• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.151-152
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• 2010

WWW images locate in structural, networking documents, so the importance of a word can be indicated by its location, frequency. There are two patterns for multi-cues ingegration annotation. The multi-cues integration algorithm shows initial promise as an indicator of semantic keyphrases of the web images. The latent semantic automatic keyphrase extraction that causes the improvement with the usage of multi-cues is expected to be preferable.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.467-468
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• 2023

현재 게임 시장에서 VR 게임이 가지는 영향력은 점차 증가하는 추세이다. 기존의 VR게임들은 대부분 Multi-Play를 지원하지 않는다. 이에 따라 본 논문에서는 Photon Network와 XR Plugin을 사용하여 2명의 플레이어가 함께 즐길 수 있는 Arcade 장르의 VR 경쟁 Multi-Game을 구현하였다. 이에 필요한 서버는 리슨 서버 방식으로 Master Client가 게임을 시작하면, Game에 참가한 다른 Client Player는 Photon Network의 RPC 기능을 사용하고 Player의 동작, Game 진행 상황 등을 실시간으로 Server에 동기화하여 Multi-Play게임을 할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.1-7
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• 2018

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.786-799
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• 2021

Road damage detection is important for road maintenance. With the development of deep learning, more and more road damage detection methods have been proposed, such as Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. However, because shallow and deep layers cannot be extracted at the same time, the existing methods do not perform well in detecting objects with fewer samples. In addition, these methods cannot obtain a highly accurate detecting bounding box. This paper presents a Multi-level Feature Pyramids method based on M2det. Because the feature layer has multi-scale and multi-level architecture, the feature layer containing more information and obvious features can be extracted. Moreover, an attention mechanism is used to improve the accuracy of local boundary boxes in the dataset. Experimental results show that the proposed method is better than the current state-of-the-art methods.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.132-138
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• 2013

Multi-modal techniques have received increasing interest in the neuroscientific and brain computer interface (BCI) communities in recent times. Two aspects of multi-modal imaging for BCI will be reviewed. First, the use of recordings of multiple subjects to help find subject-independent BCI classifiers is considered. Then, multi-modal neuroimaging methods involving combined electroencephalogram and near-infrared spectroscopy measurements are discussed, which can help achieve enhanced and robust BCI performance.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Journal of Korean Home Economics Education Association
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• v.12 no.3
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• pp.105-114
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• 2000

The purpose of this study was to investigate the effects of instructional multi-media in Home Economics Education perceived by teachers. The data for this research were attained from 139 middle & high school teachers of Home Economics. The data were analyzed by frequency of distribution, mean, stand deviation. t-test and analysis of variance, scheffe test with SPSSWin 7.5 program. The results of this study are as follows: The effects of instructional multi-media were composed of four dimensions in this study; need, frequency of use, pros and cons. 1. From these four dimensions, the need has the highest and the frequency of use has the lowest score. 2. The effects of instructional multi-media are significantly related to personal & environmental characteristics. 1)Need of the instructional multi-media effects is significantly different according to age, experience of computer education and possession of a computer at home. Low and high age groups are higher in the need of the instructional multi-media effects score than middle group age and the more experience of computer education and possession of a computer at home are higher in that score. 2) Frequency of use is significantly different according to LAN system in school. The higher score of frequency of use is in a LAN system’s school. 3) Pros of the instructional multi-media effects is significantly different according to the level of education, experience of computer education and the type of school. Undergraduate high school teachers and the lower o experience of computer education are higher in the pros of the instructional multi-media effects score. 4) Cons of the instructional multi-media effects is significantly different according to the level of education. Graduate teachers are higher in the cons of the instructional multi-media effects score.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.69-70
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• 2022

본 논문에서는 Photon Network를 사용하여 경찰과 도둑 컨셉으로 5명이 함께 즐길 수 있는 Multi Game을 구현하였다. 서버는 리슨 서버 방식으로 Master Client가 게임을 시작하면 Game에 참가한 모든 Player는 Photon Network의 RPC 기능을 사용하여 Player의 동작, Game 진행 상황 등을 실시간으로 Server에 동기화한다.