• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.147-151
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• 2020

In this paper, we propose a method to detect concept drift by applying Convolutional Neural Network (CNN) in a data stream environment. Since the conventional method compares only the final output value of the CNN and detects it as a concept drift if there is a difference, there is a problem in that the actual input value of the data stream reacts sensitively even if there is no significant difference and is incorrectly detected as a concept drift. Therefore, in this paper, in order to reduce such errors, not only the output value of CNN but also the probability vector are used. First, the data entered into the data stream is patterned to learn from the neural network model, and the difference between the output value and probability vector of the current data and the historical data of these learned neural network models is compared to detect the concept drift. The proposed method confirmed that only CNN output values could be used to reduce detection errors compared to how concept drift were detected.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.141-157
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• 2024

This study examines the impact of cryptocurrency value characteristics on cryptocurrency investment intention. Stock craze and information provided through various media, including YouTube, play an essential role in helping investors recognize the value of cryptocurrency and develop positive investment intentions. In this study, we applied the Value-Based Adoption Model (VAM) to verify the relationship between cryptocurrency value characteristics and investment intention. We surveyed 500 cryptocurrency investors to assess network externalities, awareness, compatibility, cost benefits (fees), technicality, security, perceived value, and investment intentions. SEM (Structural Equation Modeling) using AMOS 26.0 was used for data analysis. Results show that network externalities, awareness, compatibility, cost benefits (fees), security, and perceived value significantly impact investment intention. This study provides insights that help investors accurately perceive cryptocurrencies and develop strategies to increase investment intentions. It also contributes to improving investors' decision-making ability. This comprehensive approach will foster the growth of the cryptocurrency market and strengthen investor confidence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2150-2156
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• 2013

This study attempts to identify how the value network of mobile industry has evolved in the value creating process. The longitudinal case study on SK Telecom was conducted by measuring the SK Telecom's investment structure during from 1999 to 2008. Results show that the convergence services based on the advanced mobile networks changed the revenue structure, and enabled SK Telecom to reposition as a media company. For the value creation, SK Telecom's value network has flexibly adapted to convergence environment through dynamic asset reconfiguration.

• Journal of the Korean Society of Industry Convergence
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• v.5 no.2
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• pp.103-110
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• 2002

An artificial neural network is applied to the prediction of compressive strength, slump value of concrete. Standard mixed tables arc trained and estimated, and the results are compared with those of experiments. To consider the varieties of material properties, the standard mixed tables of two companies of Ready Mixed Concrete are used. And they are trained with the neural network. In this paper, standard back propagation network is used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of 210, $240kgf/cm^2$ and target slump value of 12, 15cm are used because the amount of production of that range arc the most at ordinary companies. In results, in the prediction of compressive strength and slump value, the predicted values are converged well to those of standard mixed tables at the target error of 0.10, 0.05, 0.001 regardless of two companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1773-1795
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• 2020

Unmanned aerial vehicles (UAVs) network are a very vibrant research area nowadays. They have many military and civil applications. Limited bandwidth, the high mobility and secure communication of micro UAVs represent their three main problems. In this paper, we try to address these problems by means of secure clustering, and a security clustering algorithm based on integrated trust value for UAVs network is proposed. First, an improved the k-means++ algorithm is presented to determine the optimal number of clusters by the network bandwidth parameter, which ensures the optimal use of network bandwidth. Second, we considered variables representing the link expiration time to improve node clustering, and used the integrated trust value to rapidly detect malicious nodes and establish a head list. Node clustering reduce impact of high mobility and head list enhance the security of clustering algorithm. Finally, combined the remaining energy ratio, relative mobility, and the relative degrees of the nodes to select the best cluster head. The results of a simulation showed that the proposed clustering algorithm incurred a smaller computational load and higher network security.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.189-192
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• 2000

This paper presents a new DR(Dead Reckoning) algorithm in client-server-based networked virtual environment using network delay estimation. In the algorithm, a new update packet is sent to server (or client) whenever the difference of current real value and tracking value after network delay is larger than threshold. To confirm the proposed algorithm, a test network game was implemented. Through iterative field tests, we knew that this algorithm provides fair service and stability.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.331-337
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• 2004

In the analysis of massive volume data, a neural network model is a useful tool. To implement the Neural network model, it is important to select initial value. Since the initial values are generally used as random value in the neural network, the convergent performance and the prediction rate of model are not stable. To overcome the drawback a possible method use samples randomly selected from the whole data set. That is, coefficients estimated by logistic regression based on the samples are the initial values.

• Journal of Power System Engineering
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• v.17 no.6
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• pp.115-121
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• 2013

This study was carried out to investigate the effect of hot forging on the hardness and impact value of ultra high carbon low alloy steel. With increasing hot forging ratio, thickness of the network and acicular proeutectoid cementite decreased, and than were broken up into particle shapes, when the forging ratio was 80%, the network and acicular shape of the as-cast state disappeared. Interlamellar spacing and the thickness of eutectoid cementite decreased with increasing forging ratio, and were broken up into particle shapes, which then became spheroidized. With increasing hot forging ratio, hardness, tensile strength, elongation and impact value were not changed up 50%, and then hardness rapidly decreased, while impact value rapidly increased. Hardness and impact value was greatly affected by the disappeared of network and acicular shape of proeutectoid cementite, and became particle shape than thickness reduction of proeutectoid and eutectoid cementite.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.1
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• pp.44-49
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• 2018

This study analyzed the Vulnerability of Network Communication devices when IEMI is coupled with the Network System. An Ultra Wide Band Generator (180 kV, 700 MHz) was used as the IEMI source. The EUTs are the Switch Hub and Workstation, which are used to configure the network system. The network system was monitored through the LAN system configuration, to confirm a malfunction of the network device. The results of the experiment indicate that a malfunction of the network occurs as the electric field increases. The data loss rate increases proportionally with increasing radiating time. In the case of the Switch Hub, the threshold electric field value was 10 kV/m for all conditions used in this experiment. The threshold point causing malfunction was influenced only by the electric field value. The correlation between the threshold point and pulse repetition rate was not found. However, in case of the Workstation, it was found that as the pulse repetition rate increases, the equipment responds weakly and the threshold value decreases. To verify the electrical coupling of the EUT by IEMI, current sensors were used to measure the PCB line inside the EUT and network line coupling current. As a result of the measurement, it can be inferred that when the coupling current due to IEMI exceeds the threshold value, it flows through the internal equipment line, causing a malfunction and subsequent failure. The results of this study can be applied to basic data for equipment protection, and effect analysis of intentional electromagnetic interference.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4191-4197
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• 2014

Value co-creation is an important business strategy these days in both the business-to-business (B2B) and business-to-consumer (B2C) markets. The aim of this study was to identify specialized resources and competences for value co-creation in the relationship network within a high-tech B2B market. A case of Taiwan Semiconductor Manufacturing Company Limited (TSMC) with customers and partners was chosen as the study case. Based on the observations, contents analysis of the secondary data and unstructured interviews with former TSMC employees, 4 critical resource types (financial, knowledge, efficiency and intellectual resource) and 6 competence types (relational, collaboration, strategic, innovation, managing and service capability), were performed as the principal factors for value co-creation in the relationship network. A research framework that can analyze the value co-creation phenomena in the relationship network was established.