• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.70-85
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• 2021

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.518-520
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• 2006

Due to the rapid popularization of mobile multimedia devices and the Internet as well as the realization of high-speed data transmission and large-volume data recording media, high quality content distribution and ubiquitous information services are making progress and a new type of information distribution and network sharing service has gradually emerged into the market. It is capable of utilizing terabyte sized home servers also in private homes. Under these circumstances, in distribution of content over shared networks, it is crucial to establish DRM (Digital Rights Management) technologies to protect the content from illegal copying and usage. A truly successful DRM system must be built on open worldwide specifications and provide maximum interoperability and user acceptance. An open interoperability of DRM is able to construct highly expandable PKI based DRM, targeting usage between systems, considering the expansion of recent content distribution services and clients This document gives protocol specifications for the exchange of rights information between the DRM module, description of specifications for rights information and encrypted content formats.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.427-442
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• 2022

MP3 is one of the most widely used file formats for encoding and representing audio data. One of the reasons for this popularity is their significant ability to reduce audio file sizes in comparison to other encoding techniques. Additionally, other reasons also include ease of implementation, its availability and good technical support. Steganography is the art of shielding the communication between two parties from the eyes of attackers. In steganography, a secret message in the form of a copyright mark, concealed communication, or serial number can be embedded in an innocuous file (e.g., computer code, video film, or audio recording), making it impossible for the wrong party to access the hidden message during the exchange of data. This paper describes a new steganography algorithm for encoding secret messages in MP3 audio files using an improved least significant bit (LSB) technique with high embedding capacity. Test results obtained shows that the efficiency of this technique is higher compared to other LSB techniques.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.

• Smart Media Journal
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• v.3 no.1
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• pp.28-32
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• 2014

It is practical nowadays to automate data recording in order to prevent loss and tampering of records. There are existing technologies that satisfy this needs and one of them is wireless sensor networks (WSN). Wireless body sensor networks (WBSN) are wireless networks and information-processing systems which are deployed to monitor medical condition of patients. In terms of performance, WBSNs are restricted by energy, and communication between nodes. In this paper, we focused in improving the performance of communication to achieve less energy consumption and to save power. The main idea of this paper is to prioritize nodes that exhibit a sudden change of vital signs that could put the patient at risk. Cluster head is the main focus of this study in order to be effective; its main role is to check the sent data of the patient that exceeds threshold then transfer to the sink node. The proposed scheme implemented added a time-based protocol to sleep/wakeup mechanism for the sensor nodes. We seek to achieve a low energy consumption and significant throughput in this study.

• Journal of Sasang Constitutional Medicine
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• v.33 no.4
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• Publications of The Korean Astronomical Society
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• v.19 no.1
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• pp.83-92
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• 2004

In this paper, we introduce the development of the large storage system in order to record the observed space radio signal in the Korean VLBI Network(KVN) with high-speed. The KVN is the Very Long Baseline Interferometery(VLBI) to observe the birth of star, the structure of space by constructing radio telescope with diameter 21m at the Seoul, Ulsan, Jeju from 2001 to 2007 years. To do this, Korea Astronomy Observatory joined the international consortium for developing the high-speed large storage system(Mark 5), which is developed by MIT Haystack observatory. The Mark 5 system based on hard disk has to record up to 1 Gbps the observed space radio signal. The main features of Mark 5 system are as follows; First it is able to directly record the input data to the hard disk without PC1(Peripheral Component Interconnect) internal bus, and the second, it has two hard disk banks, which are able to hot-swap ATA/IDE type very cheap up to 1 Gbps recording and playback. The third is that it follows the international VLBI standard interface hardware(VSI-H). Therefore it can be connect directly the VSI-H type system at the input/output. Finally it also supports e- VLBI(Electronic-VLBI) through the standard Gigabits Ethernet connection.

• The KIPS Transactions:PartC
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• v.9C no.1
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• pp.9-16
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• 2002

As existing analog video surveillance systems could save and retrieve data only in a limited space within short distance, it had many constraints in developing into various application systems. However, on the back of development of the Internet and computer technologies, digital video surveillance systems can be controlled from a remote location by web browser without space limits. Moreover, data compression and management technologies with Index Search algorithm make it possible to efficiently handling, storing, and retrieving a large amount of data and further motion detection algorithm enhances a recording speed and efficiency for a practical application, that is, a practical remote recordable video surveillance system using our efficient algorithms as mentioned, called WebCam. The WebCam server system can intelligently record and save video images digitized through efficient database management, monitor and control cameras in a remote place through user authentication, and search logs.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.37.2-37.2
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• 2008

Korea-Japan Joint VLBI Correlator (KJJVC), to be used for Korean VLBI Network (KVN) in Korea Astronomy & Space Science Institute (KASI), is a high-speed calculator that outputs the correlation results in the maximum speed of 1.4GB/sec.To receive and record this data keeping up with this speed and with no loss, the design of the software running on the data archive system for receving and recording the output data from the correlator is very important. But, the simple kind of programming using just single thread that receives data from network and records it by turns, can cause a bottleneck effect while processing high speed data and a probable data loss, and cannot utilize the merit of hardwares supporting multi core or hyper threading, or operating systems supporting these hardwares. In this talk we summarize the design of the data transfer software for KJJVC and high speed, large capacity data archive system using general socket programming and multi threading techniques, and the pre-BMT(Bench Marking Test) results from the tests of the storage product providers' proposals using this software.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.8
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• pp.132-141
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• 2009

In this paper, we describe finger force estimation from surface electromyogram (sEMG) data for intuitive and delicate force control of robotic devices such as exoskeletons and robotic prostheses. Four myoelectric sites on the skin were found to offer favorable sEMG recording conditions. An artificial neural network (ANN) was implemented to map the sEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using recorded sEMG signals from the selected myoelectric sites of three subjects in real-time. In addition, we discussed performance of force estimation results related to the length of the muscles. This work may prove useful in relaying natural and delicate commands to artificial devices that may be attached to the human body or deployed remotely.