• Proceedings of the Materials Research Society of Korea Conference
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• 2005.05a
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• pp.50-50
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• 2005

• Structural Engineering and Mechanics
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• v.4 no.6
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• pp.717-729
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• 1996

This paper presents the concepts of Process Innovation and Concurrent Engineering in view of their use within Integrated CAD Systems. It follows by proposing process models that support an effective implementation. The work is practically founded on the authors' experience in performing the initial steps of implementation of some of such integrated systems for the construction industry.

• Proceedings of the Materials Research Society of Korea Conference
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• 2006.05a
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• pp.17.2-17.2
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• 2006

• Transactions of Materials Processing
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• v.14 no.8 s.80
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• pp.675-680
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• 2005

This study was aimed at the design of the dies for the automobile part for the engine oil leakage prevention using the computer simulation to shorten the period of production, on the basis of the process planning which was designed by the field experts. In the computer simulation,'eesy-2-form' of 2D FEM simulation package and 'eesy-DieOpt' have been used, which are the commercial process analysis and die design program. Through the simulation of 'eesy-2-form', we could know the propriety of the forming process, the inner pressure of the die and the suitable fitting pressure between the insert and the sleeve which was not showing any positive tangential stresses in the insert. Through the simulation of 'eesy-DieOpt', we could know the number of the stress ring, the diameter ratios, the stresses of the die, the shrink fitting tolerance and temperature in the condition of the already determined maximum outer die diameter of the multi-stage former. The validity of the die design using the computer simulation was analyzed by the experiments and the results were satisfactory. As the results of this study, the new and easy die design system for cold forging has been developed.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.406-420
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• 2020

In order to make up the deficiencies of the existing research results which cannot effectively deal with the nearest neighbor query based on the line segments in obstacle space, the k nearest neighbor query method of line segment in obstacle space is proposed and the STA_OLkNN algorithm under the circumstance of static obstacle data set is put forward. The query process is divided into two stages, including the filtering process and refining process. In the filtration process, according to the properties of the line segment Voronoi diagram, the corresponding pruning rules are proposed and the filtering algorithm is presented. In the refining process, according to the relationship of the position between the line segments, the corresponding distance expression method is put forward and the final result is obtained by comparing the distance. Theoretical research and experimental results show that the proposed algorithm can effectively deal with the problem of k nearest neighbor query of the line segment in the obstacle environment.

• Nuclear Engineering and Technology
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• v.49 no.1
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• pp.113-123
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• 2017

In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and restrictions of results. Recently, a computational method, based on the Dempster-Shafer evidence theory and analytic hierarchy process, has been proposed to handle the dependence in human reliability analysis. The model can deal with uncertainty in an analyst's judgment and reduce the subjectivity in the evaluation process. However, the computation is heavy and complicated to some degree. The most important issue is that the existing method is in a positive aspect, which may cause an underestimation of the risk. In this study, a new evidential analytic hierarchy process dependence assessment methodology, based on the improvement of existing methods, has been proposed, which is expected to be easier and more effective.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.48-51
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• 2021

The article discusses the use of modern technologies in the learning process. It has been determined that the modern period of the development of society is characterized by a strong influence of computer technologies on it, a new education system is being formed, focused on entering the world information and educational space. This process is accompanied by significant changes in the pedagogical theory and practice of the educational process associated with making adjustments to the content of learning technologies, which should be adequate to modern technical capabilities, and contribute to the harmonious entry of a teenager into the information society. Computer technologies are designed to become not an additional "makeweight" in training, but an integral part of a holistic educational process, significantly increasing its effectiveness

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1148-1161
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• 2017

The success of a business process management system stands or falls on the quality of the business processes. Many experiments therefore have been devoting considerable attention to the modeling and analysis of business processes in process-centered organizations. One of those experiments is to apply the probabilistic theories to the analytical evaluations of business process models in order to improve their qualities. In this paper, we excogitate a conceptual way of applying a probability theory of proportions into modeling business processes. There are three types of routing patterns such as sequential, disjunctive, conjunctive and iterative routing patterns in modeling business processes, into which the proportion theory is applicable. This paper focuses on applying the proportion theory to the disjunctive routing patterns, in particular, and formally named proportional information control net that is the formal representation of a corresponding business process model. In this paper, we propose a conceptual approach to discover a proportional information control net from the enactment event histories of the corresponding business process, and describe the details of a series of procedural frameworks and operational mechanisms formally and graphically supporting the proposed approach. We strongly believe that the conceptual approach with the proportional information control net ought to be very useful to improve the quality of business processes by adapting to the reengineering and redesigning the corresponding business processes.

• JOURNAL OF ELECTRICAL WORLD
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• no.11 s.95
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• pp.35-39
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• 1984

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.