• 제어로봇시스템학회논문지
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• 제14권5호
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• pp.467-472
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• 2008

In this paper, we design, implement and apply network physical layer to 100 BaseFx optical cable interface module based on industrial ethernet protocol which is physical layer of EtherCAT that has ensure its open standard ethernet compatibility which having been provided with real time of control in network of intelligent service robot, can be process numerous data to sensor and motor control system. Through BLDC motor control performance tests, we try to propose suitability as internal network of intelligent service robot and automation system.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.887-892
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• 2007

본 논문에서는 지능형 서비스 로봇의 네트워크에서 제어의 실시간이 보장되면서 많은 데이터를 처리할 수 있는 개방형 표준 이더넷 호환성을 확보한 산업용 이더넷 프로토콜인 EtherCAT을 기반으로 하여 네트워크의 물리 계층을 100BaseFx인 광케이블 인터페이스 모듈을 설계하고 구현하여 센서 및 모터제어 시스템에 적용하고, 테스트를 통해 지능형 서비스 로봇 내부 네트워크로서의 적합성을 제시하고자 한다.

• 한국통신학회논문지
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• 제41권8호
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• pp.843-850
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• 2016

This paper introduces a novel modulation scheme for vehicular communication in taking advantage of existing LED lights available on a car. Our proposed 2-Phase Shift Keying (2-PSK) is a spatial modulation approach in which a pair of LED light sources in a car (either rear LEDs or front LEDs) is used as a transmitter. A typical camera (i.e. low frame rate at no greater than 30fps) that either a global shutter camera or a rolling shutter camera can be used as a receiver. The modulation scheme is a part of our Image Sensor Communication proposal submitted to IEEE 802.15.7r1 (TG7r1) recently. Also, a neural network approach is applied to improve the performance of LEDs detection and decoding under the noisy situation. Later, some analysis and experiment results are presented to indicate the performance of our system

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.683-686
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• 2002

It is fundamental to count and pack the pills in the medicine manufacture field but those tasks are time and labor consuming. Thus, the need fur automation of those tasks is necessarily getting increased in order to get effective mass production. It Is significant to perceive pills quickly and precisely. There were many trials for this processing but the performance of the existing counting machines varies about size, shape and dispersion tendency of pills. In this paper, the authors try to improve the counting performance of a pill counting machine that has optical sensors with the neural network. The passing signal of pill is acquired with optical sensor and the passage signal of the pill is extracted as input patterns. The gradient and integration of signal during passing time and the time keeping the pill interrupt the light from the LED are used as characteristic feature. The back propagation and perception algorithm are used for training. Experimental results with several pills show that the designed algorithm is a little bit effective to reduce the noise effect which is generated from interference among the machine components and unreliable environment.

• 마이크로전자및패키징학회지
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• 제24권4호
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• pp.23-29
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• 2017

본 연구에서는 선박이송용 트레슬의 표면에 부착할 수 있는 광섬유센서 패키지를 설계하고 파장다중분할방식에 기초한 센서 네트워크를 설계한 후, 모의 트레슬 유닛을 이용한 실험을 통하여 트레슬의 구조적 건전 모니터링을 위한 스마트 트레슬의 가능성을 확인하였다. 광섬유 브래그 격자 센서는 알루미늄 관으로 만들어진 원통형으로 패키징 되었다. 또한, 패키징 된 광섬유 센서를 폴리머 튜브에 삽입 한 후, 튜브 내부에 에폭시를 충전하여 센서가 해수에 대한 부식저항과 내구성을 갖도록 하였다. 패키지 된 광섬유 센서는 0.2 MPa 하의 수압테스트를 통하여 해수에서의 사용에 대한 신뢰성도 검증되었다. 트레슬의 변형에 관한 유한 요소 해석에 의해 얻어진 트레슬 부재의 변위가 큰 곳을 중심으로 트레슬에 부착할 브래그 격자의 수와 위치를 결정하였다. 최대 하중이 가해지는 트레슬 부재의 변형은 ${\sim}1000{\mu}{\varepsilon}$의 변형율로 분석되었으며, 그 때 트레슬에 걸리는 최대 하중으로 인한 센서의 브래그 파장 변화는 ~1,200 pm으로 계산되었다. 유한 요소 해석에서 얻은 결과에 따라 센서의 브래그 파장 간격을 3~5 nm로 결정하여 트레슬에 하중이 가해 졌을 때 센서 사이의 브래그 격자 파장값이 겹치지 않도록 설계하였다. 5개의 광섬유센서 패키지로 구성된 센서 모듈 5개를 연결하면 브래그 격자 센서 50개가 네트워크 될 수 있으므로, 브래그 격자 파장 검출기의 광원 중심 파장이 1550 nm에서 150 nm 광학 창 내에서 모두 검출될 수 있도록 하였다. 모의 트레슬 유닛에 부착 된 5개의 광섬유 센서 패키지의 브래그 파장 이동은 광섬유 루프미러를 사용하는 브래그 격자 파장검출기에 의해 잘 검출되었으며, 그 때 검출된 브래그 격자 센서의 값은 최대 변형률이 약 $235.650{\mu}{\varepsilon}$로 측정되었다. 센서 패키징과 네트워킹의 모델링 결과는 실험 결과와 서로 잘 일치하였다.

