• Journal of Industrial Convergence
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• v.19 no.6
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• pp.67-72
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• 2021

The elevator installed in the building consists of an elevator call button and an input button for selection to the target floor. The elevator button is input only when the elevator user directly presses it. Such passenger input can be infected with an infectious disease due to contamination of the button. A non-contact button is required as a means for solving this problem, which detects the proximity of an object by applying a capacitive method. It implements a function of measuring the body's body temperature by attaching an infrared heat sensor, and provides a sterilization function of a button by attaching a UV-LED. A button was selected, a body temperature was measured through an infrared temperature measurement sensor, and UV-LED was turned on and sterilized when there was no user. The contactless elevator button is expected to be effective in preventing infectious diseases as it can prevent infection of viruses carrying infectious diseases and can detect body temperature to select positive patients of CIVID 19.

• Applied Chemistry for Engineering
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• v.33 no.1
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• pp.78-82
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• 2022

Inorganic-organic hybrid perovskite solar cells have demonstrated considerable improvements, reaching 25.5% of certified power conversion efficiency (PCE) in 2020 from 3.8% in 2009 comparable to silicon photovoltacis. However, there remains important concern on the stability of perovskite solar cells under environmental conditions that should be solved prior to commercialization. In order to overcome the problem, we have introduced a small amount of octylammonium iodide with longer alkyl chain than volatile methylammonium iodide into MAPbI₃ perovskites. The presence of octylammonium into perovskites were confirmed using Fourier-transform infrared spectroscopy and UV-visible spectroscopy. Moreover, octylammonium-modified perovskite solar cells showed a PCE of 16.6% and enhanced moisture stability with an increased contact angle of 72.2° from 57.0°. This work demonstrated the importance of perovskite compositional engineering for improving efficiency and stability.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.5
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• pp.261-273
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• 2022

In this study, we collect water control valves that have had accidents due to existing cracks, etc. are collected, and propose investigation items for strengthening the valve structural safety evaluation through a series of analyzes from valve specifications to physicochemical properties are proposed. The results of this study are as follows. First, there was a large variation in the thickness of the body or flange of the valves to be investigated, which is considered to be very important factor, because it may affect the safety of the valve body against internal pressure and the flange connected with the bolt nut. Second, 60% of the valves under investigation had many voids in the valve body and flange, etc. and the decrease in thickness due to corrosion was relatively large on the inner surface in contact with water rather than the outer surface. It is judged that the investigation of depth included voids is very important factor. Third, all valves to be investigated are made of gray cast iron foam, and therefore it is judged that there is no major problem in chemical composition. It is judged that the chemical composition should be investigated. Fourth, as a physical investigation item, the analysis of metal morphology structure seems to be a very important factor for nodular cast iron from rather than a gray cast iron foam water valve with a flake structure. As it was found to be 46.7~68.8% of the standard recommended by KS, it could have a direct effect on damage such as cracks, and therefore it is judged that the evaluation of tensile strength is very important in evaluating the safety of the valve.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.3
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• pp.191-196
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• 2022

In this study, the anisotropic mechanical property of fused deposition modeling three-dimensional (3D) printing based on lamination direction was verified by a tensile test. Moreover, the property was applied to solid isotropic materials with penalization-based topology optimization. The case of the lower control arm, one of the automotive suspension components, was considered as a benchmark problem. The optimal topological results varied depending on the external load and anisotropic property. Based on these results, two test specimens were fabricated by varying the lamination direction of 3D printing; a tensile test utilizing 3D non-contact strain gauge was also conducted. The measured strain was compared with that obtained by computer-aided engineering response analysis. Quantitatively, the measurement and analysis results are found to have good agreement. The effectiveness of topology optimization considering the lamination direction of 3D printing was confirmed by the experimental result.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.905-910
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• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.343-347
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• 2022

Recently, the problem of global warming caused by greenhouse gases is getting serious due to the development of industry and the increase in transportation means. Accordingly, the need for a technology to reduce carbon dioxide, which accounts for most of the greenhouse gas, is increasing. Among them, a catalyst for converting carbon dioxide into fuel is being actively studied. Catalysts for reducing carbon dioxide are classified into thermal catalysts and photocatalysts. In particular, the photocatalyst has the advantage that carbon dioxide can be reduced only by irradiating ultraviolet rays at room temperature without high temperature or additional gas. TiO₂ is widely used as a photocatalyst because it is non-toxic and has high stability, but has a disadvantage of low carbon dioxide reduction efficiency. To increase the reduction efficiency, 1-propanol was used in the synthesis process. This prevents agglomeration of the catalyst and increases the specific surface area and pores of TiO₂, thereby increasing the surface area in contact with carbon dioxide. As a result of measuring the CO₂ reduction efficiency, it was confirmed that the efficiency of TiO₂ with 1-propanol and TiO₂ without 1-propanol was 19% and 12.3%, respectively, and the former showed a 1.5 times improved efficiency.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.167-175
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• 2023

Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture estimation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.

• Women's Health Nursing
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• v.29 no.1
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• pp.44-54
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• 2023

Purpose: Uncertainty and restrictions on daily life have increased fear, stress, and depression during the coronavirus disease 2019 (COVID-19) pandemic. Depression is the most common mental health problem in pregnant women. The purpose of this study was to evaluate the levels of fear and stress related to COVID-19 experienced by pregnant women, as well as their levels of depression, and to examine the factors associated with depression during pregnancy. Methods: This was a cross-sectional, correlational study conducted among 153 pregnant women who visited a maternity hospital in Busan, South Korea. A self-reported questionnaire was used for data collection from December 18, 2021 to March 8, 2022. Data were analyzed using descriptive statistics, the independent t-test, one-way analysis of variance, Pearson correlation coefficients, and multiple regression. Results: Pregnant women experienced a moderate level of fear related to COVID-19, with an average score of 21.55±4.90. The average score for depression during pregnancy was 14.86±11.10, with 50.3% of the participants experiencing depression (≥13). The factors associated with depression during pregnancy were fear of COVID-19, contact with a confirmed case of COVID-19, being in the third trimester of pregnancy, high stress levels due to difficulties experienced from social distancing measures, and unintended pregnancy. These five statistically significant factors explained 35.0% of variance in depression during pregnancy. Conclusion: Considering the prevalence of depression in pregnant women during the COVID-19 pandemic, it is necessary to develop interventions to reduce anxiety by providing correct information and alleviating the stress of social distancing.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.721-733
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• 2022

This study aimed to explore the experiences of community case managers in COVID-19 situations. For this study, the qualitative research was carried out for six social workers who are in charge of case-management in social welfare agencies. From results of research, the three themes were derived, such as 'a field of crises and opportunities'. Anxiety and efforts to resolve the problem caused by the introduction of new and unfamiliar methods' and 'responding to the clients' situations changes'. Case managers have endeavored to indirectly provide services with new, but unaccustomed ways even in the situations with keeping non-contact and being given limited resources and actively explored the ways to utilize public services to respond to the clients' needs according to the changes of conditions. Therefore, the meaning of this research was concluded to be 'A breathtaking battle to the continuous challenge'. Based on the results of research, the competences equipped by case managers and the direction of their practices were suggested to improve the community welfare currently in the middle of dealing with With-Corona-Era.