• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.238-241
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• 2010

This paper proposed an approach to integrate bridge condition assessment related information with a 3D bridge model to visualize bridge condition assessment information in the 3D bridge model. In this approach, bridge information model plays a centric role in the data access and realizes the integration of bridge initial design and historical bridge maintenance records. Behind the bridge information model is a rational database. After the system requirements for this approach, several IFC data model extensions are suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4054-4071
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• 2015

Named-Data Networking (NDN) is one of the promising approaches for the Future Internet to cope with the explosion and current usage pattern of Internet traffic. Content provider mobility in the NDN allows users to receive real-time traffic when the content providers are on the move. However, the current solutions for managing these mobile content providers suffer several issues such as long handover latency, high cost, and non-optimal routing path. In this paper, we survey main approaches for provider mobility in NDN and propose an optimal scheme to support the mobile content providers in the large-scale NDN domain. Our scheme predicts the movement of the provider and uses state information in the NDN forwarding plane to set up an optimal new routing path for mobile providers. By numerical analysis, our approach provides NDN users with better service access delay and lower total handover cost compared with the current solutions.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.590-600
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• 2014

In the wireless sensor network environment, clustering scheme has a problem that a large amount of energy is unnecessarily consumed because of frequently occurred entire re-clustering process. Some of the studies were attempted to improve the network performance by getting rid of the entire network setup process. However, removing the setup process is not worthy. Because entire network setup relieves the burden of some sensor nodes. The primary aim of our scheme is to cut down the energy consumption through minimizing entire setup processes which occurred unnecessarily. Thus, we suggest a re-clustering scheme that considers event detection, transmitting energy, and the load on the nodes. According to the result of performance analysis, our scheme reduces energy consumption of nodes, prolongs the network lifetime, and shows higher data collection rate and higher data accuracy than the existing schemes.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.73-80
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• 2018

In this paper, we propose a new type of client server environment to improve the architecture vulnerable to hacking in an existing client server environment. On the server side, move the existing Web server to the client side and This is a way for clients to communicate only the data they need and suggests a structure that completely blocks the web attack itself to the server. This can completely prevent a server from being hacked, spreading malicious code and hacking data on a server. It also presents a new paradigm that will not affect servers even if malware is infected with client PCs. This paper validates the proposed environment through BBS (Big Bad Stick) hardware in the form of USB on the client side. This study proof that secure services are provided through encryption communication with server-side security equipment, indicating that this study is a system with new security.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1353-1361
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• 2018

With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• Archives of design research
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• v.13 no.3
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• pp.313-320
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• 2000

Currently, The new wave of information technology has enormously influenced every field. In the Held of design, it is time to strive possible efforts in order to accumulate the design-related knowledge by maintaining, managing and controlling design information in a systematic manner, getting out of the old stage of mere use of data itself. Due to remarkable progress in communication media and speed, and file compression technology, text-centric data has been shifting to multimedia data such as image and motion picture. So it is currently required that methologies be developed to effectively utilize the related information. With respect to the processing of image data, it is certain that the optimal method should be come up with reflecting the unique characteristics and utilization of image data, apart from the traditional way of processing and storing the legacy text-based data. The study suggests the system of indexing and implementing design image information through the case of analyzing design image data, abstracting data elements of image itself, and finally applying it to building image-oriented database for use.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.475-487
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• 2020

The paper aims to facilitate a discussion around how big data technologies and data from citizens can be used to help public administration, society, and policy-making to improve community's lives. This paper discusses opportunities and challenges of big data strategies for government, society, and policy-making. It employs the presentation of numerous practical examples from different parts of the world, where public-service delivery has seen transformation and where initiatives have been taken forward that have revolutionized the way governments at different levels engage with the citizens, and how governments and civil society have adopted evidence-driven policy-making through innovative and efficient use of big data analytics. The examples include the governments of the United States, China, the United Kingdom, and India, and different levels of government agencies in the public services of fraud detection, financial market analysis, healthcare and public health, government oversight, education, crime fighting, environmental protection, energy exploration, agriculture, weather forecasting, and ecosystem management. The examples also include smart cities in Korea, China, Japan, India, Canada, Singapore, the United Kingdom, and the European Union. This paper makes some recommendations about how big data strategies transform the government and public services to become more citizen-centric, responsive, accountable and transparent.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.