• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.541-547
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• 2019

Recent developments in hacking technology are continuing to increase the number of new security vulnerabilities. Approximately 80,000 new vulnerabilities have been registered in the Common Vulnerability Enumeration (CVE) database, which is a representative vulnerability database, from 2010 to 2015, and the trend is gradually increasing in recent years. While security vulnerabilities are growing at a rapid pace, responses to security vulnerabilities are slow to respond because they rely on manual analysis. To solve this problem, there is a need for a technology that can automatically detect and patch security vulnerabilities and respond to security vulnerabilities in advance. In this paper, we propose the technology to extract the features of the vulnerability-discovery target binary through complexity analysis, and select a vulnerability-discovery strategy suitable for the feature and automatically explore the vulnerability. The proposed technology was compared to the AFL, ANGR, and Driller tools, with about 6% improvement in code coverage, about 2.4 times increase in crash count, and about 11% improvement in crash incidence.

• Journal of IKEEE
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• v.22 no.4
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• pp.933-940
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• 2018

In this paper, we propose a multi-block deep learning structure for improving face recognition rate. The recognition structure of the proposed deep learning consists of three steps: multi-blocking of the input image, multi-block selection by facial feature numerical analysis, and perform deep learning of the selected multi-block. First, the input image is divided into 4 blocks by multi-block. Secondly, in the multi-block selection by feature analysis, the feature values of the quadruple multi-blocks are checked, and only the blocks with many features are selected. The third step is to perform deep learning with the selected multi-block, and the result is obtained as an efficient block with high feature value by performing recognition on the deep learning model in which the selected multi-block part is learned. To evaluate the performance of the proposed deep learning structure, we used CAS-PEAL face database. Experimental results show that the proposed multi-block deep learning structure shows 2.3% higher face recognition rate than the existing deep learning structure.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.1
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• pp.57-81
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• 2019

An ontology is a complex structure dictionary that defines the relationship between terms and terms related to specific knowledge in a particular field. There have been attempts to construct various ontologies in Korea and abroad, but there has not been a case in which a large scale crime investigation record is constructed as an ontology and a service is implemented through the ontology. Therefore, this paper describes the process of constructing an ontology based on information extracted from instrusion theft field of unstructured data, a crime investigation document, and implementing an ontology-based search service and a crime spot recommendation service. In order to understand the performance of the search service, we have tested Top-K accuracy measurement, which is one of the accuracy measurement methods for event search, and obtained a maximum accuracy of 93.52% for the experimental data set. In addition, we have obtained a suitable clue field combination for the entire experimental data set, and we can calibrate the field location information in the database with the performance of F1-measure 76.19% Respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.29-33
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• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.271-271
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• 2023

수문학적 기준지점(HRS, Reference Hydrological Station)은 유량의 변동성의 장기적인 추세를 파악하기 위해 고품질의 자료를 생산하는 관측소를 의미한다. 선진외국의 경우 운영목적과 수문학적 기준지점의 정의는 조금씩 다르지만 유사한 개념의 관측소를 운영하고 있다. 호주의 경우 기후변화에 따른 장기간의 수자원 부존량의 변화를 예측하기 위한 모니터링 지점으로 정의하며, 미국의 경우 시간에 따른 수문학적 특성의 자연적인 변화 및 인간의 활동에 따른 수문환경의 변화에 대한 연구를 위한 기준값을 제공하기 위해 참조지점(HBN, Hydrological Benchmark Network)의 자료를 제공하기 위해 운영한다. 영국은 기후변화에 따른 유역의 수문학적 응답을 조사할 목적으로 참조지점(RHN, Reference Hydrological Network)을 운영하고 있으며, 주로 자연유역에 설치하여 운영하고 있다. WMO는 2006년 '기후연구를 위한 적절한 유량관측소'를 선정해 줄 것을 회원국에 요청하고, 관련 자료의 데이터베이스를 독일의 GRDC(Global Runoff Data Centre)에 수집하고 있다. 국외의 경우 '자연에 가까운 유역특성을 갖는 하천 유량관측망 중 양질의 자료를 보유하고 있는 관측소'를 고려하여 수문학적 기준지점을 선정한다. 하지만 우리나라의 경우 장기간의 유량자료를 보유하고 있는 관측소가 상대적으로 부족하고, 장기간의 유량자료를 보유한 지점 또한 홍수예보, 댐 운영 등 물관리 업무에 직접 활용하기 위해 대하천의 본류 중심으로 자료를 생산하고 있다. 따라서 현재를 기준으로 국제적으로 통용되는 기준에 부합하는 기준관측소를 선정하는 것은 곤란한 상황으로 미래에 수문학적 기준지점이 될 수 있는 관측소를 선정하여 장기간 모니터링을 통해 기준관측소를 확대해 나갈 필요가 있다. 본 연구에서는 국외의 수문학적 기준관측소 선정기준을 비교 검토하여 우리나라 실정에 맞는 기준관측소 선정기준을 개발하였다. 선정 기준은 ① 유역의 개발정도, ② 댐·저수지 등 인위적인 조절 정도, ③ 취수량 또는 방류량 등 유역간의 물 이동, ④ 유량자료의 보유기간 및 정확도 등을 고려하여 기준을 설정하였다. 또한 기준지점의 선정을 위한 절차를 ① 수위관측소 사전목록의 작성, ② 관측소 정보 분석(유역특성, 시계열자료 등), ③ 수문학적 기준관측소 후보 선정, ④ 유관기관 및 전문가 검토를 통한 우선순위 선정 등 4단계로 구분하여 제시하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.743-750
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• 2008

Due to unusual climate change and global warming, drought and flood happen frequently not only in Korea but also in all over the world. It leads to the serious damages and injuries in urban areas as well as rural areas. Since the concentration time is short and the flood flows increase urgently in urban stream basin, the chances of damages become large once heavy storm occurs. A decision support system for flood forecasting and warning in urban stream is developed as an alternative to alleviate the damages from heavy storm. It consists of model base management system based on ANFIS (Adaptive Neuro Fuzzy Inference System), database management system with real time data building capability and user friendly dialog generation and management system. Applying the system to the Tanceon river basin, it can forecast and warn the stream flows from the heavy storm in real time and alleviate the damages.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.801-809
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• 2023

Road construction projects have a long duration so cost overruns and schedule delays are occurred. However, performance assessment system that can manage and prepare for this in advance is insufficient. In addition, road construction are affected by many factors during under construction. Therefore it is necessary to conduct performance assessment considering the characteristics of roads and prepare for similar projects in the future. The purpose of this study is to provide a framework to evaluate construction phase performance and present a performance management plan using road construction information. Also, This study conducted time adjustment between the start and the finish of the project and developed performance metrics based on absolute and relative indicator. This study analyzed the cost, schedule, and changes of the road project construction process, showing the possibility of advancement of performance assessment and how to use it when planning new road construction projects.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.65-73
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• 2024

This paper aims to identify the major research trends in organizational effectiveness over the past 20 years. For this purpose, SCOPUS, an international academic database provided by Elsevier, was used to identify research trends in organizational effectiveness over the past 24 years (2000~2023). According to the frequency analysis, there were 2,789 cases of organizational, 2,714 cases of effectiveness, 850 cases of management, 689 cases of performance, 632 cases of organizations, and 597 cases of leadership. Trend analysis. While effectiveness and organizational have been consistently researched, the trends of leadership and management have been declining in recent years. LDA analysis shows that effectiveness and organizational are important topics. This shows that it is important to be able to predict the future when it is difficult to predict the future. The results of this study can be used as a guide for companies to establish organizational management at a strategic level and improve organizational effectiveness.