• Title, Summary, Keyword: dust sensor

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A Dust Detection Sensor System for Improvement of a Robot Vacuum Cleaner (청소 로봇 성능 향상을 위한 먼지 검출 시스템)

  • Kim, Dong-Hoe;Min, Byung-Cheol;Kim, Donghan
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.10
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    • pp.896-900
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    • 2013
  • In this paper, we develop a dust detection sensor system capable of identifying types of dust for an improvement of a robot vacuum cleaner. The dust detection sensor system is composed of a set of infra-red sensors: a single transmitter and multiple receivers. Given the fixed amount of light transmitted from the transmitter, the amount of light coming in multiple receiver sensors varies, depending on the type and density of dust that is passing between the transmitter and the receivers. Therefore, the type of dust can be identified by means of observing the change of the amount of light from the receiver sensors. For experiments, we use two types of dust, rice and sesame, and validate the effectiveness of the proposed method.

A Study on Environmental Micro-Dust Level Detection and Remote Monitoring of Outdoor Facilities

  • Kim, Seung Kyun;Mariappan, Vinayagam;Cha, Jae Sang
    • International journal of advanced smart convergence
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    • v.9 no.1
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    • pp.63-69
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    • 2020
  • The rapid development in modern industrialization pollutant the water and atmospheric air across the globe that have a major impact on the human and livings health. In worldwide, every country government increasing the importance to improve the outdoor air pollution monitoring and control to provide quality of life and prevent the citizens and livings life from hazard disease. We proposed the environmental dust level detection method for outdoor facilities using sensor fusion technology to measure precise micro-dust level and monitor in realtime. In this proposed approach use the camera sensor and commercial dust level sensor data to predict the micro-dust level with data fusion method. The camera sensor based dust level detection uses the optical flow based machine learning method to detect the dust level and then fused with commercial dust level sensor data to predict the precise micro-dust level of the outdoor facilities and send the dust level informations to the outdoor air pollution monitoring system. The proposed method implemented on raspberry pi based open-source hardware with Internet-of-Things (IoT) framework and evaluated the performance of the system in realtime. The experimental results confirm that the proposed micro-dust level detection is precise and reliable in sensing the air dust and pollution, which helps to indicate the change in the air pollution more precisely than the commercial sensor based method in some extent.

A Method of Obstacle Detection in the Dust Environment for Unmanned Ground Vehicle (먼지 환경의 무인차량 운용을 위한 장애물 탐지 기법)

  • Choe, Tok-Son;Ahn, Seong-Yong;Park, Yong-Woon
    • Journal of the Korea Institute of Military Science and Technology
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    • v.13 no.6
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    • pp.1006-1012
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    • 2010
  • For the autonomous navigation of an unmanned ground vehicle in the rough terrain and combat, the dust environment should necessarily be overcome. Therefore, we propose a robust obstacle detection methodology using laser range sensor and radar. Laser range sensor has a good angle and distance accuracy, however, it has a weakness in the dust environment. On the other hand, radar has not better the angle and distance accuracy than laser range sensor, it has a robustness in the dust environment. Using these characteristics of laser range sensor and radar, we use laser range sensor as a main sensor for normal times and radar as a assist sensor for the dust environment. For fusion of laser range sensor and radar information, the angle and distance data of the laser range sensor and radar are separately transformed to the angle and distance data of virtual range sensor which is located in the center of the vehicle. Through distance comparison of laser range sensor and radar in the same angle, the distance data of a fused virtual range sensor are changed to the distance data of the laser range sensor, if the distance of laser range sensor and radar are similar. In the other case, the distance data of the fused virtual range sensor are changed to the distance data of the radar. The suggested methodology is verified by real experiment.

Development of Fine Dust Analysis Technology using IoT Sensor (IoT 센서를 활용한 미세먼지 분석 기술 개발)

  • Shin, Dong-Jin;Lee, Jin;Heo, Min-Hui;Hwang, Seung-Yeon;Lee, Yong-Soo;Kim, Jeong-Joon
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.1
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    • pp.121-129
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    • 2021
  • In addition to yellow dust occurring in China, fine dust has become a hot topic in Korea through news and media. Although there is fine dust generated from the outside, the purchase rate of air purifier products is increasing as external fine dust flows into the inside. The air purifier uses a filter internally, and the sensor notifies the user through the LED alarm whether the filter is replaced. However, there is currently no product measuring how much the filter rate is reduced and determining the pressure of the blower to operate. Therefore, in this paper, data are generated directly using Arduino, fine dust sensor, and differential pressure sensor. In addition, a program was developed using Python programming to calculate how old the filter is and to analyze the wind power of the blower according to the filter rate by calculating the measured dust and pressure values.

