• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.121-125
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• 2021

Cloud service has the characteristic that it must be always available and that it must be able to respond immediately to user requests. This study suggests a method for constructing a proactive and autonomous quality and performance management system to meet these characteristics of cloud services. To this end, we identify quantitative measurement factors for cloud service quality and performance management, define a structure for applying a time series framework to cloud service application quality and performance management for proactive management, and then use big data and artificial intelligence for autonomous management. The flow of data processing and the configuration and flow of big data and artificial intelligence platforms were defined to combine intelligent technologies. In addition, the effectiveness was confirmed by applying it to the cloud service quality and performance management system through a case study. Using the methodology presented in this study, it is possible to improve the service management system that has been managed artificially and retrospectively through various convergence. However, since it requires the collection, processing, and processing of various types of data, it also has limitations in that data standardization must be prioritized in each technology and industry.

• Communications for Statistical Applications and Methods
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• v.30 no.5
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• pp.485-499
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• 2023

This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.5
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• pp.11-19
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• 2021

In this paper, we proposed an asymmetric multi-core processor scheduling scheme which is based on the mileage of each core. We considered a big-LITTLE multi-core processor structure, which consists of low power consuming LITTLE cores with general performance and high power consuming big cores with high performance. If a task needs to be processed, the processor decides a core type (big or LITTLE) to handle the task, and then investigate the core with the shortest mileage among unoccupied cores. Then assigns the task to the core. We developed a mileage-based balancing algorithm for asymmetric multi-core assignment and showed that the proposed scheduling scheme is more cost-effective compared to the traditional scheme from a management perspective. Simulation is also conducted for the purpose of performance evaluation of our proposed algorithm.

• Advances in nano research
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• v.16 no.3
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• pp.231-249
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• 2024

When the amplitude of the vibrations is equivalent to that clearance, the vibrations for small amplitudes will really be significantly nonlinear. Nonlinearities will not be significant for amplitudes that are rather modest. Finally, nonlinearities will become crucial once again for big amplitudes. Therefore, the concrete panel system may experience a big amplitude in this work as a result of the high temperature. Based on the 3D modeling of the shell theory, the current work shows the influences of the von Kármán strain-displacement kinematic nonlinearity on the constitutive laws of the structure. The system's governing Equations in the nonlinear form are solved using Kronecker and Hadamard products, the discretization of Equations on the space domain, and Duffing-type Equations. Thermo-elasticity Equations. are used to represent the system's temperature. The harmonic solution technique for the displacement domain and the multiple-scale approach for the time domain are both covered in the section on solution procedures for solving nonlinear Equations. An effective data-driven solution is often utilized to predict how different systems would behave. The number of hidden layers and the learning rate are two hyperparameters for the network that are often chosen manually when required. Additionally, the data-driven method is offered for addressing the nonlinear vibration issue in order to reduce the computing cost of the current study. The conclusions of the present study may be validated by contrasting them with those of data-driven solutions and other published articles. The findings show that certain physical and geometrical characteristics have a significant effect on the existing concrete panel structure's susceptibility to temperature change and GPL weight fraction. For building construction industries, several useful recommendations for improving the thermo-mechanics' behavior of structural concrete panels are presented.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.331-341
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• 2014

Internet of Thing(IoT), which is recently talked about with the development of information and communication technology, provides big data to all nodes such as companies and homes, means of transportation etc. by connecting all things with all people through the integrated global network and connecting all actual aspects of economic and social life with Internet of Thing through sensor and software. Defining Internet of Thing, it plays the role of a connector of providing various information required for the decision-making of companies in the cloud computing environment for the Insight usage by collecting and storing Raw Data of the production site through the sensor network and extracting big data in which data is accumulated and Insight through this. In addition, as the industry showing the largest linkage with other root industries among root industries, the mold industry is the core technology for controlling the quality and performance of the final product and realizing the commercialization of new industry such as new growth power industry etc. Recently, awareness on the mold industry is changing from the structure of being labor-intensive, relying on the experience of production workers and repeating modification without the concept of cost to technology-intensive, digitization, high intellectualization due to technology combination according to IT convergence. This study, therefore, is to provide a golden opportunity to increase the direct and indirect expected effects in poor management activities of small businesses by actually implementing and managing the entire process of mold life cycle to information system from mold planning to mass production and preservation by building SME(small and medium-sized enterprises)-type mold life cycle management information system in the cloud computing environment and applying it to the production site.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.64-69
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• 1998

