• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.419-428
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• 2011

This paper suggests a judgement method for an inclined plane before entrance of it and the detection of obstacle position. Main idea is started from the assumption that obstacle is always on the bottom plane, and corner appears at this position. The process to detect the obstacle consists of three steps. First the 3D data using stereo matching is acquired to detect an obstacle. Second a bottom plane is extracted by using limit condition. Last the obstacle position is found by using Harris corner detection. Obstacle position detection on an inclined plane was verified by outdoor and indoor experiment. In error analysis, it is confirmed that an average error of obstacle detection in outdoor was larger than the error in indoor but the error are within about 0.030 m. This method will be applied to unmanned vehicles to navigate under various environment.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.399-408
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• 2023

In this paper, we propose a solution that enables continuous indoor/outdoor positioning of smartphone users through the integration of Pedestrian Dead Reckoning (PDR) and GPS/WiFi signals. Considering that accurate step detection affects the accuracy of PDR, we propose a Deep Neural Network (DNN)-based technology to distinguish between walking and non-walking signals such as walking in place. Furthermore, in order to integrate PDR with GPS and WiFi signals, a technique is used to select a proper measurement by distinguishing between indoor/outdoor environments based on GPS Dilution of Precision (DOP) information. In addition, we propose a technology to adaptively change the measurement error covariance matrix by detecting measurement outliers that mainly occur in the indoor/outdoor transition section through a residual-based χ² test. It is verified through experiments on a testbed that these technologies significantly improve the performance of PDR and PDR/GPS/WiFi fingerprinting-based integrated pedestrian navigation.

• Journal of Korean Society for Atmospheric Environment
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• v.12 no.2
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• pp.199-209
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• 1996

The purpose of this study is to evaluate analytical method for the measurement of carbonyl compounds and to apply method to the measurement of indoor and outdoor concentrations of these compounds at public facilities. For sampling, 2.4-dinitrophenylhydrazine (DNPH) coated DNPH-Silica cartridges were uwed in this study. DNPH reacts with carbonyl compounds and forms carbonyl hydrazone, The carbonyl hydarzone was eluted from the cartridge with acetonitrile and analyzed by reverse-phase HPLC with UV detection. Possible interference caused by ozone during sampling was eliminated by using KI trap commected in series with the DNPH-Silica cartridge. A number of experimental studies were undertaken to evaluate and validate the analytical method, including collection efficiency, recovery, repeatability, lower limits of detection, and effect of ozone. Indoor and outdoor measurements of carbonyl compounds were simultaneously carried out at 14 public facilities located in Taegu city and Kyungsan city from June to July, 1995. Except for one or two sites, the indoor concentrations were found to be higher than the outdoor concentrations for carbonyl compounds. And the concentrations of carbonyl compounds measured in the morning and afternoon were showed higher than the concentrations measured in the evening.

• Journal of KIISE
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• v.43 no.7
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• pp.763-772
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• 2016

With the spread of smartphones containing multiple on-board sensors, the market for context aware applications have grown. However, due to the limited power capacity of a smartphone, users feel discontented QoS. Additionally, context aware applications require the utilization of many forms of context and sensing information. If context transition has occurred, types of needed sensors must be changed and each sensor modules need to turn on/off. In addition, excessive sensing has been found when the context decision is ambiguous. In this paper, we focus on power consumption associated with the context transition that occurs during indoor/outdoor detection, modeling the activities of the sensor associated with these contexts. And we suggest a freezing algorithm that reduces power consumption in context transition. We experiment with a commercial application that service is indoor/outdoor location tracking, measure power consumption in context transition with and without the utilization of the proposed method. We find that proposed method reduces power consumption about 20% during context transition.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.37-44
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• 2022

With the outbreak of COVID-19, non-face-to-face activities such as remote learning and telecommuting have increased rapidly. As a result, the number of people staying at home and the number of hours spent inside the house have also increased since the pandemic. Our team had previously worked on methods for detecting abnormal conditions in a person's health in various circumstances within the house by converging single sensor-based algorithms. In our previous research, we installed IoT sensors indoors to detect people emergency situations requiring aids, the scope of detection was limited to indoor space due to the limitation in sensors. In this study, we have come up with a system that integrates our previous study with a new method for detecting abnormal conditions in outdoor environments using outdoor security cameras and wearable devices. The proposed system enables users to be notified of emergency situations in both indoor and outdoor areas and respond to them as quickly as possible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3965-3983
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• 2017

