• 한국정보처리학회논문지
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• 제6권9호
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• pp.2343-2349
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• 1999

최근에 개발되어 성공적으로 적용되고 있는 초기하분포 소프트웨어 신뢰성 성장 모델은 이 모델에서 중요한 역할을 하는 반응계수(sensitivity factor)를 추정 대상인 모수로 가정하고 있다. 본 논문은 먼저 디버깅과정의 무작위성을 반영하기 위해 반응계수를 이항분로를 하는 확률변수로 가정하여 초기하분포 신뢰성 성장 모델을 일반화한다. 이러한 일반화는 초기하분포 소프트웨어 신뢰성 성장 모델의 통계적 특성을 쉽게 파악할 수 있게 한다. 특히 일반화 된 모델의 모수를 최소자승법으로 추정하면 기존 모델에 최소자승법을 적용한 것과 같은 결과를 얻을 수 있음을 보이고, 더불어 최우추정치를 최소자승법으로 구하는 방법과 예측방법도 제시한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2001년도 추계학술발표논문집 (상)
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• pp.535-538
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• 2001

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical measurements to generate the enough number of training patterns. The more training patterns, the better the generalization of MLP is. The researches about the schemes of generating artificial training patterns and adding to the original ones have been progressed and gave me the motivation of developing VTG schemes in 1996. Virtual term is an estimated measurement, X(t+0.5) between X(t) and X(t+1), while the given measurements in the series are called actual terms. VTG (Virtual Tern Generation) is the process of estimating of X(t+0.5), and VTG schemes are the techniques for the estimation of virtual terms. In this paper, the alternative VTG schemes to the VTG schemes proposed in 1996 will be proposed and applied to multivariate time series prediction. The VTG schemes proposed in 1996 are called deterministic VTG schemes, while the alternative ones are called stochastic VTG schemes in this paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• 한국콘텐츠학회논문지
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• 제13권9호
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• pp.10-19
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• 2013

본 논문에서는 그래프 패턴 인식을 신속히 처리하기 위한 새로운 자성 센서의 개발과 인식 시스템을 제안하고자 하였다. 그래픽을 입력받아 세션화와 균형화를 수행하는데 있어서 특징점의 사전 처리를 선결 수행함으로써 인식 속도를 증강하고 선처리된 특징점을 이용하여 끝점, 굴곡점, 분기점의 특징점을 별도로 추출하지 않는 방법으로 조사하여 모음이나 자음의 부분패턴의 그래프 사전을 비교하는 간단한 구조해석과 인식을 도모하였다. 본 논문의 성능 비교를 위하여 사용자의 필기체를 사전에 등록 인식하고 입력 필기체를 비교 인식하여 Unicode로 변환시켜 비교한 결과 70%의 초기 인식률에서 누적 인공학습 지능 처리 결과 95%의 이상의 인식률을 보여주고 있다.

• 한국측량학회지
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• 제25권4호
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• pp.289-297
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• 2007

대축척 수치지도를 이용하여 소축척 수치지도를 제작하기 위해서는 일반화 방법이 사용된다. 이중 가장 많은 처리시간과 수작업을 요하는 것이 대상물의 연결공정이다. 국토지리정보원에서 사용하는 대상물 연결기법은 수치지도 Ver. 1.0을 기반으로 하였기 때문에 속성정보가 포함된 수치지도 Ver. 2.0에 적용시킬 경우 저조한 연결율과 많은 처리시간이 필요하다. 이에, 본 연구에서는 속성정보와 도형정보를 고려하여 수치지도 Ver. 2.0에 적합하도록 대상물 연결기법을 개선하였다. 개선된 기법을 적용한 결과 대상물의 연결율을 높이고 처리시간의 감소효과를 얻을 수 있었다.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.521-525
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• 2021

An essential area is the creation of a single digital market between the EU and Ukraine through information technology. Purpose: to investigate and analyze civil law relations in the field of adaptation of Ukrainian civil law to civil law regulations of the EU. The object of research: Ukrainian civil law and civil law of the EU. The subject of the study is civil law in the context of adaptation of civil law to the legislation of the EU. The following methods of scientific cognition were used during the research: semantic, historical, comparison, analysis and synthesis, generalization. The results of the study show that the harmonization of the legal system of Ukraine with EU law is caused by several goals: successful integration of Ukraine into the EU, legal reforms based on the positive example of EU countries, promoting access of Ukrainian enterprises to the EU market; attracting foreign investment, increasing the welfare of Ukrainian citizens. The adaptation includes three stages, the final of which is the preparation of an expanded program of harmonization of Ukrainian legislation with EU legislation. In the process of adaptation, it is important to take into account the legal history, tradition, features and mentality of Ukraine and before borrowing legal structures to analyze the feasibility of their application in the Ukrainian legal field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.632-657
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• 2022

Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2003년도 춘계학술대회논문집
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• pp.91-100
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• 2003

In this paper we investigate the role that warfare played In the formation of the network of alliances between sites that are associated with the formation of the state in the Valley of Oaxaca, Mexico. A model of state formation proposed by Marcos and Flannery (1996) is used as the basis for an agent-based simulation model. Agents reside in sites and their actions are constrained by knowledge extracted from the Oaxaca Surface Archaeological Survey (Kowalewski 1989). The simulation is run with two different sets of constraint rules for the agents. The first set is based upon the raw data collected in the surface survey. This represents a total of 79 sites and constitutes a minimal level of warfare (raiding) in the Valley. The other site represents the generalization of these constraints to sites with similar locational characteristics. This set corresponds to 987 sites and represents a much more active role for warfare in the Valley. The rules were produced by a data mining technique, Decision Trees, guided by Genetic Algorithms. Simulations were run using the two different rule sets and compared with each other and the archaeological data for the Valley. The results strongly suggest that warfare was a necessary process in the aggregations of resources needed to support the emergence of the state in the Valley.

• 한국과학교육학회지
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• 제26권5호
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• pp.637-645
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• 2006

This study reviewed and discussed the nature of scientific hypothesis described in philosophy, the philosophy of science, science, and science education. In these descriptions, a hypothesis was defined as one of five types: hypothesis as an assumption, hypothesis as a prediction, hypothesis as a tentative explanation, hypothesis as a tentative law, and hypothesis as a tentative causal explanation. Most scholars agreed that a hypothesis is a proposition or a set of propositions proposed as an explanation for an observed situation. In this view, a hypothesis is a possible answer to or an explanation of a question that accounts for all the observed facts. Also, it is a statement that explains why things happen in nature or an explanation for an observation that can be tested. In the five types of hypothesis meanings, a tentative explanation includes a tentative law and a tentative causal explanation. However, tentative laws are not explanation but description which are general statements drawn from specific experiences by way of a process known as induction. A number of studies also have distinguished hypothesis from assumption, tentative explanation, tentative law, and prediction. Therefore, a hypothesis is concluded to be a proposition or a set of propositions proposed as a tentative causal explanation for an observed situation.