The chlorophyll-a concentration measured by OCTS could be used for observing the physical phenomena such as eddies, fronts, and up welling in the oceans as well as for studying the ecology of phytoplankton. In this study, biological and physical features in the East Sea/Japan Sea (the East Sea) and the Yellow Sea observed by OCTS are analyzed in comparison with other satellite data. And in situ chlorophyll data were compared with OCTS Level 2 chlorophyll data. There was a striking correspondence between the satellite chlorophyll structure and other satellite data in the East Sea in the spring. Very complicated ring structures in the 557 are reflected in chlorophyll structure. In the Yellow Sea, the surface structure was rather simple. While the discrepancies between in situ and OCTS algorithm version 3 chlorophyll were small in the East Sea, those for the Yellow Sea were rather big. Comparison with CZCS data for similar time of the year (May-June) shows that OCTS chlorophyll is higher in general. Although the error is partly due to the fact that NASDA chlorophyll algorithm is an empirical algorithm for case 1 water, how much of this error is also due to the errors in sensor calibration or in atmospheric correction is not clear.

• Transactions of Materials Processing
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• v.12 no.6
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• pp.542-549
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• 2003

Three-dimensional forging analysis becomes an inevitable tool to make design process more reliable and more producible. In this study, in order to make the investigation for three-dimensional forging analysis more conveniently and accurately, a new post processor was developed. For post-processing of multi-stage forging simulation, efficient data structure was proposed and applied by using STL. New file architecture was developed to handle successive and huge data efficiently, common in three-dimensional forging analysis. Since sectioning and flow tracing plays an important role in the investigation of analysis result, we developed an algorithm suitable for 4-node and 10-node tetrahedron. This flow tracing algorithm can trace and reverse-trace flow through remeshing. Developed program shows good performance and functionality. Especially, a big size problem can be handled easily due to proposed data structure and file architecture.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.99-106
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• 2020

The purpose of this study is to find out whether image similarity between celebrities and brands on social network service be a determinant to predict advertising effectiveness. To this end, an advertising effect prediction model for celebrity endorsed advertising was created and its validity was verified through a machine learning method which is a big data analysis technique. Firstly, the celebrity-brand image similarity, which was used as an independent variable, was quantified by the association network theory with social big data, and secondly a multiple regression model which used data representing advertising effects as a dependent variable was repeatedly conducted to generate an advertising effect prediction model. The accuracy of the prediction model was decided by comparing the prediction results with the survey outcomes. As for a result, it was proved that the validity of the predictive modeling of advertising effects was secured since the classification accuracy of 75%, which is a criterion for judging validity, was shown. This study suggested a new methodological alternative and direction for big data-based modeling research through celebrity-brand image similarity structure based on social network theory, and effect prediction modeling by machine learning.

• Journal of Information Technology Services
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• v.21 no.5
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• pp.15-28
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• 2022

Recently, government agencies are actively adopting the platform model as a means of public policy. However, existing studies on the public platform are minimal and have focused on user experiences or the possibility of public usage of the platform model. Now the research concerning building governance structure and utilizing network effects of the platform after adopting the platform model in the public sector is keenly required. This study intended to ignite academic dialogue on the governance of public platforms in the context of digital transformation. This study focused on a case of the 'Special delivery,' a public delivery app established by Gyeonggi-do. In order to analyze the characteristics of the public platform and its governance structure, data were collected from press releases, policy reports, and news articles. Data was analyzed using the frame of Hagui's platform design factors and Ansell & Gash's collaborative governance model. The results of the public platform analyses showed 1) incompleteness in the value trade-off accounting, which was designed for platform business based on general cost-benefit analysis, and 2) a closed governance structure that limits direct participation of diverse user groups(i.e., service provider, customer) in order to enhance providers' utility by preventing customers' excessive online activities. The results of this study provided theoretical and policy implications regarding designing the strategy for accounting for value trade-offs and functioning governance structure for public platforms.