• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2099-2106
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• 2018

This study presents a novel approach of discriminative feature vectors based on manifold learning using nonlinear dimension reduction (DR) technique to improve loss function, and combine with the Adversarial examples to regularize the object function for image classification. The traditional convolutional neural networks (CNN) with many new regularization approach has been successfully used for image classification tasks, and it achieved good results, hence it costs a lot of Calculated spacing and timing. Significantly, distrinct from traditional CNN, we discriminate the feature vectors for objects without empirically-tuned parameter, these Discriminative features intend to remain the lower-dimensional relationship corresponding high-dimension manifold after projecting the image feature vectors from high-dimension to lower-dimension, and we optimize the constrains of the preserving local features based on manifold, which narrow the mapped feature information from the same class and push different class away. Using Adversarial examples, improved loss function with additional regularization term intends to boost the Robustness and generalization of neural network. experimental results indicate that the approach based on discriminative feature of manifold learning is not only valid, but also more efficient in image classification tasks. Furthermore, the proposed approach achieves competitive classification performances for three benchmark datasets : MNIST, CIFAR-10, SVHN.

• Korean Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.133-149
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• 1996

In order to reduce the trial-and-errors in design and production of injection molded plastic parts, there has been much research effort not only on CAE systems which simulate the injection molding process, but also on CAD systems which support initial design and re-design of plastic parts and their molds. The CAD systems and CAE systems have been developed independently with being built on different basis. That is, CAD systems manipulate the part shapes and the design features in a complete solid model, while CAE systems work on shell meshes generated on the abstract sheet model or medial surface of the part. Therefore, it is required to support the two types of geometric models and feature information in one environment to integrate CAD and CAE systems for accelerating the design speed. A feature-based non-manifold geometric modeling system has been developed to provide an integrated environment for design and analysis of injection molding products. In this system, the geometric models for CAD and CAE systems are represented by a non-manifold boundary representation and they are merged into a single geometric model. The suitable form of geometric model for any application can be extracted from this model. In addition, the feature deletion and interaction problem of the feature-based design system has been solved clearly by introducing the non-manifold Boolean operation based on 'merge and selection' algorithm. The sheet modeling capabilities were also developed for easy modeling of thin plastic parts.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.558-566
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• 2005

We propose a feature-based multi-resolution representation of B-rep solid models using history-based Boolean operations based on the merge-and-select algorithm. Because union and subtraction are commutative in the history-based Boolean operations, the integrity of the models at various levels of detail (LOD) is guaranteed for the reordered features regardless of whether the features are subtractive or additive. The multi-resolution solid representation proposed in this paper includes a non-manifold topological merged-set model of all feature primitives as well as a feature-modeling tree reordered consistently with a given LOD criterion. As a result, a B-rep solid model for a given LOD can be provided quickly, because the boundary of the model is evaluated without any geometric calculation and extracted from the merged set by selecting the entities contributing to the LOD model shape.

• Korean Journal of Computational Design and Engineering
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• v.10 no.6
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• pp.432-443
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• 2005

Recent three-dimensional feature-based CAD systems based on solid or non-manifold modelling functionality have been widely used for product design in manufacturing companies. When product models associated with features are used in various downstream applications such as analysis, however, simplified and abstracted models at various levels of detail (LODs) are frequently more desirable and useful than the full detailed model. To provide multi-resolution models, the features need to be rearranged according to a criterion that measures the significance of the feature. However, if the features are rearranged, the resulting shape is possibly different from the original because union and subtraction Boolean operations are not commutative. To solve this problem, in this paper, the new concept of the effective zone of a feature is defined and identified using Boolean algebra. By introducing the effective zone, an arbitrary rearrangement of features becomes possible and arbitrary LOD criteria may be selected to suit various applications. Besides, because the effective zone of a feature is independent of the data structure of the model, the multi-resolution modelling algorithm based on the effective zone can be implemented on any 3D CAD system based on conventional solid representations as well as non-manifold topological (NMT) representations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4317-4335
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• 2018

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

• Korean Journal of Computational Design and Engineering
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• v.10 no.6
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• pp.444-454
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• 2005

