• BMB Reports
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• v.55 no.11
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• pp.565-570
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• 2022

Pulmonary arterial hypertension (PAH) is a progressive and devastating disease whose pathogenesis is associated with a phenotypic switch of pulmonary arterial vascular smooth muscle cells (PASMCs). Bone morphogenetic protein (BMP) signaling and potassium two pore domain channel subfamily K member 3 (KCNK3) play crucial roles in PAH pathogenesis. However, the relationship between BMP signaling and KCNK3 expression in the PASMC phenotypic switching process has not been studied. In this study, we explored the effect of BMPs on KCNK3 expression and the role of KCNK3 in the BMP-mediated PASMC phenotypic switch. Expression levels of BMP receptor 2 (BMPR2) and KCNK3 were downregulated in PASMCs of rats with PAH compared to those in normal controls, implying a possible association between BMP/BMPR2 signaling and KCNK3 expression in the pulmonary vasculature. Treatment with BMP2, BMP4, and BMP7 significantly increased KCNK3 expression in primary human PASMCs (HPASMCs). BMPR2 knockdown and treatment with Smad1/5 signaling inhibitor substantially abrogated the BMP-induced increase in KCNK3 expression, suggesting that KCNK3 expression in HPASMCs is regulated by the canonical BMP-BMPR2-Smad1/5 signaling pathway. Furthermore, KCNK3 knockdown and treatment with a KCNK3 channel blocker completely blocked BMP-mediated anti-proliferation and expression of contractile marker genes in HPAMSCs, suggesting that the expression and functional activity of KCNK3 are required for BMP-mediated acquisition of the quiescent PASMC phenotype. Overall, our findings show a crosstalk between BMP signaling and KCNK3 in regulating the PASMC phenotype, wherein BMPs upregulate KCNK3 expression and KCNK3 then mediates BMP-induced phenotypic switching of PASMCs. Our results indicate that the dysfunction and/or downregulation of BMPR2 and KCNK3 observed in PAH work together to induce aberrant changes in the PASMC phenotype, providing insights into the complex molecular pathogenesis of PAH.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.257-270
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• 2022

In this paper, we design a smart factory risk improvement system and risk improvement method using AI edge devices. The smart factory risk improvement system collects, analyzes, prevents, and promptly responds to the worker's work performance process in the smart factory using AI edge devices, and can reduce the risk that may occur during work with improving the defect rate when workers perfom jobs. In particular, based on worker image information, worker biometric information, equipment operation information, and quality information of manufactured products, it is possible to set an abnormal risk condition, and it is possible to improve the risk so that the work is efficient and for the accurate performance. In addition, all data collected from cameras and IoT sensors inside the smart factory are processed by the AI edge device instead of all data being sent to the cloud, and only necessary data can be transmitted to the cloud, so the processing speed is fast and it has the advantage that security problems are low. Additionally, the use of AI edge devices has the advantage of reducing of data communication costs and the costs of data transmission bandwidth acquisition due to decrease of the amount of data transmission to the cloud.

• Safety and Health at Work
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• v.13 no.2
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• pp.163-169
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• 2022

Background: It is estimated that about 13 million artisanal and small-scale miners carry out their activities under harsh, precarious, unfriendly, and risky conditions. Yet, our understanding of the extent to which these workers use personal protective equipment (PPE) and navigate through the various risks and hazards they face is still limited. This article has two main objectives. First, it explores the extent of usage of PPE among artisanal and small-scale miners for the prevention of hazards and risks. Second, it examines the coping strategies used by these miners as a response to experiences of occupational injuries and risks Methods: A cross-sectional survey of small-scale miners was conducted in six communities across three districts in Ghana, West Africa. The mixed methods approach was adopted. A total of 148 small-scale miners participated in the study. Six focus group discussions (FGDs) were held across the six communities. The data were analysed using descriptive statistics. Chi-square tests were used to analyse the relationship between some socio-demographic characteristics (sex, age, and educational background) and the usage of PPE. Open-ended questions and responses from FGDs were analysed based on the content and verbatim quotations from miners. Results: Findings suggest that 78% of the miners interviewed do not use the appropriate PPE citing reasons such as cost, and their personal discomfort associated with use of PPE. There was no significant relationship between socio-demographic characteristics (i.e., sex, age, education and major mining activity) and the usage of PPE. The study further revealed four main coping strategies used by miners to handle the risks. These are rest, taking unprescribed medication and hard drugs, registration with health insurance scheme and savings and investments. Conclusion: This study shows that very few artisanal miners use PPE despite the significant hazards and risks to which they are exposed. The study recommends to the government to put in place measures to ensure that miners adhere to health and safety regulations before undertaking mining activities. This means that health and safety plans and use of PPE should be linked to the license acquisition process for miners.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• Journal of Science Education
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• v.46 no.3
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• pp.312-325
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• 2022

