• Journal of the Society of Naval Architects of Korea
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• v.50 no.5
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• pp.349-354
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• 2013

The blades of flexible propellers are formed by overlaying and adhering many layers of thin glass-fiber fabric sheets, are compressed and dried in the rigid mold. The current manufacturing process can not avoid the rather irregular deformation of the blades composed of non-isotropic non-uniform fabric structures, and inevitably introduces the different shape-forming errors between blades. In this paper, several flexible model propellers are precisely measured with three-dimensional optical instrument and compared with the original design geometry. The model propellers with the as-measured geometry are evaluated with the lifting-surface-theory-based propeller analysis code. The open-water performance are presented and discussed. The importance of the manufacturing accuracy is addressed to be able to apply the flexible propellers for propulsion of marine vehicles.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.1
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• pp.114-122
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• 2009

An adaptive Kalman filter is designed as a post-navigation filter to improve the accuracy of GPS receiver's navigation performance in high dynamic environments. Not only the adaptive Kalman filter reduces the large noise error of navigation data which is obtained by least square method, but also the filter is not degraded as normal Kalman filter in high acceleration movements because the system noise is estimated. Also an initialization structure of the filter is desisted in consideration for irregular output condition of navigation data by least squared method such as reacquisition status in GPS receiver. The filter performance is verified by GPS simulator which has the simulation capability of high velocity and acceleration. Finally, a vehicle test including DGPS is executed to conform the real improvement of that filter performance. This filter can be applied to various data measurement systems to improve accuracy in high dynamic conditions besides GPS receiver.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.2
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• pp.59-63
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• 2007

We propose a controller design analysis for a cross-coupling control system, which is essential for achieving high contouring accuracy in multi-axis CNC systems. The proposed analysis combines three axial controllers for each individual feed drive system together with a cross-coupling controller at the beginning of the design stage in an integrated manner. These two types of controllers used to be separately designed and analyzed since they have different control objectives. The proposed scheme is based on a mathematical formulation of a three-dimensional contour error model and includes a stability analysis for the overall control system and a performance analysis in terms of contouring and tracking accuracy at steady state. A computer simulation was used to demonstrate the validity of the proposed methodology. The performance variation was investigated under different operating conditions and controller gains, and a design range was elicited that met the given performance specifications. The results provide basic guidelines in systematic and comprehensive controller designs for multi-axis CNC systems. A cross-coupling control system was also implemented on a PC-based three-axis CNC testbed, and the experimental results confirmed the usefulness of the proposed control system in terms of contouring accuracy.

• Journal of Power Electronics
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• v.13 no.3
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• pp.349-361
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• 2013

As a result of the advantages such as high efficiency, continuous current and high stability margin, push-pull converter with continuous current (PPCWCC) is competitive for battery discharge regulator (BDR) which plays an important role in power conditioning unit (PCU). Leakage inductance yields current spike in low-ripple current of PPCWCCs. The operating modes are added due to leakage inductance. Therefore the steady state performance is affected, which is embodied in the spike of low-ripple current. PPCWCCs which are suitable for BDR can be separated into three types by current spike characteristics. Three representative topologies IIs1, IIcb2 and Is3 are analyzed in order to investigate the factors on the magnitude and duration of spike. Equivalent current sampling method (ECSM) which eliminates the sampling time delay and achieves excellent dynamic performance is adopted to prevent the spike disturbance on current sampling. However, ECSM reduces the sampling accuracy and telemetry accuracy due to neglecting the spike. In this paper, ECSM used in PPCWCCs is summarized. The current sampling error is analyzed in quality and quantity, which provides the foundation for offsetting and enhancing the telemetry accuracy. Finally, current sampling error rate of three topologies is compared by experiment results, which verify the theoretical analysis.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.9
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• pp.83-89
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• 2013

Power distribution system has been changed from radial system to closed loop or mesh system due to connection of distributed generation growth. Data from distribution equipments which are installed at distribution line is required to be accurate for the performance of DMS(Distribution Management System). This paper analyzes the voltage measurement data from distribution equipment. However, the results of the analysis are confirmed to have some errors in voltage measurement data from distribution equipment. These errors come from aging of voltage sensor in distribution equipment and inaccurate data transfer to FRTU(feeder remote terminal unit) through the controller. The main problem is that the voltage measurement data of distribution equipment can not be assessed after it's first installation at the distribution line. The voltage measurement accuracy assessment system is to assess the voltage measurement data from distribution equipment on hot-line. This study had a field test to verify the performance of system.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.5
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• pp.862-874
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• 1989

A phase evil method and spectrum analysis were instrument error which is originated from measurement system and the form error of standard specimens. An instrument with a rotating table supported by an air bearing is calibrated using standard specimens. The phase of standard specimens was measured 12 times on the rotating table with rotating 30 in turn and its measurement magnification was set by 100000 times. As a result of data analysis of all the observations, read out at each of 144 orientations(per 2.5) from recorded datafiles, the error of the performance of the instrument and those of the standard specimens are evaluated and a systematic deviation of the instrument is determined. In the particular instrument used in the present experiment, the deviation of the instrument is determined with the accuracy of 15nm and those of standard specimens with the accuracy of 23, 13 n, respectively. The reproducibility of the instrument is investigated, too. If the instrument is calibrated by using the above standard specimens, then the accuracy of the measurement of roundness error can be improved to about 15nm.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.106-118
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• 2019

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

• Smart Structures and Systems
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• v.30 no.3
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.280-282
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• 2021

A particular matter prediction model was designed using a deep learning algorithm to solve the problem of particular matter forecast with subjective judgment applied. RNN and LSTM were used among deep learning algorithms, and it was designed by applying optimal parameters by proceeding with hyperparametric navigation. The predicted performance of the two models was evaluated through RMSE and predicted accuracy. The performance assessment confirmed that there was no significant difference between the RMSE and accuracy, but there was a difference in the detailed forecast accuracy.