• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.460-461
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• 2007

본 연구에서는 반도체 플라즈마 장비 감시를 위한 CUSUM 제어 차트 설계기법에 관해 연구하였다. CUSUM 제어차트에 관여하는 설계변수의 다양한 조합에 대하여 플라즈마 장비의 감시 성능을 평가하였다. 평가를 위해 RF 정합망 감시시스템을 이용하여 플라즈마 임피던스 정합에 관여하는 정합변수에 대한 실시간 데이터를 수집하였으며, 여기에는 임피던스와 상위치에 대한 전기적 정보, 그리고 반사전력에 대한 정보가 포함된다. 평가결과, 설계변수의 조합에 대하여 감시 성능이 크게 달랐지만, 각 센서 정보의 감시 성능을 증진시키는 설계변수의 조합이 있었음을 확인하였으며, 이는 각 종 다양한 센서정보별 CUSUM 제어 차트의 설계가 필요함을 의미한다. 연구에서는 Raw 데이터 대비 성능 분석을 위해 CUSUM 제어 차트의 설계변수를 변수인 d와 ${\Theta}$값의 변화를 주어 다수의 (d, ${\Theta}$)의 조합에 따른 감시 성능을 평가하였으며, 평가에 이용된 데이터는 소스전력이 750 W, 압력이 15 mTorr, Ar 유량이 50 seem일 때 수집하였다.

• Journal of Sensor Science and Technology
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• v.28 no.4
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• pp.231-235
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• 2019

We investigated the facile and effective strategy for sensitive and selective $C_2H_5OH$ sensors based on the In-decorated NiO nanoigloos. The In-decorated NiO nanoigloos is fabricated by RF sputtering using 750 nm-diameter polystyrene beads using a soft-template. The morphological evolution based on the Van der Drift model was generated through a heterojunction between In metal and NiO, resulting in a pyramidal rough surface. Upon decorating the In on the NiO surface, high sensitivity and selectivity to $C_2H_5OH$ were observed, and gas sensing mechanism was demonstrated by a high surface-to-volume and double Schottky barrier. We are confident that the method presented in this study will have a significant impact on the fabrication of effective nanostructures and their application for the gas sensors.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.475-475
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• 2013

Cuprous oxide ($Cu_2O$)는 밴드갭이 2.17 eV p-type 산화물 반도체로써 태양에너지 변환기, photocatalysis (광촉매작용), 센서, 스위칭 메모리 등 응용이 다양한 재료이다. 산화물 반도체의 기본 특성은 나노/마이크로 범위 안에서 재료의 표면형태, 크기, 구조와 형상 공간방향등에 크게 영향을 받는다. 그렇기 때문에 원하는 $Cu_2O$ 특성을 얻기 위해서 성장 거동을 아는 것은 매우 중요하다. RF 마그네트론 스퍼터법으로 rod 성장 사례는 잘 알려지지 않았다. 그래서 RF 마그네트론 스퍼터법 $Cu_2O$ rod 형성 실험을 통하여 $Cu_2O$ 형성과 성장 거동을 알아보았다. RF 마그네트론 스퍼터법으로 $Cu_2O$ rod를 glass 기판 위에 Cu metal target을 이용하여 형성시켰다. $Cu_2O$ rod 합성을 위해 기판온도 및 산소분압 O2/(Ar+O2)=3%, 5%, 7% 증착시간 등을 변화시켜 실험하였다. 성장된 rod의 분석은 XRD, SEM으로 확인하였다. 성장 거동은 증착온도와 증착시간에 차이를 보였다. 증착온도 $550^{\circ}C$에서 rod가 생성되는 것을 관찰하였다. 증착시간이 길어질수록 rod 길이가 길어지고 일정 시간이 지나면 rod의 길이 성장보다는 두께(폭)가 성장하는 것을 확인하였다. 증착온도 $550^{\circ}C$ 그리고 산소분압 3%, 5%, 7% 조건에서 rod 합성 실험을 하였을 때 3%, 5% 조건에서 rod의 성장을 확인하였다. 이때 3%, 5% 산소분압에 따라 rod의 모양이 변화하였다. 하지만 7% 조건에서는 rod가 성장하지 않았다. 이유는 3%, 5%에서는 Cu metal peak을 확인하였지만, 7% 조건에서는 Cu metal peak이 없었다. 이로부터 Cu metal이 $Cu_2O$ rod 생성에 영향을 미치는 중요한 요소임을 예상할 수 있었다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.57-63
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• 2016

This paper describes implementation of the RF frequency synthesizer with $0.18{\mu}m$ silicon CMOS technology being used as an application of the IEEE802.15.4g SUN sensor node transceiver modules. Design of the each module like VCO, prescaler, 1/N divider, ${\Delta}-{\Sigma}$ modulator, and common circuits of the PLL has been optimized to obtain high speed and low noise performance. Especially, the VCO has been designed with NP core structure and 13 steps cap-bank to get high speed, low noise, and wide band tuning range. The output frequencies of the implemented synthesizer is 1483MHz~2017MHz, the phase noise of the synthesizer is -98.63dBc/Hz at 100KHz offset and -122.05dBc/Hz at 1MHz offset.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.33-40
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• 2015

