• Imaging Science in Dentistry
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• v.49 no.3
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• pp.219-228
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• 2019

Purpose: The aim of this study was to determine the reasons and solutions for intraoral phosphor storage plate (PSP) image artifacts and errors, and to develop an appropriate classification of the artifacts. Materials and Methods: This study involved the retrospective examination of 5,000 intraoral images that had been obtained using a phosphor plate system. Image artifacts were examined on the radiographs and classified according to possible causative factors. Results: Artifacts were observed in 1,822 of the 5,000 images. After examination of the images, the errors were divided into 6 groups based on their causes, as follows: images with operator errors, superposition of undesirable structures, ambient light errors, plate artifacts (physical deformations and contamination), scanner artifacts, and software artifacts. The groups were then re-examined and divided into 45 subheadings. Conclusion: Identification of image artifacts can help to improve the quality of the radiographic image and control the radiation dose. Knowledge of the basic physics and technology of PSP systems could aid to reduce the need for repeated radiography.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.597-605
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• 2010

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.27-36
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• 2013

Objective: This paper describes an analysis of electroencephalography(EEG) signals to identify human errors during using digital devices in nuclear power plants(NPPs). Background: The application of an advanced main control room(MCR) has accompanied with lots of changes in different forms and features by virtue of new digital technologies. The characteristics of these digital technologies and devices provide several opportunities for the use of interface management. It can integrate into a compact single workstation in an advanced MCR, allowing workers to operate the plant with minimum physical burden under any operating condition. However these devices may introduce new types of human errors, and thus we need a means to assess and prevent such errors especially those related to digital devices. Method/Conclusion: The EEG data are relatively objective, and thus we introduce several measures to EEG analysis for obtaining the feasibility of human error identification. Application: This study may support to ensure the safety when applying digital devices in NPPs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1343-1351
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• 2000

Thermally induced errors have been significant factors affecting the machine tool accuracy. In this paper, the spindle thermal error has been focused, where the 5 degree of freedom thermal error components are considered. An effective measurement system has been devised for the 5 DOF thermal errors, consisting of gap sensors and thermocouples around the micro-computer interfaced environment. Several thermal error modeling techniques are also implemented for the thermal error prediction: multiple linear regression, neural network and system identification methods, etc. The performance of the thermal error modeling techniques is evaluated and compared, giving the system identification method as the optimum model having the least deviation. The developed system for the thermal error measurement and modeling was practically applied to a CNC machining center, and the spindle thermal errors were effectively compensated around the micro computer-machine tool interfaced networks. The machine tool accuracy was improved about 4-5 times typically.

• The Korean Journal of Petroleum Geology
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• v.12 no.1
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• pp.9-13
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• 2006

Volcanic glass shard, which shows relatively homogeneous chemical composition in volcanic eruption materials, is used to determine ages of tephra layers and then to correlate tephra layers each other for understanding of evolution of Quaternary geomorphology. For reducing processing errors in age determination and correlation of units, amorphous glass shard should be separated carefully from soil sample through laboratory procedures such as washing, separation, and identification. Introduction of these processes in detail could be reduced errors in tephrochronology by using volcanic glass shard.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• MALSORI
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• no.57
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• pp.165-174
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• 2006

In this paper we use a training algorithm, MCE (Minimum Classification Error), to improve the performance of a text-independent speaker identification system. The MCE training scheme takes account of possible competing speaker hypotheses and tries to reduce the probability of incorrect hypotheses. Experiments performed on a small set speaker identification task show that the discriminant training method using MCE can reduce identification errors by up to 54% over a baseline system trained using Bayesian adaptation to derive GMM (Gaussian Mixture Models) speaker models from a UBM (Universal Background Model).

• Journal of Astronomy and Space Sciences
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• v.31 no.3
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• pp.205-214
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• 2014

In this study, a star identification algorithm which utilizes pivot patterns instead of apparent magnitude information was developed. The new star identification algorithm consists of two steps of recognition process. In the first step, the brightest star in a sensor image is identified using the orientation of brightness between two stars as recognition information. In the second step, cell indexes are used as new recognition information to identify dimmer stars, which are derived from the brightest star already identified. If we use the cell index information, we can search over limited portion of the star catalogue database, which enables the faster identification of dimmer stars. The new pivot algorithm does not require calibrations on the apparent magnitude of a star but it shows robust characteristics on the errors of apparent magnitude compared to conventional pivot algorithms which require the apparent magnitude information.

• 전기의세계
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• v.22 no.4
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• pp.17-24
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• 1973

In the view point of practical engineering the identification problem may be considered as a problem to determine the optimal model in the sense of minimizing a given criterion function using the input-output records of the plant. In the system identification the statistical approach has been known to be very effective when the topological structure of the system and the statistical characteristics of the observation noises are known a priori. But in the practical situation there are many cases when the inforhation about the observation noises or the system noises are not available a priori. Here, the authors propose a new identification method which can be used effectively even in the cases when the variances of observation noises are unknown a priori. In the method, the identification of unknown parameters of a linear diserete system is achieved by minimizing the improved quadratic criterion function which is composed of the term of square equation errors and the term to eliminate the affection of observation noises. The method also gives the estimate of noise variance. Numerical computations for several examples show that the proposed procedure gives satisfactory results even when the short time observation data are provided.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.552-559
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• 2009

Most damage identification methods for structural health monitoring developed to date utilize modal domain responses which require postprocessing and inevitably contain errors in transforming the domain of responses. In this paper, the feasibility of a damage identification method based on dynamics responses from moving loads is experimentally verified. The experiment is performed via applying periodic and non-periodic moving loads to a steel beam and acceleration and displacement responses of the beam is measured. The moving loads is applied using steel balls and the damage of a structure is simulated by saw-cutting the beam. The damage identification results using the measured responses show that the moving load response based damage identification method successfully identify all damages in the beam.