• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.87-95
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• 2002

In this paper, we proposed a fast multi-resolution motion estimation(MRME) algorithm. This algorithm exploits the half-pixel accuracy motion estimation(HPAME) for exact motion vectors in the baseband and block classification for the reduction of bit amounts and computational loads. Generally, as the motion vector in the baseband are used as initial motion vector in the high frequency subbands, it has crucial effect on quality of the motion compensated image. For this reason, we exploit HPAME in the motion estimation for the baseband. But HPAME requires additional bit and computational loads so that we use block classification for the selective motion estimation in the high frequency subbands to compensate these problems. In result, we could reduce the bit rate and computational load at the similar image quality with conventional MRME. The superiority of the proposed algorithm was confirmed by the computer simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.319-327
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• 2012

In this paper, we present a sign language recognition model which does not use any wearable devices for object tracking. The system design issues and implementation issues such as data representation, feature extraction and pattern classification methods are discussed. The proposed data representation method for sign language patterns is robust for spatio-temporal variances of feature points. We present a feature extraction technique which can improve the computation speed by reducing the amount of feature data. A neural network model which is capable of incremental learning is described and the behaviors and learning algorithm of the model are introduced. We have defined a measure which reflects the relevance between the feature values and the pattern classes. The measure makes it possible to select more effective features without any degradation of performance. Through the experiments using six types of sign language patterns, the proposed model is evaluated empirically.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.15-23
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• 2016

As various IoT devices appear recently, Cloud Services such as DropBox, Amazon S3, Microsoft Azure Storage, etc are widely use for data sharing across the devices. Although, cryptographic algorithms like AES is prevalently used for data security, there is no mechanisms to allow selectively and flexibly use wider spectrum of lightweight cryptographic algorithms such as LEA, SEED, ARIA. With this, IoT devices with lower computation power and limited battery life will suffer from overly expensive workload and cryptographic operations are slower than what is enough. In this paper, we designed and implemented a CloudGate that allows client programs of those cloud services to flexibly select a cryptographic algorithms depending on the required security level. By selectively using LEA lightweight algorithms, we could achieve the cryptographic operations could be maximum 1.8 faster and more efficient than using AES.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2313-2321
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• 2014

A concentration meter is widely used at purification plants, sewage treatment plants and waste water treatment plants to sort and transfer high concentration sludge and to control the amount of chemical input. This study has been prepared for improving efficiency of operation on sludge processes and to establish a basic for factory automation by accuracy improvement and problem solution of sludge concentration meter. The concentration meter's accuracy and stability is improved by applying multi-beam sensors and minimum deviation linear average filtering. Furthermore maintenance without cut-off of water in sludge operation is possible by detachable sensors. The performance of multi-beam concentration meter has been variously verified by the pilot plant experiment.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.525-536
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• 2004

Gene expression profile is numerical data of gene expression level from organism, measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify disease with gene expression profile. Because all genes are not related to disease, it is needed to select related genes that is called feature selection, and it is needed to classify selected genes properly. This paper Proposes GA based method for searching optimal ensemble of feature-classifier pairs that are composed with seven feature selection methods based on correlation, similarity, and information theory, and six representative classifiers. In experimental results with leave-one-out cross validation on two gene expression Profiles related to cancers, we can find ensembles that produce much superior to all individual feature-classifier fairs for Lymphoma dataset and Colon dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3A
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• pp.272-280
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• 2009

Measurement of the background radio noise is very important process being used in survey of radio noise environment, calculating the threshold level for the frequency occupancy measurement and so forth. First step of background radio noise measurement is to select the sample sub-band which is mostly dominated by the background white Gaussian noise (WGN) within the target band. The second step is to carry out the main measurement of radio noise on this selected sample sub-band for the representative value of the noise power. In this paper, a method for selection of sample sub-band for the effective background radio noise measurement using SVD is proposed under the assumption that background radio noise is WGN. The performance of the proposed method is compared with that of the APD method which is widely used for the same purpose. Simulation results are shown to demonstrate the high performance of the proposed method in comparison with the existing APD method.

• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.105-115
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• 2018

The descriptive-type question is appropriate for deep thinking ability evaluation, but it is not easy to grade. Since, even though same grading criterion, the graders produce different scores, we need the objective evaluation system. However, the system needs the Korean analysis. As the descriptive-type answering is described with the compound sentence, the system has to analyze the compound sentence. This paper develops the Korean syntactic-semantic analysis system for compound sentence and evaluates performance of the system. This system selects the modifiee of the word phrase using syntactic-semantic constraint and semantic dictionary. The 93% accurate rate shows that the system is effective. This system will be utilized in descriptive-type grading and Korean processing.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.1-7
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• 2021

In this paper, optimized media data retrieval and transmission based on the Hybrid CDN/P2P architecture and selective storage through user's prediction of requestability enable seamless data transfer to users and reduction of unnecessary traffic. We also propose a new media management method to minimize the possibility of transmission delay and packet loss so that media can be utilized in real time. To this end, we construct each media into logical segments, continuously compute weights for each segment, and determine whether to store segment data based on the calculated weights. We also designate scattered computing nodes on the network as local groups by distance and ensure that storage space is efficiently shared and utilized within those groups. Experiments conducted to verify the efficiency of the proposed technique have shown that the proposed method yields a relatively good performance evaluation compared to the existing methods, which can enable both initial latency reduction and seamless transmission.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.553-559
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• 2020

In order to prevent subsidence in urban areas, which is a major cause of damage to aging underground facilities, an integrated underground space map is being produced for systematic management of underground facilities. However, there is a problem of delaying the update time because an unupdated underground facility object is included in the process of updating the underground space integrated map. In this paper, we proposed a method to shorten the update time of the integrated map by selecting only the updated objects required for the update process of the underground space integrated map based on the central point of the underground facilities. Through the comparison of the centroid, the number of search targets is greatly reduced to shorten the search speed, and the distance of the actual location values between the two objects is calculated whether or not the objects are the same. The proposed method shows faster performance as the number of data increases, and the updated object can be reflected in the underground space integrated map about four times faster than the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.355-363
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• 2020

In this paper, we propose a deep learning training method for accurately classifying Kuzushiji-MNIST and Kuzushiji-49 datasets for ancient and medieval Japanese characters. We analyze the latest convolutional neural network networks through experiments to select the most suitable network, and then use the networks to select the number of training to classify Kuzushiji-MNIST and Kuzushiji-49 datasets. In addition, the training is conducted with high accuracy by applying learning methods such as Mixup and Random Erase. As a result of the training, the accuracy of the proposed method can be shown to be high by 99.75% for MNIST, 99.07% for Kuzushiji-MNIST, and 97.56% for Kuzushiji-49. Through this deep learning-based technology, it is thought to provide a good research base for various researchers who study East Asian and Western history, literature, and culture.