• 한국해양공학회지
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• 제33권6호
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• pp.620-626
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• 2019

With the development of robot technology, the expectation of autonomous mission operations has increased, and the research on robot control architectures and mission planners has continued. A scalable and robust control architecture is required for unmanned surface vehicles (USVs) to perform a variety of tasks, such as surveillance, reconnaissance, and search and rescue operations, in unstructured and time-varying maritime environments. In this paper, we propose a robot control architecture along with a new utility function that can be extended to various applications for USVs. Also, an additional structure is proposed to reflect the operator's command and improve the performance of the autonomous mission. The proposed architecture was developed using a robot operating system (ROS), and the performance and feasibility of the architecture were verified through simulations.

• 대한임베디드공학회논문지
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• 제15권3호
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.570-581
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• 2018

Recently, a software program has been used in nuclear power plants (NPPs) to digitalize many instrumentation and control systems. To guarantee NPP safety, the reliability of the software used in safetycritical instrumentation and control systems must be quantified and verified with proper test cases and test environment. In this study, a software testing method using a simulation-based software test bed is proposed. The test bed is developed by emulating the microprocessor architecture of the programmable logic controller used in NPP safety-critical applications and capturing its behavior at each machine instruction. The effectiveness of the proposed method is demonstrated via a case study. To represent the possible states of software input and the internal variables that contribute to generating a dedicated safety signal, the software test cases are developed in consideration of the digital characteristics of the target system and the plant dynamics. The method provides a practical way to conduct exhaustive software testing, which can prove the software to be error free and minimize the uncertainty in software reliability quantification. Compared with existing testing methods, it can effectively reduce the software testing effort by emulating the programmable logic controller behavior at the machine level.

• Asia pacific journal of information systems
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• 제26권2호
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• pp.211-230
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• 2016

Social commerce is a new type of e-commence that is based on social networking technologies and aggressive marketing strategies, such as one-deal-a-day. However, although social commerce has become very popular, little is known of customers' substantive purchasing behaviors when using social commerce sites. These behaviors, namely visiting and purchasing behaviors, are the focus of this study. Hence, this study aims to provide comprehensive understanding of the visiting and purchasing behaviors of customers in relation to social commerce sites. A research model based on the utilitarian and hedonic values of shopping, social influence, and convenience, which represent social commerce features, was developed and empirically analyzed using data from social commerce site users. The results revealed that purchasing behaviors of consumers when they use social commerce sites are affected directly by the utilitarian value (perceived usefulness) of the site as well as their purchase intention. Purchase intention is affected by perceived usefulness, subjective norm, and visiting behaviors. The visiting behaviors of consumers in relation to social commerce sites are also affected directly by the hedonic value (playfulness) of the site as well as their intention to visit the site. The findings of this study have implications for practitioners with regard to understanding and promoting the use of social commerce sites.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2170-2190
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• 2012

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.

• 한국정보시스템학회지:정보시스템연구
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• 제25권1호
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• pp.201-223
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• 2016

Purpose As social network games (SNG) enjoy rapid growth in the market and become a major sector of the gaming industry, it is of great interest to examine the how users continuously use SNG. In SNG, the users' social interaction is the most prominent advantage of the social network, as well as the entertainment afforded by the game. This study explores the relationship between altruism, which is considered the most prominent characteristic of SNS, and the continuance usage intention, as well as the moderating role of social capital. Based on social capital theory and organizational citizenship behavior, this research model considers social bonding and bridging that are divided by social capital. Design/methodology/approach An AMOS analysis based on survey data from 223 SNG users indicated that SNG with greater altruism enhance social capital (social bonding, social bridging), which is related to the user's satisfaction and the continuance intention of SNG. Findings Social bonding is positively related to the user's satisfaction with SNG. In other words, social bridging positively affects the continuous usage intention of SNG. These findings help managers in developing and implementing altruistic relationships and social capital for continuous usage of SNG.

