gestating sows were allowed to graze freely on the corn field measuring 100 × 50 m². The images were corrected to a bird's-eye view, and then divided into 32 segments and sequentially inputted into the YOLOv4 detector to detect the corn images according to their condition. The 43 raw training images selected randomly out of 320 segmented images were flipped to create 86 images, and then these images were further augmented by rotating them in 5-degree increments to create a total of 6,192 images. The increased 6,192 images are further augmented by applying three random color transformations to each image, resulting in 24,768 datasets. The occupancy rate of corn in the field was estimated efficiently using You Only Look Once (YOLO). As of the first day of observation (day 2), it was evident that almost all the corn had disappeared by the ninth day. When grazing 20 sows in a 50 × 100 m² cornfield (250 m²/sow), it appears that the animals should be rotated to other grazing areas to protect the cover crop after at least five days. In agricultural technology, most of the research using machine and deep learning is related to the detection of fruits and pests, and research on other application fields is needed. In addition, large-scale image data collected by experts in the field are required as training data to apply deep learning. If the data required for deep learning is insufficient, a large number of data augmentation is required.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.