We propose a novel eye tracking method that can estimate 3D world coordinates using an infrared (IR) stereo camera for indoor and outdoor environments. This method first detects dark evidences such as eyes, eyebrows and mouths by fast multi-level thresholding. Among these evidences, eye pair evidences are detected by evidential reasoning and geometrical rules. For robust accuracy, two classifiers based on multiple layer perceptron (MLP) using gradient local binary patterns (GLBPs) verify whether the detected evidences are real eye pairs or not. Finally, the 3D world coordinates of detected eyes are calculated by region-based stereo matching. Compared with other eye detection methods, the proposed method can detect the eyes of people wearing sunglasses due to the use of the IR spectrum. Especially, when people are in dark environments such as driving at nighttime, driving in an indoor carpark, or passing through a tunnel, human eyes can be robustly detected because we use active IR illuminators. In the experimental results, it is shown that the proposed method can detect eye pairs with high performance in real-time under variable illumination conditions. Therefore, the proposed method can contribute to human-computer interactions (HCIs) and intelligent transportation systems (ITSs) applications such as gaze tracking, windshield head-up display and drowsiness detection.

• Journal of Environmental Health Sciences
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• v.41 no.6
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• pp.369-379
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• 2015

Objectives: The lack of reliable information on environmental pollution and health impacts related to asbestos contamination from abandoned mines has drawn attention to the need for a community health study. This study was performed to evaluate asbestos-related health symptoms among residents near abandoned asbestos mines located in the Chungcheong Provinces. In addition, exposure assessment for asbestos is needed although the exposure to asbestos was in the past. Methods: Past exposure to asbestos among inhabitants near abandoned asbestos mines was estimated by using surface sampling of deposited dust in indoor and outdoor residences. A total of 54 participants were divided into two groups with (34 cases) and without (20 controls) diseases related to asbestos. Surface sampling of deposited dust was carried out in indoor and outdoor residences by collecting 105 samples. Deposited dust for sampling was analyzed by polarization microscope (PLM) and scanning electron microscope?energy dispersive x-ray spectrometer (SEM-EDX) to detect asbestos. Subsequently, the elements of the deposited dust with asbestos were analyzed by SEM-EDX to assess the contribution of sources such as abandoned mines, slate and soil. Results: Among the 105 samples, asbestos was detected by PLM in 29 (27.6%) sampling points, and detected by SEM in 56 (48.6%) sampling points. Asbestos in indoor residences was detected by PLM in four sampling points, and by SEM in 12 sampling points. Asbestos detection in indoor residences may be due to ventilation between indoors and outdoors, and indicates long-term exposure. The asbestos detection rate for outdoor residences in the case group was higher than that in the control group. This can be explained as the case group having had higher exposure to asbestos, and there has been continuous exposure to asbestos in the control group as well as the case group. Conclusion: Past residential asbestos exposure may be associated with asbestosis among local residents near abandoned asbestos mines. Odds ratios were calculated for asbestos detection in outdoor residence by logistic regression analysis. Odds ratio between asbestos detection and asbestosis pulmonum was 3.36 (95% CI 0.90-12.53) (p=0.072), adjusting for age, sex, smoking status and work history with multi-variable logistic regression by PLM analysis method.

• ETRI Journal
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• v.35 no.1
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• pp.80-88
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• 2013

A passive infrared or pyroelectric infrared (PIR) sensor is mainly used to sense the existence of moving objects in an indoor environment. However, in an outdoor environment, there are often outbreaks of false alarms from environmental changes and other sources. Therefore, it is difficult to provide reliable detection outdoors. In this paper, two algorithms are proposed to reduce false alarms and provide trustworthy quality to surveillance systems. We gather PIR signals outdoors, analyze the collected data, and extract the target features defined as window energy and alarm duration. Using these features, we model target and false alarms, from which we propose two target decision algorithms: window energy detection and alarm duration detection. Simulation results using real PIR signals show the performance of the proposed algorithms.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.181-194
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• 2017

In recent years, research has been actively conducted on the navigation in an indoor environment where Global Navigation Satellite System signals are unavailable. Among them, a study performed indoor navigation by integrating pseudolite carrier and Inertial Measurement Unit (IMU) sensor. However, in this case, there was no solution for the cycle slip occurring in the carrier. In another study, cycle slip detection and compensation were performed by integrating Global Positioning System (GPS) and IMU in an outdoor environment. However, in an indoor environment, cycle slip occurs more easily and frequently, and thus the occurrence of half cycle slip also increases. Accordingly, cycle slip detection based on 1 cycle unit has limitations. Therefore, in the present study, the aforementioned problems were resolved by performing indoor navigation through the integration of pseudolite and ultra-low-cost IMU embedded in a smartphone and by performing half cycle slip detection and compensation based on this. In addition, it was verified through the actual implementation of real-time navigation.

• Tuberculosis and Respiratory Diseases
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• v.86 no.1
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• pp.23-32
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• 2023

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.