• Journal of Radiation Protection and Research
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• v.41 no.2
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• pp.87-91
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• 2016

Background: A hand-held detector for an emergency response was developed for nuclide identification and to estimate the information of the ambient dose rate in the scene of an accident as well as the radioactivity of the contaminants. Materials and Methods: To achieve this, the most suitable sensor was first selected as a cadmium zinc telluride (CZT) semiconductor and the signal processing unit from a sensor and the signal discrimination and storage unit were successfully manufactured on a printed circuit board. Results and Discussion: The performance of the developed signal processing unit was then evaluated to have an energy resolution of about 14 keV at 662 keV. The system control unit was also designed to operate the CZT detector, monitor the detector, battery, and interface status, and check and transmit the measured results of the ambient dose rate and radioactivity. In addition, a collimator, which can control the inner radius, and the airborne dust sampler, which consists of an air filter and charcoal filter, were developed and mounted to the developed CZT detector for the quick and efficient response of a nuclear accident. Conclusion: The hand-held CZT detector was developed to make the in-situ gamma-ray spectrometry and its performance was checked to have a good energy resolution. In addition, the collimator and the airborne dust sampler were developed and mounted to the developed CZT detector for a quick and efficient response to a nuclear accident.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.159-166
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• 2019

Objectives: The purpose of this study was to evaluate the personal exposure to $PM_{10}$ by body parts for the development of dust monitoring wearable device for swine farmers. Methods: Tasks were classified by using motion pictures taken by action cameras attached to swine farmers. Concentrations of $PM_{10}$ were measured by attaching direct-reading instruments at the head, neck and waist of worker. Differences of $PM_{10}$ exposure between body parts were analyzed with linear regression. Results: We identified three tasks(vaccination, moving pigs, and manure treatment). $PM_{10}$ concentration during vaccination was the highest among the tasks, and the body part showing the highest concentration of $PM_{10}$ was the waist regardless of task. In all tasks, the closer distance between the body parts, the higher were the R-squared values(vaccination 0.4221, moving pigs 0.6990, and manure treatment 0.2164). Conclusions: We presumed that $PM_{10}$ concentrations were affected by the parts of the body in which they were measured. In order to develop swine farmer's wearable device for monitoring dust concentration in air, the determination of the positions of monitoring sensor to ensure accurate measurement is essential. Considering the results of this study, wearable sensor should be positioned at the waist.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.183-190
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• 2021

This study is a study on the implementation and operation of smart air cleaning system to improve indoor air quality. Recently, the problem of indoor air quality is getting serious due to various environmental factors. In this study, to improve the problems of indoor air quality, we implement an air cleaning system using IoT sensor. In particular, we proposed a system that can measure air pollution in real time and change different air flow paths according to pollution level. Through this, we examined efficient air quality improvement, extension of filter life, and system energy reduction. In addition, the main functions of the indoor air quality improvement system were constructed and prototypes were manufactured to confirm the operability. Finally, the utility of fine dust resolution through the implementation of the indoor air quality improvement system was examined.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.447-450
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• 2005

This paper describes an implementation of system based on ZIGBEE wireless communication technique to prevent for diseases of skin ailments and respiratory ailments as sensing the air pollutions that breaks out in the inside and purifying. ZIGBEE wireless communication technique has features - low battery consumption, low cost, acceptance of the maximum 256 node and simple protocol structure of below 32Kbyte. Hardware platform is implemented by using ATmega128L in ATmel corporation and 2.4GHz RF-IC CC2420 in Chipcon corporation and dust sensor(GP2Y1010AU) and gas sensor(GSBT11) that confirm degree of inside air pollution for ZIGBEE wireless communication technique.

