• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.46 no.4
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• pp.349-360
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• 2020

Bio-conversion manufacturing technology has been developed to produce ginsenoside Rd which is increasingly in demand as a cosmetic material due to various possibilities related to improving skin function. In order to convert ginsenoside Rb1 which is contained in red ginseng saponin (RGS) into Rd, several commercial enzymes were tested. Viscoflow MG was found to be the most efficient. In order to optimize the conversion of RGS to ginsenoside Rd by enzymatic transition was carried out using response surface methodology (RSM) based on Box-Behnken design (BBD). The main independent variables were RGS concentration, enzyme concentration, and reaction time. Conversion of ginsenoside Rd was performed under 17 conditions selected according to BBD model and optimization conditions were analyzed. The concentration of the converted ginsenoside Rd ranged from 0.3113 g/L to 0.5277 g/L, and the highest production volume was obtained under condition of reacting 2% RGS and 1.25% enzyme for 13.5 hours. Consequently, RGS concentration, enzyme concentration which is 0.05 less than p-value and among the interactions between the independent variables, the interaction between enzyme concentration and reaction time was confirmed to be the most influential.

• 한국노년학
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• v.40 no.2
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• pp.239-255
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• 2020

The purpose of this study is to examine whether family relationships of elderly single-person households affect community participation and whether these relationships differ by gender. Based on Baltes and Baltes (1990) 's selection, optimization, and compensation theory (SOC) and the argument that family members are a social capital by Prandini (2014), we test whether family relationships can affect community participation in old age. In order to verify this, single-person households were extracted from the 2017 National Survey of Living Conditions and Welfare Needs conducted by the Korea Institute for Health and Social Affairs (Male sample=370, Female sample=1770), multiple regression analysis were conducted with the dependent variables of friends·neighbors and the participation at Kyungrodang·welfare centers for the elderly. The results are as follows. In the case of men, family relations showed no significant effect on their participation in friends·neighbors, or Kyungrodang·welfare centers. However, in the case of women, the frequency of contact with family had a positive effect on the frequency of meeting friends·neighbors. Family contact frequency and child relationship satisfaction had a positive (+) effect on Kyungrodang·welfare center participation, while family meeting frequency had a negative effect on participation in Kyungrodang·welfare centers. For women, although Prandini's (2014) claim that family members are a social capital seems to be supported, it was found that the impact could vary depending on the type of community participation. In addition, practical discussions and suggestions were presented.

• Resources Recycling
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• v.31 no.4
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• pp.12-18
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• 2022

Korea depends on the import of raw materials such as iron ore and coal for the steel industry. These raw materials have a major impact on the cost, productivity, and quality competitiveness in the global steel industry. To secure the competitiveness of steel companies, it is necessary to reduce the country's dependence on raw materials. This can be achieved using byproducts with a high Fe content, which are primarily generated by the steel industry. These byproducts are available in the form of a very fine powder, which can disperse as dust when used directly in plant processes. Dust dispersion has a negative impact on the environment and can lead to the loss of raw materials. To enable the use of a wide range of Fe-containing byproducts, it is necessary to pretreat them in the form of larger aggregates such as pellets and briquettes. There are several methods to achieve such aggregates. There are two ways to produce briquettes: using a hot briquette, which supplies additional heat to produce briquettes, or using a cold briquette, which does not use heat. A method for producing cold briquettes using Fe-containing byproducts was investigated in this study. The yield ratio and briquette strength were examined under various manufacturing conditions.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.2
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• pp.102-110
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• 2022

