• Journal of the Semiconductor & Display Technology
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• v.6 no.2 s.19
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• pp.59-63
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• 2007

Radio Frequency Identification (RFID) is a common term for technologies using micro chips that are able to communicate over short-range radio and that can be used for identifying physical objects. RFID technology already has several application areas and more are being envisioned all the time. While it has the potential of becoming a really ubiquitous part of the information society over time, there are many security and privacy concerns related to RFID that need to be solved. This paper proposes a method which could protect private information and ensure RFID's identification effectively storing face feature information on RFID tag. This method improved linear discriminant analysis has reduced the dimension of feature information which has large size of data. Therefore, face feature information can be stored in small memory field of RFID tag. The proposed algorithm in comparison with other previous methods shows better stability and elevated detection rate and also can be applied to the entrance control management system, digital identification card and others.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.461-471
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• 2017

In this paper we propose a feature selection method identifying important features in the semivarying coefficient model. One important issue in semivarying coefficient model is how to estimate the parametric and nonparametric components. Another issue is how to identify important features in the varying and the constant effects. We propose a feature selection method able to address this issue using generalized cross validation functions of the varying coefficient least squares support vector regression (LS-SVR) and the linear LS-SVR. Numerical studies indicate that the proposed method is quite effective in identifying important features in the varying and the constant effects in the semivarying coefficient model.

• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.53-57
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• 2003

Chaos theory is a study researching the irregular, unpredictable behavior of deterministic and non-linear dynamical system. The interpretation using Chaos makes us evaluate characteristic existing in status space of system by tine series, so that the extraction of Chaos characteristic understanding and those characteristics enables us to do high precision interpretation. Therefore, This paper propose the new method which is adopted in extracting character features and recognizing characters using the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from input character images. And their feature is converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of character image after calculating Box-counting dimension, Natural Measure, information bit and information dimension which are meant fractal dimension. Finally, character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An experimental result shows 99% character classification rates for 2,350 Korean characters (Hangul) using proposed method in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.4
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• pp.187-194
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• 2002

Recently, the application of the linear machine far industrial field is remarkable increased, especially for the gantry machine, machine tool system and CNC. In these application fields, high dynamics position control performance Is essentially required in both the steady and the transient state. This pacer presents simple but powerful position control loop based on traditional PID controller. The presented position control algorithm, named 'Unified PID Position Controller'has great features for the linear machine drives such as no over-shoot phenomena and simple gain tuning strategy. Through the experimental results with commercial linear motors, it is shown that the proposed algorithm has excellent dynamics suitable fur linear motions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.98-101
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• 2001

Linear motor feature a direct connection to the machine tool, therefore a direct route for heat transfer. The heat dissipation of linear motor machine is affected by the maximum temperature rise of the primary part, coil and the cooling method. To minimize temperature induced dimension changes and decrements of performance, linear motor machine require effective cooling mechanism. To evaluate cooling performance of existing linear motor machine, some experiments about temperature profile are performed using thermocouple recorder. Due to the lack of information about internal structure, only some finite element modeling is prepared and analyzed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.414-419
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• 1997

Linear motor feature a direct connection to the machine tool, therefore a direct route for heat transfer. The heat dissipation of linear motor machine is affected by the maximum temperature rise of the primary part, coil and the cooling method. To minimize temperature induced dimension changes and decrements of performance, linear motor machine require effective cooling mechanism. 1'0 evaluate cooling performance of existing linear motor machine, some experiments about temperature profile are performed and evaluated using thermocouple recorder. Due to the lack of information about internal structure. only some finite element modelling is prepared and analyzed.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.53-62
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• 2004

The processing of emotional information is very important in Human-Computer Interaction (HCI). In particular, it is very important in video information processing to deal with a user's affection. To handle emotional information, it is necessary to represent meaningful features and detect them efficiently. Even though it is not an easy task to detect emotional events from low level features such as colour and motion, it is possible to detect them if we use statistical analysis like Linear Discriminant Analysis (LDA). In this paper, we propose a representation scheme for emotion-related features and a defection method. We experiment with extracted features from video to detect emotional events and obtain desirable results.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.121-130
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• 2009

In this paper, feature extraction methods, which is one field of reducing dimensions of high-dimensional data, are empirically investigated. We selected the traditional PCA(Principal Component Analysis), ICA(Independent Component Analysis), NMF(Non-negative Matrix Factorization), and sNMF(Sparse NMF) for comparisons. ICA has a similar feature with the simple cell of V1. NMF implemented a "parts-based representation in the brain" and sNMF is a improved version of NMF. In order to visually investigate the extracted features, handwritten digits are handled. Also, the extracted features are used to train multi-layer perceptrons for recognition test. The characteristic of each feature extraction method will be useful when applying feature extraction methods to many real-world problems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.757-762
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• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.