• Journal of Advanced Marine Engineering and Technology
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• v.40 no.5
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• pp.437-446
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• 2016

Feature selection has become an essential technique to reduce the dimensionality of data sets. Many features are frequently irrelevant or redundant for the classification tasks. The purpose of feature selection is to select relevant features and remove irrelevant and redundant features. Applications of the feature selection range from text processing, face recognition, bioinformatics, speaker verification, and medical diagnosis to financial domains. In this study, we focus on filter methods based on information entropy : IG (Information Gain), FCBF (Fast Correlation Based Filter), and mRMR (minimum Redundancy Maximum Relevance). FCBF has the advantage of reducing computational burden by eliminating the redundant features that satisfy the condition of approximate Markov blanket. However, FCBF considers only the relevance between the feature and the class in order to select the best features, thus failing to take into consideration the interaction between features. In this paper, we propose an improved FCBF to overcome this shortcoming. We also perform a comparative study to evaluate the performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4510-4526
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• 2017

Most available methods of facial gender recognition work well under a constrained situation, but the performances of these methods have decreased significantly when they are implemented under unconstrained environments. In this paper, a method via low-rank and collaborative representation is proposed for facial gender recognition in the wild. Firstly, the low-rank decomposition is applied to the face image to minimize the negative effect caused by various corruptions and dynamical illuminations in an unconstrained environment. And, we employ the collaborative representation to be as the classifier, which using the much weaker $l_2-norm$ sparsity constraint to achieve similar classification results but with significantly lower complexity. The proposed method combines the low-rank and collaborative representation to an organic whole to solve the task of facial gender recognition under unconstrained environments. Extensive experiments on three benchmarks including AR, CAS-PERL and YouTube are conducted to show the effectiveness of the proposed method. Compared with several state-of-the-art algorithms, our method has overwhelming superiority in the aspects of accuracy and robustness.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.579-587
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• 2008

Promotion of the line no.2 in Seoul Metro was changing from the existing signaling facilities for ATS(Automatic Train Stop) vehicles to the up-to-date signaling facilities for ATO(Automatic Train Operation). But, in consequence of conducting a trial run after being equipped with the ATO signaling facilities, the matter related to mix-operation with the existing ATS signaling facilities appeared. The operation of the existing ATS signaling system in combination with the ATO signaling system has made improper working related to frequency recognition of the ATS On-board Computerized Equipment. This obstructs operation of a working ATS vehicle. That is, as barring operation of an ATS vehicle that should proceed, it may make the proceeding ATS vehicle stop suddenly and after all, it will cause safety concerns. In this paper, we designed a wayside track occupancy detector that previously prevents improper working related to frequency recognition of the ATS On-board Computerized Equipment by gripping classification and working processes of operating trains throughout transmission of local signaling information from the existing facilities, which does not need to change or replace the existing signaling facilities. Furthermore, we described general characteristics of the wayside track occupancy detector and modeled the IFC(InterFace Contrivance) device and the logical circuit recognizing signal information. Then, we made an application program of PLC(programmable Logic Computer) based on the stated model. We, in relation to data transfer method, used the frame in TCP/IP transfer mode as the standard, and we demonstrated that ATO transmission frequency is intercepted.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.51-57
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• 2017

Digital signage is becoming part of daily life across a wide range of visual advertisements segments market used in stations, hotels, retail stores, hotels, etc. The current digital signage system used in market is generally works on limited user interactivity with static contents. In this paper, a new approach is proposed using computer vision based dynamic audience adaptive cost-effective digital signage system. The proposed design uses the Camera attached Raspberry Pi Open source platform to employ the real-time audience interaction using computer vision algorithms to extract facial features of the audience. The real-time facial features are extracted using Haar Cascade algorithm which are used for audience gender specific rendering of dynamic digital signage content. The audience facial characterization using Haar Cascade is evaluated on the FERET database with 95% accuracy for gender classification. The proposed system, developed and evaluated with male and female audiences in real-life environments camera embedded raspberry pi with good level of accuracy.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• The Korean Journal of Pain
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• v.24 no.1
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• pp.31-35
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• 2011

Background: Circumcision is a painful intervention frequently performed in pediatric surgery. We aim to compare the efficacy of caudal block versus dorsal penile block (DPNB) under general anesthesia for children undergoing circumcision. Methods: This study was performed between July 1, 2009 and October 16, 2009. Fifty male children American Society of Anesthesiolgists physical status classification I, aged between 3 and 12 were included in this randomized, prospective, comparative study. Anesthetic techniques were standardized for all children. Patients were randomized into 2 groups. Using 0.25% 0.5 ml/kg levobupivacain, we performed DPNB for Group 1 and caudal block for Group 2. Postoperative analgesia was evaluated for six hours with the Flacc Pain Scale for five categories; (F) Face, (L) Legs, (A) Activity, (C) Cry, and (C) Consolability. For every child, supplemental analgesic amounts, times, and probable local or systemic complications were recorded. Results: No significant difference between the groups (P > 0.05) was found in mean age, body weight, anesthesia duration, FLACC pain, and sedation scores (P > 0.05). However, on subsequent measurements, a significant decrease of pain and sedation scores was noted in both the DPNB group and the caudal block group (P < 0.001). No major complication was found when using either technique. Conclusions: DPNB and caudal block provided similar postoperative analgesic effects without major complications for children under general anesthesia.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.644-646
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• 2017

In this study, the university conducted an inspection of the classes of students in the school-grain classes of liberal arts classes and conducted a thorough classification. Each of the students' characteristics is divided into those who have experienced programming in elementary and secondary schools. As a result, classes were classified and followed by Scratch programming classes. The difference between pre-scan and post-test is the identification of the centre of gravity and the different methods of teaching the teaching methods accordingly. Through this study, we learned how to understand and appreciate the difficulties we face while studying computing.