• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.32-42
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• 2023

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.4
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• pp.120-130
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• 2019

In this study, based on the correlation between traffic volume data and vertical displacement data developed in previous research using the bridge maintenance big data of 2006, the vertical displacement estimation model using the traffic volume data of Gwangan Bridge for 10 years A comparison of the performance of the developed model with the current applicability is presented. The present applicability of the developed model is analyzed that the estimated displacement is similar to the actual displacement and that the displacement estimation performance of the model based on the structured regression analysis and the principal component analysis is not significantly different from each other. In conclusion, the vertical displacement estimation model using the traffic volume data developed by this study can be effectively used for the analysis of the behavior according to the traffic load of Gwangan Bridge.

• International Journal of CAD/CAM
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• v.11 no.1
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• pp.1-10
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• 2011

Product lifecycle management (PLM) is a new business strategy for enterprise's product R&D. A PLM system holds and maintaining the integrity of the product data produced throughout its entire lifecycle. There is, therefore, a need to build a safe and effective product data model to support PLM system. The paper proposes a domain-based product data model for PLM. The domain modeling method is introduced, including the domain concept and its defining standard along the product evolution process. The product data model in every domain is explained, and the mapping rules among these models are discussed. Mapped successively among these models, product data can be successfully realized the dynamic evolution and the historical traceability in PLM system.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.262-273
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• 2020

This paper analyzes the existing copyright management and copyright sharing model and discusses the limitations. It then proposes a consortium Blockchain-based copyright management model in which the service platform participates as a node, and discusses how to combine the My Data concept with Blockchain and smart contracts. Also, Blockchain-based CP-ABE is introduced and applied to the proposed model as a way for users to define access policies and store copyright data in encrypted form on the storage of the online service providers (OSP). Compared with the existing copyright management model, the proposed model allows the copyright holder to focus on copyright registration, license content design, and sharing, as the data subject. And it is expected to be able to transparently manage the usage records and the basis for the settlement of the copyrighted data that are shared and used on each platform.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• ETRI Journal
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• v.39 no.4
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• pp.558-569
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• 2017

In this study, a non-preemptive M/G/1 queueing model of a spectrum handoff scheme for cognitive wireless networks is proposed. Because spectrum handoff gives secondary users an opportunity to carry on their transmissions, it is crucially important to determine the actions of primary users. In our queueing model, prioritized data traffic is utilized to meet the requirements of the secondary users. These users' packets are categorized into three different priority classes: urgent, real-time, and non-real time. Urgent data packets have the highest priority, while non-real time data packets have the lowest priority. Riverbed (OPNET) Modeler simulation software was used to simulate both reactive and proactive decision spectrum handoff schemes. The simulation results were consistent with the analytical results obtained under different load and traffic conditions. This study also revealed that the cumulative number of handoffs can be drastically decreased by exploiting priority classes and utilizing a decent spectrum handoff strategy, such as a reactive or proactive decision-based strategy.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.261-270
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• 2002

Recently, the demand of the Intelligent Transportation System(ITS) has been increased to a large extent, and a real-time traffic information service based on the internet system became very important. When ITS companies carry out real-time traffic services, they find some traffic data missing, and use the conventional method of reconstructing missing values by calculating average time trend. However, the method is found unsatisfactory, so that we develop a new method based the spatial and spatial-temporal models. A cross-validation technique shows that the spatial-temporal model outperforms the others.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.6
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• pp.75-81
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• 2008

The purpose of this study was to develop an optimum equipment model considering various objective functions and variables of agricultural reservoir. Traditional optimum function for feasibility assessment is based on economic benefit, but we tried new approach of feasibility assessment based on the number of beneficiary. The data of Yuraimi reservoir and Yongbong reservoir in Yesan-gun such as numbers of related people, construction costs, safe diagnosis have been gathered for applying developed model. Data are used for determining optimum strategy with restricted cost. For verifying results of optimum maintenance, real maintenance data of Yuraimi reservoir were compared with simulated strategy. Results show that simulated maintenance strategies are 3 times more effective than real maintenance data.