• Journal of Korea Multimedia Society
• /
• v.18 no.9
• /
• pp.1039-1046
• /
• 2015

We propose a feature generation method for low-resolution face recognition. For this, we first generate new features from the input features (pixels) of a low-resolution face image by adding the higher-order terms. Then, we evaluate the separability of both of the original input features and new features by computing the discriminant distance of each feature. Finally, new data sample used for recognition consists of the features with high separability. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed method gives good recognition performance for low-resolution face images compared with other method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.1
• /
• pp.82-89
• /
• 2015

The researches of image recognition have been processed traditionally. Especially, face recognition technology has been received attractions with advance and applied to various areas according as camera sensor embedded into many devices such as smart phone. In this study, we design and develop a feature vector generation technique of face for making animation caricatures using methods for face detection which are previous stage of face recognition. At first, we detect both face region and detailed eye region of component element by Viola&Johns's realtime detection method which are called as ROI(Region Of Interest). And then, we generate feature vectors of eye region by utilizing factors as opposed to the periphery and by using appearance information of eye. At this point, we focus on the embedded information in many color spaces to overcome the problems which can be occurred by using one color space. We propose a feature vector generation method using information from many color spaces. Finally, we experiment the test of feature vector generation by the proposed method with enough quantity of sample picture data and evaluate the proposed method for factors of estimating performance such as error rate, accuracy and generation time.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2209-2212
• /
• 2003

In this paper, we propose the method to extract the important frequency bands from the EMG signal, and for generation of feature vector using the important frequency bands. The EMG signal is measured with 4 sensor and is recorded as 4 channel’s time series data. The same frequency bands from 4 channel’s frequency components are selected as the important frequency bands. The feature vector is calculated by the function formed using the combination of selected same important frequency bands. The EMG signals acquired from seven wrist motion type are recognized by changing into the feature vector formed. Then, the extraction and generation is performed by using the double combination of the genetic algorithm (GA) and the neural network (NN). Finally, in order to illustrate the effectiveness of the proposed method, computer simulations are done.

• Korean Journal of Computational Design and Engineering
• /
• v.8 no.2
• /
• pp.122-132
• /
• 2003

STEP AP218 has a standard schema to represent the structural model of a midship section. While it helps to exchange ship structural models among heterogeneous automation systems, most shipyards and classification societies still exchange information using 2D paper drawings. We propose a feature parameter input method to generate a 3D STEP model of a ship structure from 2D drawings. We have analyzed the ship structure information contained in 2D drawings and have defined a data model to express the contents of the drawing. We also developed a QUI for the feature parameter input. To translate 2D information extracted from the drawing into a STEP AP2l8 model, we have developed a shape generation library, and generated the 3D ship model through this library. The generated 3D STEP model of a ship structure can be used to exchange information between design departments in a shipyard as well as between classification societies and shipyards.

• 한국전산유체공학회:학술대회논문집
• /
• 2007.04a
• /
• pp.103-107
• /
• 2007

Computational Fluid Dynamics (CFD in short) approach is now playing an important role in the engineering process recently. Generating proper grid system for the region of interest in time is prerequisite for the efficient numerical calculation of flow physics using CFD approach. Grid generation is, however, usually considered as a major obstacle for a routine and successful application of numerical approaches in the engineering process. CFD approach based on the unstructured grid system is gaining popularity due to its simplicity and efficiency for generating grid system compared to the structured grid approaches. In this paper an automated triangular surface grid generation using CAD surface data is proposed According to the present method, the CAD surface data imported in the STL format is processed to identify feature edges defining the topology and geometry of the surface shape first. When the feature edges are identified, node points along the edges are distributed. The initial fronts which connect those feature edge nodes are constructed and then they are advanced along the CAD surface data inward until the surface is fully covered by triangular surface grid cells using Advancing Front Method. It is found that this approach can be implemented in an automated way successfully saving man-hours and reducing human-errors in generating triangular surface grid system.

