• Transactions of the Korean hydrogen and new energy society
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• v.23 no.3
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• pp.243-249
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• 2012

A lightweight 200W direct methanol fuel cell (DMFC) stack is designed and fabricated to power a small scale Unmanned Aerial Vehicle (UAV). The DMFC stack consists of 33-cells in which membrane-electrode assemblies (MEAs) having an active area of 88 $cm^2$ are sandwiched with lightweight composite bipolar plates. The total stack weight is around 3.485 kg and stack performance is tested under various methanol feed concentrations. The DMFC stack delivers a maximum power of 248 W at 13.2 V and $71.3^{\circ}C$ under methanol feed concentration of 1.2 M. In addition, the voltage of individual cell in the 33-cell stack is measured at various current levels to ensure the stability of DMFC stack operations. The cell voltage distribution data exhibit the maximum cell voltage deviation of 28 mV at 15 A and hence the uniformity of cell voltages is acceptable. These results clearly demonstrate that DMFC technology becomes a potential candidate for small-scale UAV applications.

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.5
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• pp.492-501
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• 2017

A 900 W scale direct methanol fuel cell (DMFC) stack is designed and fabricated for unmanned aerial vehicle (UAV) applications. To meet the volume and weight requirements, metallic bipolar plates are applied to the DMFC stack for the first time wherein POS470FC was chosen as bipolar plate material. To ensure good robustness of the metallic bipolar plate based DMFC stack, finite element method based simulations are conducted using a commercial ANSYS Fluent software. The stress buildup and deformation characteristics on bipolar plates and end plates are analyzed in details. The present DMFC stack exhibits the performance of 1,130 W at 32 V and 35.3 A, clearly demonstrating that it could successfully operate for UAVs requiring around 1,000 W of power.

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.5
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• pp.481-491
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• 2017

In this study, we developed 300 W lightweight DMFC system for charging secondary battery in small unit military operation. In order to reduce the volumetric shape and weight of the system considering the environment of the individual soldier's, the arranging of system components has been optimized. A metal bipolar plates made of STS-470FC have been implemented to the DMFC stack to meet the weight demand of the system. As a result of the performance test of the stack, the target value was satisfied by outputting 561 W exceeding 24% of the stack output 450 W required to output 300 W required for the entire system. Moreover, 2,655 hours exceeding 1,000 hours also has been satisfied. To ensure good robustness of the metallic bipolar plate based DMFC stack, finite element method based simulations are conducted using a commercial ANSYS Fluent software.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2505-2528
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• 2023

The field of facial expression recognition (FER) has been actively researched to improve human-computer interaction. In recent years, deep learning techniques have gained popularity for addressing FER, with numerous studies proposing end-to-end frameworks that stack or widen significant convolutional neural network layers. While this has led to improved performance, it has also resulted in larger model sizes and longer inference times. To overcome this challenge, our work introduces a novel lightweight model architecture. The architecture incorporates three key factors: Depth-wise Separable Convolution, Residual Block, and Attention Modules. By doing so, we aim to strike a balance between model size, inference speed, and accuracy in FER tasks. Through extensive experimentation on popular benchmark FER datasets, our proposed method has demonstrated promising results. Notably, it stands out due to its substantial reduction in parameter count and faster inference time, while maintaining accuracy levels comparable to other lightweight models discussed in the existing literature.

• New & Renewable Energy
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• v.3 no.1 s.9
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• pp.46-53
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• 2007

Direct Methanol Fuel Cell, DMFC is a potential power source for portable IT application. DMFC works at low temperature ($<100^{\circ}C$) without fuel processing. Methanol has high energy density, fuel economy, and easiness to handle. This paper focuses high efficient catalyst to increase utilization in the electrode, new membrane reducing methanol crossover, new material parts, and optimization of system integration. Lightweight and small-sized DMFC based on new materials, efficient stack, and improved system control will be applied to the 50W prototype system for the notebook computer.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.188-194
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• 1999

Fiber reinforced composite materials are widely used in automotive industry to produce parts that are large, thin. lightweight. strong and stiff. It is very important to know a charge shape in order to have good products in the compression molding. In particular, the product such as a bumper beam is composed of the random and unidirectional fiber mats. This study analyzes numerically the characteristics of flow fronts such as a bulging phenomenon made by changing viscosity of random mat and unidirectional fiber mat and slip parameters. And it is discussed that the effect of ratio of viscosity A and stack type on mold filling parameters

• Composites Research
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• v.12 no.5
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• pp.23-30
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• 1999

Fiber reinforced composite materials are widely used in automotive industry to produce parts that are large, thin, lightweight, strong and stiff. It is very important to know a charge shape in order to have good products in the compression molding. In particular, the product such as a bumper beam is composed of the random and unidirectional fiber mats. The characteristics of flow fronts such as a bulging phenomenon for random mat and unidirectional fiber mat and slip parameters are studied numerically. And the effects of viscosity ratio and stack type on mold filling parameters are also discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1301-1306
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• 2011

RTOS in the embedded system is a powerful tool for the design of multi-tasking. However, previous RTOS has large proportion in the MCU with limited memory. So it is difficult to apply RTOS. In this paper, I removed less frequently used features from the traditional RTOS, and designed lightweight RTOS that schedules and manages the resources with minimal code. I used techniques to obtain user memory using sharing stack, and to reduce the overhead at context. Considering ratio of kernel and applications, the RTOS designed in this paper is available on the MCU with more than 4KB of program memory.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.26-33
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• 2018

A web application running on the Internet is causing many difficulties for a program developer, and it requires to process multiple sessions at the same time due to the occurrence of excessive traffic. Web applications should be able to process concurrent requests efficiently and in real time. Node.js is a single-threaded server-side JavaScript environment implemented in C and C ++ as one of the latest frameworks to implement event models across the entire stack. Nodes implement JavaScript quickly and robust to achieve the best performance using a JavaScript V8 engine developed by Google. In this paper, it will be explained the operation principle of Node.js, which is a lightweight real-time web server that can be implemented in JavaScript for real-time responsive web applications. In addition, this application was practically implemented through automatic alignment system for group scheduling to demonstrate event-based real-time response web processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1188-1203
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• 2020

The human expression contains a lot of information that can be used to detect complex conditions such as pain and fatigue. After deep learning became the mainstream method, the traditional feature extraction method no longer has advantages. However, in order to achieve higher accuracy, researchers continue to stack the number of layers of the neural network, which makes the real-time performance of the model weak. Therefore, this paper proposed an expression recognition framework based on densely concatenated convolutional neural networks to balance accuracy and latency and apply it to humanoid robots. The techniques of feature reuse and parameter compression in the framework improved the learning ability of the model and greatly reduced the parameters. Experiments showed that the proposed model can reduce tens of times the parameters at the expense of little accuracy.