• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.367-372
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• 2001

Feature restoration is that restore feature to 3D solid model using the feature information in STEP AP224. Feature is very important in CAPP, but feature information is defined very complicated in STEP AP224. This paper recommends the algorithm of extraction the feature information in physical STEP AP224file. This program import STEP AP224 file, parse the geometric and topological information, the tolerance data, and feature information line-by-line. After importation and parsing, store data into database. Feature restoration module analyze database including feature information, extract feature information, e.g. feature type, feature's parameter, etc., analyze the relationship and then restore feature to 3D solid model.

• Current Optics and Photonics
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• v.8 no.3
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.17-27
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• 2005

In this study, it proposes the neural network system for character recognition and restoration. Proposes system composed by recognition part and restoration part. In the recognition part. it proposes model of effective pattern recognition to improve ART Neural Network's performance by restricting the unnecessary top-down frame generation and transition. Also the location feature extraction algorithm which applies with Hangeul's structural feature can apply the recognition. In the restoration part, it composes model of inputted image's restoration by Hopfield neural network. We make part experiments to check system's performance, respectively. As a result of experiment, we see improve of recognition rate and possibility of restoration.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.547-551
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• 2009

In recent years, a film restoration has gained increasing attention by many researchers, to emergence of variety multimedia and to importance of video preservation. Blotch is the most frequent degradation in old film. This paper presents a blotch detection method using color and shape feature. The proposed method is two major modules: a SROD detector using impulsive feature and NN-based detector using shape feature. To assess the validity of the proposed method, the experiments have been performed on several old films.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.7
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• pp.535-544
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• 1990

In this paper, we represent the restoration and feature extraction of fingerprint image. The purpose of restoration of fingerprint image are to com pensate distortion which is affected by noise and to preserve various features of fingerprint image. To extracte the central point of fingerprint, we used sample matrix, and restore fingerprint, we used direction in formation of thinned image and the gray scale of the original images.

• Ocean and Polar Research
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• v.24 no.1
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• pp.87-92
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• 2002

The emergy (spelled with an 'm') concept was introduced to provide a new insight into ecosystem restoration efforts in Korea. The emergy is defined as the available energy of one kind previously required directly and indirectly to make a product or service. It is an effort to evaluate the true contributions of natural resources to our economy. It tries to include both contributions from natures free works and human services to develop and process natural resources. The emergy evaluation can be used to select a restoration alternative that yields more to the economy with less stress to the environment, by comparing different alternatives with indices expressed in emergy. It can also be used to assess the success of ecosystem restoration projects. Pulsing dynamics in which a slow build-up of production is followed by a frenzied consumption in relatively short time period seems to be a general feature of all systems. Any ecosystem restoration effort, therefore, should consider the whole pulsing cycle for a successful implementation.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.11
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• pp.49-58
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• 2010

In this paper, we present a methodology for fast segment restoration under multiple simultaneous link failures in mesh-type MPLS networks. The salient feature of the methodology is that both resilience and QoS constraint conditions have been taken into account for fast segment restoration. For fast restoration, a sufficient condition for testing the existence of backup segments with guaranteed-resilience has been derived for a mesh-type MPLS network. The algorithms for constructing backup segments which can meet both resilience and QoS constraint conditions are then presented with illustrating examples. Finally, simulation has been done to show the efficiency of the proposed segment restoration algorithms.

• 보존과학연구
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• s.28
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• pp.105-119
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• 2007

Restoration of earthenware is largely composed of selection of clay, making(forming), and firing. This study lays emphasis on the making method and open-air firing. For making methods, This study used coiling method partly with priority given to ring method. The most significant feature of this restoration work is the making method of tap-forming, in which 외박자(out tap instrument) and 내박자(inter tap instrument) would be tapped and formed. For firing, This study used open-air firing method in the most primitive way. This method needs no special device and equipment and makes the work more simple and easy. The previous study was on the making method by archeological and preservation-scientific research but this study emphasized the restoration work in an actual earthenware maker's position. Through the result of this study, This study wish this would be an opportunity to present another model of various restoration methods for other researchers those who wanted to participate in the restoration and openair firing.