• 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.

• 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.

• 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.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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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.

• 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.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.61-64
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• 2003

Technological progress in integrated, low-power, CMOS communication devices and sensors makes a rich design space of networked sensors viable. These sensors can be deeply embedded in the physical world and spread throughout sensor network environment like smart dust. So ubiquitous computing will be come true. SmartDust project is the one of ubiquitous computing approach. It produces TinyOS, mote(mica, mica2, rene2, mica2dot, etc.), NesC, TinyDB, etc. We constructs sensor network test bed and tests to approach sensor network and ubiquitous computing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.140-145
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• 2022

In an advanced modern society, among air pollutants caused by urban industrialization and public transportation, fine dust flows into indoors from the outdoors. The fine dust meter used indoors provides limited information and measures the pollution level differently, so there is a problem that users cannot monitor and monitor the data they want. To solve this problem, in this paper, indoor air quality data fine dust and ultra-fine dust (PM1.0, PM2.5, PM10), VOC (Volatile Organic Compounds) and PIR (Passive Infrared Sensor) are used to measure fine dust. and a monitoring system were designed and implemented. We propose a fine dust meter and monitoring system that is installed in a designated area to measure fine dust in real time, collects, stores, and visualizes data through App Engine of Google Cloud Platform and provides it to users.

• Proceedings of the KIEE Conference
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• 2006.10c
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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.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.7-15
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• 2023

Recently, as interest in air quality in vehicles increases, the use of fine dust detection sensors for air quality measurement is becoming common. An axial-flow fan is inserted in the fine dust sensor installed in the air conditioning system in the vehicle to prevent dust from sinking directly on the sensor. When the sensor operates, the flow noise caused by the rotation of the axial-flow fan acts as a major noise source of the fine dust sensor. flow noise is recognized as one of the product competitiveness of fine dust sensors. In this study, the noise was gradually reduced at the same flow rate by improving the flow performance of the small axial flow fan. First, a virtual fan performance tester consisting of about 20 million grids was developed to analyze the aerodynamic performance of the target small axial-flow fan. In addition, the flow field was simulated by using compressible Large Eddy Simulation for direct computation of flow noise as well as high-accurate prediction of flow rate. The validity of numerical method are confirmed through the comparison of predicted results with experimental ones. After the effects of pitch angle on flow performance were analyzed using the verified numerical method, the pitch angle was determined to maximize the flow rate. It was found that the flow rate was increased by 8.1 % and noise was reduced by 0.8 dBA when the axial-flow fan with the optimum pitch angle was used.