• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.

• 융합보안논문지
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• 제24권1호
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• pp.77-82
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• 2024

최근 연구를 통해 다양한 물체 탐지 및 분류 모델은 전반적으로 크게 성능 향상이 이루워졌지만, 물체가 부분적으로 노출된 상황에서의 물체 탐지 및 분류에 대한 연구는 미흡한 실정이다. 특히, 군사 분야에서 무인전투체계가 물체를 탐지하고 분류하는 데 사용되는 경우, 군사적 상황에서 물체는 일반적으로 부분적으로 가려진 상태나 위장된 상태일 가능성이 높다. 본 연구에서는 부분적으로 가려진 물체의 분류 성능을 향상시키는 방법을 제안한다. 이 방법은 물체 이미지 상에 특정 부분을 주변 환경을 고려하여 가리는 부분을 추가하여 은·엄폐 및 위장된 물체에 대한 분류 성능을 개선시켰다. 실험결과로 제안 방법을 적용하였을 때 은·엄폐 및 위장된 물체에 대해서 기존 방법에 비해 물체 분류 향상이 있음을 볼 수가 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3756-3777
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• 2019

Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.

• 한국정보통신학회논문지
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• 제18권7호
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• pp.1531-1539
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• 2014

조기심실수축(PVC)은 가장 보편적인 부정맥으로 심실세동, 심실빈맥 등과 같은 위험한 상황을 유발할 수 있는 가능성을 가지고 있기 때문에 이의 조기 검출은 매우 중요하다. 하지만 ECG 신호의 개인 차이가 있음에도 불구하고, 일반적인 신호의 판단 규칙에 따라 진단을 수행함으로써 성능하락이 나타날 수 밖에 없다. 이러한 문제점을 극복하기 위해서는 개인에 따른 이상 신호를 검출한 후 다양한 QRS 패턴을 고려하여 PVC를 분류할 수 있는 알고리즘이 필요하다. 본 연구에서는 개인별 이상신호 검출과 QRS 패턴 변화에 따른 PVC 분류 기법을 제안한다. 이를 위해 전 처리 과정과 차감기법을 통해 R파를 검출하였으며, 개인별 이상신호를 검출하였다. 이후 QRS 패턴에 따른 QS 간격과 R파의 진폭 변화율에 따라 PVC를 분류하였다. 제안한 알고리즘의 이상 신호 검출 및 PVC 분류 성능을 평가하기 위해서 MIT-BIH 부정맥 데이터베이스를 사용하였다. 성능평가 결과, 이상 신호 검출률은 98.33%, PVC는 각각 94.46%의 평균 분류율을 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• 방송공학회논문지
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• 제19권6호
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• pp.967-970
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• 2014

이 논문에서는 M-FSK(frequency shift keying) 변조신호를 자동으로 분류하는데 필요한 효율적인 첨두 검출 방법을 제안하였다. 다른 디지털 변조신호와 FSK 신호는 진폭 스펙트럼의 특성을 이용하여 분류할 수 있다. FSK 신호의 진폭 스펙트럼은 다른 디지털 변조신호와 다르게 변조차수와 동일한 수의 첨두를 나타낸다. 일반적으로 신호의 첨두를 검출하기 위해서는 임계치가 필요한데, 변조인식과 같이 사전에 신호에 대한 정보가 없는 경우 임계치를 정하기 어려운 점이 있다. 이 논문에서는 진폭 스펙트럼의 히스토그램을 이용하여 자동으로 간단하게 임계치를 결정하는 방법을 제시하였다. 모의실험 결과 적은 수의 표본과 잡음이 많은 환경에서도 매우 우수한 분류확률을 나타내었다.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1943-1950
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• 2016

Recently, gender classification has attracted a great deal of attention in the field of video surveillance system. It can be useful in many applications such as detecting crimes for women and business intelligence. In this paper, we proposed a method which can detect pedestrians from CCTV video and classify the gender of the detected objects. So far, many algorithms have been proposed to classify people according the their gender. This paper presents a gender classification using convolutional neural network. The detection phase is performed by AdaBoost algorithm based on Haar-like features and LBP features. Classifier and detector is trained with data-sets generated form CCTV images. The experimental results of the proposed method is male matching rate of 89.9% and the results shows 90.7% of female videos. As results of simulations, it is shown that the proposed gender classification is better than conventional classification algorithm.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• 한국콘텐츠학회논문지
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• 제13권8호
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• pp.1-10
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• 2013

본 논문에서는 선분분류를 이용하여 실내영상의 소실점을 검출하는 방법을 제안한다. 실내영상을 효율적으로 검출하기 위하여 2 단계로 소실점을 추출한다. 1 단계에서는 이미지가 1 점 투시인지 2 점 투시인지 판별한다. 만일 이미지가 2 점 투시이면, 선분분류를 위하여 검출된 소실점을 지나는 수평선을 구한다. 2 단계에서는 선분분류를 이용하여 2 개의 소실점을 정확히 검출한다. 또 본 논문에서는 인공영상과 이미지 DB를 이용하여 제안한 방법을 평가하였다. 노이즈를 첨가한 인공 영상에 대해서는 노이즈가 60%를 차지할 때까지 검출한 소실점과 실제 소실점과의 차이가 16 픽셀 이하였다. A. Quattoni 와 A. Torralba가 제안한 이미지 DB를 이용한 평가에서는 87% 이상의 이미지에 대하여 소실점을 검출하였다.