• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.1003-1011
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• 2008

Mixed-effect Poisson regression models are widely used for analysis of correlated count data such as those found in longitudinal studies. In this paper, we consider kernel extensions with semiparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.210-212
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• 2005

The personal video recorder is a consumer electronics device that records television shows to a hard disk in digital format. In this paper, we propose an implementation method of personal video recorder for digital TV. The proposed personal video recorder includes cpu and system control modules, graphics and display module, audio DSP module, digital I/O module, NIM module, graphic software library, and embedded software modules for providing a lot of PVR functions such as live or reserved recordings, browsing of recorded content list, trick play and time shifting. Especially, combining trick play with time shifting makes much more convenient functions such as pausing live TV, instant replay of interesting scenes, and skipping advertising.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.383-388
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• 2009

In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.141-144
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• 2006

The personal digital recorder is a consumer electronics device that records television shows to a hard disk in digital format. In this paper, we propose an implementation method of personal digital recorder for HDTV. The proposed personal recorder includes CPU and system control modules, graphics and display module, audio DSP module, digital I/O module, NIM module, graphic software library, and embedded software modules for providing a lot of personal digital recorder functions such as live or reserved recordings, browsing of recorder content list, trick lay and time shifting. Especially, combining trick play with time shifting makes much more convenient functions such as pausing live TV, instant replay of interesting scenes, and skipping advertising.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.607-625
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• 2011

Two-party key exchange protocol is a mechanism in which two parties communicate with each other over an insecure channel and output the same session key. A key exchange protocol that is secure against an active adversary who can control and modify the exchanged messages is called authenticated key exchange (AKE) protocol. LaMacchia, Lauter and Mityagin presented a strong security definition for public key infrastructure (PKI) based two-pass protocol, which we call the extended Canetti-Krawczyk (eCK) security model, and some researchers have provided eCK-secure AKE protocols in recent years. However, almost all protocols are provably secure in the random oracle model or rely on a special implementation technique so-called the NAXOS trick. In this paper, we present a PKI-based two-pass AKE protocol that is secure in the eCK security model. The security of the proposed protocol is proven without random oracles (under three assumptions), and does not rely on implementation techniques such as the NAXOS trick.

• Journal of Oral Medicine and Pain
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• v.45 no.2
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• pp.29-33
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• 2020

Tardive dyskinesia (TD) is continuous, repetitive movement disorder of tongue, lip or jaw, induced by medication. It causes pain and dysfunction of oral structures but also interferes with dental treatment and overall social life of patients. As a dentist, it is imperative to recognize and manage TD, although currently, there is no definitive treatment for TD. This article reports a patient with TD of tongue, successfully managed with an oral appliance mimicking sensory tricks. Considering the limited treatment options for TD, an oral appliance, a simple and conservative approach, can be a meaningful treatment for some patients with orofacial dyskinesia.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.385-392
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• 2012

Logistic regression is a well known binary classification method in the field of statistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel extensions with semiparametric fixed effects and parametric random effects for the logistic regression. The estimation is performed through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of optimal hyperparameters, cross-validation techniques are employed. Numerical results are then presented to indicate the performance of the proposed procedure.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.280-286
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• 2024

In this paper, layered UNet with warmup and dropout tricks was used to segment teeth instantly by using data labeled for each individual tooth and increase performance of the result. The layered UNet proposed before showed very good performance in tooth segmentation without distinguishing tooth number. To do instance segmentation of teeth, we labeled teeth CBCT data according to tooth numbering system which is devised by FDI World Dental Federation notation. Colors for labeled teeth are like AI-Hub teeth dataset. Simulation results show that layered UNet does also segment very well for each tooth distinguishing tooth number by color. Layered UNet model using warmup trick was the best with IoU values of 0.80 and 0.77 for training, validation data. To increase the performance of instance segmentation of teeth, we need more labeled data later. The results of this paper can be used to develop medical software that requires tooth recognition, such as orthodontic treatment, wisdom tooth extraction, and implant surgery.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.479-480
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• 2014

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1020-1033
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• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.