• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.515-530
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• 2022

A factor graph and belief propagation can be used for finding stochastic values of link weights in biological networks. However it is not easy to follow the process of use and so we presented the process with a toy network of three nodes in our prior work. We extend this work more generally and present numerical example for a network of 100 nodes.

• Journal of the Ergonomics Society of Korea
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• v.34 no.6
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• pp.549-561
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• 2015

Objective: The purpose of this study was to investigate the job stress factors of mobile communication network construction workers using survey based on 'Job stress factors evaluation tool for Koreans' that was developed by KOSHA in 2003. Background: Due to the rapid growth of penetration rate of smartphone, the necessity of LTE service changing from 3G network was brought up. The demand of LTE network construction in a short period of time leads to the aggravation of the job stress of mobile communication network construction workers. Method: Two hundred and fifty workers who were in the mobile communication network industry participated in this study, and among them 206 responses were analyzed for this study due to the unreliability and insincerity of responses. The eight job stress factors which are physical environment, job demand, job autonomy, relation conflict, job instability, organizational system, inadequate compensation, workplace culture were analyzed. Results: The job stress factors of mobile communication network construction workers were compared to those of other industry workers, and other work related characteristics were analyzed. The results showed that the stress level of a physical environment and job requirement were relatively higher than those of manufacturing industry workers, meaning that mobile communication network construction workers have rough working conditions and increased amount of work due to the demand of LTE network construction. The stress level of physical environment for outdoor job workers was relatively higher than that of indoor job workers. With the analytical result for level of job satisfaction, significant difference was observed (p <0.05) with every factor, and the job stress was found the highest with those not satisfied with every factor Conclusion: From the results of this study, the work loss due to the job stress could be prevented, and accurate stress factors could be removed at the workplace. Application: The results of this study may not represent the whole mobile network construction workers, the effort for job stress management is needed to improve the work efficiency and the workers' quality of life.

• Bioinformatics and Biosystems
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• v.2 no.1
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• pp.24-27
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• 2007

Human cell regulatory mechanism is one of suspicious problems among biologists. Here we tried to uncover the human breast cancer cell regulatory mechanism from gene expression data (Marc J. Van de vijver, et. al., 2002) using a module network algorithm which is suggested by Segal, et. al.(2003) Finally, we derived a module network which consists of 50 modules and 10 tree depths. Moreover, to validate this candidate network, we applied a GO enrichment test and known transcription factor-target relationships from Transfac(R) (V. Matys, et. al, 2006) and HPRD database (Peri, S. et al., 2003).

• Geomechanics and Engineering
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• v.27 no.6
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• pp.605-614
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• 2021

Water inrush from fault is one of the most severe hazards during tunnel excavation. However, the traditional evaluation methods are deficient in both quantitative evaluation and uncertainty handling. In this paper, a comprehensive methodology method combined intuitionistic fuzzy AHP with a Bayesian network for the risk assessment of water inrush from fault in the subsea tunnel was proposed. Through the intuitionistic fuzzy analytic hierarchy process to replace the traditional expert scoring method to determine the prior probability of the node in the Bayesian network. After the field data is normalized, it is classified according to the data range. Then, using obtained results into the Bayesian network, conduct a risk assessment with field data which have processed of water inrush disaster on the tunnel. Simultaneously, a sensitivity analysis technique was utilized to investigate each factor's contribution rate to determine the most critical factor affecting tunnel water inrush risk. Taking Qingdao Kiaochow Bay Tunnel as an example, by predictive analysis of fifteen fault zones, thirteen of them are consistent with the actual situation which shows that the IFAHP-Bayesian Network method is feasible and applicable. Through sensitivity analysis, it is shown that the Fissure development and Apparent resistivity are more critical comparing than other factor especially the Permeability coefficient and Fault dip. The method can provide planners and engineers with adequate decision-making support, which is vital to prevent and control tunnel water inrush.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.621-625
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• 2018

This paper proposes a method for bridge damage factor recognition from inspection reports using deep learning. Bridge inspection reports contains inspection results including identified damages and causal analysis results. However, collecting such information from inspection reports manually is limited due to their considerable amount. Therefore, this paper proposes a model for recognizing bridge damage factor from inspection reports applying Named Entity Recognition (NER) using deep learning. Named Entity Recognition, Word Embedding, Recurrent Neural Network, one of deep learning methods, were applied to construct the proposed model. Experimental results showed that the proposed model has abilities to 1) recognize damage and damage factor included in a training data, 2) distinguish a specific word as a damage or a damage factor, depending on its context, and 3) recognize new damage words not included in a training data.

