• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1133-1138
• /
• 2000

This paper describes a signal recognition method for diagnosing the rotating machinery using wavelet-aided Self-Organizing Feature Map(SOFM). The SOFM specialized from neural network is a new and effective algorithm for interpreting large and complex data sets. It converts high-dimensional data items into simple order relationships with low dimension. Additionally the Learning Vector Quantization(LVQ) is used for reducing the error from SOFM. Multi-resolution and wavelet transform are used to extract salient features from the primary vibration signals. Since it decomposes the raw timebase signal into two respective parts in the time space and frequency domain, it does not lose either information unlike Fourier transform. This paper is focused on the development of advanced signal classifier in order to automatize vibration signal pattern recognition. This method is verified by the experiment and several abnormal vibrations such as unbalance and rubbing are classified with high flexibility and reliability by the proposed methods.

• Journal of Power Electronics
• /
• v.2 no.1
• /
• pp.25-31
• /
• 2002

Servo drives with microcomputer control provide the possibility of using modern and sophisticated control algorithms. As an additional feature it is possible to implement parallel and/or redundant software and hardware structures to realise safe motion or similar security functions. Unfortunately microcomputer control also has some impact on the behaviour of servo drives. Control algorithm, cycle time, sensors and interface have to be perfectly synchronised. Special control schemes are necessary on the line side (power supply) to meet the actual requirements concerning EMC. This contribution presents experiences and results obtained from a modern digital drive system pointing out the influences of low and high accuracy position sensors and the interdependencies mentioned above.

• 한국정보디스플레이학회:학술대회논문집
• /
• 2009.10a
• /
• pp.1022-1024
• /
• 2009

Stereoscopic Images provide depth information with the relative distances between the objects in the images. There are many different ways to extract disparity maps from the visible spectral images. For the infrared spectral range, the same approach cannot be utilized for the innate low resolution and colorless features because typical methods require corresponding features between the images. The authors suggest a new approach that makes use of image segmentation to obtain depth information for stereoscopic millimeter-wave images. For image segmentation a selective visual attention model based on the theory of a feature-integration of attention is used. Experimental results show the proposed method provides reasonable depth information for object shape recognition and display.

• Journal of the Semiconductor & Display Technology
• /
• v.9 no.4
• /
• pp.51-56
• /
• 2010

As the price of image acquisition component becomes low enough, the compact and easily accessible handheld devices are generally equipped with image acquisition functionality. This trend speeds up various applications in diverse areas such as image related services and software. Therefore users strongly need to identify their images effectively and efficiently so that the duplicated images are perceived as one physical entity. In order to handle this environment, we propose a number of methods that generate image identifiers utilizing fundamental image features. In this paper, we analyze the identifier generation methods in terms of various size patterns, especially for tiny size cases, since the small images does not contain abundant pixels for feature extraction. In this paper, experimental evaluation over identifier generation methods' behavior according to different sizes is demonstrated.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2013.11a
• /
• pp.8-11
• /
• 2013

We implement the foveation and frequency sensitivity feature of human visual system in discrete cosine transform (DCT) domain. Resolution of human visual perception decays as distance from the eye-focused point, known as foveation property, and the middle frequency components give most pleasant image quality to human than the low and high frequency components, which is the frequency sensitivity property of human visual system. For satisfying the foveation property, we enhanced the local contrast at the focused regions and smoothed local contrast at the non-focused regions in the DCT domain without bringing the blocking and ringing artifacts. Moreover, the energies at each DCT frequency components is modified with various degree to fulfill the frequency sensitivity property. The proposed method is verified by the subjective and objective evaluations that it can the improve the human perceptual visual quality.

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.392-395
• /
• 2003

This paper describes a new iris segmentation and recognition method, which is robust to noises. Combining statistical classification and elastic boundary fitting, the iris is first segmented. Then, the localized iris image is smoothed by a convolution with a Gaussian function, down-sampled by a factor of filtered with a Laplacian operator, and quantized using the Lloyd-Max method. Since the quantized output is sensitive to a small shift of the full-resolution iris image, the outputs of the Laplacian operator are computed for all space shifts. The quantized output with maximum entropy is selected as the final feature representation. An appropriate formulation of similarity measure is defined for the classification of the quantized output. Experimentally we showed that the proposed method produces superb performance in iris segmentation and recognition.

• Korea-Australia Rheology Journal
• /
• v.12 no.1
• /
• pp.39-53
• /
• 2000

Using a variety of examples from the recent literature on extensional flow of polymer solutions, this paper shows that simple constitutive equations are unable to capture the diversity of chain conformations in such flows. Such diversity is a feature of extensional flows and arises because deformation leads to significant chain extension. Substantial local extension appears even at low strains and the behaviour of these stretched out portions influences the dynamics of the chain and makes a dominant contribution to the stress. Both the distribution function and the chain conformation appear to follow different paths during stretching and relaxation. As a result the second moment of the distribution function does not contain enough information to correctly predict the dynamics. Resolution of this deficiency in simple constitutive models is one of the challenges for rheology.

• ETRI Journal
• /
• v.40 no.6
• /
• pp.788-793
• /
• 2018

3D reconstruction of urban architecture, land, and roads is an important part of building a "digital city." Unmanned aerial vehicles (UAVs) are gradually replacing other platforms, such as satellites and aircraft, in geographical image collection; the reason for this is not only lower cost and higher efficiency, but also higher data accuracy and a larger amount of obtained information. Recent 3D reconstruction algorithms have a high degree of automation, but their computation time is long and the reconstruction models may have many voids. This paper decomposes the object into multiple regional parallel reconstructions using the clustering principle, to reduce the computation time and improve the model quality. It is proposed to detect the planar area under low resolution, and then reduce the number of point clouds in the complex area.

• Journal of KIISE:Software and Applications
• /
• v.32 no.12
• /
• pp.1247-1259
• /
• 2005

This paper proposes a real-time face recognition system which detects multiple faces from low resolution video such as web-camera video. Face recognition system consists of the face detection step and the face classification step. At First, it finds face region candidates by using AdaBoost based object detection method which have fast speed and robust performance. It generates reduced feature vector for each face region candidate by using principle component analysis. At Second, Face classification used Principle Component Analysis and multi-SVM. Experimental result shows that the proposed method achieves real-time face detection and face recognition from low resolution video. Additionally, We implement the auto-tracking face recognition system using the Pan-Tilt Web-camera and radio On/Off digital door-lock system with face recognition system.

• Journal of Advanced Navigation Technology
• /
• v.27 no.2
• /
• pp.182-189
• /
• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.