• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.36 no.10
• /
• pp.979-988
• /
• 2012

The 3D structure of GDL for fuel cells was measured using high-resolution X-ray tomography in order to study material transport in the GDL. A computational algorithm has been developed to remove noise in the 3D image and construct 3D elements representing carbon fibers of GDL, which were used for both structural and fluid analyses. Changes in the pore structure of GDL under various compression levels were calculated, and the corresponding volume meshes were generated to evaluate the anisotropic permeability of gas within GDL as a function of compression. Furthermore, the transfer of liquid water and reactant gases was simulated by using the volume of fluid (VOF) and pore-network model (PNM) techniques. In addition, the simulation results of liquid water transport in GDL were validated by analogous experiments to visualize the diffusion of fluid in porous media. Through this research, a procedure for simulating the material transport in deformed GDL has been developed; this will help in optimizing the clamping force of fuel cell stacks as well as in determining the design parameters of GDL, such as thickness and porosity.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.2
• /
• pp.294-301
• /
• 2017

In this study, we found the optimal manufacturing conditions of livestock manure sludge RDF with the oil-drying method. We performed oil evaporation, oil drying and pelletizing of the sludge to evaluate the value of the product (sludge RDF), and measured the performance of the product using calorimeter and PXRF equipment. Also, we conducted the calorie comparison test between sludge RDF manufactured in this study and wood RDF generally used in the field. Experimental results showed that 30g of the sludge treated by vegetable oil at $130^{\circ}C$ for 25 minutes were the optimal conditions to make the sludge RDF (considering the aspects of eco-friendly and mass production). The caloric value of the sludge RDF manufactured in this study was 5211kcal/kg which is higher than that of wood RDF used widely in the market. Finally, PXRF results showed sludge RDF contains no heavy metals with the exception of sulfur. Therefore, we recommend more study about the sulfur control process for future development of the industrial manufacturing process.

• Journal of the Korean Society of Radiology
• /
• v.11 no.7
• /
• pp.619-625
• /
• 2017

The sudy was intended to evaluate the optimal equipment selection by quantitatively assessing the SNR(signal to noise ratio) and CNR(contrast to noise ratio) on the abdominal organ. This study performed on 1.5 T and 3.0 T MRI units focusing on HASTE, HASTE(f/s) and FFE(in of phase), FFE(out of phase) without using the contrast medium(Gadolinium). The data analysis was performed by randomly selecting on 1.5 T and 3.0 T abdominal MRI images. As a results, SNR and CNR values of 3.0 T is higher than 1.5 T at liver, kidney and spleen(p<0.05). Stomach, abdominal fat and pancreas was obtained a higher value at 1.5 T(p<0.05). On conclusion, the organs of outer part in the body showed generally a high value at 3.0 T, and the organs of inner part in the body including the gas showed a high value at 3.0 T because of a large difference on magnetic susceptibility.

• Journal of Internet of Things and Convergence
• /
• v.6 no.3
• /
• pp.75-80
• /
• 2020

Recently, the size of the IoT sensor has been decreased and the collecting direction of the IoT sensor for acquiring the data have been changed from 2D to 3D. It makes sensor structure complex. In the fabrication of the complex structure, 3D printing technology has more useful than traditional manufacturing technologies. Among 3D printing technologies, FDM (fused deposition modeling) is a candidate technology to fabricate a small IoT sensor because the price of the machine and the material is cheap. In the FDM process, a 3D shape is made by depositing the melted filament. Recently, the patent of FDM technology is expired and cheat machines are developed based on the open-source. In the FDM process, mechanical properties of a fabricated part is affected by a lots of factors such as the kind of material and process parameters. Among them, infill is affecting the mechanical properties and the production lead time as well. In this work, a new method to optimize the FDM process with the consideration of mechanical property and production lead time was proposed. To verify the method, the fabrications were performed with the different infill rates. The results of tensile tests were analyzed to verify the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1777-1783
• /
• 2014

In this paper, we designed and fabricated a high-speed semiconductor sensor for use in power control devices and analyzed the characteristics with pulsed radiation tests. At first, radiation sensitive circular Si PIN diodes with various diameters(0.1 mm ~5.0 mm) were designed and fabricated using Si epitaxial wafer, which has a $42{\mu}m$ thick intrinsic layer. The reverse leakage current of the diode with a radius of 2 mm at a reverse bias of 30 V was about 20.4 nA. To investigate the characteristic responses of the developed diodes, the pulsed gamma-radiation tests were performed with the intensity of 4.88E8 rad(Si)/sec. From the test results showing that the output currents and the rising speeds have a linear relationship with the area of the sensors, we decided that the optimal condition took place at a 2 mm diameter. Next, for the selected 2 mm diodes, dose rate tests with a range of 2.47E8 rad(Si)/sec to 6.21E8 rad(Si)/sec were performed. From the results, which showed linear characteristics with the radiation intensity, a large amount of photocurrent over 60mA, and a high speed response under 350ns without saturation, we can conclude that the our developed PIN diode can be a good candidate for the sensor of power control devices.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.373-376
• /
• 2018