• Journal of Korea Game Society
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• v.13 no.3
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• pp.55-68
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• 2013

This study examined the factors determining experience of flow in mobile social network games. Built upon the literature on flow experience in the Internet and online games, this study classified the determining factors into three categories. The first category is the content factor which includes graphic design, challenge, and incentive; the second is the device factor including ease of access and ease of control; the third is the social factor including social interaction and community activities. A correlation analysis was conducted to examine the association between each of the seven determining factors and flow experience. Additionally, a hierarchical regression analysis was performed to evaluate which of the selected factors would exert a relatively strong influence on experience of flow. Both analyses reached the same conclusions as follows: Graphic design, incentive, and community activities increase flow experience while challenge and ease of control exert little influence on flow experience. In addition, graphic design was the most influential element in determining flow experience, followed by community activities and incentive, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1929-1950
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• 2020

Reliable and secure data transmission in the application environment assisted by the wireless sensor network is one of the major challenges. Problem like blind forwarding and data inaccessibility affect the efficiency of overall infrastructure performance. This paper proposes routing protocol for forwarding and error recovery during packet loss. The same is achieved by energy and hops distance-based formulation of the routing mechanism. The reachability of the intermediate node to the source node is the major factor that helps in improving the lifetime of the network. On the other hand, intelligent hop selection increases the reliability over continuous data transmission. The number of hop count is factor of hop weight and available energy of the node. The comparison over the previous state of the art using QualNet-7.4 network simulator shows the effectiveness of proposed work in terms of overall energy conservation of network and reliable data delivery. The simulation results also show the elimination of blind forwarding and data inaccessibility.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• 전기의세계
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• v.27 no.4
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• pp.41-44
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• 1978

Intermittent failures exercise their effects only part of the time but constitute a dominant factor for the field failures. We consider the data raliability of the partially self-checking network with which a single intermittent failure will be recovered by a rollback method. Even if the self-testingness of partially self-checking network is guranteed for a set of permanent failures, it sometimes may not be so for intermittent failures. We introduce the notion of error residual and provide the basis for calculating the data reliability. Both the duration of each intermittent failure and the occurrence interval of successive ones are assumed to be negative exponentially distributed; the convolution of the intervals is distributed according to an Erlangen distribution.

• Asia Marketing Journal
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• v.12 no.3
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• pp.1-24
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• 2010

Typically, marketing literature on innovation diffusion has focused on the pre-adoption process and only a few studies explicitly examined consumers' post-adoption behavior of innovative mobile services. Besides, prior use diffusion research has considered the variables that determine the consumers' initial adoption in explaining the post adoption usage behavior. However, behavioral sciences and individual psychology suggest that social influences are a potentially important determinant of usage behavior as well. The purpose of this study is to investigate into the effects of network factor and brand identification as social influences on the consumers' use diffusion or continued usage intention of a mobile service. Network factor designates consumer perception of the usefulness of a network, which embraces the concept of network externality and that of critical mass. Brand identification captures distinct aspects of social influence on technology acceptance that is not captured by subjective norm in situations where the technology use is voluntary. Additionally, this study explores the effect of the use diffusion on the brand switching intention, a generally unexplored form of post-adoption behavior. There are only a few empirical studies in the literature addressing the issue of IT user switching. In this study, the use diffusion comprises of rate of use and variety of use. The research hypotheses are as follows; H1. Network factor will have a positive influence on the rate of use of mobile services. H2. Network factor will have a positive influence on variety of use of mobile services. H3. Network factor will have a positive influence on continued usage intention. H4. Brand identification will have a positive influence on the rate of use. H5. Brand identification will have a positive influence on variety of use. H6. Brand identification will have a positive influence on continued usage intention. H7. Rate of use of mobile services are positively related to continued usage intention. H8. Variety of Use of mobile services are positively related to continued usage intention. H9. Rate of use of mobile services are negatively related to brand switching intention. H10. Variety of Use of mobile services are negatively related to brand switching intention. With the assistance of a marketing service company, a total of 1023 questionnaires from an online survey were collected. The survey was conducted only on those who have received or given a mobile service called "Gifticon". Those who answered insincerely were excluded from the analysis, so we had 936 observations available for a further stage of data analysis. We used structural equation modeling and overall fit was good enough (CFI=0.933, TLI=0.903, RMSEA=0.081). The results show that network factor and brand identification significantly increase the rate of use. But only brand identification increases variety of use. Also, network factor, brand identification and the use diffusion are positively related to continued usage intention. But the hypotheses that the use diffusion are positively related to brand switching intention were rejected. This result implies that continued usage intention cannot guarantee reducing brand switching intention.