• Title/Summary/Keyword: Sensors

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Traffic Correction System Using Vehicle Axles Counts of Piezo Sensors (피에조센서의 차량 축 카운트를 활용한 교통량보정시스템)

  • Jung, Seung-Weon;Oh, Ju-Sam
    • The Journal of the Korea Contents Association
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    • v.21 no.1
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    • pp.277-283
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    • 2021
  • Traffic data by vehicle classification are important data used as basic data in various fields such as road and traffic design. Traffic data is collected through permanent and temporary surveys and is provided as an annual average daily traffic (AATD) in the statistical yearbook of road traffic. permanent surveys are collected through traffic collection equipment (AVC), and the AVC consists of a loop sensor that detects traffic volume and a piezo sensor that detects the number of axes. Due to the nature of the buried type of traffic collection equipment, missing data is generated due to failure of detection equipment. In the existing method, it is corrected through historical data and the trend of traffic around the point. However, this method has a disadvantage in that it does not reflect temporal and spatial characteristics and that the existing data used for correction may also be a correction value. In this study, we proposed a method to correct the missing traffic volume by calculating the axis correction coefficient through the accumulated number of axes acquired by using a piezo sensor that can detect the axis of the vehicle. This has the advantage of being able to reflect temporal and spatial characteristics, which are the limitations of the existing methods, and as a result of comparative evaluation, the error rate was derived lower than that of the existing methods. The traffic volume correction system using axis count is judged as a correction method applicable to the field system with a simple algorithm.

EF Sensor-Based Hand Motion Detection and Automatic Frame Extraction (EF 센서기반 손동작 신호 감지 및 자동 프레임 추출)

  • Lee, Hummin;Jung, Sunil;Kim, Youngchul
    • Smart Media Journal
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    • v.9 no.4
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    • pp.102-108
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    • 2020
  • In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.

Evaluation of Field Application and Estimation of Bedload Discharge in the Forest Watershed using the Hydrophone (하이드로폰을 이용한 산림유역 소류사 유출량 산정 및 현장 적용성 검토)

  • Seo, Jun-Pyo;Kim, Ki-Dae;Woo, Choong-Shik;Lee, Chang-Woo;Lee, Heon-Ho
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.12
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    • pp.807-818
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    • 2020
  • In this study, hydrophones using acoustic sensors were used to estimate the amount of bedload discharge in a forested watershed. The reaction characteristics were analyzed through hydrophone flume tests and field tests, and the quantitative bedload discharge was calculated and compared with that measured by a pit sampler. The hydrophone reaction changed the pulse according to the flow rate change, but did not react to standard sand. The pulse was different depending on the particle size and weight, and accordingly, there was a specific channel showing a suitable response. For a hydrophone installed in the field, by using an automatic impact device, the reaction characteristics of each channel were analyzed to confirm normal operation of the sensor and the suitability of the output value of each channel. In addition, a suitable channel was selected for the estimation of bedload discharge. The bedload discharge formula was developed using a hydrophone pulse and the average flow rate, and was compared with the measured data in the pit sampler in the study site. As a result of the study, if a hydrophone is used for monitoring the bedload in forested watersheds, it is considered effective in quantitatively estimating the weight of bedload discharge.

Class 1·3 Vehicle Classification Using Deep Learning and Thermal Image (열화상 카메라를 활용한 딥러닝 기반의 1·3종 차량 분류)

  • Jung, Yoo Seok;Jung, Do Young
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.19 no.6
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    • pp.96-106
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    • 2020
  • To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.

Analysis of UAV-based Multispectral Reflectance Variability for Agriculture Monitoring (농업관측을 위한 다중분광 무인기 반사율 변동성 분석)

  • Ahn, Ho-yong;Na, Sang-il;Park, Chan-won;Hong, Suk-young;So, Kyu-ho;Lee, Kyung-do
    • Korean Journal of Remote Sensing
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    • v.36 no.6_1
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    • pp.1379-1391
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    • 2020
  • UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

Laser Fabrication of Graphene-based Materials and Their Application in Electronic Devices (레이저 유도에 의한 그래핀 합성 및 전기/전자 소자 제조 기술)

  • Jeon, Sangheon;Park, Rowoon;Jeong, Jeonghwa;Hong, Suck Won
    • Journal of the Microelectronics and Packaging Society
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    • v.28 no.1
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    • pp.1-12
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    • 2021
  • Here, we introduce a laser-induced graphene synthesis technology and its applications for the electric/electronic device manufacturing process. Recently, the micro/nanopatterning technique of graphene has received great attention for the utilization of these new graphene structures, which shows progress developments at present with a variety of uses in electronic devices. Some examples of practical applications suggested a great potential for the tunable graphene synthetic manners through the control of the laser set-up, such as a selection of the wavelength, power adjustment, and optical techniques. This emerging technology has expandability to electric/electronic devices combined together with existed micro-packaging technology and can be integrated with the new processing steps to be applied for the operation in the fields of biosensors, supercapacitors, electrochemical sensors, etc. We believe that the laser-induced graphene technology introduced in this paper can be easily applied to portable small electronic devices and wearable electronics in the near future.

