• 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.

• Design & Manufacturing
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• v.16 no.4
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• pp.24-33
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• 2022

Fiber-reinforced plastics(FRPs) have excellent specific stiffness and strength, so they are usually used as automotive parts that require high rigidity and lightweight instead of metal. However, it is difficult to predict the mechanical properties of injection molded parts due to the fiber orientation and breakage of FRPs. In this paper, the fiber orientation characteristics and mechanical properties of injection molded specimens were evaluated in order to fabricate automotive transmission side covers with FRPs and design a rib structure for improvement of their rigidity. The test molds were designed and manufactured to confirm the fiber orientation characteristics of each position of the injection molded standard plate-shaped specimens, and the tensile properties of the specimens were evaluated according to the injection molding conditions and directions of specimens. A gusset-rib structure was designed to improve the additional structural rigidity of the target products, and a proper rib structure was selected through the flexural tests of the rib-structured specimens. Based on the evaluation of fiber orientation and mechanical characteristics, the optimization analyses of gate location were performed to minimize the warpage of target products. Also, the deformation analyses against the internal pressure of target product were performed to confirm the rigidity improvement by gusset-rib structure. As a result, it could be confirmed that the deformation was reduced by 27~37% compared to the previous model, when the gusset-rib structure was applied to the joining part of the target products.

• Journal of Powder Materials
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• v.29 no.6
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• pp.459-467
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• 2022

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.59-71
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• 2023

As the frequency of drought increases due to climate change, water scarcity in agriculture would be a main issue. However, it seems difficult to solve the water scarcity by securing alternative water sources. The aim of this study is to analyze optimal water supply capacity of agricultural reservoir for emergency operation connecting reservoirs and dams. First, we simulated the water storage of agricultural reservoir playing the role emergency water supplier to other water facility such as dams and other reservoirs. In particular, the results of simulation of water storage through K-HAS model was calibrated using the optimization process based on ratio correction factors of outflow and inflow. Finally, the optimal amount of water supply securing water supply reliability in emergency interconnection operation was analyzed. The results of this study showed that Janchi reservoir could supply 12.8 thousand m³/day maintaining 90 % water supply reliability. The result of this study could suggest the standard for connecting water facilities as emergency water supply.

• Journal of the Korean Society of Food Culture
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• v.25 no.6
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• pp.779-787
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• 2010

The principal objective of this study was to determine the optimal mixing conditions for three amounts of onion powder, sugar, and butter to prepare onion powder cookies. The experimental design was based on the central composite design methodology of response surface, which included 16 experimental points including two replicates for onion powder, sugar, and butter. The mechanical and sensory properties of the cookies were measured, and these values were applied to the mathematical models. A canonical form and perturbation plot showed the influence of each ingredient on the mixed final product. The results of the spread ratio did not show significant results, but hardness increased with increasing quantities of onion powder and sugar but decreased with butter (p<0.01). The color lightness "L" value increased with increasing quantities of sugar and butter but decreased with added onion powder. In contrast, the redness color "a" value increased with increasing quantities of onion powder and sugar. Sugar did not affect the yellowness color "b" value, but the color b value increased with increasing onion powder and sugar. The results of a sensory evaluation using the predicted model showed significant values for flavor (p<0.01), texture (p<0.05), taste (p<0.05), and overall quality (p<0.01). As a result, the optimum formulation by numerical and graphical methods was calculated as 12.58 g onion powder, 35 g sugar, and 52.38 g butter.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.43-43
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• 2022

