• The Journal of Society for e-Business Studies
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• v.18 no.1
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• pp.33-55
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• 2013

Information sharing behavior in the Internet has raised much interest. Recently, social network sites provide a new information sharing channel for the users who want to connect with others based on common social background or tastes. Especially, we focus that a social network site becomes one of major routes for information sharing about socially influential issues. Therefore, studying how information is diffused via a social network site may give theoretically, practically significant implication. Based on the assertion, we investigated user's behavior to mediate other user's information messages. We define information mediating behavior as concurrent actions of filtering and distributing behavior of the digital content that is originated from one of the connected users. In this study, we intended to understand the effects of information mediating behavior, and tried to understand characteristics of re-mediating of previously mediated information. Using an agent-based simulation model, we found that information mediating behavior increased the extent of information diffusion significantly. In addition, even a small degree of mediating probability could boost up the level of information diffusion in the case of a re-mediating condition. We believe that those findings provide remarkable insight of research and business application on both of information sharing and diffusion in a social network site.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• Journal of Distribution Science
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• v.22 no.9
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• pp.97-106
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• 2024

Purpose: In view of increased social awareness of today's consumers, it is very important to understand how retail customers perceive their sense of social responsibility. This study aims to explore the decision processes of university students that affect the patronage of social enterprises in retail sector. Research design, data and methodology: This study proposes and tests whether and how social network traits, firm's image, and perceived trustworthiness serve as predictors of value co-creation behavior specific to two different industries (social enterprises and regular firms) operating in retail sector of South Korea. This study incorporated theoretical premise of value co-creation to verify the structural relationships among the predictors of value co-creation. Results: The result demonstrates that social network and firm's image both significantly influence consumers' value co-creation behavior. The study further found that the firm's image is overall more effective for eliciting consumers' value co-creation behavior than social network traits. Conclusions: As the result of comparing the industry type (social enterprises vs. regular firms), the study confirmed a meaningful difference such that consumers indicated greater impact of firm's image on value co-creation for social enterprises than for regular firms. The findings are expected to provide useful industrial insights for the management of social enterprises.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.984-990
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• 2005

The aim of this paper is to investigate a control framework for mobile robots, operating in shared environment with humans. The Intelligent Space (iSpace) can sense the whole space and evaluate the situations in the space by distributing sensors. The mobile agents serve the inhabitants in the space utilizes the evaluated information by iSpace. The iSpace evaluates the situations in the space and learns the walking behavior of the inhabitants. The human intelligence manifests in the space as a behavior, as a response to the situation in the space. The iSpace learns the behavior and applies to mobile agent motion planning and control. This paper introduces the application of fuzzy-neural network to describe the obstacle avoidance behavior teamed from humans. Simulation results are introduced to demonstrate the efficiency of this method.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.121-128
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• 2022

There are much processing techniques of microwave circuits, whose dimensions are small compared to the wavelength, but the disadvantage is that they cannot be directly applied to circuits working at high and/or low frequencies. In this article, we will consider the bond graph approach as a tool for analyzing and understanding the behavior of microwave circuits, and to show how basic circuit and network concepts can be extended to handle many microwaves analysis and design problems of practical interest. This behavior revealed in the scattering matrix filter, and which will be operated from its reduced bond graph model. So, we propose in this paper, a new application of bond graph approach jointly with the scattering bond graph for a high frequency study.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.199-205
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• 2022

The extensive growth in data rate demand by the smart gadgets and mobile broadband application services in wireless cellular networks. To achieve higher data rate demand which leads to aggressive frequency reuse to improve network capacity at the price of Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) has been recognized as an effective scheme to get a higher data rate and mitigate ICI for perfect geometry network scenarios. In, an irregular geometric multicellular network, ICI mitigation is a challenging issue. The purpose of this paper is to develop distributed dynamic power allocation scheme for FFR based on game theory to mitigate ICI. In the proposed scheme, each cell region in an irregular multicellular scenario adopts a self-less behavior instead of selfish behavior to improve the overall utility function. This proposed scheme improves the overall data rate and mitigates ICI.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.6
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• pp.48-55
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• 2005

In this paper, a mathematical model and a simulation method for the response time analysis of Local Interconnect Network(LIN) based network systems are proposed. Network-induced delays in a network based control system can vary widely according to the transmission time of message and the overhead time of transmission. Therefore, in order to design a distributed control system using LIN network, a method to predict and verify the timing behavior of LIN protocol is required at the network design phase. Furthermore, a simulation environment based on a timing model of LIN protocol is beneficial to predict the timing behavior of LIN. The model equation is formulated with six timing parameters deduced from timing properties of LIN specification. Additionally, LIN conformance test equations to verify LIN device driver are derived with timing constraints of the parameters. The proposed model equation and simulation method are validated with a result that is measured at real LIN based network system.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.69-87
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• 2013

Recently a large number of people have been using social networking and microblogging services such as Facebook and Twitter. These mediums play a pivotal communication channel in a business-to-customer (B2C) relationship. Given its importance in today's business, companies have invested in the strategic application of social network services to reach out to customers. This study provides a blueprint for mechanisms for successful execution of social network services in the context of developing an effective B2C relationship, such as customer participation behavior. The S-O-R(Stimulus-Organism-Response) framework lays out the foundation for developing our research model and provides a structured view for understanding customer participation behavior on brand loyalty. For the methodology, this study employed a mixed-method approach. Additionally, in order to provide empirical evidences, a total of 121 respondents have completed the survey. All the data were compiled and analyzed through structural equation modeling and were implemented in partial least square (PLS). To sum up, this study presented theoretical and practical implications by providing the effect of customer participation behavior on brand loyalty through B2C microblogging.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1576-1589
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• 2006

Most of surface water ways in Egypt suffer from the infestation of aquatic weeds especially submerged ones which cause lots of problems for the open channels and the water structures such as increasing water losses, obstructing the water flow, and reducing the efficiency of the water structures. Accurate simulation of the water flow behavior in such channels is very essential for water distribution decision makers. Artificial Neural Network (ANN) has been widely utilized in the past ten years in civil engineering applications for the simulation and prediction of the different physical phenomena and has proven its capabilities in the different fields. The present study aims towards introducing the use of ANN technique to model and predict the impact of the existence of submerged aquatic weeds on the hydraulic performance of open channels. Specifically the current paper investigates utilizing the ANN technique in developing a simulation and prediction model for the flow behavior in an open channel experiment that simulates the existence of submerged weeds as branched flexible elements. This experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results of current manuscript showed that ANN technique was very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds' cases that were considered in the ANN development process.