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Kim, Jun Ho,Lee, Hankyeol,Na, Jin-Young,Kim, Sun-Kyung,Yoo, Young-Zo,Seong, Tae-Yeon Elsevier 2015 Current Applied Physics Vol.15 No.4
<P><B>Abstract</B></P> <P>We report on the optimization of the optical and electrical properties of IGZO/Ag/IGZO multilayer films as a function of IGZO thickness. The transmission window slightly widened and shifted toward lower energies with increasing IGZO thickness. The IGZO(39 nm)/Ag(19 nm)/IGZO(39 nm) showed transmittance 88.7% at 520 nm. The optical transmittance spectra were examined by finite-difference time-domain (FDTD) simulations. The carrier concentration decreased from 1.73 × 10<SUP>22</SUP> to 4.99 × 10<SUP>21</SUP> cm<SUP>−3</SUP> with increasing the IGZO thickness, while the charge mobility insignificantly changed from 19.07 to 19.62 cm<SUP>2</SUP>/V. The samples had sheet resistances of 4.17–4.39 Ω/sq with increasing IGZO thickness, while the resistivity increased from 1.89 × 10<SUP>−5</SUP> to 6.43 × 10<SUP>−5</SUP> Ω cm. The 39 nm-thick IGZO multilayer sample had a smooth surface with a root mean square roughness of 0.63 nm. The IGZO(39 nm)/Ag(19 nm)/IGZO(39 nm) multilayer showed a Haacke's FOM of 49.94 × 10<SUP>−3</SUP> Ω<SUP>−1</SUP>.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The opto-electrical properties of IGZO/Ag/IGZO multilayer films are investigated. </LI> <LI> Transmission window widens and shifts to lower energies with increasing IGZO thickness. </LI> <LI> IGZO(39 nm)/Ag(19 nm)/IGZO(39 nm) shows maximum transmittance at 520 nm. </LI> <LI> Carrier concentration decreases, but sheet resistance is constant with IGZO thickness. </LI> </UL> </P>
Smartphone based Indoor Localization Technology using 1D CNN -BLSTM
Changsoo Yu,Beomju Shin,Chung G. Kang,Jung Ho Lee,Hankyeol Kyung,Taehun Kim,Taikjin Lee 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
The study of indoor localization technology using smart phone has been continuously studied. Fingerprinting is a representative indoor positioning technology. This technology estimates the location by comparing Radio Signal Strength (RSS) information received in one-shot at a specific location with the previously constructed Radio Map. Since the RSS received in one-shot is used, the ability to discriminate signals according to space is low. To solve this problem, the use of RSS spatial patterns based on Pedestrian Dead Reckoning (PDR) improves signal discrimination according to space and increases accuracy. However, since PDR is used, there is a problem that it is difficult to use a spatial pattern if PDR distortion occurs due to a heading drift error and a change motion. We propose an indoor positioning technology using 1D Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (BLSTM). We estimated the position by learning the 1D RSS pattern. In order to generate a large amount of data, we used the pre-built Radio Map. We use a model that combines 1D CNN and BLSTM. 1D CNN is used to extract RSS patterns, and BLSTM is used to learn the relationship of sequential data in both directions. Through this, it is possible to estimate the position using only the RSS. To verify the proposed technology, we compared it with the previous technology. As a result, the previous technology showed 2.19m error and the proposed technology showed 4.663m error. However, the calculation speed is 30 times faster than the proposed technology. It was confirmed that indoor positioning technology using deep learning technology can provide position information with only 1D RSS pattern.
A Study on Altitude Estimation using Smartphone Pressure Sensor for Emergency Positioning
Shin, Donghyun,Lee, Jung Ho,Shin, Beomju,Yu, Changsu,Kyung, Hankyeol,Choi, Dongwook,Kim, Yeji,Lee, Taikjin 항법시스템학회 2020 Journal of Positioning, Navigation, and Timing Vol.9 No.3
This paper introduces a study to estimate the user altitude in need of rescue in an emergency. The altitude is estimated by using the barometric pressure sensor embedded in the smartphone. Compared to GPS, which is degraded in urban or indoor environments, it has the advantage of not having spatial restrictions. With the endless development of smartphone hardware, it is possible to estimate the absolute altitude using the measured value if only the bias of the embedded barometric pressure sensor is applied. The altitude information of the person in need of rescue in an emergency is a great help in reducing rescue time. Since time is tight, we propose online calibration that provides the barometric pressure sensor bias used for altitude estimation through database. Furthermore, experiments were conducted to understand the characteristics of the barometric pressure sensor, which is greatly affected by wind. At the end, the altitude estimation performance was confirmed through an actual field tests in various floors in the building.
Performance Enhancement of Emergency Rescue System using Surface Correlation Technology
Shin, Beomju,Lee, Jung Ho,Shin, Donghyun,Yu, Changsu,Kyung, Hankyeol,Lee, Taikjin 항법시스템학회 2020 Journal of Positioning, Navigation, and Timing Vol.9 No.3
In emergency rescue situations, the localization accuracy of the rescue requestor is a very important factor in determining the success or failure of the rescue. Indoors where Global Navigation Satellite System (GNSS) is not operated, there is no choice but to use Wi-Fi or LTE signals. However, the performance of the current emergency rescue system utilizing those RF signals is exceedingly low. In this study, the effectiveness of the surface correlation technology using the accumulated signal pattern of RF signals was verified in relation to the emergency localization technology. To validate the proposed system, we configured and tested an emergency rescue scenario in multi-floors building. When the emergency rescue was requested, it was confirmed that the initial localization error was large owing to the short length of the accumulated signal pattern. However, the localization error decreased over time, which eventually led to the accurate location information being delivered to the rescuer.
Taehun Kim,Beomju Shin,Chung G. Kang,Jung Ho Lee,Changsoo Yu,Hankyeol Kyung,Donghyun Shin,Taikjin Lee 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Pedestrians use their smartphones to determine their location. Accordingly, there is a growing demand for seamless localization that can estimate the location regardless of indoor and outdoor space. However, the Global Navigation Satellite System used for outdoor location estimation has poor reception in the indoor environment, making it difficult to use in indoor space. Therefore, we propose the indoor localization technology based on Surface Correlation (SC). This indoor localization technology can estimate the location of pedestrians on only one floor. In this study, floor detection was performed using only RF signal without using other sensors such as barometric pressure sensor in the multi-floor building. The most important thing in floor detection is the reliability of the current floor and floor change detection. We can estimate the coarse floor using the unique ID of the RF source installed on each floor. Then, the virtual trajectory is generated using only RF signal, and the degree of similarity with the floor is determined by identifying the fine floor of the coarse floor estimated by applying the existing SC-based localization. Once the fine floor of pedestrians is identified, the final absolute location of pedestrians in the multi-floor building can be estimated by calculating the indoor location of the estimated floor using conventional SC-based localization. To verify the performance of the proposed algorithm in real-time, the algorithm was implemented in Google Cloud Platform. Pedestrians can check the indoor location results through real-time connection with the smartphone. In the actual multi-floor building, the similarity between the floor estimated by the proposed algorithm and the floor estimated using the barometric pressure sensor is about 95.0%. And the RMSE of the indoor localization results of the proposed system is about 3.662m.