유해 조류 대발생은 전국 각지의 인공호소나 하천에서 다발적으로 발생하며, 경관을 해치고 수질을 오염시키는 등 수자원에 부정적인 영향을 미친다. 본 연구에서는 인공호소에서 발생하는 유해 조류 대발생을 예측하기 위해 심층학습 기법을 이용하여 예측 모델을 개발하고자 하였다. 대상 지점은 대청호의 추동 지점으로 선정하였다. 대청호는 금강유역 중류에 위치한 댐으로, 약 150만명에 달하는 급수 인구수를 유지 중이기에 유해 남조 대발생 관리가 매우 중요한 장소이다. 학습용 데이터 구축은 대청호의 2011년 1월부터 2019년 12월까지 측정된 수질, 기상, 수문 자료를 입력 자료를 이용하였다. 수질 예측 모델의 구조는 다중 레이어 퍼셉트론(Multiple Layer Perceptron; MLP)으로, 입력과 한 개 이상의 은닉층, 그리고 출력층으로 구성된 인공신경망이다. 본 연구에서는 인공신경망의 은닉층 개수(1~3개)와 각각의 레이어에 적용되는 은닉 노드 개수(11~30개), 활성함수 5종(Linear, sigmoid, hyperbolic tangent, Rectified Linear Unit, Exponential Linear Unit)을 각각 하이퍼파라미터로 정하고, 모델의 성능을 최대로 발휘할 수 있는 조건을 찾고자 하였다. 하이퍼파라미터 최적화 도구는 Tensorflow에서 배포하는 Keras Tuner를 사용하였다. 모델은 총 3000 학습 epoch 가 진행되는 동안 최적의 가중치를 계산하도록 설계하였고, 이 결과를 매 반복마다 저장장치에 기록하였다. 모델 성능의 타당성은 예측과 실측 데이터 간의 상관관계를 R2, NSE, RMSE를 통해 산출하여 검증하였다. 모델 최적화 결과, 적합한 하이퍼파라미터는 최적화 횟수 총 300회에서 256 번째 반복 결과인 은닉층 개수 3개, 은닉 노드 수 각각 25개, 22개, 14개가 가장 적합하였고, 이에 따른 활성함수는 ELU, ReLU, Hyperbolic tangent, Linear 순서대로 사용되었다. 최적화된 하이퍼파라미터를 이용하여 모델 학습 및 검증을 수행한 결과, R2는 학습 0.68, 검증 0.61이었고 NSE는 학습 0.85, 검증 0.81, RMSE는 학습 0.82, 검증 0.92로 나타났다.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Advances in nano research
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• v.16 no.4
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• pp.341-352
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• 2024

This study investigates the heat and mass transfer characteristics of a MoS₂ nanoparticle suspension in ethylene glycol over a porous stretching sheet. MoS₂ nanoparticles are known for their exceptional thermal and chemical stability which makes it convenient for enhancing the energy and mass transport properties of base fluids. Ethylene glycol, a common coolant in various industrial applications is utilized as the suspending medium due to its superior heat transfer properties. The effects of variable thermal conductivity, variable mass diffusivity, thermal radiation and thermophoresis which are crucial parameters in affecting the transport phenomena of nanofluids are taken into consideration. The governing partial differential equations representing the conservation of momentum, energy, and concentration are reduced to a set of nonlinear ordinary differential equations using appropriate similarity transformations. R software and MATLAB-bvp5c are used to compute the solutions. The impact of key parameters, including the nanoparticle volume fraction, magnetic field, Prandtl number, and thermophoresis parameter on the flow, heat and mass transfer rates is systematically examined. The study reveals that the presence of MoS₂ nanoparticles curbs the friction between the fluid and the solid boundary. Moreover, the variable thermal conductivity controls the rate of heat transfer and variable mass diffusivity regulates the rate of mass transfer. The numerical and statistical results computed are mutually justified via tables. The results obtained from this investigation provide valuable insights into the design and optimization of systems involving nanofluid-based heat and mass transfer processes, such as solar collectors, chemical reactors, and heat exchangers. Furthermore, the findings contribute to a deeper understanding of stretching sheet systems, such as in manufacturing processes involving continuous casting or polymer film production. The incorporation of MoS₂-C₂H₆O₂ nanofluids can potentially optimize temperature distribution and fluid dynamics.

• The Korean Journal of Food And Nutrition
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• v.37 no.3
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• pp.162-170
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• 2024

The domestic swine industry is currently facing a threat due to the recent increase in pork imports. This study aims to determine what factors influence consumers' intention to consume imported pork and suggest measures to support the domestic pork industry. To achieve this, we analyzed data from the Korea Rural Economic Institute's Food Consumption Behavior Survey using a binary logistic regression model. The results revealed that a higher intention to consume imported pork is linked to a higher intention to consume imported rice, purchasing meat online, frequent purchases of HMR, and procuring U.S. beef, especially among urban residents. On the other hand, a lower intention to consume imported pork is associated with a higher awareness of animal welfare certification, frequently dining out, and older age. Based on these findings, we propose the following response measures for the domestic swine industry: implementing educational programs, marketing, and advertising specifically targeting urban residents to improve their perception of domestic agricultural products; enhancing price competitiveness through distribution optimization; and developing policies to promote the use of domestic pork as an ingredient in processed foods.