• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.69-71
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• 2005

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.

• Smart Structures and Systems
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• 제17권5호
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• pp.743-751
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• 2016

An ongoing research project is devoted to the design and implementation of a satellite based asset tracking for supporting emergency management in crisis operations. Due to the emergency environment, one has to rely on a low power consumption wireless communication. Therefore, the communication hardware and software must be designed to match requirements, which can only be foreseen at the level of more or less likely scenarios. The latter aspect suggests a deep use of a simulator (instead of a real network of sensors) to cover extreme situations. The former power consumption remark suggests the use of a minimal computer (Raspberry Pi) as data collector.

• Journal of the Optical Society of Korea
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• 제9권1호
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• pp.1-5
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• 2005

Using tightly focused femtosecond laser pulses, we produce an optical waveguide and optical devices in transparent materials. This technique has the potential to generate not only channel waveguides, but also three-dimensional optical devices. In this paper, an optical splitter and U-grooves, which are used for fiber alignment, are simultaneously fabricated in a fused silica glass using near-IR femtosecond laser pulses. The fiber aligned optical splitter has a low insertion loss, less than 4㏈, including an intrinsic splitting loss of 3㏈ and excess loss due to the passive alignment of a single-mode fiber. Finally, we demonstrate the utility of the femtosecond laser writing technique by fabricating gratings at the surface and inside the silica glass.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.487-489
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• 2005

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and In an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.

• 열처리공학회지
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• 제32권1호
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• pp.29-35
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• 2019

The remedial junction is found in the network of single walled carbon nanotubes after the irradiation of protons not only for the better mechanical strength but also for the higher property of electrical conductivity. The irradiated proton formed a beam transferred sufficient energy to change the sp2 structure of atomic carbon as much as damage of crystalline formation, however it is shown the cross bonding while recovery of structure. This improved network in 2-D atomic chain of carbon is expected to use in a critical part in space energy harvesting system related with the solar radiation.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.203-207
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• 2023

A frequency reconfigurable antenna based on graphene and used for multi-band wireless communications is presented in this article. The proposed antenna, which consists of two radiating rectangular loops with a graphene extension, is analyzed for Global Positioning System (GPS) and Iridium applications. Its operating frequency is tuned through the implementation of a layer of graphene and thereby adjusting the applied gate bias. Furthermore, the results show a novel use of graphene for microwave frequencies while achieving a frequency reconfiguration with an improvement of the impedance matching and the gain. The results also prove the importance of graphene, with its exceptional properties, for a promising future in nano-electronics.

• International journal of advanced smart convergence
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• 제6권1호
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• pp.76-81
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• 2017

This paper presents a low cost and flexible IoT enabled smart LED controller using Arduino that is used for emergency exit signs. The Internet of Things (IoT) is become a global network that put together physical objects using network communications for the purpose of inter-communication of devices, access information on internet, interaction with users as well as permanent connected environment. A crucial point in this paper, is underlined on the potential key points of applying the Arduino platform as low cost, easy to use microcontroller with combination of various sensors applied in IoT technology to facilitate and establishment of intelligent products. To demonstrate the feasibility and effectiveness of the system, devices such as LED strip, combination of various sensors, Arduino, power plug and ZigBee module have been integrated to setup smart emergency exit sign system. The general concept of the proposed system design discussed in this paper is all about the combination of various sensor such as smoke detector sensor, humidity, temperature sensor, glass break sensors as well as camera sensor that are connected to the main controller (Arduino) for the purpose of communicating with LED exit signs displayer and dedicated PC monitors from integrated system monitoring (controller room) through gateway devices using Zig bee module. A critical appraisal of the approach in the area concludes the paper.

• Journal of Astronomy and Space Sciences
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• 제26권2호
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• pp.221-228
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• 2009

한반도 상공의 전리층 총전자수(TEC, Total Electron Content)를 추정하는 방법을 통해 GPS 수신기와 위성의 코드 바이어스(DCB, Differential Code Bias)를 함께 추정하였다. 한국천문연구원에서 운영하고 있는 GPS 기준국망 데이터를 사용하였으며, 가중치 최소자승법을 이용하여 매 1시간 간격으로 DCB를 산출하였다. 총 3일간의 데이터를 처리한 결과 9개 GPS 수신기의 DCB는 ${\pm}2m$ 이내에서 변화하는 것으로 나타났으며, 3일 동안 크게 변하지 않았다. 또한 일일 평균값으로 산출된 위성의 DCB는 최대 약 4.09ns(nano-second), 최소 약 -6.28ns를 갖는 것으로 나타났다. 그리고 산출된 DCB를 전리층 총전자수 산출에 적용한 결과, 적용 전에 비해 특정시점에서 최대 약 9TECU 이상의 총전자수 변화가 검출됨을 확인 할 수 있었다.

• Nano
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• 제13권11호
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• pp.1850126.1-1850126.10
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• 2018

Flexible strain sensors, as the core member of the family of smart electronic devices, along with reasonable sensing range and sensitivity plus low cost, have rose a huge consumer market and also immense interests in fundamental studies and technological applications, especially in the field of biomimetic robots movement detection and human health condition monitoring. In this paper, we propose a new flexible strain sensor based on thick CVD graphene film and its low-cost fabrication strategy by using the commercial adhesive tape as flexible substrate. The tensile tests in a strain range of ~30% were implemented, and a gage factor of 30 was achieved under high strain condition. The optical microscopic observation with different strains showed the evolution of cracks in graphene film. Together with commonly used platelet overlap theory and percolation network theory for sensor resistance modeling, we established an overlap destructive resistance model to analyze the sensing mechanism of our devices, which fitted the experimental data very well. The finding of difference of fitting parameters in small and large strain ranges revealed the multiple stage feature of graphene crack evolution. The resistance fallback phenomenon due to the viscoelasticity of flexible substrate was analyzed. Our flexible strain sensor with low cost and simple fabrication process exhibits great potential for commercial applications.

• Advances in nano research
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• 제12권2호
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.