• 한국레이저가공학회지
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• 제2권3호
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• pp.19-31
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• 1999

This paper introduces the monitoring scheme of laser welding quality using neural network. The developed monitoring scheme detects light signal emitting from plasma formed above the weld pool with optic sensor and DSP-based signal processor, and analyzes to give a guidance about the weld quality. It can automatically detect defects of laser weld and further give an information about what kind of defects it is, specially partial penetration and porosity among the interior defects. Those could be detected only by naked eyes or X-ray after welding, which needs more processes and costs in mass production. The monitoring scheme extracts four feature vectors from signal processing results of optical measuring data. In order to classify pattern for extracted feature vectors and to decide defects, it uses single-layer neural network with perceptron learning. The monitoring result using only the first feature vector shows confidence rate in recognition of 90%($\pm$5) and decides whether normal status or defects status in real time.

• ETRI Journal
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• 제39권5호
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• pp.756-763
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• 2017

A switchable single-wideband (SWB)-to-dual-wideband (DWB) bandpass filter (BPF), which is realized by using lumped switches, is presented in this paper. By alternating the operation modes-ON and OFF-in which the ON mode is achieved by placing the capacitors at the switching spots and the OFF mode is achieved by replacing the capacitors with inductors, DWB-to-SWB BPF can be achieved on the same device. In addition, by changing the capacitor values, the center frequency (CF) of the lower passband of DWB BPF can be easily tuned from 1.69 GHz to 2.22 GHz, while the higher passband stays almost unchanged. As an example, an SWB-to-DWB BPF is designed, fabricated, and measured. This BPF exhibits good performance including wideband, high isolation, compact size, and ability to switch.

• Journal of Astronomy and Space Sciences
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• 제34권4호
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• pp.303-314
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• 2017

With increased human activity in space, the risk of re-entry and collision between space objects is constantly increasing. Hence, the need for space situational awareness (SSA) programs has been acknowledged by many experienced space agencies. Optical and radar sensors, which enable the surveillance and tracking of space objects, are the most important technical components of SSA systems. In particular, combinations of radar systems and optical sensor networks play an outstanding role in SSA programs. At present, Korea operates the optical wide field patrol network (OWL-Net), the only optical system for tracking space objects. However, due to their dependence on weather conditions and observation time, it is not reasonable to use optical systems alone for SSA initiatives, as they have limited operational availability. Therefore, the strategies for developing radar systems should be considered for an efficient SSA system using currently available technology. The purpose of this paper is to analyze the performance of a radar system in detecting and tracking space objects. With the radar system investigated, the minimum sensitivity is defined as detection of a $1-m^2$ radar cross section (RCS) at an altitude of 2,000 km, with operating frequencies in the L, S, C, X or Ku-band. The results of power budget analysis showed that the maximum detection range of 2,000 km, which includes the low earth orbit (LEO) environment, can be achieved with a transmission power of 900 kW, transmit and receive antenna gains of 40 dB and 43 dB, respectively, a pulse width of 2 ms, and a signal processing gain of 13.3 dB, at a frequency of 1.3 GHz. We defined the key parameters of the radar following a performance analysis of the system. This research can thus provide guidelines for the conceptual design of radar systems for national SSA initiatives.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.