• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• International Journal of Internet, Broadcasting and Communication
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• 제13권4호
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• pp.22-29
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• 2021

This study evaluates the accuracy of corrective feedback from Grammarly, an online grammar checker, on essays written by cyber university learners in terms of detected errors, suggested replacement forms, and false alarms.The results indicate that Grammarly has a high overall error detection rate of over 65%, being particularly strong at catching errors related to articles and prepositions. In addition, on the detected errors, Grammarly mostly provide accurate replacement forms and very rarely make false alarms. These findings suggest that Grammarly has high potential as a useful educational tool to complement the drawbacks of teacher feedback and to help learnersimprove grammatical accuracy in their written work. However, it is still premature to conclude that Grammarly can completely replace teacher feedback because it has the possibility (approximately 35%) of failing to detect errors and the limitationsin detecting errors in certain categories. Since the feedback from Grammarly is not entirely reliable, caution should be taken for successful integration of Grammarly in English writing classes. Teachers should make judicious decisions on when and how to use Grammarly, based on a keen awareness of Grammarly's strengths and limitations.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1187-1194
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• 2020

본 논문은 온라인 퀴즈 시스템에서 핵심인 문제은행 구축 자동화를 위한 Deep Quiz Cropping 기법을 제시했다. 이것은 문제지를 스캔한 그림 파일에서 개별문제에 대한 질의영역과 선다영역을 딥러닝 기반 검출기를 통해 검출하는 것과, 문제생성을 위해 질의영역과 선다영역을 짝지우고 영역오류를 수정하는 Box Coupling으로 이루어졌다. 문제지 및 시험지를 스캔한 영상파일에 Deep Quiz Coupling 기법을 적용한 다수의 실험에서 질의영역과 선다영역을 검출하는데 있어서 성공적인 결과를 도출했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.305-318
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• 2022

Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.504-509
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• 1995

In this paper, we developed a position correction system of industrial robot for door-chassis assembly task. With the aid of a dedicated vision system, industrial robot accomplished visually acceptable door-chassis's assembly task. The alogorithm of the position detection of notch and 2 dimesional position correction algorithm are noteworthy. The obtained algorithms were satisfatorily implemented for a real door-chassis model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1722-1741
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• 2017

Intrusion detection techniques based on virtual machine introspection (VMI) provide high temper-resistance in comparison with traditional in-host anti-virus tools. However, the presence of semantic gap also leads to the performance and compatibility problems. In order to map raw bits of hardware to meaningful information of virtual machine, detailed knowledge of different guest OS is required. In this work, we present VDSM, a lightweight and general approach based on driver separation mechanism: divide semantic view reconstruction into online driver of view generation and offline driver of semantics extraction. We have developed a prototype of VDSM and used it to do intrusion detection on 13 operation systems. The evaluation results show VDSM is effective and practical with a small performance overhead.

• Journal of Information Technology Applications and Management
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• 제16권1호
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• pp.21-36
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• 2009

As the World Wide Web (WWW) has become a major channel for information delivery, the data accumulated in the Internet increases at an incredible speed, and it derives the advances of information search technologies. It is the search engine that solves the problem of information overloading and helps people to identify relevant information. However, as search engines become a powerful tool for finding information, the opportunities of plagiarizing have increased significantly in e-Learning. In this paper, we developed an online plagiarism detection system for detecting plagiarized documents that incorporates the functions of search engines and acts in exactly the same way of plagiarizing. The plagiarism detection system uses morpheme analysis to improve the performance and sentence-based comparison to investigate document comes from multiple sources. As a result of applying this system in e-Learning, the performance of plagiarism detection was improved.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2014년도 전력전자학술대회 논문집
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• pp.522-523
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• 2014

Analysis of faults in induction motors has become a major field of research due to importance of loss and damage reduction and maximum online performance of motors. There are several methods to analyze the faults in an induction motor from conventional Fourier transform to modern decision-making neural networks. Considering detectability of fault among all methods, a new fault detection solution has been proposed; it is called as frequency-domain Discrete Wavelet Transform (FD-DWT). In this method, the stator current is decomposed through series of low- and high-pass filters and consequently, the fault characteristics are more visible, because additional components have been reduced. The objective of this paper is early detection of input voltage unbalance in induction motor using wavelet transform in frequency domain. Experimental results show the effectiveness of the proposed method in early detection of faults.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.377-379
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• 2023

Concept drift detection on data stream is the major issue to maintain the performance of the machine learning model. Since the online stream is to be a function of time, the classical statistic methods are hard to apply. In particular case of seasonal time series, a novel window strategy with Fourier analysis however, gives a chance to adapt the classical methods on the series. We explore the KS-test for an adaptation of the periodic time series and show that this strategy handles a complicate time series as an ordinary tabular dataset. We verify that the detection with the strategy takes the second place in time delay and shows the best performance in false alarm rate and detection accuracy comparing to that of arbitrary window sizes.

• Bulletin of the Korean Chemical Society
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• 제33권10호
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• pp.3298-3302
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• 2012

Although offline enrichment of phosphorylated peptides is widely used, enrichment for phosphopeptides using $TiO_2$ is often performed manually, which is labor-intensive and can lead to irreproducible results. To address the problems associated with offline enrichment and to improve the effectiveness of phosphopeptide detection, we developed an automated online enrichment system for phosphopeptide analysis. A standard protein mixture comprising BSA, fetuin, crystalline, ${\alpha}$-casein and ${\beta}$-casein, and ovalbumin was assessed using our new system. Our multidimensional system has four main parts: a sample pump, a 20-mm $TiO_2$-based column, a weak anion-exchange, and a strong cation-exchange (2:1 WAX:SCX) separation column with LC/MS. Phosphorylated peptides were successfully detected using the $TiO_2$-based online system with little interference from nonphosphorylated peptides. Our results confirmed that our online enrichment system is a simple and efficient method for detecting phosphorylated peptides.