• Asia pacific journal of information systems
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• 제17권1호
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• pp.95-121
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• 2007

There are recently several studies of online auctions which have focused on exploring a bidder's bidding behavior in IS area. Those studies, however, have been limited to account for bidders' bidding behavior in the view of TAM and trust, not considering perceived price fairness and competition intensity. Although this view point seems reasonable in the online auction sites, few previous studies employing this perspective are found in the relevant literatures. Based on it, this study developed a comprehensive model based on trust and TAM in terms of perceived price fairness to explain competition intensity in the online auction sites. This study collected 269 survey responses from online bidders who have prior experiences with online auction sites. The survey data are used to empirically verify 11 research hypothesis by using LISREL. The results indicate that trust in websites, trust in sellers and perceived usefulness have significant impacts on perceived price fairness. Finally, perceived price fairness is strongly related to competition intensity in the online auction. This study ends with theoretical and managerial implications, as well as limitations and future research.

• Structural Engineering and Mechanics
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• 제77권1호
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• pp.19-35
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• 2021

Existing methods to estimate the probability of seismic pounding occurrence of adjacent buildings do not account for nonlinear behavior or only apply to simple lumped mass systems. The present study proposes an efficient method based on subset simulation for fragility and risk assessment of seismic pounding occurrence between nonlinear adjacent buildings neglecting pounding effects with application to finite element models. The proposed method is first applied to adjacent buildings modeled as elastoplastic systems with substantially different dynamic properties for different structural parameters. Seismic pounding fragility and risk of adjacent frame structures with different floor levels is then assessed, paying special attention to modeling the non-linear material behavior in finite element models. Difference in natural periods and impact location are identified to affect the pounding fragility simultaneously. The reliability levels of the minimum code-specified separation distances are also determined. In addition, the incremental dynamic analysis method is extended to assess seismic pounding fragility of the adjacent frame structures, resulting in higher fragility estimates for separation distances larger than the minimum code-specified ones in comparison with the proposed method.

• 한국정보시스템학회지:정보시스템연구
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• 제32권1호
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• pp.53-84
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• 2023

Purpose The purpose of this study is to understand the factors that influence the purchasing behavior of NFT collectibles consumers, using the value-based acceptance model (VAM). As the use of NFTs is predicted to become more widespread in the near future, it is important to explore how these consumers make purchasing decisions. Design/methodology/approach This study employed text analysis and in-depth interviews to identify the factors of benefits and sacrifices perceived by consumers. Based on the results of the exploratory study, a research model and hypotheses were established. To test the hypotheses, an online survey was conducted, and the data was analyzed using a structural equation model. Findings The major findings of this study showed that perceived benefit factors had a significant positive impact on consumers' perceived value of NFT collectibles, whereas perceived sacrifice factors did not have a significant effect on perceived value. Also, when consumers' social needs were met, their perceived value was highest. Lastly, the effect of perceived value on purchase intention was not affected by the level of psychological ownership.

• Coupled systems mechanics
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• 제12권5호
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• pp.429-444
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• 2023

The comprehension and structural modeling of masonry constructions is fundamental to safeguard the integrity of built cultural assets and intervene through adequate actions, especially in earthquake-prone regions. Despite the availability of several modeling strategies and modern computing power, modeling masonry remains a great challenge because of still demanding computational efforts, constraints in performing destructive or semi-destructive in-situ tests, and material uncertainties. This paper investigates the shear behavior of masonry walls by applying a plane-stress FE continuum model with the Modified Masonry-like Material (MMLM). Epistemic uncertainty affecting input parameters of the MMLM is considered in a probabilistic framework. After appointing a suitable probability density function to input quantities according to prior engineering knowledge, uncertainties are propagated to outputs relying on gPCE-based surrogate models to considerably speed up the forward problem-solving. The sensitivity of the response to input parameters is evaluated through the computation of Sobol' indices pointing out the parameters more worthy to be further investigated, when dealing with the seismic assessment of masonry buildings. Finally, masonry mechanical properties are calibrated in a probabilistic setting with the Bayesian approach to the inverse problem based on the available measurements obtained from the experimental load-displacement curves provided by shear compression in-situ tests.