This paper presents the design and implementation of the educational remote controlled robot system including remote sensing in the embedded environment. The design of sensing information processing, software design and template design mechanism for the programming practice are introduced. LPC1769 using Cortex-M3 core as CPU, LPCXPRESSO as debugging environment, C language as firmware development language and FreeRTOS as OS are used in development environment. The control command is received via RF communication by the server and the robot system which is operated by driving the various sensors. The educational procedure is from robot demo operation program as hands-on practice and then compiling, loading of the basic robot operation program, already supplied. Thereafter the verification is checked by using the basic robot operation to allow demo operation such as hands-on-training procedure. The original protocol is designed via RF communication between server and robot system, and the satisfied performance result is presented by analyzing the robot sensing data processing.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.166-166
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• 2007

ZnO는 육방정계(wurtzite) 결정구조를 지니며 상온에서 3.37eV의 wide band gap을 갖는다. ZnO의 엑시톤 결합 에너지는 GaN에 비해 2.5배 높은 60meV로서 고효율의 광소자 적용 가능성이 높다. 또한 고품위의 박막합성이 가능하다. 이러한 특성 때문에 display소자의 투명전극, 광전소자, 바리스터, 압전소자, 가스센서 등에 폭 넓게 응용되고 있다. ZnO박막의 제조는 스퍼터링, CVD, 진공증착법, 열분해법 등이 있다. 본 논문에서는 RF 마그네트론 스퍼터에 의해 제작된 ZnO 박막에 CMP공정을 수행하여 연마율과 비균일도 특성 및 광투과 특성을 연구하였다. ZnO박막은 $2{\times}2Cm$의 Corning glass위에 증착되었다. 로터리 펌프와 유확산 펌프를 이용하여 초기진공을 $2{\times}10^{-6}$ Torr까지 도달시킨 후 Ar과 $O_2$를 주입하였다. 증착은 상온에서 이루어졌으며 공정압력은 $6{\times}10^{-2}$Torr이였다. 초기의 불안정한 상태의 풀라즈마를 안정시키기 위해 셔터를 이용하여 pre-sputtering을 하였다. CMP 공정조건은 플레이튼 속도, 슬러리 유속, 압력은 칵각 60rpm, 90ml/min, $300g/cm^2$으로 일정하게 유지하였으며 헤드속도는 20rpm에서 100rpm까지 증가시키면서 연마특성을 조사하였다. 실리카슬러리의 적합성을 알아보기 위해 DIW와 병행하여 CMP공정을 수행하고 비교 분석하였다. CMP공정 결과 광투과도는 굉탄화된 표면의 확보로 인해 향상된 특성을 보였다. 실리카 슬러리를 사용하여 CMP를 할 경우는 헤드속도는 저속으로 하여야 양호한 연마특성을 얻을 수 있었다.

• Korean Journal of Optics and Photonics
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• v.20 no.2
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• pp.71-75
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• 2009

A compact electric field probe sensor incorporating two different electro-optic materials of $LiNbO_3$ and GaAs was proposed and fabricated, and it was used to measure the strength of the horizontal and vertical fields generated by a microstrip ring-resonator filter. The sensitivities of the sensors in $LiNbO_3$ and GaAs were $9.315{\mu}V/\sqrt{Hz}$ and ${\sim}49.346{\mu}V/\sqrt{Hz}$ respectively, and their signal to noise ratios were approximately ${\sim}50\;dB$ and ${\sim}40\;dB$ respectively. And the operating frequency range was up to ${\sim}1.2\;GHz$. The electric field profile for the test circuit was scanned and found to be in good agreement with that obtained by using the HFSS simulation.

• Journal of the Korean Institute of Gas
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• v.22 no.1
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• pp.53-59
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• 2018

In Korea, The number of installed LPG Charging stations is about 2000, increasing by 26 every year. In these, about 500 charging stations are older above 15 year, accounting about 25% of total stations. About 86% of them are located in the city, which is causing serious damage if accident occurs. In this paper, we developed a duel gas sensor module and integrated control system software that can prevent and correspondence to gas leaks and fire accidents at LPG/CNG charging stations. The dual type sensor module has the function of collecting and transmitting the measured data to the sensors of methane, butane and hydrogen through RF433Mhz communication. In addition, each sensor is attached with two to improve stability and accuracy. The integrated control system software detects real-time data of the devices measured by the sensors and it send to the PC and smart phone of manager. Therefore, if accident occurs, the manager can check the status of the charging station regardless of time and place.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.