• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.545-551
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• 2008

The MOCT(Ministry of Construction & Transportation) gave a order named as "The guideline for computerization and application of geotechnical investigation result" to an affiliated organization in March 2007. Today, pilot project of construction of geotechnical information database is in process to be stable for its system after applying this guideline, and discipline how to input investigated data for related users. We have developed standard for geotechnical investigation result-format, metadata for distribution of geotechnical information and to coordinate based on world geodetic system. Also, We had a introduce to status with respect to use the input system, collect a statistics of input contents. At a result, improvement items of input system is proposed. It was analyzed that most users put to practical use easily as a result of education for making use of on the spot of the developed GIIS. But There were problems with the GIIS as well as complexity of metadata formation, such as error of moving part of information window, and a part of recognition error of install program in accordance with computer OS circumstances. Particularly, to improve some parts of GIIS is needed, because of use of or KNHC (Korea National Housing Corporation)-specific format and difference of input process followed by MOCT's guideline. In this study, it is planning to make up for occurred problems, and improvements when operating and managing the Geotechnical Information DB center in 2008.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.91-99
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• 2010

Recently, recognizing user resource for personalized service has been needed in digital content service fields. Especially, to analyze user taste, recommend music and service music related information need recognition of user music file in case of online music service. Music related information service is offered through recognizing user music based on tag information. Recognition error has grown by weak points like changing and removing of tag information. Techniques of content based user music recognition with music signal itself are researched for solving upper problems. In this paper, we propose user music recognition on the internet by extracted feature from music signal. Features are extracted after suitable preprocessing for structure of content based user music recognition. Recognizing on music server consist of feature form are progressed with extracted feature. Through this, user music can be recognized independently of tag data. 600 music was collected and converted to each 5 music qualities for proving of proposed recognition. Converted 3000 experiment music on this method is used for recognition experiment on music server including 300,000 music. Average of recognition ratio was 85%. Weak points of tag based music recognition were overcome through proposed content based music recognition. Recognition performance of proposed method show a possibility that can be adapt to online music service in practice.

• Journal of the Korean Society of Radiology
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• v.9 no.4
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• pp.227-234
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• 2015

In this paper, we studied possibility about application for CAD on diffuse liver disease through pixel texture analysis parameters(average gray level, skewness, entropy) which based statistical property brightness histogram and image analysis using brightness difference liver and kidney parenchyma. The experiment was set by ROI ($50{\times}50$ pixels) on liver ultrasound images.(non specific, fatty liver, liver cirrhosis) then, evaluated disease recognition rates using 4 types pixel texture analysis parameters and brightness gap liver and kidney parenchyma. As a results, disease recognition rates which contained average brightness, skewness, uniformity, entropy was scored 100%~96%, they were high. In brightness gap between liver and kidney parenchyma, non specific was $-1.129{\pm}12.410$ fatty liver was $33.182{\pm}11.826$, these were shown significantly difference, but liver cirrhosis was $-1.668{\pm}10.081$, that was somewhat small difference with non specific case. Consequently, pixel texture analysis parameter which scored high disease recognition rates and CAD which used brightness difference of parenchyma are very useful for detecting diffuse liver disease as well as these are possible to use clinical technique and minimize reading miss. Also, it helps to suggest correct diagnose and treatment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.981-989
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• 2015

In a log likelihood ratio(LLR) calculation of the detected symbol, multiple-input multiple-output(MIMO) system applying an optimal or suboptimal algorithm such as a maximum likelihood(ML) detection, sphere decoding(SD), and QR decomposition with M-algorithm Maximum Likelihood Detection(QRM-MLD) suffers from exponential complexity growth with number of spatial streams and modulation order. In this paper, we propose a LLR calculation method with very low complexity in the QRM-MLD based symbol detector for a high order modulation based $N_T{\times}N_R$ MIMO system. It is able to approach bit error rate(BER) performance of full maximum likelihood detector to within 1 dB. We also analyze the BER performance through computer simulation to verify the validity of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.5 s.335
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• pp.55-62
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• 2005

In this paper, we propose an efficient symbol detection algorithm for space-frequency OFDM (SF-OFDM) transmit diversity scheme and present the implementation results of the SF-OFDM WLAN baseband processor with the proposed algorithm. When the number of sub-carriers in SF-OFDM scheme is small, the interference between adjacent sub-carriers may be generated. The proposed algorithm eliminates this interference in a parallel manner and obtains a considerable performance improvement over the conventional detection algorithm. The bit error rate (BER) performance of the proposed detection algorithm is evaluated by the simulation. In the case of 2 transmit and 2 receive antennas, at $BER=10^{-4}$ the proposed algorithm obtains about 3 dB gain over the conventional detection algorithm. The packet error rate (PER), link throughput, and coverage performance of the SF-OFDM WLAN with the proposed detection algorithm are also estimated. For the target throughput at $80\%$ of the peak data rate, the SF-OFDM WLAN achieves the average SNR gain of about 5.95 dB and the average coverage gain of 3.98 meter. The SF-OFDM WLAN baseband processor with the proposed algorithm was designed in a hardware description language and synthesized to gate-level circuits using 0.18um 1.8V CMOS standard cell library. With the division-free architecture, the total logic gate count for the processor is 945K. The real-time operation is verified and evaluated using a FPGA test system.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.35-42
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• 2020