This study was conducted to optimize the processing process for the oyster Crassostrea gigas gratin with cream sauce (OG-CS). The optimum concentration of added milk for oyster extract with milk (OE-M) was 35.0% based on the frozen-boiled oyster (F-BO), as suggested by the results of sensory evaluation. Response surface methodology was performed with whipping cream (WC)/[OE-M+mixed powder (garlic powder:onion powder=1:1) (MP)] (X₁) and OE-M/MP (X₂) as independent variables and viscosity (Y₁), amino acid nitrogen (Y₂), and overall acceptance for sensory evaluation (Y₃) as dependent variables. The optimal proportions were 74.55% of WC, 20.25% of OE-M, and 5.2% of MP, and the predicted multiple response optimal values for the dependent variables were 3,735.6 cP of Y₁, 197.0 mg/100 g of Y₂, and 6.2 score of Y₃. Under optimal conditions, the experimental values for Y₁, Y₂, and Y₃ were 3,711.9±30.0 cP, 198.1±1.9 mg/100 g, and 6.3±0.5 score, respectively, which were not significantly different from the predicted values (P>0.05). Further, the results of sensory evaluation suggested that the optimum concentration of macaroni:cheese (1:2) to be 46.2% based on the F-BO. The OG-CS prepared under these optimal conditions was superior to the commercial seafood gratin in overall acceptance.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.4
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• pp.47-54
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• 2021

In this study, the performance of a honeycomb-type hydrogen oxidation catalyst to remove hydrogen in a hydrogen economy society to secure leaking hydrogen. The Pd/TiO₂ catalyst was prepared based on a liquid phase reduction method that is not exposed to a heat source, and it was showed through H₂-chemisorption analysis that it existed as very small active particles of 2~4 nm. In addition, it was found that the metal dispersion decreased and the active particle size increased as the reduction reaction temperature increased. It was meant that the active metal particle size and the hydrogen oxidation performance were in a proportional correlation, so that it was consistent with the hydrogen oxidation performance reduction result. The prepared catalyst was coated on a support in the form of a honeycomb so that it could be applied to the hydrogen industrial process. When 20 wt% or more of the AS-40 binder was coated, oxidation performance of 90% or more was observed under low-concentration hydrogen conditions. It was showed through SEM analysis that long-term catalytic activity can be expected by enhancing the adhesion strength of the catalyst and preventing catalyst desorption. It is a basic research that can secure safety in a hydrogen society such as gasification, organic resource, and it can be utilized as a system that can respond to unexpected safety accidents in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.195-211
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• 2023

For the commercialization For the commercialization of autonomous vehicles (AV), the operational design domain (ODD) of automated driving systems (ADS) is to be clearly defined. A common language and consistent format must be prepared so that AV-related stakeholders can understand ODD at the same level. Therefore, overseas countries are presenting a standardized ODD framework and developing scenarios that can evaluate ADS-specific functions based on ODD. However, ODD includes conditions reflecting the characteristics of each country, such as road environment, weather environment, and traffic environment. Thus, it is necessary to clearly understand the meaning of the items defined overseas and to harmonize them to reflect the specific domestic conditions. Therefore, in this study, domestic optimization of the ODD classification system was performed by analyzing the domestic driving environment based on international standards. The driving environment of currently operating self-driving car test districts (Sangam, Seoul, and Gwangju) was investigated using the developed domestic ODD items. Then, based on the results obtained, the ranges of the ODDs in each test district were determined and compared.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.163-171
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• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.9
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• pp.917-925
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• 2006

The aim of our research was to apply experimental design methodology in the optimization of photocatalytic degradation of azo dye(Reactive orange 16). The reactions were mathematically described as a function of parameters amount of $TiO_2(x_1)$, and dye concentration($x_2$) being modeled by the use of the Box-Behnken method. The results show that the responses of color removal(%)($Y_1$) in photocatalysis of dyes were significantly affected by the synergistic effect of linear term of $TiO_2(x_1)$ and dye concentration($x_2$). Significant factors and synergistic effects for the $COD_{Cr}$, removal(%)($Y_2$) were the linear term of $TiO_2(x_1)$ and dye concentration($x_2$). However, the quadratic term of $TiO_2(x_1^2)$ and dye concentration($x_2^2$) had an antagonistic effect on $Y_1$ and $Y_2$ responses. Canonical analysis indicates that the stationary point was a saddle point for $Y_1$ and $Y_2$, respectively. The estimated ridge of maximum responses and optimal conditions for $Y_1:(X_1,\;X_2)$=(1.11 g/L, 51.2 mg/L) and $Y_2:(X_1,\;X_2)$=(1.42 g/L, 72.83 mg/L) using canonical analysis was 93% and 73%, respectively.