• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.163-168
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• 1998

Extracting a Digital Elevation Model (DEM) from spaceborne imagery is important for cartographic applications of remote sensing data. The procedure for such DEM generation can be divided into stereo matching, sensor modelling and DEM interpolation. Among these, DEM interpolation contributes significantly to the completeness and accuracy of a DEM and, yet, this technique is often considered "trivial". However, na\ulcornere DEM interpolation may result in a less accurate and sometimes meaningless DEM. This paper reports the performance analysis of various DEM interpolation techniques. Using a manually derived DEM as reference, a number of sample points were created randomly. Different interpolation techniques were applied to the sample points to generate DEMs. The performance of interpolation was assessed by the accuracy of such DEMs. The results showed that kriging gave the best results at all times whereas nearest neighborhood interpolation provided a fast solution with moderate accuracy when sample points were large enough.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.107-113
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• 2011

An OFDMA(Orthogonal Frequency Division Multiple Access) includes a MIMO(Multi-Input Multi-Output) scheme for improving spectral efficiency and data throughput. Recognizing that the performance of MIMO system is heavily dependent upon the accuracy of channel estimation, we propose a novel channel estimation for the MIMO scheme based on OFDMA. Conventional interpolation-based channel estimation suffers from poor estimation error at specific subcarriers. Proposed scheme makes use of a planar interpolation instead of linear interpolation for those subcarriers of bad accuracy. Simulation results show that the proposed scheme improves the performance of MIMO system by improving the accuracy in channel estimation especially for the adverse subcarrier positions. It is observed that the proposed scheme outperforms the conventional method by about 2dB in terms of both mean squared error and overall bit error rate with a reasonable computational complexity.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.233-250
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• 2021

This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.50-64
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• 2018

This paper describes a visual object detection approach utilizing ensemble based machine learning. Object detection methods employing 1D features have the benefit of fast calculation speed. However, for real image with complex background, detection accuracy and performance are degraded. In this paper, we propose an ensemble learning algorithm that combines a 1D feature classifier and 2D DNF (Disjunctive Normal Form) classifier to improve the object detection performance in a single input image. Also, to improve the computing efficiency and accuracy, we propose a feature selecting method to reduce the computing time and ensemble algorithm by combining the 1D features and 2D DNF features. In the verification experiments, we selected the Haar-like feature as the 1D image descriptor, and demonstrated the performance of the algorithm on a few datasets such as face and vehicle.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.35-42
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• 2021

The position accuracy of the stand-alone Regional Navigation Satellite System (RNSS) users is more than tens of meters because of various error sources in satellite navigation signals. This paper focuses on wide-area differential (WAD) positioning technique, which is already applied in Global Navigation Satellite System (GNSS), in order to improve the position accuracy of RNSS users. According to the simulation results in the very narrow ground network in regional area, the horizontal position error of stand-alone RNSS is about RMS 11.6 m, and that of RNSS with WAD technique, named the WAD-RNSS, is about RMS 2.5 m. The accuracy performance has improved by about 78%.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.13-21
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• 1992

Standards concerning the determination of positioning accuracy and repeatability of numerically controlled(NC) machine tools have been published relentlessly over the last 20 years. Since the publication in 1988 of the International Standard 230-2 there has been a pronounced move, both at national and international standards level, to embrace further test procedures for a complete machine tool performance assessment. For example, measurements of angular (pitch, roll, and yaw) and straightness errors along linear axes are now commonplace and complement the existing positioning accuracy and repeatablity tests. More recently the subject of circularity evalutaion has also gained considerable interest. Here dynamic tests, using a kinematic ballbar or circular masterpiece, give an instant overview of the contouring ability of the machine in two axes at specific feedrates. This information is extremely important in optimising machining accuracy. This paper describes moves to complete the machine calibration package in national and international standardis- ation for the assessment of machine tool performance.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• ETRI Journal
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• v.40 no.4
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• pp.531-536
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• 2018

The weighted K-nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH-KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K-Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of $20m{\times}20m$, $30m{\times}30m$, $40m{\times}40m$ and $50m{\times}50m$. Simulation results show that the maximum position accuracy of DH-KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well-known algorithms.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.113-121
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• 1999

It is necessary to calibrate measurement systems to enhance its measurement accuracy. The visual sensing system that is presented in our previous work has to be calibrated, too. It is a multiple mirror system for three-dimensional measurement, which is composed of a camera and a series of mirrors. It is important to calibrate the positions and orientations of the mirrors relative to the camera because they have direct influence on the relationship between the image plane and the task space. This paper presents the calibration method for the visual sensing system. To confirm the measurement performance of the implemented system. its measurement accuracy in measuring the locations in three-dimensional space is investigated. A series of experiments for measuring the locations of the circle-shaped marks are performed. Experimental results show that the sensing system can be effectively used for three-dimensional measurement.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.