• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2010.06a
• /
• pp.328-328
• /
• 2010

압전 세라믹스는 엑츄에이터 및 센서 등의 다양한 응용분야로 인하여 많은 연구가 진행되어왔다. 최근 친환경 무연 압전계인 Bi층상구조 (BNT) 및 알칼리 니오븀산화물계 (KNN)에 대한 연구가 집중되고 있다. 한편, 소형화 및 고성능의 압전소자에 대한 요구 증가로 고가의 내부전극인 Ag, Ag-Pd합금으로 이루어진 적층압전소자에 대한 연구개발이 진행되어 왔다. 본 연구에서는 Ni이나 Cu를 내부전극으로 사용하는 적층압전소자의 개발가능성을 타진하고자 Ni의 산화를 억제할 수 있는 환원분위기 소결시에 압전소재의 상변화 및 내환원성 정도를 조사하였다. 압전소재인 BNT 및 KNN를 공기중에서 합성한 후, 환원분위기의 영향을 조사하고자 샘플을 디스크 형태로 성형하여 $1000{\sim}1200^{\circ}C$에서 2 시간 동안 공기, 중성 (N2) 와 환원 분위기 (3 % H2 - 97 %의 N2) 에서 소결한 후 미세구조와 전기적 특성을 SEM, EDS, XRD, impedance analyzer로 조사였다. 환원분위기에서 소결된 BNT 샘플은 페롭스카이트 상이 관찰되지 않았으며, SEM/EDS 분석결과 시편의 표면에 Bi의 석출이 관찰되었다. KNN의 경우에는 공기중에서 소결 시편뿐만 아니라 환원분위기에서 소결된 시편에서도 페롭스카이트 구조를 보였으며, EDS분석결과 K 및 Na의 휘발이 비교적 적었다.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.31-36
• /
• 2021

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

• Journal of Digital Convergence
• /
• v.19 no.9
• /
• pp.463-468
• /
• 2021

Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.43-52
• /
• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

• Journal of the Semiconductor & Display Technology
• /
• v.16 no.3
• /
• pp.78-81
• /
• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.24 no.7
• /
• pp.563-567
• /
• 2011

The most widely used piezoelectric ceramics are lead oxide based ferroelectrics (PZT). However, the toxicity of lead oxide and its high vapor pressure during processing have led to a demand for alternative lead-free piezoelectric materials. We synthesized Lead-free piezoelectric ceramics of $0.96(K_{0.5}Na_{0.5})NbO_3-0.04SrZrO_3+x$ mol% $ZrO_2$ [KNN-SZ+$xZrO_2$; x= 0~0.10] doped with 0.1 wt% $MnO_2$ by a conventional solid state method. We investigated the piezoelectric properties and microstructures of these disk samples sintered in reduced atmosphere in order to evaluate the possibility of the multilayered piezoelectric ceramics having the base metal such as Ni as a internal electrode. All of these KNN-SZ samples sintered in 3%$H_2-97%N_2$ atmosphere at $1,140^{\circ}C$ exhibit pure perovskite structure irrespective of the content of $ZrO_2$. Meanwhile, the sintering denisty and piezoelectric properties such as $K_p$, $Q_m$ and $d_{33}$ of KNN-SZ samples as a function of $ZrO_2$ content show the maxima ($k_p$=28.07%, $Q_m$= 101.34, $d_{33}$= 156 pC/N) at x= 0.04 and it is likely that there is some morphotropic phase boundary(MPB) in this KNN-SZ+$xZrO_2$ composition system. These results indicate that the ceramic composition is a promising candidate material for applications in lead free multilayer piezoelectric ceramics.

• International journal of advanced smart convergence
• /
• v.4 no.2
• /
• pp.170-176
• /
• 2015

The stamp is one of the crucial information of traditional historical and cultural for nations. In this paper, we purpose to detect official stamps from scanned document and recognize the Mongolian traditional, historical stamps. Therefore we performed following steps: first, we detect official stamps from scanned document based on red-color segmentation and document standard. Then we collected 234 traditional stamp images with 6 classes and 100 official stamp images from scanned document images. Also we implemented the processing algorithms for noise removing, resize and reshape etc. Finally, we proposed a new scale invariant classification algorithm based on KNN (k-nearest neighbor). In the experimental result, our proposed a method had shown proper recognition rate.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.2
• /
• pp.249-256
• /
• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.3
• /
• pp.261-270
• /
• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• Journal of Internet Computing and Services
• /
• v.23 no.2
• /
• pp.53-59
• /
• 2022

After the 3rd level autonomous driving vehicle, the 4th and 5th level of autonomous driving technology is trying to maintain the optimal condition of the passengers as well as the perfect driving of the vehicle. However current autonomous driving technology is too dependent on visual information such as LiDAR and front camera, so it is difficult to fully autonomously drive on roads other than designated roads. Therefore this paper proposes a Braking Strength Calculation System (BSCS), in which a vehicle classifies road conditions using data other than visual information and calculates optimal braking strength according to road conditions and driving conditions. The BSCS consists of RCDM (Road Condition Definition Module), which classifies road conditions based on KNN algorithm, and BSCM (Braking Strength Calculation Module), which calculates optimal braking strength while driving based on current driving conditions and road conditions. As a result of the experiment in this paper, it was possible to find the most suitable number of Ks for the KNN algorithm, and it was proved that the RCDM proposed in this paper is more accurate than the unsupervised K-means algorithm. By using not only visual information but also vibration data applied to the suspension, the BSCS of the paper can make the braking of autonomous vehicles smoother in various environments where visual information is limited.