• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.127-143
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• 2016

This study explored the variability in the accuracy of dasymetric population estimation with different grid cell sizes. Dasymetric population maps for Fulton County, Georgia in the US were generated from 30m to 420m at intervals of 30m using an automated intelligent dasymetric mapping technique, population data, and original and simulated land use and cover data. The accuracies of dasymetric population maps were evaluated using RMSE and adjusted RMSE statistics. Lumped fractal dimension values were calculated for the dasymetric population maps generated from resolutions of 30m to 420m using the triangular prism surface area (TPSA) method. The results show that a grid cell size of 210m or smaller is required to estimate population more accurately in terms of thematic accuracy, but a grid cell size of 30m is required to meet an acceptable spatial accuracy of dasymetric population estimation in the study area. The fractal analysis also indicates that a grid cell size of 120m is the optimal resolution for dasymetric population estimation in the study area.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.156-162
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• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.536-545
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• 2020

When general statistical regression methods are applied to predict the battery remaining time of a mobile smart device, they yielded the poor accuracy of estimating battery remaining time as the deviations of battery usage time per battery level became larger. In order to improve the estimation accuracy of general statistical regression methods, a preprocessing task is required to refine the measured raw data with large deviations of battery usage time per battery level. In this paper, we propose a data preprocessing framework that preprocesses raw measured battery consumption data and converts them into refined battery consumption data. The numerical results obtained by experimenting the proposed data preprocessing framework confirmed that it yielded good performance in terms of accuracy of estimating battery remaining time under general statistical regression methods for given refined battery consumption data.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.769-774
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• 2016

In the design of sensorless control system for induction motor, high-precision speed estimation is one of the most difficult problems. To solve this problem, the common method is model reference adaptive method (MRAS). MRAS requires accurate motor parameters to estimate rotor speed precisely. However, when motor is running, the variety of temperature and magnetic saturation will lead to the change of motor parameters such as stator resistance and rotor resistance, which will lower the accuracy of the speed estimation. To improve the accuracy and rapidity of speed estimation, this paper analyses the mutual MRAS speed identification based on rotor flux linkage, and proposes an improved mutual MRAS speed identification based on back-EMF. The improved method is verified by Simulink simulation and motor experimental platform based on DSP2812. The results of simulation and experiment indicate that the method proposed by this paper can significantly improve the accuracy of speed identification, and speed up the response of identification.

• Journal of the Optical Society of Korea
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• v.20 no.3
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• pp.381-388
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• 2016

The performance of a staring infrared imaging system can be characterized based on estimating the modulation transfer function (MTF). The slant edge method is a widely used MTF estimation method, which can effectively solve the aliasing problem caused by the discrete undersampling of the infrared focal plane array. However, the traditional slant edge method has some limitations such as the low precision of the edge angle extraction and using the approximate function to fit the edge spread function (ESF), which affects the accuracy of the MTF estimation. In this paper, we propose a modified slant edge method, including an edge angle extraction method that can improve the precision of the edge angle extraction and an ESF fitting algorithm which is based on the transfer function model of the imaging system, to enhance the accuracy of the MTF estimation. This modified slant edge method presents higher estimation accuracy and better immunity to noise and edge angle than other traditional methods, which is demonstrated by the simulation and application experiments operated in our study.

• Structural Engineering and Mechanics
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• v.68 no.4
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• pp.399-408
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• 2018

Cable supported structures have been widely used in civil engineering. Cable tension estimation has great importance in cable supported structures' analysis, ranging from design to construction and from inspection to maintenance. Even though the Bernoulli-Euler beam element is commonly used in the traditional finite element method for calculation of frequency and cable tension estimation, many elements must be meshed to achieve accurate results, leading to expensive computation. To improve the accuracy and efficiency, a dynamic finite element method for estimation of cable tension is proposed. In this method, following the dynamic stiffness matrix method, frequency-dependent shape functions are adopted to derive the stiffness and mass matrices of an exact beam element that can be used for natural frequency calculation and cable tension estimation. An iterative algorithm is used for the exact beam element to determine both the exact natural frequencies and the cable tension. Illustrative examples show that, compared with the cable tension estimation method using the conventional beam element, the proposed method has a distinct advantage regarding the accuracy and the computational time.

• Journal of Ecology and Environment
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• v.32 no.1
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• pp.61-65
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• 2009

We compared two nondestructive methods for leaf area estimation using leaves of 16 common plant species classified into six types depending on leaf shape. Relatively good linear relationships between actual leaf area (LA) and leaf length (L), width (W), or the product of length and width (LW) were found for ordinary leaves with lanceolate, oblanceolate, linear and sagitttate shapes with entire margins, serrate margins, mixed margins with a entire form and shallow lobes, and ordinary incised margins. LA was better correlated with LW than L or W, with $R^2$ > 0.91. However, for deeply incised lobes, LA estimation using LW showed low correlation coefficient values, indicating low accuracy. On the other hand, a method using photographic paper showed a good correlation between estimates of area based on the mass of a cut-out leaf image on a photographic sheet (PW) and actual leaf area for all types of leaf shape. Thus, the PW method for LA estimation can be applied to all shapes of leaf with high accuracy. The PW method takes a little more time and has a higher cost than leaf estimation methods using LW based on leaf dimensions. These results indicate that researchers should choose their nondestructive LA estimation method according to their research goals.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.24-36
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• 1996

In this paper, we discuss the errors in selectivity estimation using the multilevel grid file (MLGF). We first demonstrate that the estimatio errors stem from the uniformity assumption that records are uniformly distributed in their belonging region represented by an entry in a level of an MLGF directory. Bsed on this demonstration, we then investigate five factors affecting the accuracy of estimation: (1) the data distribution in a region (2) the number of records stored in an MLFG (3) the page size, (4) the query region size, and (5) the level of an MLFG directory. Next we present the tendancy of estimation errors according to the change of values for each factor through experiments. The results show that the errors decrease when (1) the distribution of records in a region becomes closer to the uniform one, (2) the number of records in an MLFG increases, (3) the page size decreases, (4) the query region size increases, and (5) the level of an MLFG directory employed as data distribution information becomes lower. After the definition of the granule ratio, the core formula representing the basic relationship between the estimation errors and the above five factors, we finally examine the change of estimation errors according to the change of the values for the granule ratio through experiments. The results indicate that errors tend to be similar depending on the values for the granule ratio regardless of the various changes of the values for the five factors. factors affecting the accuracy of estimation:

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.10-14
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• 2005

This report deals with a feeding system of the Coaxal grinding machine, processing optical ferrule. This report also examines the applicability of using the feeding system for the Coaxial grinding machine, by mean of conducting performance estimation. The results are as follow; Repeatability of regulating wheel is $17{\mu}m$, R/W rotation accuracy is between $30\;\~\;40{\mu}m$. This means 'Rotation accuracy' is lower than the concentricity level. Backlash generation level at the feeding system of the grinding wheel is under $1{\mu}m$, thereby positioning accuracy is controlled within $2{\mu}m$ In terms of repeatability, you can find occasional error at the returning process from the starting point. This error is resulted from the measurement tolerance of the starting point sensor. We will get the repeatability level under control by $1{\mu}m$, through improving the soft-ware used and up-grading the sensor at the starting point.