• Journal of KIISE:Databases
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• v.36 no.6
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• pp.422-433
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• 2009

As an effort to secure Semantic Web, in this paper, we introduce an RDF access authorization model based on an ontology hierarchy and an RDF triple pattern. In addition, we apply the authorization model to RDF query validation for approved access authorizations. A subscribed SPARQL or RQL query, which has RDF triple patterns, can be denied or granted according to the corresponding access authorizations which have an RDF triple pattern. In order to efficiently perform the query validation process, we first analyze some primary authorization conflict conditions under RDF subsumption inference, and then we introduce an efficient query validation algorithm using the conflict conditions and Dewey graph labeling technique. Through experiments, we also show that the proposed validation algorithm provides a reasonable validation time and when data and authorizations increase it has scalability.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.19-26
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• 2019

In this paper, the classification performance of learning algorithms is compared for TAB digit recognition. The TAB digits that are segmented from TAB musical notes contain TAB lines and musical symbols. The labeling method and non-linear filter are designed and applied to extract fret digits only. The shift operation of the 4 directions is applied to generate more data. The selected models are Bayesian classifier, support vector machine, prototype based learning, multi-layer perceptron, and convolutional neural network. The result shows that the mean accuracy of the Bayesian classifier is about 85.0% while that of the others reaches more than 99.0%. In addition, the convolutional neural network outperforms the others in terms of generalization and the step of the data preprocessing.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.7-13
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• 2019

The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.139-146
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• 2004

A 2D/3D complex optical system and its vision inspection algerian is proposed and implemented as a single probe system for high speed, precise vision inspection of the solder pastes. One pass un length labeling algorithm is proposed instead of the conventional two pass labeling algorithm for fast extraction of the 2D shape of the solder paste image from the recent line-scan camera as well as the conventional area-scan camera, and the optical probe path generation is also proposed for the efficient 2D/3D inspection. The Moire interferometry-based phase shift algerian and its optical system implementation is introduced, instead of the conventional laser slit-beam method, for the high precision 3D vision inspection. All of the time-critical algorithms are MMX SIMD parallel-coded for further speedup. The proposed system is implemented for simultaneous 2D/3D inspection of 10mm${\times}$10mm FOV with resolutions of 10 ${\mu}{\textrm}{m}$ for both x, y axis and 1 ${\mu}{\textrm}{m}$ for z axis. Experiments conducted on several nBs show that the 2D/3D inspection of an FOV, excluding an image capturing, results in high speed of about 0.011sec/0.01sec, respectively, after image capturing, with $\pm$1${\mu}{\textrm}{m}$ height accuracy.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.646-654
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• 2007

In recent years, the use of text inserted into TV contents has grown to provide viewers with better visual understanding. In this paper, video text is defined as superimposed text region located of the bottom of video. Video text extraction is the first step for video information retrieval and video indexing. Most of video text detection and extraction methods in the previous work are based on text color, contrast between text and background, edge, character filter, and so on. However, the video text extraction has big problems due to low resolution of video and complex background. To solve these problems, we propose a method to extract text from videos using the Harris corner detector. The proposed algorithm consists of four steps: corer map generation using the Harris corner detector, extraction of text candidates considering density of comers, text region determination using labeling, and post-processing. The proposed algorithm is language independent and can be applied to texts with various colors. Text region update between frames is also exploited to reduce the processing time. Experiments are performed on diverse videos to confirm the efficiency of the proposed method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.859-865
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• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• The Journal of the Acoustical Society of Korea
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• v.16 no.3
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• pp.64-72
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• 1997

In this paper, study and experiments are performed for finding recognition unit fit which can be used in large vocabulary recognition system. Specifically, a phoneme that is currently used as recognition unit and a syllable in which Korean is well characterized are selected. From comparisons of recognition experiments, the study is performed whether a syllable can be considered as recognition unit of Korean recognition system. For report of an objective result of the comparison experiment, we collected speech data of a male speaker and processed them by hand-segmentation for phoneme boundary and labeling to construct speech database. And for training and recognition based on HMM, we used HTK (HMM Tool Kit) 2.0 of commercial tool from Entropic Co. to experiment in same condition. We applied two HMM model topologies, 3 emitting state of 5 state and 6 emitting state of 8 state, in Continuous HMM on training of each recognition unit. We also used 3 sets of PBW (Phonetically Balanced Words) and 1 set of POW(Phonetically Optimized Words) for training and another 1 set of PBW for recognition, that is "Speaker Dependent Medium Vocabulary Size Recognition." Experiments result reports that recognition rate is 95.65% in phoneme unit, 94.41% in syllable unit and decoding time of recognition in syllable unit is faster by 25% than in phoneme.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.4
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• pp.231-241
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• 2006

Creating controllable, responsive avatars is an important problem in computer games and virtual environments. Recently, large collections of motion capture data have been exploited for increased realism in avatar animation and control. Large motion sets have the advantage of accommodating a broad variety of natural human motion. However, when a motion set is large, the time required to identify an appropriate sequence of motions is the bottleneck for achieving interactive avatar control. In this paper, we present a novel method for training avatar behaviors from unlabelled motion data in order to animate and control avatars at minimal runtime cost. Based on machine learning technique, called Q-teaming, our training method allows the avatar to learn how to act in any given situation through trial-and-error interactions with a dynamic environment. We demonstrate the effectiveness of our approach through examples that include avatars interacting with each other and with the user.