• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.203-209
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• 2019

In this work, a new land-specific resistance measuring device (GM) and a measuring probe (Grounding Rod) are connected to the WENNER quadrant as power-line communication (PLC). In groups of two (P1,P2) probes, five to ten probes are installed in series on the ground at intervals of 1m, 2m, 4m, 8m, and 16m, respectively. If the PLC signal from the GMD is detected by the receiver of the Probe 1 (P1) for measurement, the minute voltage and current for measurement flow from the PSD (power supply) attached to the probe to the ground, and then, through the soil between P1 and P2, enters the Probe 1 (P2). The resistance value is then measured by the principle of voltage drop due to ground resistance. Measure the earth resistance every T seconds up to 1 trillion and store the measured data on the Arduino Server mounted on the main equipment. Stored measurement data can be derived from formulas by Ohm's Law and from inherent resistance (here,). Data obtained in real time will be linked to CDGES programs installed on Main PC, enabling data analysis and real-time monitoring of the ground environment on land. In addition, a three-dimensional display is possible with 3D graph support by identifying seasonal characteristics such as temperature and humidity of land (soils). The limitations of the study will require specific application measures of Test Bed for commercial access to a model that has been developed and operated experimentally.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.79-86
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• 2023

This study aims to create a highly conductive E-textile made by recycling PET with a Dip-coating process. PET fiber with hydrophobic properties is characterized by the difficulty in imparting great conductivity when both Virgin and Recycled are made of electronic fibers through a Dip-coating process. To advance the effectiveness of the Dip-coating process, a sample made of recycled PET was surface modified for 50 w 5 minutes and 10 minutes employing a Covance-2mprfq model from FEMTO SCIENCE. After that, the sample was immersed in an SWCNT dispersion (.1 wt%, Carbon Co., Ltd.) for 5 minutes, and then dip coating was conducted to allow the solution to permeate well into the sample through a padder (DAELIM lab). After the procedure was completed, the resistance measurement was measured with a multimeter at both ends and then accurately remeasured with a wider electrode. As a result of this contemplation, it was affirmed that great conductivity might be given through an impregnation process through the plasma surface modification. When the surface modification was performed for 10 minutes, the resistance was reduced by up to 2.880 times. Dependent on the results of this research, E-fibers employed in the smart wearable sector can also be made of recycled materials, improving smart wearable products that can save oil resources and reduce carbon emissions.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.