Traditional paprika smart farm systems are often harmful to paprika growth because they are set to follow the values of several sensors to the reference value, so the system is often unable to make optimal judgement. Using decision tree techniques, the expert system for the paprika smart farm is designed to create a control system with a decision-making structure similar to that of farmers using data generated by factors that depend on their surroundings. With the current smart farm control system, it is essential for farmers to intervene in the surrounding environment because it is designed to follow sensor values to the reference values set by the farmer. To solve this problem even slightly, it is going to obtain environmental data and design controllers that apply decision tree method. The expert system is established for complex control by selecting the most influential environmental factors before controlling the paprika smart farm equipment, including criteria for selecting decisions by farmers. The study predicts that each environmental element will be a standard when creating smart farms for professionals because of the interrelationships of data, and more surrounding environmental factors affecting growth.

• Korean Chemical Engineering Research
• /
• v.57 no.4
• /
• pp.525-530
• /
• 2019

The performance and stability of solid oxide fuel cells (SOFCs) depend on the microstructure of the electrode and electrolyte. In anode, porosity and pore distribution affect the active site and fuel gas transfer. In an electrolyte, density and thickness determine the ohmic resistance. To optimizing these conditions, using costly method cannot be a suitable research plan for aiming at commercialization. To solve these drawbacks, we made high performance unit cells with low cost and highly efficient ceramic processes. We selected the NiO-YSZ cermet that is a commercial anode material and used facile methods like die pressing and dip coating process. The porosity of anode was controlled by the amount of carbon black (CB) pore former from 10 wt% to 20 wt% and final sintering temperature from $1350^{\circ}C$ to $1450^{\circ}C$. To achieve a dense thin film electrolyte, the thickness and microstructure of electrolyte were controlled by changing the YSZ loading (vol%) of the slurry from 1 vol% to 5 vol. From results, we achieved the 40% porosity that is well known as an optimum value in Ni-YSZ anode, by adding 15wt% of CB and sintering at $1350^{\circ}C$. YSZ electrolyte thickness was controllable from $2{\mu}m$ to $28{\mu}m$ and dense microstructure is formed at 3vol% of YSZ loading via dip coating process. Finally, a unit cell composed of Ni-YSZ anode with 40% porosity, YSZ electrolyte with a $22{\mu}m$ thickness and LSM-YSZ cathode had a maximum power density of $1.426Wcm^{-2}$ at $800^{\circ}C$.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.27-35
• /
• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.26 no.4
• /
• pp.303-314
• /
• 2024

Recently, the consecutive shield tunnel boring machine (TBM) has gained attention for its potential to enhance TBM penetration rates. However, its development requires a thorough risk assessment due to the unconventional nature of its equipment and hydraulic systems, coupled with the absence of design or construction precedents. This study investigated the causal relationships between four accidents and eight relevant sources associated with the consecutive shield TBM. Subsequently, risk levels were determined based on expert surveys and a risk matrix technique. The findings highlighted significant impacts associated with collapses or surface settlements and the likelihood of causal combinations leading to misalignment. Specifically, this study emphasized the importance of proactive mitigation measures to address collapses or surface settlements caused by inadequate continuous tail void backfill or damaged thrust jacks. Furthermore, it is recommended to develop advanced non-destructive testing technology capable of comprehensive range detection across helical segments, to design a sequential thrust jack propulsion system, and to determine an optimal pedestal angle.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.10 no.2
• /
• pp.133-144
• /
• 2006

In order to catch out such Bond Strength, the preceding researchers had ever examined the Bond Strength of FRP Plate through their experimentations by setting up of various fluent. However, since the experiment for research on such Bond Strength takes much of expenditure for equipment structure and time-consuming, also difficult to carry out, it is conducting limitedly. This Study purposes to develop the most suitable Artificial Neural Network Model by application of various Neural Network Model and Algorithm to the adhering experiment data of the preceding researchers. Output Layer of Artificial Neural Network Model, and Input Layer of Bond Strength were performed the learning by selection as the variable of the thickness, width, adhered length, the modulus of elasticity, tensile strength, and the compressive strength of concrete, tensile strength, width, respectively. The developed Artificial Neural Network Model has applied Back-Propagation, and its error was learnt to be converged within the range of 0.001. Besides, the process for generalization has dissolved the problem of Over-Fitting in the way of more generalized method by introduction of Bayesian Technique. The verification on the developed Model was executed by comparison with the resulted value of Bond Strength made by the other preceding researchers which was never been utilized to the learning as yet.