A Study on the Suction Power Control of Vacuum Cleaner with a Dust Sensor (먼지센서에 의한 진공청소기의 흡입력 제어에 관한 연구)

  • 백승면;김성진;이만형
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.304-307
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    • 1995
  • In this paper, an optical sensing system has been developed to detect the dust in vacuum cleaner. The system works well through self-tuning mechanism, even though there are systemic variance and characteristic change which is caused by the pollution on the surface of the optical elements. Using the developed sensing system, a novel suction power control system has been proposed, which is able to be used for a long time.

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Deformation Analysis for Dust Cap of Automotive Wheel Bearing (자동차용 휠 베어링의 Dust Cap 변형 해석)

  • Lee, Seung-Pyo;Lee, In-Ha;Kim, Bong-Chul;Jin, Sung-Kyu
    • Journal of The Korean Society of Manufacturing Technology Engineers
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    • v.20 no.5
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    • pp.576-581
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    • 2011
  • In this paper, deformation of dust cap in the automotive wheel bearing produced during press-fit process was numerically analyzed. The commercial software, MSC.MARC which is based on the finite element method was used to calculate the deformation. From those results, interference between dust cap and sensor was investigated. To verify the analysis results, experiments were performed and compared experiment results with analysis results. To avoid the interference between dust cap and sensor, 4 modified designs were proposed and the best design was derived from them.

Development of An Operation Monitoring System for Intelligent Dust Collector By Using Multivariate Gaussian Function (Multivariate Gaussian Function을 이용한 지능형 집진기 운전상황 모니터링 시스템 개발)

  • Han, Yun-Jong;Kim, Sung-Ho
    • Proceedings of the KIEE Conference
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    • pp.470-472
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    • 2006
  • Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as environment and health, industry scene system monitoring, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modem learning techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes having the capability of simple processing and wireless communication. The proposed system is able to perform context classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to intelligent dust collecting system.

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Optical Characterization of Smart Dust Based on Photonic Crystals and Its Sensing Applications

  • Kim, Sung Gi
    • Journal of the Chosun Natural Science
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    • v.4 no.1
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    • pp.7-10
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    • 2011
  • Various types of smart dust based on photonic crystal exhibiting unique reflectivity were successfully obtained by an electrochemical etching of silicon wafer using square wave currents. Smart dust containing Bragg structure obtained from the sonication of DBR porous silicon film in solution retained its optical reflectivity. Field emission scanning electron micrograph (FE-SEM) was used to measure the size of optically encoded smart dust and its size can be tuned from few hundred nanometers to few microns depending on the duration of sonication. Optical characteristics of smart dust were used to investigate a possible applications such as chemical sensors.

A Study on the Design and Implementation of Fine Dust Measurement LED Using Drone

  • Park, Jong-Youel;Ko, Chang-Bae
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.4
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    • pp.48-54
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    • 2020
  • Researchers recognized air pollution changes causing diseases and difficulties in living due to environmental pollution following various human activities, and have studied how to avoid fine dust harmful to the human respiratory system to be healthy. To this end, Arduino is used to equip fine dust level sensors in drones to measure the fine dust levels, visualize the measurements with LED indicator colors depending on the measurements to inform users of the danger of fine dust, and use the benefits of drones to specify dangerous fine dust zones and measure the fine dust levels. Users can see the changes depending on the fine dust levels in real time with the LED indicators. This will contributes to measuring fine dust levels easily in dangerous areas. Mission Planner (ArduPilot) is used to set up the GPS of drone, and store the data from the dust sensor as contents. This study aims to establish a method for improving the environment to measure fine dust levels with drones with LED indicators for fine dust, and reduce fine dust.

Development of Energy Saving System Using the Microwave Sensor (마이크로웨이브 센서를 이용한 에너지 절약시스템 개발)

  • Jung, Soon-Won;Lee, Jae-Jin;Koo, Kyung-Wan
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.57 no.4
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    • pp.404-407
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    • 2008
  • Because of directly receiving the thing in which a microwave is reflected and comparing the frequency, the microwave sensor with doppler effect completely overcomes the problem of the passive infrared sensor. The microwave sensor with doppler effect well operates about a temperature, the dust, and the peripheral noise because of being dull in the most of ambient conditions. The system developed in this research is the electricity saving detection sensor which it senses the real time action of a man as the microwave sensor and automatically turns on the electric lamp and turns off, minimizes the electrical energy consumption. Since the microwave sensor is not influenced in the light, the dust, and the natural element like the ambient temperature, the effectiveness is considered to be superior to the passive infrared sensor being used currently. There was the energy reduction effect more than about 60% in the performed example which established this system. When this was compared with the construction cost, the cost of establishing payback period was about 1-1.5 year. The microwave sensor with doppler effect developed from this research result is convinced in the future to do enough for the electric energy saving.