This study aims to provide a science history content system necessary in the course design process of liberal arts subjects, along with the application of flip learning in liberal arts science classes for humanities and social sciences students. For the research, we analyzed the current state of the liberal arts and history of science classes at universities. Then we developed the 'Understanding the History of Science' subject by applying the flip learning method through the analysis of various previous studies. As the goal of science history lectures that can reach the essential purposes of science liberal arts education, including knowledge acquisition and strengthening various competencies, scientific attitude cultivation was set, and the content system of week 15 was designed to consider this. The four topics corresponding to the "History of Science" part of the "Understanding Science History" content system consisted of flipped learning classes and teaching and learning activities, including online video materials and group discussion activities. As a result of opening courses for students in the humanities and social sciences and operating classes for 56 college students, it was confirmed that students' interest and awareness of science increased. This study provides educational evidence for science history and liberal arts education.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.41-49
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• 2022

Recently interests on the application of thermal cameras have increased with the advance of image analysis technology. Aside from a simple image acquisition, applications such as digital twin and thermal image management systems have gained popularity. To this end, we studied the effect of emissivity on the DN (Digital Number) value in the process of derivation of a relational expression for converting DN to an actual surface temperature. The DN value is a number representing the spectral band value of the thermal image, and is an important element constituting the thermal image data. However, the DN value is not a temperature value indicating the actual surface temperature, but a brightness value indicating high and low heat as brightness, and has a non-linear relationship with the actual surface temperature. The reliable relationship between DN and the actual surface temperature is critical for a thermal image processing. We tested the relationship between the actual surface temperature and the DN value of the thermal image, and then the radiation adjustment was performed to better estimate actual surface temperatures. As a result, the relation graph between the actual surface temperature and the DN value similarly show linear pattern with the relation graph between the radiation-controlled non-contact thermometer and the DN value. And the non-contact temperature after adjusting the emissivity was closer to the actual surface temperature than before adjusting the emissivity.

• The Korean Journal of Franchise Management
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• v.2 no.2
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• pp.70-83
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• 2011

The number of agricultural products due to open in response to international competitiveness of the farming press and the need for structural adjustment in agriculture and in agricultural crops receive a fair market price, fair trade, such as improving efficiency sidaejeo appropriate response to the request of the government must have. For this purpose, compared to the communist bojaphan agricultural water efficiently changing the structure of the distribution, agricultural products originating from the acquisition phase choice of shipping a stable product supply and plans to expand production system to induce a smooth supply of agricultural products, expand processing capacity and sales control should be. Also, in the distribution process by eliminating various immoral conduct commerce retail establishment and enforcement of policies for efficient and accurate distribution statistics, information is needed. The ultimate goal of agricultural restructuring and ensure fair price for producers sangpuui and improvement of production facilities for maximizing and affordable for consumers, according to your preferences to receive the best offer will be Foo. Therefore, management increases the efficiency of just distribution costs, or margins, lowering the improvement is not practical to restructure the distribution structure of costs for the best product and must supply the next country of agricultural products, strengthen the consumer's purchase desire to meet will be.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.171-179
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• 2023

Due to man-made climate change, global abnormal weather phenomena have occurred, increasing disasters. Major developed countries(military) are preparing for disasters caused by extreme weather appearances. However, currently, disaster prevention plans and facilities have been implemented based on the frequency and intensity method based on statistical data, it is not enough to prepare for disasters caused by frequent extreme weather based on probability basis. The U.S. and British forces have been the fastest to take research and policy approaches related to climate change and the threat of disaster change, and are considering both climate change mitigation and adaptation. The South Korean military regards the perception of disasters to be storm and flood damage, and there is a lack of discussion on extreme weather and disasters due to climate change. In this study, the process of establishing disaster management systems in developed countries(the United States and the United Kingdom) was examined, and the response policies of each country(military) were analyzed using literature analysis techniques. In order to maintain tight security, our military should establish a response policy focusing on sustainability and resilience, and the following three policy approaches are needed. First, it is necessary to analyze the future operational environment of the Korean Peninsula in preparation for the environment that will change due to climate change. Second, it is necessary to discuss climate change 'adaptation policy' for sustainability. Third, it is necessary to prepare for future disasters that may occur due to climate change.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.