Modbus TCP based Solar Power Plant Monitoring System using Raspberry Pi (라즈베리파이를 이용한 Modbus TCP 기반 태양광 발전소 모니터링 시스템)

  • Park, Jin-Hwan;Kim, Chang-Bok
    • Journal of Advanced Navigation Technology
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    • v.24 no.6
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    • pp.620-626
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    • 2020
  • This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

Characteristics of KOMPSAT-3A Key Image Quality Parameters During Normal Operation Phase (정상운영기간동안의 KOMPSAT-3A호 주요 영상 품질 인자별 특성)

  • Seo, DooChun;Kim, Hyun-Ho;Jung, JaeHun;Lee, DongHan
    • Korean Journal of Remote Sensing
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    • v.36 no.6_2
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    • pp.1493-1507
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    • 2020
  • The LEOP Cal/Val (Launch and Early Operation Phase Calibration/Validation) was carried out during 6 months after KOMPSAT-3A (KOMPSAT-3A Korea Multi-Purpose Satellite-3A) was launched in March 2015. After LEOP Cal/Val was successfully completed, high resolution KOMPSAT-3A has been successfully distributing to users over the past 8 years. The sub-meter high-resolution satellite image data obtained from KOMPSAT-3A is used as basic data for qualitative and quantitative information extraction in various fields such as mapping, GIS (Geographic Information System), and national land management, etc. The KARI (Korea Aerospace Research Institute) periodically checks and manages the quality of KOMPSAT-3A's product and the characteristics of satellite hardware to ensure the accuracy and reliability of information extracted from satellite data of KOMPSAT-3A. To minimize the deterioration of image quality due to aging of satellite hardware, payload and attitude sensors of KOMPSAT-3A, continuous improvement of image quality has been carried out. In this paper, the Cal/Val work-flow defined in the KOMPSAT-3A development phase was illustrated for the period of before and after the launch. The MTF, SNR, and location accuracy are the key parameters to estimate image quality and the methods of the measurements of each parameter are also described in this work. On the basis of defined quality parameters, the performance was evaluated and measured during the period of after LEOP Cal/Val. The current status and characteristics of MTF, SNR, and location accuracy of KOMPSAT-3A from 2016 to May 2020 were described as well.

Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans

  • Go, Seung-Hwan;Park, Jin-Ki;Park, Jong-Hwa
    • Korean Journal of Remote Sensing
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    • v.37 no.4
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    • pp.699-717
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    • 2021
  • South Korea is pushing for the advancement of crop production technology to achieve food self-sufficiency and meet the demand for safe food. A medium-sized satellite for agriculture is being launched in 2023 with the aim of collecting and providing information on agriculture, not only in Korea but also in neighboring countries. The satellite is to be equipped with various sensors, though reference data for ground information are lacking. Hyperspectral remote sensing combined with 1st derivative is an efficient tool for the identification of agricultural crops. In our study, we develop a system for hyperspectral analysis of the ground-based reflectance spectrum, which is monitored seven times during the cultivation period of three soybean crops using a PSR-2500 hyperspectral sensor. In the reflection spectrum of soybean canopy, wavelength variations correspond with stages of soybean growths. The spectral reflection characteristics of soybeans can be divided according to growth into the vegetative (V)stage and the reproductive (R)stage. As a result of the first derivative analysis of the spectral reflection characteristics, it is possible to identify the characteristics of each wavelength band. Using our developed monitoring system, we observed that the near-infrared (NIR) variation was largest during the vegetative (V1-V3) stage, followed by a similar variation pattern in the order of red-edge and visible. In the reproductive stage (R1-R8), the effect of the shape and color of the soybean leaf was reflected, and the pattern is different from that in the vegetative (V) stage. At the R1 to R6 stages, the variation in NIR was the largest, and red-edge and green showed similar variation patterns, but red showed little change. In particular, the reflectance characteristics of the R1 stage provides information that could help us distinguish between the three varieties of soybean that were studied. In the R7-R8 stage, close to the harvest period, the red-edge and NIR variation patterns and the visible variation patterns changed. These results are interpreted as a result of the large effects of pigments such as chlorophyll for each of the three soybean varieties, as well as from the formation and color of the leaf and stem. The results obtained in this study provide useful information that helps us to determine the wavelength width and range of the optimal band for monitoring and acquiring vegetation information on crops using satellites and unmanned aerial vehicles (UAVs)

Enzyme-Free Glucose Sensing with Polyaniline-Decorated Flexible CNT Fiber Electrode (Polyaniline을 이용한 CNT fiber 유연 전극 기반의 비효소적 글루코스 검출)

  • Song, Min-Jung
    • Korean Chemical Engineering Research
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    • v.60 no.1
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    • pp.1-6
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    • 2022
  • As the demand for wearable devices increases, many studies have been studied on the development of flexible electrode materials recently. In particular, the development of high-performance flexible electrode materials is very important for wearable sensors for healthcare because it is necessary to continuously monitor and accurately detect body information such as body temperature, heart rate, blood glucose, and oxygen concentration in real time. In this study, we fabricated the nonenzymatic glucose sensor based on polyaniline/carbon nanotube fiber (PANI/CNT fiber) electrode. PANI layer was synthesized on the flexible CNT fiber electrode through electrochemical polymerization process in order to improve the performance of a flexible CNT fiber based electrode material. Surface morphology of the PANI/CNT fiber electrode was observed by scanning electron microscopy. And its electrochemical characteristics were investigated by chronoamperometry, cyclic voltammetry, electrochemical impedance spectroscopy. Compared to bare CNT fiber electrode, this PANI/CNT fiber electrode exhibited small electron transfer resistance, low peak separation potential and large surface area, resulting in enhanced sensing properties for glucose such as wide linear range (0.024~0.39 and 1.56~50 mM), high sensitivity (52.91 and 2.24 ㎂/mM·cm2), low detection limit (2 μM) and good selectivity. Therefore, it is expected that it will be possible to develop high performance CNT fiber based flexible electrode materials using various nanomaterials.