• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.231-234
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• 2007

The magnetic sensor using electromagnetic principle. which transfers magnatic into electric. is the electric component.It has been widely applied to the industry, university and the reseach. However there are some problems. Not only the korean domestic sensor manufacture skills are still lower then the advanced manufacture's but also production of sensor is not well organized yet. Due to cahnging excitation cvurrent, excitation freq and the rate magnetic permeability core, there sometimes would be distorted phenomena or loaded phenomena which result in limited measurment range. Therefore, the signal conversion device should support to receive undistorted and nice output. This paper focuses on both the design of signal transform circuit using inductive proximity sensor and the signal transfer equipment (Z device) which detects thickness of painted material.

• Journal of Biomedical Engineering Research
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• v.36 no.1
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• pp.31-36
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• 2015

In this study, we evaluated the effects of static magnetic fields of earphones and headphones on pacemakers and implantable cardioverter defibrillators(ICDs). Five pacemakers and three ICDs were subjected to in-vitro test with three headphones which were an in-ear earphone, clip-on headphone, and closed-back headphone. Each implantable device was placed in close proximity(within 3 mm) to the ear-pad of each of the earphone and headphones for 3 min. As a result, no effects were observed on the pacemakers for the earphone and headphones during the test, but an effect was observed on one ICD for the clip-on and closed-back headphone during the test. When the ICD was placed in close proximity to the headphones, the ICD temporarily suspended functions of tachyarrhythmia detection and therapy. The effect was not observed in this study when the headphones were at least 2 cm from the ICD. Based on these findings, patients with ICDs should be advised to keep earphones and headphones at least 2 cm apart from their ICDs.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.63-69
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• 2020

In this paper, a small microwave sensor that can be applied to a wearable device is proposed because it can detect the heartbeat signal of a human body moving irregularly at low speed. It consist of balanced microstrip radiation patches in the 2.4 GHz ISM band, self-oscillation detection circuit, and feedback circuit. Based on the theoretical development and simulation, the validity of the proposed structure was confirmed and the manufactured prototype was tested. The board size of the circuit is as small as 65mm × 85㎟, and has a low power consumption of 60mW thanks to the simple RF circuit structure. Finally heartbeat signal has been obtained from a human body moving at low speed (0.5Hz) within a linear distance of 2 to 30mm close to the sensor and a lateral distance of ±20mm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.121.4-121
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• 2001

We propose an off-line methodology for detecting a faulty backup roll that generates eccentricity components, under the condition that the feeding velocity, equivalently the angular velocity of roll, is not constant. From a newly devised speed angle conversion algorithm, we transform all process data into those of a virtual process under a constant feeding speed. This indirectly way, we can apply a spectral analysis to the original process. In addition, we develop an online detection method of roll eccentricity based on newly designed PLG sensor. This PLG sensor is robust because of applying magnetic proximity sensnors and non-contact measurement method.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.4
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• pp.253-257
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• 2017

Nowadays, it is increasingly important to detect whether cables are live for the operator's safety if there is a sudden power failure. It is especially hard to detect the electrical field of an underground line because of shielding. This paper on detection of live-line states in cables studied the detection characteristics of the change in the magnetic field and axis as the frequency, voltage, and distance at the same load are changed using 3 axes. A search coil type was used as a magnetic field sensor with non-contact. We found that magnetic fields decrease proportionally to the square of the distance and the decrease of rated voltage with load effected to magnetic field. The magnetic field was detected by 3-axis sensors given correct proximity, but appeared as noise components beyond a distance of 2 cm.

• Journal of the Korean Chemical Society
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• v.5 no.1
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• pp.26-28
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• 1961

Microgram quantities of thallirum in meteorites, rocks, minerals, alloys and biological samples have been analyzed by rapid neutron activation analysis. A l0-minute radiochemical separation procedure coupled with a gas-flow proportional detector for 4.2-minute half life measurement and a gamma or beta scintillation detector placed in close proximity to the sample permitted detection of the 4.2-min Tl206. Samples were irradiated for 10-minutes at a thermal neutron flux of approximately $0.95{\times}10^{11}$ neutron-$cm^{-2}-sec^{-1}.$ The low limits of detection was about $10^{-7}$ gm of thallium.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.169-177
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• 2021

In this paper, we propose a novel global navigation satellite system (GNSS) spoofing detection technique through an array antenna-based direction of arrival (DoA) estimation of satellite and spoofer. Specifically, we consider a sophisticated GNSS spoofing attack scenario where the spoofer can accurately mimic the multiple pseudo-random number (PRN) signals since the spoofer has its own GNSS receiver and knows the location of the target receiver in advance. The target GNSS receiver precisely estimates the DoA of all PRN signals using compressed sensing-based orthogonal matching pursuit (OMP) even with a small number of samples, and it performs spoofing detection from the DoA estimation results of all PRN signals. In addition, considering the initial situation of a sophisticated spoofing attack scenario, we designed the algorithm to have high spoofing detection performance regardless of the relative spoofing signal power. Therefore, we do not consider the assumption in which the power of the spoofing signal is about 3 dB greater than that of the authentic signal. Then, we introduce design parameters to get high true detection probability and low false alarm probability in tandem by considering the condition for the presence of signal sources and the proximity of the DoA between authentic signals. Through computer simulations, we compare the DoA estimation performance between the conventional signal direction estimation method and the OMP algorithm in few samples. Finally, we show in the sophisticated spoofing attack scenario that the proposed spoofing detection technique using OMP-based estimated DoA of all PRN signals outperforms the conventional spoofing detection scheme in terms of true detection and false alarm probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1264-1286
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• 2018

In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.

• Journal of Sensor Science and Technology
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• v.21 no.3
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• pp.211-216
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• 2012

Fires are the most common disaster and early fire detection is of great importance to minimize the consequent damage. Simple sensors including smoke detectors are widely used for the purpose but they are able to sense fires only at close proximity. Recently, due to the rapid advances of relevant technologies, vision-based fire sensing has attracted growing attention. In this paper, a novel visual sensing technique to automatically detect fire is presented. The proposed technique consists of multiple steps of image processing: pixel-level, block-level, and frame level. At the first step, fire flame pixel candidates are selected based on their color values in YIQ space from the image of a camera which is installed as a vision sensor at a fire scene. At the second step, the dynamic parts of flames are extracted by comparing two consecutive images. These parts are then represented in regularly divided image blocks to reduce pixel-level detection error and simplify following processing. Finally, the temporal change of the detected blocks is analyzed to confirm the spread of fire. The proposed technique was tested using real fire images and it worked quite reliably.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.1-7
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• 2021

The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.