• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.784-791
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• 2004

In this study, we deal with face recognition using fuzzy-based Fisherface method. The well-known Fisherface method is more insensitive to large variation in light direction, face pose, and facial expression than Principal Component Analysis method. Usually, the various methods of face recognition including Fisherface method give equal importance in determining the face to be recognized, regardless of typicalness. The main point here is that the proposed method assigns a feature vector transformed by PCA to fuzzy membership rather than assigning the vector to particular class. In this method, fuzzy membership degrees are obtained from FKNN(Fuzzy K-Nearest Neighbor) initialization. Experimental results show better recognition performance than other methods for ORL and Yale face databases.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.735-739
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• 2005

This paper presents a new system to estimate the head pose of human in interactive indoor environment that has dynamic illumination change and large working space. The main idea of this system is to suggest a new morphological feature for estimating head angle from stereo disparity map. When a disparity map is obtained from stereo camera, the matching confidence value can be derived by measurements of correlation of the stereo images. Applying a threshold to the confidence value, we also obtain the specific morphology of the disparity map. Therefore, we can obtain the morphological shape of disparity map. Through the analysis of this morphological property, the head pose can be estimated. It is simple and fast algorithm in comparison with other algorithm which apply facial template, 2D, 3D models and optical flow method. Our system can automatically segment and estimate head pose in a wide range of head motion without manual initialization like other optical flow system. As the result of experiments, we obtained the reliable head orientation data under the real-time performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1817-1827
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• 2014

As a new type of wireless network, Ad hoc network does not depend on any pre-founded infrastructure, and it has no centralized control unit. The computation and transmission capability of each node are limited. In this paper, a self-updating one-time password mutual authentication protocol for Ad hoc network is proposed. The most significant feature is that a hash chain can update by itself smoothly and securely through capturing the secure bit of the tip. The updating process does not need any additional protocol or re-initialization process and can be continued indefinitely to give rise to an infinite length hash chain, that is, the times of authentication is unlimited without reconstructing a new hash chain. Besides, two random variable are added into the messages interacted during the mutual authentication, enabling the protocol to resist man-in-the-middle attack. Also, the user's identity information is introduced into the seed of hash chain, so the scheme achieves anonymity and traceability at the same time.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.111-118
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• 2019

Recent trends in modern warfare are increasing in importance for air warfare, information warfare, and warfare. The technology of the weapon system software is rapidly developing, and the silent information war to hack it is still going on. Currently, the FA-50 aircraft has a function that can be initialized by a simple switch operation to protect the main military information in the event of an emergency. However, there are limitations in the existing Zeroize function, and this study was carried out to supplement this. First, we compare and analyze the memory structure of aircraft operating in our military, and examined the currently implemented Zeroize function. Second, we reviewed various methods to overcome the limitation of existing Zeroize function. Third, we implement the existing Zeroize function without additional manipulation. In this paper, we propose that the implementation of this feature will enable us to protect our military data more securely and suggest that we should continue to look for ways to enhance security for our technology in the future.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.357-363
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• 2005

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.246-254
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• 2020

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

• Atmosphere
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• v.34 no.1
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• pp.55-67
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• 2024

This paper aims to provide a detailed introduction to the concept of the Ratio of Predictable Component (RPC) and the Signal-to-Noise Paradox. Then, we derive insights from them by exploring the paradoxical features by conducting a seasonal and regional analysis through ensemble expansion in KMA's climate prediction system (GloSea). We also provide an explanation of the ensemble generation method, with a specific focus on stochastic physics. Through this study, we can provide the predictability limits of our forecasting system, and find way to enhance it. On a global scale, RPC reaches a value of 1 when the ensemble is expanded to a maximum of 56 members, underlining the significance of ensemble expansion in the climate prediction system. The feature indicating RPC paradoxically exceeding 1 becomes particularly evident in the winter North Atlantic and the summer North Pacific. In the Siberian Continent, predictability is notably low, persisting even as the ensemble size increases. This region, characterized by a low RPC, is considered challenging for making reliable predictions, highlighting the need for further improvement in the model and initialization processes related to land processes. In contrast, the tropical ocean demonstrates robust predictability while maintaining an RPC of 1. Through this study, we have brought to attention the limitations of potential predictability within the climate prediction system, emphasizing the necessity of leveraging predictable signals with high RPC values. We also underscore the importance of continuous efforts aimed at improving models and initializations to overcome these limitations.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.341-346
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• 2009

The motion data glove is a representative human-computer interaction tool that inputs human hand gestures to computers by measuring their motions. The motion data glove is essential equipment used for new computer technologiesincluding home automation, virtual reality, biometrics, motion capture. For its popular usage, this paper attempts to develop an inexpensive visual.type motion data glove that can be used without any special equipment. The proposed approach has the special feature; it can be developed as a low-cost one becauseof not using high-cost motion-sensing fibers that were used in the conventional approaches. That makes its easy production and popular use possible. This approach adopts a visual method that is obtained by improving conventional optic motion capture technology, instead of mechanical method using motion-sensing fibers. Compared to conventional visual methods, the proposed method has the following advantages and originalities Firstly, conventional visual methods use many cameras and equipments to reconstruct 3D pose with eliminating occlusions But the proposed method adopts a mono vision approachthat makes simple and low cost equipments possible. Secondly, conventional mono vision methods have difficulty in reconstructing 3D pose of occluded parts in images because they have weak points about occlusions. But the proposed approach can reconstruct occluded parts in images by using originally designed thin-bar-shaped optic indicators. Thirdly, many cases of conventional methods use nonlinear numerical computation image analysis algorithm, so they have inconvenience about their initialization and computation times. But the proposed method improves these inconveniences by using a closed-form image analysis algorithm that is obtained from original formulation. Fourthly, many cases of conventional closed-form algorithms use approximations in their formulations processes, so they have disadvantages of low accuracy and confined applications due to singularities. But the proposed method improves these disadvantages by original formulation techniques where a closed-form algorithm is derived by using exponential-form twist coordinates, instead of using approximations or local parameterizations such as Euler angels.