• 한국정보통신학회논문지
• /
• 제7권4호
• /
• pp.673-677
• /
• 2003

본 연구에서는 적응 필터 뱅크와 유사한 신경망을 이용한 시간영역에서의 새로운 필터뱅크(뉴럴 필터 뱅크)와 필터 창 함수를 가진 새로운 필터 뉴런을 제안하였다. 제안된 뉴럴 필터 뱅크의 성능을 검증하기 위해 두 가지의 예를 들어 실험하였다. 실험에서 제안된 기법은 기존의 방법인 주파수 영역에서의 필터뱅크보다 간단한 구조와 고속처리가 가능한 특성을 보였다. 따라서 제안된 방법은 시간 영역에서의 신호의 특징 검출에 있어 높은 성능을 제공할 것으로 사료된다.

• Journal of Electrical Engineering and Technology
• /
• 제3권2호
• /
• pp.263-270
• /
• 2008

We studied partial discharge detection by sensing electromagnetic waves emitted from the partial discharge source in an HV Rotating Machine using a UWB sensor. In this study, we design a new type of compact low frequency UWB sensor based on micro-strip technology. We also perform many experiments of offline and dismantled testing compared with the existing HFCT on stator winding of the HV generator. We mention the detailed design of a new compact UWB sensor along with the time domain PRPD pattern and frequency domain results of partial discharge in the stator winding of a 6.6kV rotating machine by offline testing performed in a laboratory.

• 방송공학회논문지
• /
• 제6권3호
• /
• pp.270-282
• /
• 2001

이 논문은 현재 잘 알려진 3종류의 블라인드 워터마크 삽입 및 검출방법을 신호처리 관점에서 소개한다. 이들 3가지는 각각 상 관관계기반 방법, 에코기반 방법, 그리고 패치워크 방법이다. 이들 방법은 시간영역 (또는 공간영역) 또는 변환영역에서 적용할 수 있다. 이 논문에서는 이들 세 방법을 구현하는데 필요한 기초이론 및 구현방법을 제공한다. 아울러 실제 약간의 실험 결과들을 포함시켰다.

• 한국멀티미디어학회논문지
• /
• 제24권10호
• /
• pp.1319-1325
• /
• 2021

It is important to remove fog for accurate object recognition and detection during preprocessing because images taken in foggy adverse weather suffer from poor quality of images due to scattering and absorption of light, resulting in poor performance of various vision-based applications. This paper proposes an end-to-end deep learning-based single image de-fogging method using U-Net architecture. The loss function used in the algorithm is a loss function based on Mahalanobis distance with fog features, which solves the problem of domain shifts, and demonstrates superior performance by comparing qualitative and quantitative numerical evaluations with conventional methods. We also design it to generate fog through the VGG19 loss function and use it as the next training dataset.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2008년도 학술대회 1부
• /
• pp.569-572
• /
• 2008

The possibility to extent the solution in human detection problem for plug-in on vision-based Human Computer Interaction domain is very attractive, since the successful of the machine leaning theory and computer vision marriage. Bayesian logistic regression is a powerful classifier performing sparseness and high accuracy. The difficulties of finding people in an image will be conquered by implementing this Bavesian model as classifier. The comparison with other massive classifier e.g. SVM and RVM will introduce acceptance of this method for human detection problem. Our experimental results show the good performance of Bavesian logistic regression in human detection problem, both in trade-off curves (ROC, DET) and real-implementation compare to SVM and RVM.

• ETRI Journal
• /
• 제39권6호
• /
• pp.832-840
• /
• 2017

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception-based spatial and spectral-domain noise-reduced harmonic features are extracted from multichannel audio and used as high-resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short-term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

• ETRI Journal
• /
• 제35권4호
• /
• pp.644-654
• /
• 2013

In this paper, a signal detection scheme for cognitive radio (CR) based on the Bussgang theorem is proposed. The proposed scheme calculates the statistical difference between Gaussian noise and the primary user signal by applying the Bussgang theorem to the received signal. Therefore, the proposed scheme overcomes the noise uncertainty and gives scalable complexity according to the zero-memory nonlinear function for a mobile device. We also present the theoretical analysis on the detection threshold and the detection performance in the additive white Gaussian noise channel. The proposed detection scheme is evaluated by computer simulations based on the IEEE 802.22 standard for the wireless regional area network. Our results show that the proposed scheme is robust to the noise uncertainty and works well in a very low signal-to-noise ratio.

• 전자통신동향분석
• /
• 제36권5호
• /
• pp.82-91
• /
• 2021

This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.

• ETRI Journal
• /
• 제44권2호
• /
• pp.346-354
• /
• 2022

The SYN flooding attack is widely used in cyber attacks because it paralyzes the network by causing the system and bandwidth resources to be exhausted. This paper proposed a self-information approach for detecting the SYN flooding attack and provided a detection algorithm with a hierarchical policy on a detection time domain. Compared with other detection methods of entropy measurement, the proposed approach is more efficient in detecting the SYN flooding attack, providing low misjudgment, hierarchical detection policy, and low time complexity. Furthermore, we proposed a detection algorithm with limiting system resources. Thus, the time complexity of our approach is only (log n) with lower time complexity and misjudgment rate than other approaches. Therefore, the approach can detect the denial-of-service/distributed denial-of-service attacks and prevent SYN flooding attacks.

• Asia pacific journal of information systems
• /
• 제31권3호
• /
• pp.335-357
• /
• 2021

As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.