• Journal of Korea Multimedia Society
• /
• v.9 no.11
• /
• pp.1435-1444
• /
• 2006

We present an effective and real-time face detection method based on Principal Component Analysis(PCA) and Support Vector Machines(SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training data set in a significant way, so that it showed 90.1 % detection rates with a small quantity of training data. it can process 8 frames per second for $320{\times}240$ pixel images. This is an acceptable processing time for a real-time system.

• The Journal of the Korea Contents Association
• /
• v.9 no.4
• /
• pp.81-94
• /
• 2009

In ubiquitous environments, the real-time features are necessary to insure the QoS of the intelligent service robots. In this paper, we design and implement a real-time framework for intelligent service robots to support real-time features. The real-time framework to support real-time scheduling services is implemented on the general operating systems. We solve the problem that the scheduler of a general operating system can not support real-time features. This paper also proposes realtime scheduling services to guarantee the QoS of real-time robot applications. We implemented the proposed real-time framework on the Windows operating system and conducted some performance experiments. The experimental results show that the proposed real-time framework can improve thread response times and it has slight performance overhead of $62{\mu}s$.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.5
• /
• pp.718-723
• /
• 2008

Bearing is one of the important mechanical elements used in various industrial equipments. Most of failures occurred during the equipment operation result from bearing defects and breakages. Therefore, monitoring of bearings is essential in preventing equipment breakdowns and reducing unexpected loss. The purpose of this paper is to present an online monitoring method to predict bearing states using vibration signals. Bearing vibrations, which are collected as a form of profile signal, are first analyzed by a discrete wavelet transform. Next, some statistical features are obtained from the resultant wavelet coefficients. In order to select significant ones among them, analysis of variance (ANOVA) is employed in this paper. Statistical features screened in this way are used as input variables to support vector machine (SVM). An hierarchical SVM tree is proposed for dealing with multi-class problems. The result of numerical experiments shows that the proposed SVM tree has a competent performance for classifying bearing fault states.

• Smart Structures and Systems
• /
• v.13 no.3
• /
• pp.453-471
• /
• 2014

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.

• The Journal of the Korea Contents Association
• /
• v.12 no.12
• /
• pp.471-478
• /
• 2012

Various studies on sentiment classification of documents have been performed. Recently, they have been applied to twitter sentiment classification. However, they did not show good performances because they did not consider the characteristics of tweets such as tweet structure, emoticons, spelling errors, and newly-coined words. In this paper, we perform experiments on various input features (emoticon polarity, retweet polarity, author polarity, and replacement words) which affect twitter sentiment classification model based on machine-learning techniques. In the experiments with a sentiment classification model based on a support vector machine, we found that the emoticon polarity features and the author polarity features can contribute to improve the performance of a twitter sentiment classification model. Then, we found that the retweet polarity features and the replacement words features do not affect the performance of a twitter sentiment classification model contrary to our expectations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.10a
• /
• pp.285-288
• /
• 2008

In many intrusion detection applications, n-gram approach has been widely applied. However, n-gram approach has shown a few problems including double counting of features. To address those problems, we applied n-gram augmented Naive Bayes directly to classify intrusive sequences and compared performance with those of Naive Bayes and Support Vector Machines (SVM) with n-gram features by the experiments on host-based intrusion detection benchmark data sets. Experimental results on the University of New Mexico (UNM) benchmark data sets show that the n-gram augmented method, which solves the problem of independence violation that happens when n-gram features are directly applied to Naive Bayes (i.e. Naive Bayes with n-gram features), yields intrusion detectors with higher accuracy than those from Naive Bayes with n-gram features and shows comparable accuracy to those from SVM with n-gram features.

• Journal of The Korea Institute of Healthcare Architecture
• /
• v.16 no.3
• /
• pp.69-79
• /
• 2010

Canada is a democratic country, yet it keeps a social democratic system in which the government is in charge of welfare of its people. And this is one of the most significant features about the country. Her public and private pension system has been effective since the 1920s, securing its people's fundamental income. In particular, the public medical system applies to its every citizen and performs its role. This system is called the National Medical System as well as "MEDICARE" named after its related law. However, there has been a significant change in the national medical and welfare policy due to the budget deficit. In other words, the policy was mainly implemented to welfare facilities in the past, but the policy changed to a welfare policy for the elderly with a concentration on the support for self-reliance of senior citizens since the reform. The purpose of this study is to provide data and implications for Korea through the analysis of the current situation and distinct features of the housing welfare system in Canada. This study has researched the literature on the subject with an analytic focus on three aspects that are the fundamental frame of the system, essential content (support for self-reliance and facility composition), and distinct features of the housing for the elderly. In other words, they are, first, how the fundamental frame of the housing welfare system for the elderly is composed; second, how the service for self-reliance welfare and facility service are composed; and third, what their scale and distinct spatial features of general houses for the elderly with self-reliance are. A comparative study was conducted in detail on courses and characteristics of the housing welfare system for senior citizens in Canada and the USA of North America. In particular, it reveals the scale and distinct spatial features of public houses for the elderly with self-reliance in British Columbia (BC) which is one of the main provinces of Canada.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.12 no.3
• /
• pp.344-353
• /
• 2009

In this paper, I have proposed a design of process support system based on CMMI process which can support integrated management and repository for CMMI model processes. The system assists small organization to improve business process and offers institutionalized automatic environment to users for use of CMMI processes. The proposed system also can enhance quality and productivity of project by including features such as convenience of project management, planned project administration and steady process improvement.

• Journal of The Korean Astronomical Society
• /
• v.36 no.spc1
• /
• pp.21-27
• /
• 2003

In the present study, we have investigated morphology and evolution of small-scale Ha dynamic features on the quiet sun by analyzing video magnetograms and high resolution Ha images simultaneously taken for 5 hours at Big Bear Solar Observatory on April 18, 1997. From comparisons between time sequential longitudinal magnetograms and H$\alpha$ images covering $150" {\times} 150"$, several small-scale H$\alpha$ dynamic features have been observed at a site of magnetic flux cancellation. A close relationship between such features and cancelling magnetic fluxes has been revealed temporarily and spatially. Our results support that material injection by chromospheric magnetic reconnect ion may be essential in supporting numerous small-scale H$\alpha$ dynamical absorption features, being in line with recent observational studies showing that material injection by chromospheric magnetic reconnect ion is essential for the formation of solar filaments.

• Journal of Korea Multimedia Society
• /
• v.21 no.4
• /
• pp.478-484
• /
• 2018

The choice of the motion features influences the result of the human action recognition method directly. Many factors often influence the single feature differently, such as appearance of the human body, environment and video camera. So the accuracy of action recognition is restricted. On the bases of studying the representation and recognition of human actions, and giving fully consideration to the advantages and disadvantages of different features, the Dense Trajectories(DT) algorithm is a very classic algorithm in the field of behavior recognition feature extraction, but there are some defects in the use of optical flow images. In this paper, we will use the improved Dense Trajectories(iDT) algorithm to optimize and extract the optical flow features in the movement of human action, then we will combined with Support Vector Machine methods to identify human behavior, and use the image in the KTH database for training and testing.