• Journal of Drive and Control
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• v.20 no.1
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• pp.8-15
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• 2023

With the recent trend of intelligence and automation of construction work, a double-blade road cutter is being developed that automatically enables cutting along the cutting line marked on the road using a vision system. The road cutter can recognize the cutting line through the camera and correct the driving route in real-time, and it detects the load of the cutting blade in real-time to control the driving speed in case of overload to protect workers and cutting blades. In this study, a vision system mounted on a double-blade road cutter was developed. A cutting route recognition technology was developed to stably recognize cutting lines displayed on non-uniform road surfaces, and performance was verified in similar environments. In addition, a vision sensor protection module was developed to prevent foreign substances (dust, water, etc.) generated during cutting from being attached to the camera.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.224-226
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• 2021

Recently, in order to increase the efficiency of the product production process, the automation of facilities and devices in the factory is in progress, and a smart factory is being built using ICT and IoT technologies. In order to organically solve many problems occurring in the smart factory, a system for monitoring the wireless communication function between facilities and devices and the manufacturing process environment of the smart factory is required. In this paper, we propose a monitoring system using a Bluetooth module, a temperature/humidity sensor and a fine dust sensor to remotely monitor the process environment of a smart factory. The proposed monitoring system collect Arduino sensor values wirelessly through Bluetooth communication.

• Fire Science and Engineering
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• v.32 no.3
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• pp.134-139
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• 2018

Extremely fine particles seriously affect people and are becoming a social problem. Conventional methods using the type of beta ray absorption are difficult to have real-time measurements and miniaturization for the acquisition of fine dust. In this paper, a light scattering method was used. The sensors were configured internally with semiconductor laser diodes for miniaturization, low cost and lightweight. The use of the FFT method makes it easier to separate fine dust according to size compared to conventional light scattering sensors. Bluetooth communication also allows the connection, monitoring and control of devices using smart phones.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.397-400
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• 2019

급격히 증가하는 자동차 수, 발전량 증가 등으로 인하여 미세먼지로 인한 환경오염이 심각한 사회문제로 대두되고 있는 실정이다. 50개가 넘는 국가들이 권고치 이상의 미세먼지로 인해 피해를 받고 있으며 각 피해국들은 미세먼지 저감 대책 및 발생을 최소화하기 위한 방안을 연구하고 있다. 하지만 현재 고정형 미세먼지 취득 드론으로는 다양한 포인트의 미세먼지 데이터를 수집하기 힘든 상황이며, 기존 드론을 활용한 방법에서 도 회전 날개의 영향으로 인해 정확한 데이터를 수집하기 힘든 실정이다. 본 논문에서는 드론과 특정 구조물을 활용한 미세먼지 수집 방법을 제안하고 이의 효율성을 보여주고자 한다.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.409-414
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• 1998

To examine the detectability of the aerosol and/or Yellow dust from China crossing over the Yellow sea, three works carried out as follows , Firstly, a comparison was made of the visible(VIS), water vapor(WV), and Infrared(IR) images of the GMS-5 and NOAA/AVHRR on the cases of yellow sand event over Korea. Secondly, the spectral radiance and reflectance(%) was observed during the yellow sand phenomena on April, 1998 in Seoul using the GER-2600 spectroradiometer, which observed the reflected radiance from 350 to 2500 nm in the atmosphere. We selected the optimum wavelength for detecting of the yellow sand from this observation, considering the effects of atmospheric absorption. Finally, the atmospheric radiance emerging from the LOWTRAN-7 radiative transfer model was simulated with and without yellow sand, where we used the estimated aerosol column optical depth ($\tau$ 673 nm) in the Meteorological Research Institute and the d'Almeida's statistical atmospheric aerosol radiative characteristics. The image analysis showed that it was very difficult to detect the yellow sand region only by the image processing because the albedo characteristics of the sand vary irregularly according to the density, size, components and depth of the yellow sand clouds. We found that the 670-680 nm band was useful to simulate aerosol characteristics considering the absorption band from the radiance observation. We are now processing the simulation of atmospheric radiance distribution in the range of 400-900 nm. The purpose of this study is to present the preliminary results of the aerosol and/or Yellow dust detectability using the Ocean Scanning Multispectral Imager(OSMI), which will be mounted on KOMPSAT-1 as the ocean color monitoring sensor with the range of 400-900 nm wavelength.

• The Journal of Bigdata
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• v.6 no.2
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• pp.39-49
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• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.