• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2200-2203
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• 2003

In an active noise control(ANC) system using the Filtered-X least mean square(LMS) algorithm, the online secondary path modeling method by exploiting a random noise generator is applied. This method is suitable for secondary path modeling. However, it is increased the residual error of the ANC system. In this paper, we presents an ANC system improved online secondary path modeling method which is modified Kuo and Zhang model that is the secondary path estimation by the additive noise. In addition, our proposed model is used that additive noise is transformed into the signal multiplied reference signal by gain control parameter and delayed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5674-5691
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• 2016

Fast IDentity Online(FIDO) supports biometric authentications in an online environment without transmitting biometric templates over the network. For a given FIDO client, the "Fuzzy Vault" securely stores biometric templates, houses additional biometric templates, and unlocks private keys via biometrics. The Fuzzy Vault has been extensively researched and some vulnerabilities have been discovered, such as brute force, correlation, and key inversions attacks. In this paper, we propose a simple fingerprint Fuzzy Vault for FIDO clients. By using the FIDO feature, a simple minutiae alignment, and point-to-point matching, our Fuzzy Vault provides a secure algorithm to combat a variety of attacks, such as brute force, correlation, and key inversions. Using a case study, we verified our Fuzzy Vault by using a publicly available fingerprint database. The results of our experiments show that the Genuine Acceptance Rate and the False Acceptance Rate range from 48.89% to 80% and from 0.02% to 0%, respectively. In addition, our Fuzzy Vault, compared to existing similar technologies, needed fewer attempts.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2016년도 추계학술대회 논문집
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• pp.85-86
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• 2016

본 논문은 내부 저항을 추정하여 배터리 노화 상태 (State of Health)를 판단하는 알고리즘을 제안한다. 내부 저항은 차량 구동 시 발생하는 전류 변화량과 그에 따른 전압강하 특성을 이용하여 추정되며, 정확도를 높이기 위해 차량의 주행 특성을 고려하여 추정 조건을 결정한다. 알고리즘의 정확도는 MATLAB 시뮬레이션을 통해 검증한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.396-399
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• 2012

Topic detection is a problem of discovering the topics of online publishing documents. For topic detection, it is important to extract correct topic words and to show the topical words easily to understand. We consider a topic tree-based approach to more effectively and more briefly show the result of topic detection for online text documents. In this paper, to achieve the topic tree-based topic detection, we propose a new term weighting method, called CTF-CDF-IDF, which is simple yet effective. Moreover, we have modified a conventional clustering method, which we call incremental k-medoids algorithm. Our experimental results with Reuters-21578 and Google news collections show that the proposed method is very useful for topic detection.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• International Journal of Advanced Culture Technology
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• 제9권1호
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• pp.218-223
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• 2021

Pervasive enhancement and required enforcement of the Internet of Things (IoTs) in a distributed massively multiplayer online architecture have effected in massive growth of Big-Data in terms of server over-load. There have been some previous works to overcome the overloading of server works. However, there are lack of considered methods, which is commonly applicable. Therefore, we propose a combing Sparse Auto-Encoder and Load-Balancing, which is ProGReGA for Big-Data of server loads. In the process of Sparse Auto-Encoder, when it comes to selection of the feature-pattern, the less relevant feature-pattern could be eliminated from Big-Data. In relation to Load-Balancing, the alleviated degradation of ProGReGA can take advantage of the less redundant feature-pattern. That means the most relevant of Big-Data representation can work. In the performance evaluation, we can find that the proposed method have become more approachable and stable.

• Journal of Multimedia Information System
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• 제9권1호
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• pp.61-68
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• 2022

The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word's coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.109-118
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• 2022

Workers' anomalous trajectories allow us to detect emergency situations in the workplace, such as accidents of workers, security threats, and fire. In this work, we develop a scheme to detect abnormal trajectories of workers using the edit distance on real sequence (EDR) and density method. Our anomaly detection scheme consists of two phases: offline phase and online phase. In the offline phase, we design a method to determine the algorithm parameters: distance threshold and density threshold using accumulated trajectories. In the online phase, an input trajectory is detected as normal or abnormal. To achieve this objective, neighbor density of the input trajectory is calculated using the distance threshold. Then, the input trajectory is marked as an anomaly if its density is less than the density threshold. We also evaluate performance of the proposed scheme based on the MIT Badge dataset in this work. The experimental results show that over 80 % of anomalous trajectories are detected with a precision of about 70 %, and F1-score achieves 74.68 %.

• 한국정보통신학회논문지
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• 제26권11호
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• pp.1751-1754
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• 2022

The news articles collected with keyword "non face-to-face" were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-face-to-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing.

• 방송공학회논문지
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• 제19권6호
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• pp.819-825
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• 2014

본 논문에서는 비음수 행렬 분해 (NMF) 기법을 이용하여 단일 채널에서 배경음 성분을 추출하는 알고리즘에 대해 서술한다. 이러한 배경음 성분 추출은 오디오 업믹싱 시스템을 고려하여 개발되었으며, 기존의 연구를 통하여 분리된 배경음 신호가 업믹싱 시스템에 적용될 경우 공간감을 향상시킬 수 있다는 사실이 이미 확인된 바 있다. 다만 기존의 기법은 음향 신호를 모두 축적하여 일괄적으로 처리해야 한다는 단점이 있어, 스트리밍 시스템이나 디지털 시그널 프로세서 (DSP) 등을 이용한 시스템에서 사용되기 어렵다. 본 논문에서는 이를 해소하기 위하여 실시간 비음수 행렬 분해 기법을 이용한 배경음 추출 시스템을 고안하여 실험하였다. 실험에서 처리된 음원을 스펙트럼 평활도를 이용하여 분석한 결과, 고안된 배경음 추출 시스템이 기존의 일괄 추출 시스템과 유사한 정도로 배경음 성분을 추출했음을 확인할 수 있었다.