• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.45-50
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• 2004

In this paper, we propose a new PCA-based LDA Mixture Algorithm(PLMA) for real-time face recognition system. This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction part we applied subtraction image, color filtering, eyes and mouth region detection, and normalization method, and in the face recognition part we used the method mixing PCA and LDA in extracted face candidate region images. The existing recognition system using only PCA showed low recognition rates, and it is hard in the recognition system using only LDA to apply LDA to the input images as it is when the number of image pixels ire small as compared with the training set. To overcome these shortcomings, we reduced dimension as we apply PCA to the normalized images, and apply LDA to the compressed images, therefore it is possible for us to do real-time recognition, and we are also capable of improving recognition rates. We have experimented using self-organized DAUface database to evaluate the performance of the proposed system. The experimental results show that the proposed method outperform PCA, LDA and ICA method within the framework of recognition accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.155-164
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.231-235
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• 2005

Rapid increase of maritime traffic volume and the increase of vessel size make it indispensible for the fairway designer to estimate the traffic capacity of a fairway at its early design stage. In this paper, as one of the methods to estimate the maritime traffic capacity of a fairway, operating rate of a fairway is defined and reviewed together with its basic characteristics, which is a brief estimation model based on bumper model around a ship. The method is applied to the approach channels of major harbors in Korea to give some guidelines on the acceptable traffic capacity of a fairway. In spite of its simplicity, this method can be used as an effective tool to discriminate whether the principal dimension of a fairway is enough or not from the viewpoint of maritime traffic capacity at its initial design stage.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.76-85
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• 2011

In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.

• Journal of the Society of Naval Architects of Korea
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• v.47 no.1
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• pp.99-111
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• 2010

Recently, a very large vessel's maneuvering performance, rudder performance and rudder design's importance is considered to be an important subject. There have been few studies on the design process of rudder device before. The aim of this paper is to investigate a design process of rudder device and to propose a generalized design process of rudder device. Firstly, we investigated the rudder device design process of Korean major shipyards. And the differences of a torque calculation method, rudder section design, maneuvering performance examination method, etc were analyzed theoretically. Secondly, the design process of rudder device was divided into concept design, initial design and detail design. In concept design, a rudder area was estimated and its validity was examined. In initial design, rudder profile and design method has been selected through rudder form determination process. And principal dimension and steering gear capacity were determined. Maneuvering performance was also examined by simulation tool. In detail design, design criteria considered in rudder initial design has been investigated thoroughly. Also a rudder torque, rudder cavitation performance and rudder structure analysis were estimated. And maneuvering performance was also examined by model test. Finally, based on the results of investigation, the design process of rudder device was generalized and proposed.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.425-430
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• 2015

This paper is a study to improve the classification efficiency of rotating objects by using deep neural networks to which a deep learning algorithm was applied. For the classification experiment of rotating objects, COIL-20 is used as data and total 3 types of classifiers are compared and analyzed. 3 types of classifiers used in the study include PCA classifier to derive a feature value while reducing the dimension of data by using Principal Component Analysis and classify by using euclidean distance, MLP classifier of the way of reducing the error energy by using error back-propagation algorithm and finally, deep learning applied DBN classifier of the way of increasing the probability of observing learning data through pre-training and reducing the error energy through fine-tuning. In order to identify the structure-specific error rate of the deep neural networks, the experiment is carried out while changing the number of hidden layers and number of hidden neurons. The classifier using DBN showed the lowest error rate. Its structure of deep neural networks with 2 hidden layers showed a high recognition rate by moving parameters to a location helpful for recognition.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.417-424
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• 2010

In this paper the real-time face region was detected by suggesting the rectangular feature-based classifier and the robust detection algorithm that satisfied the efficiency of computation and detection performance was suggested. By using the detected face region as a recognition input image, in this paper the face recognition method combined with PCA and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input face image, this method computes the eigenface through PCA and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the face recognition is performed by inputting the multi-layer neural network.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.1
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• pp.161-177
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• 2013

High-speed vessels require good resistance and seakeeping performance for safe operations in rough seas. The resistance and seakeeping performance of high-speed vessels varies significantly depending on their hull forms. In this study, three planing hulls that have almost the same displacement and principal dimension are designed and the hydrodynamic characteristics of those hulls are estimated by high-speed model tests. All model ships are deep-V type planing hulls. The bows of no.2 and no.3 model ships are designed to be advantageous for wave-piercing in rough water. No.2 and no.3 model ships have concave and straight forebody cross-sections, respectively. And length-to-beam ratios of no.2 and no.3 models are larger than that of no.1 model. In calm water tests, running attitude and resistance of model ships are measured at various speeds. And motion tests in regular waves are performed to measure the heave and pitch motion responses of the model ships. The required power of no.1 (VPS) model is smallest, but its vertical motion amplitudes in waves are the largest. No.2 (VWC) model shows the smallest motion amplitudes in waves, but needs the greatest power at high speed. The resistance and seakeeping performance of no.3 (VWS) model ship are the middle of three model ships, respectively. And in regular waves, no.1 model ship experiences 'fly over' phenomena around its resonant frequency. Vertical accelerations at specific locations such as F.P., center of gravity of model ships are measured at their resonant frequency. It is necessary to measure accelerations by accelerometers or other devices in model tests for the accurate prediction of vertical accelerations in real ships.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.1
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• pp.52-61
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• 1998

We studied behaviour pattern of anchovy (Engraulis japonicus) shoal by a method of shoal echo integration and tested species identification by a method of artificial neural network using the acoustic data collected in the East China Sea in March 1994 and in the southern coastal waters of the East Sea of Korea in March 1995. Between areas, frequency distribution of 10 shoal descriptors was different, which showed characteristics of shoal behaviour in size, bathymetric position and acoustic strength. The range and mean of shoal size distribution in length and height was wider and bigger in the southern coastal waters of the East Sea than in the East China Sea. Relative shoal size of China Sea. Fractal dimension of shoal was almost same in both areas. Mean volume reverbration index of shoal was 3 dB higher in the southern coastal waters of the East Sea than in the East China Sea. The depth layer of shoal distribution was related to bottom depth in the southern coastal waters of the East Sea, while it was between near surface and central layer in the East China Sea. Principal component analysis of shoal descriptors showed the correlation between shoal size and acoustic strength which was higher in the southern coastal waters of the East Sea, than in the East China Sea. Correlation was also found among the bathymetric positions of shoal to some degree higher in the southern coastal waters of the East Sea than in the East China Sea. The anchovy shoal of two areas was identified by artificial neural network. The contribution factor index (Cio) of the shoal descriptors between two areas were almost identical feature. The shoal volume reverberation index (Rv) was showed the highest contribution to the species identification, while shoal length and shoal height showed relatively high negative contribution to the species identification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.