• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.125-133
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• 2019

Because military weapons systems for special purposes are small and complex, they are not easy to maintain. Therefore, it is very important to maintain combat strength through quick maintenance in the event of a breakdown. In particular, naval ships are complex weapon systems equipped with various equipment, so other equipment must be considered for maintenance in the event of equipment failure, so that skilled maintenance personnel have a great influence on rapid maintenance. Therefore, in this paper, we analyzed maintenance data of defense equipment maintenance information system through text mining and social network analysis(SNA), and tried to identify the naval ship maintenance expertise. The defense equipment maintenance information system is a system that manages military equipment efficiently. In this study, the data(2,538cases) of some naval ship maintenance teams were analyzed. In detail, we examined the contents of main maintenance and maintenance personnel through text mining(word cloud, word network). Next, social network analysis(collaboration analysis, centrality analysis) was used to confirm the collaboration relationship between maintenance personnel and maintenance expertise. Finally, we compare the results of text mining and social network analysis(SNA) to find out appropriate methods for finding and finding naval ship maintenance expertise.

• Journal of Technology Innovation
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• v.23 no.1
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• pp.1-36
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• 2015

The purpose of this research is to find individual user's preference for cloud storage service such as Daum Cloud, Naver N-Drive, GoogleDrive, Dropbox, SkyDrive and iCloud. Through literature reviewed and pilot tests, 6 attributes of cloud storage service (storage capacity, perceived cost, collaboration, accessibility, social influence and perceived security) were selected and all 6 attributes had significant effects on the preference of cloud storage service by conjoint analysis. The results shows that the user's willingness to pay is estimated 10,553 won for the free storage, 4,646 won for the function for mobile accessibility, and 2,443 won for more reliable cloud computing service provider. This study has significance to apply conjoint analysis with economic, technological, and environmental factors to cloud storage service (SaaS) and shed light on policy promotion of next generation of cloud computing ecosystem by user perception with willingness to pay on the storage service.

• Journal of KIBIM
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• v.6 no.1
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• pp.9-17
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• 2016

Building information modeling refers to combination or set of technologies and organizational solutions that are expected to increase collaboration in the construction industry and to improve the productivity and quality of the design, construction, and maintenance of buildings. For enhanced communication among project participants, various information which BIM model usually includes is provided, furthermore data which contain exchange of unstructured information is needed. If the extension of BIM standard file format for practical use of design Issue information about collaborative design process is fulfilled, the productivity and quality of design will be improved.

• Journal of Information Science Theory and Practice
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• v.2 no.1
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• pp.22-34
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• 2014

This paper analyzes India's contribution to world tribology research during the period 2001-2012 based on SCOPUS records. India's global publication share, annual output, and its citation impact of Indian contribution, partner countries, leading contributors, leading institutes, and highly cited papers were analyzed. Additionally, a cloud technique is used to map frequently used single words in titles. It is observed that India ranks in the $7^{th}$ position with a global publication share of 3.83% and an annual average growth rate of 25.58% during the period 2001-2012. The citation impact of India's contribution is 6.05 which decreased from 12.74 during 2001-2006 to 4.62 during 2007-2012. 17.4% of India's total research output was published with international collaboration.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.573-574
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• 2016

Before the Machine Socialization System used to connected between server and router. However, the data flow increases due to the poor performance of the router increased traffic, as a result, the loss of data when the problem occurred Collaboration between devices increases that have been interrupted. This action moves the server connected to the router is required to solve these problems. In this paper, by utilizing the cloud server to reduce bottlenecks proposed a system that can reduce the loss of data during cooperation between devices. In addition, by dividing the management unit and the sensor using the virtualization technology, we designed a system that can efficiently make use of the resource.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.183-186
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• 2013

Modern day despite technology advancements that manufacture a new generation of mobile devices with generous resources, the fact that they can offer only limited processing capacity still remains a painful experience. So far, a number of research studies have been carried out, trying to eliminate problems arising from shortcomings in the connection between thin clients and cloud networks, yet little have been found efficient. In this paper, we present a novel approach, taking advantage of collaboration of thin and thick clients, particularly aiming at optimizing data distribution by splitting data and utilizing cloud computing (CC) resources so that expected Quality-of-Service (QoS) requirements can be met. Moreover, we conduct simulations to evaluate our approach. Our results evaluation shows that our approach has better performance than existing approaches.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.135-142
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• 2015

As the importance of software to society has grown, more and more schools worldwide teach coding basics in the classroom. Despite the rapid spread of coding instruction in grade schools, experience in the classroom is certainly limited because there is a gap between the curriculum and the existing computing environment such as the mobile and cloud computing. We propose an approach to fill this gap by using a mobile environment and the robot on the cloud-based platform for effective teaching. In this paper, we propose an architecture called Cloudboard that enables knowledge sharing and collaboration among knowledge providers in the cloud-based robot platforms. We also describe five representative architectural patterns that are referenced and analyzed to design the Cloudboard architecture. Our early experimental results show that the Cloudboard can be effective in the development of collective robotic systems.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.