• Journal of Computing Science and Engineering
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• 제8권2호
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• pp.65-77
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• 2014

To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

• 한국IT서비스학회지
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• 제3권2호
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• pp.9-23
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• 2004

Interest about Information infrastructure construction is enlarged socially according to arrival of information age, and various information systems are constructed for efficient business processing, customer service in public sector. According to subjective method for performance improvement for information system of public sector and engine that propel information system construction because it is no definite hardware sizing guidelines for information system caterer, is calculating resource volume of information system. It is situation that problem of excess of scale or reduce sizing is happening and is causing various kind of problems that is waste of information budget and service decline thereby. In this research, we proposed hardware sizing framework for information system that is applied to pubic sector.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1345-1357
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• 2017

A prototype selection method chooses a small set of training points from a whole set of class data. As the data size increases, the selected prototypes play a significant role in covering class regions and learning a discriminate rule. This paper discusses the methods for selecting prototypes in a classification framework. We formulate a prototype selection problem into a set covering optimization problem in which the sets are composed with distance metric and predefined classes. The formulation of our problem makes us draw attention only to prototypes per class, not considering the other class points. A training point becomes a prototype by checking the number of neighbors and whether it is preselected. In this setting, we propose a greedy algorithm which chooses the most relevant points for preserving the class dominant regions. The proposed method is simple to implement, does not have parameters to adapt, and achieves better or comparable results on both artificial and real-world problems.

• 대한수학회지
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• 제60권2호
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• pp.341-358
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• 2023

Our aim is to study the properties of Fischer-Marsden conjecture and Ricci-Bourguignon solitons within the framework of generalized Sasakian-space-forms with 𝛽-Kenmotsu structure. It is proven that a (2n + 1)-dimensional generalized Sasakian-space-form with 𝛽-Kenmotsu structure satisfying the Fischer-Marsden equation is a conformal gradient soliton. Also, it is shown that a generalized Sasakian-space-form with 𝛽-Kenmotsu structure admitting a gradient Ricci-Bourguignon soliton is either ψ∖T^k × M^2n+1-k or gradient 𝜂-Yamabe soliton.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.116-122
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• 2023

Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4968-4986
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• 2017

In this paper, we primarily address the difficulty of automatic generation of a plausible depth map from a single image in an unstructured environment. The aim is to extrapolate a depth map with a more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Our technique, which is fundamentally based on a preexisting DepthTransfer algorithm, transfers depth information at the level of superpixels. This occurs within a framework that replaces a pixel basis with one of instance-based learning. A vital superpixels feature enhancing matching precision is posterior incorporation of predictive semantic labels into the depth extraction procedure. Finally, a modified Cross Bilateral Filter is leveraged to augment the final depth field. For training and evaluation, experiments were conducted using the Make3D Range Image Dataset and vividly demonstrate that this depth estimation method outperforms state-of-the-art methods for the correlation coefficient metric, mean log10 error and root mean squared error, and achieves comparable performance for the average relative error metric in both efficacy and computational efficiency. This approach can be utilized to automatically convert 2D images into stereo for 3D visualization, producing anaglyph images that are visually superior in realism and simultaneously more immersive.

• 한국정보통신학회논문지
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• 제26권2호
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• pp.271-277
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• 2022

PBFT(Practical Byzantine Fault Tolerant)는 분산 네트워크 환경에서 비의도적·의도적 결함을 해결하여 합의를 달성할 수 있는 합의 알고리즘으로 높은 성능과 절대적 최종성을 보장할 수 있다. 하지만 합의 과정에서 반복적으로 발생하는 메시지 브로드캐스팅으로 인해 네트워크의 규모가 증가할수록 네트워크 부하도 커진다. PBFT 알고리즘의 특성상 소규모·프라이빗 블록체인에는 적합하지만, 대규모·퍼블릭 블록체인에 적용하기엔 한계가 있다. PBFT는 블록체인 네트워크의 성능에 영향을 끼치기 때문에 산업에서는 PBFT가 제품 및 서비스에 적합한지 테스트할 수 있어야 하며, 학계에서는 PBFT 성능 향상 연구를 위한 통일된 평가지표와 평가 기술이 필요하다. 본 논문에서는 PBFT 계열 합의 알고리즘을 평가할 수 있는 정량적 지표와 평가 프레임워크에 대해 연구한다. 또한 제안한 PBFT 평가 프레임워크를 사용하여 PBFT의 처리량, 지연시간, 내결함성을 평가한다.

• 경영정보학연구
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• 제14권1호
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• pp.1-19
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• 2012

일반적으로 대규모 정보시스템 개발 프로젝트는 장기간 진행되는 특징이 있어, 프로젝트의 위험관리에 대한 관심이 특히 더 중요하다. 특히 IS 프로젝트는 소프트웨어 개발 생명주기 중 이행단계에서 오픈을 위한 진행상태 점검 및 컷오버 진행여부에 대한 의사결정을 하는 작업활동을 가지게 되는데 대규모 IS의 경우 오픈 후 그 파급효과가 대체로 크기 때문에 컷오버 의사결정을 보다 신중히 내려야 할 필요가 있다. 이것은 프로젝트를 오픈 하기 위한 매우 중요한 작업이지만 기존 프로젝트들의 컷오버 의사결정은 대부분 다양한 세부영역별 지표로 관리하기 보다는 시스템이행, 어플리케이션이행, 데이터이행 관점에서 단순히 이행의 정상 여부에만 초점을 두고 관리되어 실질적인 오픈에 대한 객관적인 기준으로 삼기에는 부족한 면이 있었다. 이에 본 연구에서는 컷오버 의사결정 관리 지표를 기능완성도, 비기능완성도, 이행리허설 등 컷오버 단계에 직접적으로 연관이 있는 실질적인 상세 항목으로 구성된 지표를 제시하고 이를 적용한 실제 사례를 통하여 그 효과를 확인하고자 한다.

• Journal of Electrical Engineering and Technology
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• 제9권5호
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• pp.1739-1745
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• 2014

Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naive Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.

• Geomechanics and Engineering
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• 제18권6호
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• pp.661-668
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• 2019

Seismic vulnerability assessment is a useful tool for rational safety analysis and planning of large and complex structural systems; it can deal with the effects of uncertainties on the performance of significant structural systems. In this study, an efficient dynamic reliability approach, probability density evolution methodology (PDEM), is proposed for seismic vulnerability analysis of earth dams. The PDEM provides the failure probability of different limit states for various levels of ground motion intensity as well as the mean value, standard deviation and probability density function of the performance metric of the earth dam. Combining the seismic reliability with three different performance levels related to the displacement of the earth dam, the seismic fragility curves are constructed without them being limited to a specific functional form. Furthermore, considering the seismic fragility analysis is a significant procedure in the seismic probabilistic risk assessment of structures, the seismic vulnerability results obtained by the dynamic reliability approach are combined with the results of probabilistic seismic hazard and seismic loss analysis to present and address the PDEM-based seismic probabilistic risk assessment framework by a simulated case study of an earth dam.