• Journal of Advanced Marine Engineering and Technology
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• 제28권1호
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• pp.90-97
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• 2004

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as speed detectors. but they increase cost and size of the motor and restrict the industrial drive applications. So in these days. many Papers have reported on the sensorless operation or DC motor(3)-(5). This paper Presents a new sensorless strategy using neural networks(6)-(8). Neural network structure has three layers which are input layer. hidden layer and output layer. The optimal neural network structure was tracked down by trial and error and it was found that 4-16-1 neural network has given suitable results for the instantaneous rotor speed. Also. learning method is very important in neural network. Supervised learning methods(8) are typically used to train the neural network for learning the input/output pattern presented. The back-propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• 한국항행학회논문지
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• 제17권3호
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• pp.354-360
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• 2013

모션 및 무인 차량의 깊이 제어를 위해 스테레오 비전 분석을 하였다. 스테레오 비전에서 3차원 좌표의 깊이 정보는 스테레오 이미지 사이의 포인트를 식별한 후 삼각 측량을 통해 얻을 수 있다. 그러나 항상 몇몇 이유 때문에 삼각 측량의 오류가 발생한다. 비전 삼각 측량의 이러한 오류는 카메라의 위치와 방향에 주의하여 배열함으로써 완화 될 수 있다. 본 논문에서는 무인 차량을 위해 카메라의 최적의 위치와 방향을 결정하는 방법을 제시하였다.

• 한국항행학회논문지
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• 제24권4호
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• pp.267-272
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• 2020

선박의 상태를 공간 제약 없이 광범위하게 측정하고 이를 정밀히 분석하기 위한 수단들 중 하나로 무선 네트워크의 활용이 하나의 방법이 될 수 있다. 여러 개의 센서들을 적절한 곳에 배치하고 이들 간 네트워크가 자동으로 구성이 되고나면 선박의 상태를 한 장소에서 모니터링 할 수 있다. 아울러 배치된 모든 센서들이 동일한 시각정보를 갖는다면, 이들로부터 감지된 동일한 이벤트에 대하여 위치 또는 이동방향 등도 알아낼 수 있다. 따라서 동기화된 센서들의 활용은 메타정보의 생산을 위한 중요한 요소가 될 수 있다. 이 논문은 선박에서의 효율적 활용을 위한 센서 간 시각동기 알고리즘을 제안한다.

• 한국운동역학회지
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• 제29권1호
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• pp.1-8
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• 2019

Objective: The purpose of this study was to confirm that the cross-country ski sprint course in PyeongChang, where the 2018 Winter Olympics course was to utilize wearable devices equipped with inertial measurement unit (IMU), global positioning system (GPS) and heart rates sensor. Method: For the data collection, two national level cross-country (XC) skiers performed classic technique on the entire sprint course. We analyzed cycle characteristics, range of motion on double poling (DP) technique, average velocity, and displacement of 3 points according to the terrain. Results: The absolute cycle time gradually decreased during starting, middle and finish sections. While the length of the DP increased and the heart rates tended to increase for men skier. In addition, the results indicated that range of motion of knee joint during starting and finish section decreased more than middle section. The errors of latitude and longitude data collected through GPS were within 3 m from 3 points. Conclusion: Through the first case study in Korea, which analyzed the location and condition of XC skiers in the entire sprint course in real time, confirmed that feedback was available in the field using various wearable sensors.

• 센서학회지
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• 제31권6호
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• pp.383-388
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• 2022

Currently, photodetectors are being extensively studied and developed for next-generation applications, such as in autonomous vehicles and image sensors. In this regard, all-inorganic metal halide perovskite (CsPbX₃; X = Cl, Br, and I) nanocrystals (NCs) have emerged as promising building blocks for various applications owing to their high absorption coefficients, tunable bandgaps, high defect tolerances, and solution processability. These features, which are typically required for the development of advanced optoelectronics, can be engineered by modifying the chemical compositions and surface chemistry of the NCs. Herein, we briefly review various strategies adopted for the application of CsPbX₃ perovskite NCs in photodetectors and for improving device performance. First, modifications of the chemical compositions of CsPbX₃ NCs to tune their optical bandgaps and improve the charge-transport mechanism are discussed. Second, the application of surface chemistry to improve oxidation resistance and carrier mobility is described. In the future, perovskite NCs with prospective features, such as non-toxicity and high resistance to external stimuli, are expected to be developed for practical applications.

• 센서학회지
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• 제31권6호
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• pp.371-375
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• 2022

Antimony-doped tin oxide (ATO) thin films, one type of transparent conductive oxide (TCO) films, were prepared on a SiO₂-coated glass substrate with different substrate temperatures by a radio-frequency magnetron sputtering system. Structural, optical, and electrical characteristics of the deposited ATO films were analyzed using X-ray diffraction, scanning electron microscopy, alpha-step, ultraviolet-visible spectrometer, and Hall effect measurement. The substrate temperature during deposition did not affect the basic crystal structure of the films but changed the grain size and film thickness. The optical transmittance of the ATO films deposited at different substrate temperatures was over 70%. The lowest sheet resistance and resistivity were 8.43 × 10² Ω/sq, and 0.3991 × 10^-2 Ω·cm, respectively, and the highest carrier concentration and mobility were 2.36 × 10²¹ cm^-3 and 6.627 × 10^-2 cm²V^-1s^-1, respectively, at a substrate temperature of 400 ℃.

• 센서학회지
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• 제31권4호
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• pp.214-217
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• 2022

The effect of inductively coupled plasma (ICP) treatment with O₂ gas on the surface properties of poly(ether sulfone) (PES) was investigated. X-ray photoelectron spectroscopy (XPS) was used to analyze the chemical characteristics of the O₂ plasma-treated PES films. The surface roughness of the pristine and O₂ plasma-treated PES films for different RF powers of the ICP was determined using an atomic force microscope (AFM). The contact angles of the PES films were also measured, using which the surface free energies were calculated. The O1s XPS spectra of the PES films revealed that the number of polar functional groups increased following the O₂ plasma treatment. The AFM analysis showed the average surface roughness increased from 1.01 to 4.48 nm as the RF power of the ICP was increased. The contact angle measurements revealed that the PES films became more hydrophilic as the RF power of the ICP was increased. The total surface energy increased with the RF power of the ICP, resulting from the increased polar energy component.

• 한국항행학회논문지
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• 제27권1호
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• pp.50-56
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• 2023

관성항법장치는 내장된 관성센서만을 이용하여 외부의 도움 없이 항체의 가속도 및 각속도를 이용하여 항법 정보를 계산한다. 하지만, 장시간 운용 시 관성항법장치는 시간이 지남에 따라 오차가 누적되어 항법 성능이 저하된다. 이러한 관성항법장치의 누적 오차를 줄이기 위하여 관성센서조립체를 일정한 절차로 회전시켜 관성센서 오차가 회전을 통해 상쇄되도록 항법 성능을 개선 시키는 연구가 활발히 진행되고 있다. 본 논문에서는 2축 회전형 관성항법장치의 성능에 영향을 미칠 수 있는 오차 요소를 식별하고 각 오차가 항법 성능에 어떠한 영향을 미치는지 분석하였다. 오차 분석 수행 후 시뮬레이션을 통해 관성항법장치의 항법 성능분석 결과를 제시하였다.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.475-483
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• 2023

The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system; simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model.

• Smart Structures and Systems
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• 제32권3호
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.