In this study, we propose a crack detection method using limited data with a U-Net based image inpainting technique that is a modified unsupervised anomaly detection method. Concrete cracking occurs due to a variety of causes and is a factor that can cause serious damage to the structure in the long term. In general, crack investigation uses an inspector's visual inspection on the concrete surfaces, which is less objective in judgment and has a high possibility of human error. Therefore, a method with objective and accurate image analysis processing is required. In recent years, the methods using deep learning have been studied to detect cracks quickly and accurately. However, when the amount of crack data on the building or infrastructure to be inspected is small, existing crack detection models using it often show a limited performance. Therefore, in this study, an unsupervised anomaly detection method was used to augment the data on the object to be inspected, and as a result of learning using the data, we confirmed the performance of 98.78% of accuracy and 82.67% of harmonic average (F1_Score).

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.25-27
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• 2011

본 논문에서는 경제의 글로벌화를 반영하는 세계적인 현상인 국제회계기준 채택에 대하여 회계 관련사항을 내재하고 있는 기업의 전사적 관리 소프트웨어인 국산 ERP 시스템에 대하여 세금처리의 투명성을 향상시킬 수 있는 방법론적 모델을 해외 ERP의 장점을 통하여 연구한다. 국제회계기준은 기업요소에서 가장 중요한 자본의 국제적인 이동이 기본적 사항으로 기업의 소재지에 대한 국가표시와 관계없이 재무제표의 정보가 투명하고 비교 가능하도록 국제적으로 단일한 회계기준이 사용을 요구한다. 이러한 환경 변화에 따라 국제회계기준(International Financial Reporting Standards: IFRS)의 필요성과 중요성이 확대되어지고 있으며 국제회계기준위원회(International Accounting Standards Board: IASB)의 영향력까지 강화되어지고 있다. 본 논문은 국제회계기준에 맞춘 회계처리 부분의 투명성 향상 모델을 연구하기 위하여 국내에서 많이 사용되고 있는 국산 ERP 소프트웨어 프로그램에 대하여 국제회계기준과 상반되는 현상을 유발하는 기준이 되는 부분을 상호 보완할 수 있는 해외 ERP 소프트웨어 프로그램을 기반으로 기업 시스템을 분석, 구현한다. 국산 ERP 시스템의 국제회계기준 처리와 관련된 단점은 송장의 수정, 삭제가 용이하며 수정, 삭제 후 이력이 남지 않는 부분이다. 이 부분은 국제회계기준에서는 신뢰성에 대한 중대한 문제를 유발하므로 외산 ERP패키지의 장점인 수정과 삭제 단계가 계층적이며 수정, 삭제를 하더라도 필수적으로 이력전체가 저장되는 시스템을 연구하며 특히 세금처리 부분이 상이한 점을 보완하기 위한 모듈을 추가한다. 수정, 삭제에 대한 이력관리는 담당자의 전문적 능력평가와 동시에 실수와 오류부분에 대한 통계를 통하여 관리의 향상성을 추구하여 투명성이 향상된 모델 구축에 활용할 수 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.901-906
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• 2019

UAV (Unmanned Aerial Vehicle) is widely used in various areas such as civil and military applications including entertainment industries. Among them, UAV based communication system is also one of the important application areas. Relays have been received much attention in communication system due to its benefits of performance enhancement and coverage extension. In this paper, we investigate UAVs as relays especially focusing on physical layer. First, we introduce the research on UAV application for the relays, then the basic performance of relay networks in dual-hop communication system is analyzed by adopting decode-and-forward (DF) relaying protocol. The performance is represented using symbol error rate (SER) and UAV channels are applied by assuming asymmetric environments. Based on the performance analysis, we discuss performance enhancement issues by considering physical layer.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.71-80
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• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

• Journal of the Korean Society for Marine Environment & Energy
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• v.4 no.4
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• pp.61-69
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• 2001

Recently, according to the rapid development of computer and electronic technology, most crude oil carriers adopt automated cargo handling system. But an excessive automation makes system so complicate that it could increase the Possibility of accidents due to human error. Although a cargo handling process is done by an expert, the potential of accidents by human factor lies through the whole cargo handling procedure and the current automated system lacks of the functionality to prevent a mis-operation and diagnose the abnormal status of the system. Because the oil concerned accident could be almost a disaster, the primary goal of system development should not be a fully automated system but be a perfectly safe system. This paper deals with the analysis and design of an expert system which can provide mariner with the operational guidance and the facility of crisis management by monitoring system's abnormal condition and human's mis-operation.