• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.89-96
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• 2009

As increasing the number of digital control devices installed on aircrafts and their transmission speed, various digital data buses have been introduced to provide reliable and high-speed characteristics. These characteristics of avionics data bus are highly related on the fault-tolerant performance which can make minimize jitter and loss during data transfer. In this paper, we concerned about a new traffic shaping scheme for increasing the reliability of Avionics Full Duplex Switched Ethernet (AFDX) systems based on ARINC 664 standard. We note that the conventional AFDX with a single regulator per virtual link system may produce aggregated traffics as the number of virtual links increasing. The aggregated traffic results in large jitters among frames. To remedy for the jitter and loss of data, we propose a dual regulator scheme for the AFDX system. The purpose of the additional regulator is to additionally regulate aggregated traffics from a number of per virtual link regulators. Using NS-2 simulator, we show that the proposed scheme provides a better performance than the single regulator one. It is worthwhile note that the proposed AFDX with Dual Regulator scheme can be employed to not only aircraft networks but other QoS sensitive networks for robot and industrial control systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.415-424
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• 2019

In this paper, a minimum rotation radius was designed and fabricated to overcome the threshold so that elderly or disabled people who have difficulty moving can move and transfer safely and conveniently in a narrow room. In the indoor environment, where the sedentary culture develops, this study aimed to provide convenience for passengers with fracture diseases, geriatric diseases, and other knee and waist diseases. First, links, seats, armrests, covers, motors, batteries, chargers, controllers, etc. were attached to the frame so that they could be moved and lifted indoors. The product design and structure were designed considering the user's environment and physical characteristics, and IoT functions were added. A driving experiment was performed to confirm the operating performance of the manufactured indoor moving and lifting wheelchair. The performance tests, such as continuous running time, turning radius, maximum actuator load, maximum lift height, sound pressure level, minimum sensing distance of the driving aid sensor, interworking of server and app programs, device compatibility, and duty cycle error rate, were performed. As a result of the test, the built-in wheelchair could achieve the performance test target of each item and operate successfully.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.103-108
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• 2019

Non-equilibrium molecular dynamics simulation on the thermal boundary resistance(TBR) of an aluminum(Al)/silicon(Si) interface was performed in the present study. The constant heat flux across the Si/Al interface was simulated by adding the kinetic energy in hot Si region and removing the same amount of the energy from the cold Al region. The TBR estimated from the sharp temperature drop at the interface was independent of heat flux and equal to $5.13{\pm}0.17K{\cdot}m^2/GW$ at 300K. The simulation result was experimentally confirmed by the time-domain thermoreflectance technique. A 90nm thick Al film was deposited on a Si(100) wafer using an e-beam evaporator and the TBR on the film/substrate interface was measured using the time-domain thermoreflectance technique based on a femtosecond laser system. A numerical solution of the transient heat conduction equation was obtained using the finite difference method to estimate the TBR value. Experimental results were compared to the prediction and discussions on the nanoscale thermal transport phenomena were made.

• Resources Recycling
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• v.31 no.4
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• pp.3-11
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• 2022

Owing to the increasing demand for electric vehicles (EVs), appropriate management of their waste batteries is required urgently for scrapped vehicles or for addressing battery aging. With respect to technological developments, data-driven diagnosis of waste EV batteries and management technologies have drawn increasing attention. Moreover, robot-based automatic dismantling technologies, which are seemingly interesting, require industrial verifications and linkages with future battery-related database systems. Among these, it is critical to develop and disseminate various advanced battery diagnosis and assessment techniques to improve the efficiency and safety/environment of the recirculation of waste batteries. Incorporation of lithium-related chemical substances in the public pollutant release and transfer register (PRTR) database as well as in-depth risk assessment of gas emissions in waste EV battery combustion and their relevant fire safety are some of the necessary steps. Further research and development thus are needed for optimizing the lifecycle management of waste batteries from various aspects related to data-based diagnosis/classification/disassembly processes as well as reuse/recycling and final disposal. The idea here is that the data should contribute to clean design and manufacturing to reduce the environmental burden and facilitate reuse/recycling in future production of EV batteries. Such optimization should also consider the future technological and market trends.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.