• Journal of computational fluids engineering
• /
• v.12 no.4
• /
• pp.68-73
• /
• 2007

Computational Fluid Dynamics (CFD) approach is now playing an important role in the engineering process in these days. Generating proper grid system in time for the region of interest is prerequisite for the efficient numerical calculation of flow physics using CFD approach. Grid generation is, however, usually considered as a major obstacle for a routine and successful application of numerical approaches in the engineering process. CFD approach based on the unstructured grid system is gaining popularity due to its simplicity and efficiency for generating grid system compared to the structured grid approaches, especially for complex geometries. In this paper an automated triangular surface grid generation using CAD(Computer Aided Design) surface data is proposed. According to the present method, the CAD surface data imported in the STL(Stereo-lithography) format is processed to identify feature edges defining the topology and geometry of the surface shape first. When the feature edges are identified, node points along the edges are distributed. The initial fronts which connect those feature edge nodes are constructed and then they are advanced along the CAD surface data inward until the surface is fully covered by triangular surface grid cells using Advancing Front Method. It is found that this approach can be implemented in an automated way successfully saving man-hours and reducing human-errors in generating triangular surface grid system.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1279-1290
• /
• 2021

As vehicle technology has grown, autonomous driving that does not require driver intervention has developed. Accordingly, CAN security, an network of in-vehicles, has also become important. CAN shows vulnerabilities in hacking attacks, and machine learning-based IDS is introduced to detect these attacks. However, despite its high accuracy, machine learning showed vulnerability against adversarial examples. In this paper, we propose a adversarial CAN frame generation method to avoid IDS by adding noise to feature and proceeding with feature selection and re-packet for physical attack of the vehicle. We check how well the adversarial CAN frame avoids IDS through experiments for each case that adversarial CAN frame generated by all feature modulation, modulation after feature selection, preprocessing after re-packet.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.2919-2937
• /
• 2020

Post-Quantum Cryptography (PQC) is rapidly developing as a stable and reliable quantum-resistant form of cryptography, throughout the industry. Similarly to existing cryptography, however, it does not prevent a third-party from using the secret key when third party obtains the secret key by deception, unauthorized sharing, or unauthorized proxying. The most effective alternative to preventing such illegal use is the utilization of biometrics during the generation of the secret key. In this paper, we propose a biometric-based secret key generation scheme for multivariate quadratic signature schemes, such as Rainbow. This prevents the secret key from being used by an unauthorized third party through biometric recognition. It also generates a shorter secret key by applying Principal Component Analysis (PCA)-based Confidence Interval Analysis (CIA) as a feature extraction method. This scheme's optimized implementation performed well at high speeds.

• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.4
• /
• pp.217-221
• /
• 2013

Recently, researches for methods of generating binary feature descriptors have been actively done. Since matching of binary feature descriptors uses Hamming distance which is based on bit operations, it is much more efficient than that of previous general feature descriptors which uses Euclidean distance based on real number operations. However, since increase in the number of features linearly drops matching speed, in applications such as object tracking where real-time applicability is a must, there has been an increasing demand for methods of further improving the matching speed of binary feature descriptors. In this regard, this paper proposes a method that improves the matching speed greatly while maintaining the matching accuracy by splitting high dimensional binary feature descriptors to several low dimensional ones and sequentially analyzing their partial Hamming distances. To evaluate the efficiency of the proposed method, experiments of comparison with previous matching methods are conducted. In addition, this paper discusses schemes of generating binary feature descriptors for maximizing the performance of the proposed method. Based on the analysis on the performance of several generation schemes, we try to find out the most effective scheme.

• Journal of Korean Port Research
• /
• v.14 no.1
• /
• pp.97-105
• /
• 2000

This paper presents an automatic mesh generation considering water depth, which is based on the depth interpolation. The key feature of this method is that the position of a mesh on any depth in the shallow water area can be generated. The Examples are carried out, and the results are shown to be good. This method is shown to be a useful and powerful tool for the flow calculation for the seabed topography.