• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.419-422
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• 2001

To safely control a group of trains, it is essential to detect the position of train. The system to detect the position of train by the train itself will become a mainstream of the new systems using radio transmission. This paper introduces the methods of train position detection that have been studied, introduces the trend of these methods. It also described the fundamental concept and approach to realize a system to detect the position of train which is applicable to systems.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.147-154
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• 2008

First, this paper compute difference value between frames using the composed method of $X^2$ histogram and color histogram and the normalization. Next, cluster representative frame was decided by using the clustering for distance and the k-mean grouping. Finally, representative frame of group was decided by using the likelihood ratio. Proposed method can be known by experiment as outstanding of detection rather than other methods, due to computing of difference value, clustering and grouping, and detecting of representative frame.

• Bulletin of the Korean Chemical Society
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• v.33 no.11
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• pp.3696-3700
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• 2012

The colorimetric and fluorescent cyanide recognition properties of 2-[(9-ethyl-9H-carbazol-3-yl)methylene]-propanedinitrile (1) in $CH_3CN-H_2O$ (2/1, v/v, HEPES 10 mM, pH = 7.0) solution were evaluated. The optical recognition process of probe 1 exhibited high sensitivity and selectivity to cyanide ion with the detection limit of $2.04{\times}10^{-6}$ M and barely interfered by other coexisting anions. The sensing mechanism of probe 1 is speculated to undergo a nucleophilic addition of cyanide to dicyanovinyl group present in compound 1. The colorimetric and fluorescent dual-modal response to cyanide makes probe 1 has a potential utility in cyanide detection.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• Bulletin of the Korean Chemical Society
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• v.15 no.12
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• pp.1035-1037
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• 1994

An ultrasensitive and selective stripping voltammetric scheme for the determination of rhodium is described. By the use of combined accumulation and catalytic effects in formaldehyde-hydrochloric acid medium, substantial improvement in the limit of detection can be obtained. Optimal experimental conditions were found to be 0.42 M hydrochloric acid solution containing 0.008${\%}$ formaldehyde, an accumulation potential of -0.70 V (vs. Ag/AgCl) and an accumulation time of 20 s. The stripping mode was differential pulse voltammetry. In these conditions the limit of detection lies at 2 ${\times}$ l0$^{-12}$ M (0.21 ppt). The relative standard deviation at 5 ${\times}$ l0$^{-11}$ M was 4.9${\%}$ (n=5). There were no serious interferences from other platinum group metal ions being the tolerable amounts more than 500 times that of rhodium.

• Smart Structures and Systems
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• v.2 no.2
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• pp.171-188
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• 2006

This paper deals with damage detection using active Lamb waves. The wavelet transform and empirical mode decomposition methods are discussed for measuring the Lamb wave's arrival time of the group velocity. An experimental system to diagnose the damage in the composite plate is developed. A method to optimize this system is also given for practical applications of active Lamb waves, which involve optimal arrangement of the piezoelectric elements to produce single mode Lamb waves. In the paper, the single mode Lamb wave means that there exists no overlapping among different Lamb wave modes and the original Lamb wave signal with the boundary reflection signals. Based on this optimized PZT arrangement method, five damage localizations on different plates are completed and the results using wavelet transform and empirical mode decomposition methods are compared.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.317-322
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• 2018

We use edge detection technique for the input image to extract the entire edges of the object in the image and then select only the edges that construct the outline of the object. By examining the positional relation between these pixels composing the outline, a simplified version of the outline of the object in the input image is generated by removing unnecessary pixels while maintaining the condition of connection of the outline. For each pixel constituting the outline, its direction is calculated by examining the positional relation with the next pixel. Then, we group the consecutive pixels with same direction into one and then change them to a line segment instead of a point. Among those line segments composing the outline of the object, a line segment whose length is smaller than a predefined minimum length of acceptable line segment is removed by merging it into one of the adjacent line segments. As a result, an outline composed of line segments of over a certain length is obtained through this process.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.16-26
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• 2020

Improving operator performance in complex and time-critical situations is critical to maintain plant safety and operability. These situations require quick detection, diagnosis, and mitigation actions to recover from the root cause of failure. One of the key challenges for operators in nuclear power plants is information management and following the control procedures and instructions. Nowadays Digital Twin technology can be used for analyzing and fast detection of failures and transient situations with the recommender system to provide the operator or maintenance engineer with recommended action to be carried out. Systems engineering approach (SE) is used in developing a digital twin for the CEDM system to support operator actions when there is a misalignment in the control element assembly group. Systems engineering is introduced for identifying the requirements, operational concept, and associated verification and validation steps required in the development process. The system developed by using a machine learning algorithm with a text mining technique to extract the required actions from limiting conditions for operations (LCO) or procedures that represent certain tasks.

• International Journal of High-Rise Buildings
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• v.9 no.4
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• pp.335-342
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• 2020

This article comes up with a new method which is based on the visual characteristic of the objects and machine learning technology to achieve semi-automated recognition of the personnel, machine & materials of the construction sites. Balancing the real-time performance and accuracy, using Faster RCNN (Faster Region-based Convolutional Neural Networks) with transfer learning method appears to be a rational choice. After fine-tuning an ImageNet pre-trained Faster RCNN and testing with it, the result shows that the precision ratio (mAP) has so far reached 67.62%, while the recall ratio (AR) has reached 56.23%. In other word, this recognizing method has achieved rational performance. Further inference with the video of the construction of Huoshenshan Hospital also indicates preliminary success.