• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.359-362
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• 2003

The aerostatic stage. which is used in semiconductor process, is demanded higher velocity and more precise accuracy for higher productivity and integrated performance. So, in the case of XY stage, H type structure, which is designed two co-linear axis of guide-way, driving force in one surface, has advantage of velocity and accuracy compared to conventional tacked type XY stage. To analyze characteristics of H type aerostatic stage, H type aerostatic surface XY stage is made, which is driven by linear motor and detected position with precise optical linear scale. And, analyze characteristics of motion error, effect of angular motion on positioning accuracy error and effect of simultaneous control on variation of velocity.

• 대한기계학회논문집A
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• 제21권10호
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• pp.1712-1717
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• 1997

Stability and accuracy for the trapezoidal rule of the Newmark time integration method are analyzed when variable time step sizes are adopted. A new analytic approach to stability and accuracy analysis is also proposed for time integration methods with variable time step sizes. The trapezoidal rule with variable time step sizes has the "actual" unconditional stability which is the same as that of the method with constant time step sizes. However, the method with variable time step sizes is first-order accurate while the method with constant time step sizes is second-order accurate. accurate.

• 한국산학기술학회논문지
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• 제14권1호
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• pp.94-100
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• 2013

본 연구에서는 VRS GNSS(GPS/GLONASS)를 이용하여 지적측량에서의 위치결정 정확도를 분석하여 보고, 이를 토대로 성과결정의 정확도 향상 가능성을 제시하고자 하였다. 연구결과 GPS 위성데이터만을 수신 한 결과 보다 GPS/GLONASS 위성데이터을 통합으로 수신해 위치를 결정한 결과가 대략 3cm 정도 위치정확도가 높은 것으로 분석되었다. 따라서 VRS GNSS 통합 수신 방식이 지적측량에 도입된다면 위치 정확도 향상을 기대할 수 있을 것으로 보인다.

• 한국산업융합학회 논문집
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• 제22권6호
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• pp.599-606
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• 2019

In this study, it was aimed to find the optimal cutting conditions by measuring and analyzing the dimensional accuracy of SNCM 616 alloy steel, which is commonly used in industry, by precision hole machining using Ø25 mm and 8-blade reamer in CNC-HBM to be. As a result of the roundness and dimensional accuracy, it was found that the spindle speed had a significant effect on the dimensional tolerance value. Optimum cutting conditions are spindle speed 25 rpm and feed rate 20 mm / min.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3498-3512
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• 2021

This article describes how events that make up text stories can be represented and extracted. We also address the results from our simple experiment on extracting and clustering events in terms of emotions, under the assumption that different emotional events can be associated with the classified clusters. Each emotion cluster is based on Plutchik's eight basic emotion model, and the attributes of the NLTK-VADER are used for the classification criterion. While comparisons of the results with human raters show less accuracy for certain emotion types, emotion types such as joy and sadness show relatively high accuracy. The evaluation results with NRC Word Emotion Association Lexicon (aka EmoLex) show high accuracy values (more than 90% accuracy in anger, disgust, fear, and surprise), though precision and recall values are relatively low.

• 대한방사선기술학회지:방사선기술과학
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• 제45권4호
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• pp.321-330
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• 2022

Echocardiography and cardiac enzymes test are the tests to assess ischemic heart disease. The purpose of this study was to verify the accuracy by comparing and analyzing two tests for the diagnosis of ischemic heart disease. A retrospective study was conducted on 393 study subjects who underwent echocardiography and cardiac enzymes test. As a result of the study, regional wall motion abnormality (RWMA) increased as the age of the study subjects increased. As a result of ROC analysis, RWMA showed a larger area under the curve (AUC) than cardiac enzymes. RWMA showed the highest accuracy with 81.1% of all cardiac enzymes. Among cardiac enzymes, cTnI showed the highest accuracy. Thus, It was confirmed that RWMA of echocardiography is more accurate than cardiac enzyme is in diagnosing ischemic heart disease.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.293-301
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• 2022

Direction of arrival (DOA) estimation of space signals is a basic problem in array signal processing. DOA estimation based on the multiple signal classification (MUSIC) algorithm can theoretically overcome the Rayleigh limit and achieve super resolution. However, owing to its inadequate real-time performance and accuracy in practical engineering applications, its applications are limited. To address this problem, in this study, a DOA estimation algorithm with high parallelism and precision based on an analysis of the characteristics of complex matrix eigenvalue decomposition and the coordinate rotation digital computer (CORDIC) algorithm is proposed. For parallel and single precision, floating-point numbers are used to construct an orthogonal identity matrix. Thus, the efficiency and accuracy of the algorithm are guaranteed. Furthermore, the accuracy and computation of the fixed-point algorithm, double-precision floating-point algorithm, and proposed algorithm are compared. Without increasing complexity, the proposed algorithm can achieve remarkably higher accuracy and efficiency than the fixed-point algorithm and double-precision floating-point calculations, respectively.

• 자동차안전학회지
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• 제10권2호
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• pp.29-35
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• 2018

This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).