• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.134-139
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• 1998

The objective of this study is to estimate and to compensate for the volumetric error of a machine tool. In this paper, the volumetric error is defined and error synthesis model is presented. Then, the volumetric error of workpiece is compared through the virtual machining and a new tool-path is generated to compensate for the error in the post-processor of CAM system using the error synthesis model. By this method, the error is compensated without modification or replacement of a machine tool being in use.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.345-350
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• 1999

The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.73-80
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• 1991

Identifying linear form of the parts of precision machine, precise monitoring is indispensable. Therefore, in this study, close-range photogrammetry being tried to screw one of the parts of precision machine, using non-metric camera that is calibrated by plumb line method. Also, it is analyzed three dimensional values of tortien, offset, section and thickness. From results of this study, monitoring of the parts of precision machine was conducted efficiently using non-metric camera and possibility of this application was proved.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.492-496
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• 1988

As computer systems get complex more and more, the powerful but easy-to-use man machine interfaces are required. This paper describes the man machine interface in the TDX-10 switching system currently being developed at ETRI. The man machine interface in TDX-10 provides a reliable and user-friendly system interface through which system operators can manage the system in a convenient way. It incorporates the window, menu, form, on-line help, history recording, command file batching and color graphics capabilities.

• Journal of the Korean Society for Railway
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• v.12 no.3
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• pp.364-369
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• 2009

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection doe to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.693-712
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• 2012

The paper presents three machine learning based keyphrase extraction methods that respectively use Decision Trees, Na$\ddot{i}$ve Bayes, and Artificial Neural Networks for keyphrase extraction. We consider keyphrases as being phrases that consist of one or more words and as representing the important concepts in a text document. The three machine learning based keyphrase extraction methods that we use for experimentation have been compared with a publicly available keyphrase extraction system called KEA. The experimental results show that the Neural Network based keyphrase extraction method outperforms two other keyphrase extraction methods that use the Decision Tree and Na$\ddot{i}$ve Bayes. The results also show that the Neural Network based method performs better than KEA.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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• pp.45-49
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• 2022

In this paper, we propose a method for estimating the error of the Ultra-Wideband (UWB) distance measurement using the channel impulse response (CIR) of the UWB signal based on machine learning. Due to the recent demand for indoor location-based services, wireless signal-based localization technologies are being studied, such as UWB, Wi-Fi, and Bluetooth. The constructive obstacles constituting the indoor environment make the distance measurement of UWB inaccurate, which lowers the indoor localization accuracy. Therefore, we apply machine learning to learn the characteristics of UWB signals and estimate the error of UWB distance measurements. In addition, the performance of the proposed algorithm is analyzed through experiments in an indoor environment composed of various walls.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.275-279
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• 2022

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.