Recently, the requirements of multi-resolution models of a solid model, which represent an object at multiple levels of feature detail, are increasing for engineering tasks such as analysis, network-based collaborative design, and virtual prototyping and manufacturing. The research on this area has focused on several topics: topological frameworks for representing multi-resolution solid models, criteria for the level of detail (LOD), and generation of valid models after rearrangement of features. As a solution to the feature rearrangement problem, the new concept of the effective zone of a feature is introduced in the former part of the paper. In this paper, we propose a feature-based non-manifold modeling system to provide multi-resolution models of a feature-based solid or non-manifold model on the basis of the effective feature zones. To facilitate the implementation, we introduce the class of the multi-resolution feature whose attributes contain all necessary information to build a multi-resolution solid model and extract LOD models from it. In addition, two methods are introduced to accelerate the extraction of LOD models from the multi-resolution modeling database: the one is using an NMT model, known as a merged set, to represent multi-resolution models, and the other is storing differences between adjacent LOD models to accelerate the transition to the other LOD. We also suggest the volume of the feature, regardless of feature type, as a criterion for the LOD. This criterion can be used in a wide range of applications, since there is no distinction between additive and subtractive features unlike the previous method.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.509-516
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• 2013

Since traditional linear feature extraction methods are unable to handle nonlinear characteristics often exhibited in hyperspectral imagery, nonlinear feature extraction, also known as manifold learning, is receiving increased attention in hyperspectral remote sensing society as well as other community. A most widely used manifold Isomap is generally promising good results in classification and spectral unmixing tasks, but significantly high computational overhead is problematic, especially for large scale remotely sensed data. A small subset of distinguishing points, referred to as landmarks, is proposed as a solution. This study proposes a new robust and controllable landmark selection method based on the maximum volume of the simplex spanned by landmarks. The experiments are conducted to compare classification accuracies with standard deviation according to sampling methods, the number of landmarks, and processing time. The proposed method could employ both classification accuracy and computational efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4568-4587
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• 2014

Authentication of videos and images based on the content is becoming an important problem in information security. Unfortunately, previous studies lack the consideration of Kerckhoffs's principle in order to achieve this (i.e., a cryptosystem should be secure even if everything about the system, except the key, is public knowledge). In this paper, a solution to the problem of finding a relationship between a frame's index and its content is proposed based on the creative utilization of a robust manifold feature. The proposed solution is based on a novel semi-fragile watermarking scheme for H.264/AVC video content authentication. At first, the input I-frame is partitioned for feature extraction and watermark embedding. This is followed by the temporal feature extraction using the Isometric Mapping algorithm. The frame index is included in the feature to produce the temporal watermark. In order to improve security, the spatial watermark will be encrypted together with the temporal watermark. Finally, the resultant watermark is embedded into the Discrete Cosine Transform coefficients in the diagonal positions. At the receiver side, after watermark extraction and decryption, temporal tampering is detected through a mismatch between the frame index extracted from the temporal watermark and the observed frame index. Next, the feature is regenerate through temporal feature regeneration, and compared with the extracted feature. It is judged through the comparison whether the extracted temporal watermark is similar to that of the original watermarked video. Additionally, for spatial authentication, the tampered areas are located via the comparison between extracted and regenerated spatial features. Experimental results show that the proposed method is sensitive to intentional malicious attacks and modifications, whereas it is robust to legitimate manipulations, such as certain level of lossy compression, channel noise, Gaussian filtering and brightness adjustment. Through a comparison between the extracted frame index and the current frame index, the temporal tempering is identified. With the proposed scheme, a solution to the Kerckhoffs's principle problem is specified.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04a
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• pp.559-564
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• 1995

Feature based design approach is widely studied for the application of mechanical part design and process planning. Mechanical parts are associated with volumetric form features in nature. Therfore, one of the important characteristics that reside in the form feature research until now is that features have been studied in connection with CAPP for material removal. We studied the application of feature based design for ship structure design. Ship structure has interesting nature that tis distinct from mechanical parts. Among these are multiple cell structure, non-volumetric part and production by welding or assembling. An idea of applying feature based design paradigm for design, process planning, cost analysis and engineering calculation was shown. Non-manifold geometric modeler ACIS was adopted to fully benefit from the non-manifold nature of ship structure.