• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.1-21
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• 2003

In this research, we developed a Multi-Agent based Negotiation Support System to be able to increase the competitive power of a company in dynamic environment and correspond to various orders of customers by diffusion of electronic commerce. The system uses the agent technology that is being embossed as new paradigm in dynamic environment and flexible system framework. The multi-agent technology is used to solve these problems through cooperation of agent. The system consists of six sub agents: Mediator, manufacturability Analysis Agent, Process Planning Agent, Scheduling Agent, Selection Agent, Negotiation-strategy Building Agent. In this paper, the proposed Multi-Agent based Negotiation Support System takes aim at the automation of transaction process from ordering to manufacturing plan through the automation of negotiation that is the most important in order-taking transaction.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Computers and Concrete
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• v.27 no.5
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.105-118
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• 2021

As we enter the era of the 4th industrial revolution, information and communication technologies, including artificial intelligence and big data, are converging throughout society. Especially, as the importance of the social foundation of hyper-connection grows, the social influence of IoT, a network of connecting objects, people, and various entities, is also gradually expanding. In addition, as a pandemic, COVID-19, continues, interests in untact-oriented technology and service development are growing more than ever, and each company is trying to establish a core competency strategy to gain an edge in competition in the changing society. This study is a case study centered on Dawon DNS, a company that provides an IoT-based AI smart plug platform. Dawon DNS is broadening its services while developing products by applying advanced technologies, and this study is aiming to investigate the core competencies of the business evolution process. The obtained result of this study will provide implications for companies to become more competitive by suggesting the attitudes and strategies that startups should have during the transforming business environment.

• Electronics and Telecommunications Trends
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• v.37 no.4
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• pp.70-80
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• 2022

With the rapid growth in media service platforms providing broadcast programs or content services, content production has become more important and competitive. As a strategy to meet the diverse needs of global consumers for a variety of content and to retain them as long-term repeat customers, global over-the-top service providers are increasing not only the number of content productions but also their production efficiency. Moreover, a considerable amount of scene composition in the flow of content production work appears to be combined with rendering technology from a game engine and converted to object-based computer programming, thereby enhancing the creativity, diversity, quality, and efficiency of content production. This study examines the latest technology trends in content production such as virtual studio technology, which has emerged as the center of content production, the use cases in various fields of artificial intelligence, and the metadata standards for content search or scene composition. This study also examines the possibility of using object-based computer programming as one of the future candidate technologies for content production.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.105-113
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• 2022

Purpose - An increase in automation has been as a result of digital technologies. The data will be instrumental in the determination of the services that are more necessary so that more resources can be allocated for them. The purpose of the current research is to investigate how big data utilization will help increase the profitability in the industry 4.0 era. Research design, Data, and methodology - The present research has conducted the comprehensive literature content analysis. Quantitative approaches allow respondents to decide, but qualitative methods allow them to offer more information. In the next step, respondents are given data collection equipment, and information is collected. Result - The According to qualitative literature analysis, there are five ways in which big data utilization will help increase the profitability in the industry 4.0 era. The five solutions are (1) Better Customer Insight, (2) Increased Market Intelligence, (3) Smarter Recommendations and Audience Targeting, (4) Data-driven innovation, (5) Improved Business Operations. Conclusion - Modern companies have been seeking a competitive advantage so that they can have the edge over other companies in the same industries providing the same services and products. Big data is that technology that businesses have always wanted for an extended period of time to revolutionize their operations, making their businesses more profitable.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.

• Journal of Distribution Science
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• v.20 no.9
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• pp.39-46
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• 2022

Purpose: At the industrial sites of aviation enterprises there is a significant optimization of the main production processes through the use of advanced digital technologies. The most promising are the latest technologies of industrial Internet of Things, active use of big data and practical application of artificial intelligence in production. Research design, data and methodology:The process of creating a competitive product in the high-tech aviation sector is actively linked to the investment appeal of aircraft and helicopter construction products, which is built on the basis of reducing production and time costs through the creation of an effective digital system. Results: The aviation cluster of Rostec State Corporation is currently being transformed in a significant way. The leading enterprises of the Russian aviation industry are actively mastering cooperation schemes using integrated digital management principles and the widespread introduction of digital products from leading Russian vendors. Conclusions: Following the transition to electronic aircraft design technologies and modern materials in the production of aircraft, UAC continues to improve all production processes through robotization and optimization of technological processes, due to the introduction of aircraft assembly technology in accordance with digital models.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.697-706
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• 2023

This study examines the history of string quartet performances and analyzes contemporary integrated performances to propose a new performance paradigm for future audiences. It examines past developments and audience interactions, and how modern classical performance can gain a competitive edge internationally through tech integration. Building on this foundation, a future vision is proposed for Korean string quartet performances, drawing from novel performances that are interconnected with their historical context. The study concludes that modern string quartets necessitate innovative and original performance directions that can be achieved through various technological integrations.