• Korean Journal of Materials Research
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• v.6 no.11
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• pp.1061-1066
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• 1996

RF magnetron reactive sputtering법으로 Fe75.5Zr8.3N16.2/SiO2(250$\AA$) 다층 박막을 FeZrN의 두께를 변화시키면서 제조하고, 제조된 박막을 진공 열처리하여 열처리 온도에 따른 포화자화, 보자력, 고주파에서의 투자율 그리고 열적 안정성을 조사하였다. Fe75.5Zr8.3N16.2/SiO2(250$\AA$) 다층박막은 FeZrN의 두께가 800$\AA$이상일 때 좋은 연자성을 나타내었다. Fe75.5Zr8.3N16.2/SiO2(250$\AA$)다층 박막을 45$0^{\circ}C$로 열처리 했을 때 포화자속밀도(1.08 T), 보자력 0.41 Oe, 1 MHz에서의 실효 투자율은 3000이상의 연자성을 나타내었다. 그 이유는 X-선 회절 분석 결과 열처리에 의해서 ZrN 미결정이 석출하여 $\alpha$-Fe 결정 성장이 억제되어 우수한 연자기적 성질이 나타난다고 판단된다. 이때 $\alpha$-Fe 입자 크기는 40-50$\AA$, ZrN의 입자 크기는 10-15$\AA$이다. 그리고 실효 투자율의 주파수 의존성에서 단층막에서는 5 MHz 이상에서 실효 투자율이 급격히 감소하는 경향을 보였으나, 다층막에서는 40MHz까지 실효 투자율이 1600이 되어 고주파에서의 연자성이 개선되었다.

• Journal of the Korean institute of surface engineering
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• v.54 no.3
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• pp.124-132
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• 2021

Graphene which grown on the cobalt or nickel sputtered copper foil depending on the annealing time was studied. Graphene on the copper foil grown by chemical vapor deposition was compared to those on cobalt or nickel sputtered copper foil by using a RF (Radio Frequency) magnetron sputtering at room temperature. FLG(few-layer graphene) was identified independent of substrates by Raman and X-Ray Photoelectron Spectroscopy analyses. On copper foil, size and area fraction of the graphene growth increased until 30 minutes annealing and then didn't changed. Comparing to that, graphene on the cobalt refined till 50 minutes annealing, after then the effect disappeared which means a similar shape to that on copper foil. On nickel the graphene refined irrespective of annealing time that is possibly because of the complete solid solution of nickel with copper.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.143-146
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• 2002

Unsized AS-4 carbon fibers were etched by RF plasma and then coated via plasma polymerization in order to enhance adhesion to vinyl ester resin. The gases utilized for the plasma etching were Ar, $N_2 and O_2$, while the monomers used for the plasma polymerization coating were acetylene, butadiene and acrylonitrile. The conditions for the plasma etching and the plasma polymerization were optimized by measuring interfacial adhesion with vinyl ester resin via micro-droplet tests. Among the treatment conditions, the combination of Ar plasma etching and acetylene plasma polymerization provided greatly improved interfacial shear strength (IFSS) of 69MPa compared to 43MPa with as-received carbon fiber. Based on the SEM analysis of failure surface and load-displacement curve, it was assume that the failure might be occurred at the carbon fiber and plasma polymer coating. The plasma etched and plasma polymer coated carbon fibers were subjected to analysis with SEM, XPS, FT-IR or Alpha-Step, and dynamic contact angles and tensile strengths were also evaluated. Plasma polymer coatings did not change tensile strength and surface roughness of fibers, but decreased water contact angle except butadiene plasma polymer coating, possibly owing to the functional groups introduced, as evidenced by FT-IR and XPS.

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.131-131
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• 2000

We witnessed the enhancement of mangetoresistance (MR) in La0.75Ca0.25MnO3 thin films grown on Si (100) substrates by RF magnetron sputtering. The films are polycrystalline with (100) and (110) orientations. The lattice constants of films are reduced as much as 0.9% compared to the one of the bulk sample, which proves that the compressive stress on films was imposed by Si sbustrate. It is found that the MR value (Δ$\rho$/$\rho$0) of films are 0.33, 0.29 and 0.27 under a magnetic field of 1.5T for each films with deposition temperature of $700^{\circ}C$, 75$0^{\circ}C$ and 80$0^{\circ}C$, respectively. The correlation between the MR values and lattice constants of films is discussed. It is concluded that the compressive stress on films cause the enhancement of MR values of thin films grown on Si (1000 substrates. Some mechanism of compressive stress induced by Si substrate is suggested.

• Transactions of the Society of Information Storage Systems
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• v.1 no.1
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• pp.93-98
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• 2005

Rewritable optical disk is one of the essential data storage media in these days, which takes advantage of the different optical properties in the amorphous and crystalline states of phase change materials. As well known, data transfer rate is one of the most important parameter of the phase change optical disks, which is mostly limited by the crystallization speed of recording media. Therefore, we doped Sn/Bi to $Ge_2Sb_2Te_5$ alloy in order to improve the crystallization speed and investigated the dependence of phase change characteristics on Sn/Bi doping concentration. The Sn/Bi doped $Ge_2Sb_2Te_5$ thin film was deposited by RF magnetron co-sputtering system and phase change characteristics were investigated by X-ray diffraction (XRD), static tester, UV-visible spectrophotometer, electron probe microanalysis (EPMA), inductively coupled plasma mass spectrometer (ICP-MS) and atomic force microscopy (AFM). Optimum doping concentration of Bi and Sn were 5${\~}$6 at.$\%$ and the minimum time for crystallization was below than 20 ns. This improvement is correlated with the simple crystalline structure of Sn/Bi doped $Ge_2Sb_2Te_5$ and the reduced activation barrier arising from Sn/Bi doping. The results indicate that Sn/Bi might play an important role in the transformation kinetics of phase change materials..

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.185-188
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• 2021

본 논문에서는 기술의 빠른 발전에도 불구하고 줄어들지 않는 화재 사고, 그 중에서도 많은 인명피해를 내는 실내 화재 사고에 대하여 기존 실내 화재 알림 시스템의 한계점인 알림의 양치기 소년화로 인한 안전 불감증 증가와 알림의 사각지대 문제를 해결하고자 새로운 대안 시스템을 설계 및 구현하고, 실험 검증을 진행하였다. 위 두 가지 문제점을 해결하기 위해, 본 연구에서는 스마트폰이 매우 대중적으로 보급되어 있다는 점을 기반으로 IoT, 엣지 컴퓨팅, 비콘 등을 응용한다. 비콘 신호를 broadcasting 하는 엣지 노드의 신호 범위 내에 진입하면 사용자 정보를 수집하여 대상 건물에 출입한 대상을 특정한다. 말단 센서 노드와 엣지 노드 간의 무선 RF 통신으로 화재를 모니터링하며 화재가 발생했을 시 특정된 대상들에게만 스마트폰 어플의 푸시 알림으로 화재 발생 상황을 전송하는 시스템을 설계 및 구현하였다. 시스템 성능 평가를 위해 동국대학교 건물 내에서 수평, 수직으로 이동하며 실험을 진행하였고, 그 결과를 통해 대안 시스템의 성능과 한계를 분석하여 이를 실내 공간에 적용하기 위한 적합성을 평가하였다.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.