• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.2
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• pp.123-134
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• 2023

Disordered eating behaviors, such as overeating, are known to be contagious in the general population. The objective of our research is to find an optimal control strategy to reduce the social burden of unhealthy overeating behavior by establishing and analyzing a mathematical model for the social transmission dynamics of unhealthy overeating. We consider four compartments in the population: normal weight with normal eating behavior, normal weight with overeating behavior, overweight with normal eating behavior, and overweight with overeating behavior. Simulation results under various control scenarios show that integrated control measures may be necessary to reduce the growth rate of the overeating population.

• Journal of Internet Technology
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• v.22 no.4
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• pp.903-911
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• 2021

As cyber-attacks on Cyber-Physical System (CPS) become more diverse and sophisticated, it is important to quickly detect malicious behaviors occurring in CPS. Since CPS can collect sensor data in near real time throughout the process, there have been many attempts to detect anomaly behavior through normal behavior learning from the perspective of data-driven security. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) model using only a normal dataset for anomaly detection. We use auto-encoder to reduce the dimensions of the dataset and the K-means clustering algorithm to classify the normal data into the optimal cluster size. The DL model trains to predict clusters of normal data, and we can obtain logit values as outputs. The derived logit values are datasets that can better represent normal data in terms of knowledge distillation and are used as inputs to the OCC model. As a result of the experiment, the F1 score of the proposed model shows 0.93 and 0.83 in the SWaT and HAI dataset, respectively, and shows a significant performance improvement over other recent detectors such as Com-AE and SVM-RBF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1583-1598
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• 2024

With the popularization and application of artificial intelligence technology in daily life, new ethical and moral problems constantly appear in human society. These ethical and moral problems have been associated with people's moral behavior and have become crucial issues. In traditional social situations, researches have proved that moral intensity affects people's moral behavior. However, in the context of applying artificial intelligence technology, the mechanism between moral intensity and moral behavior is unknown. Therefore, this study focuses on the relationship between moral intensity and moral behavior in the context of applying artificial intelligence technology, and introduces a new concept - technology moral sense (TMS) into the theoretical model. Research method: We set various situations of applying artificial intelligence technology and adopt the situational experiment method to analyze the relationship between moral intensity and moral behavior in different application scenarios. The results show that moral intensity has a significant influence on moral behavior, while the technology moral sense performs a mediating function.

• Journal of Microbiology and Biotechnology
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• v.17 no.1
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• pp.146-153
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• 2007

A mathematical competition model between normal flora and an invading pathogen was devised to allow analysis of bacterial infections in a host. The normal flora includes the various microorganisms that live on or within the host and act as a primary human immune system. Despite the important role of the normal flora, no mathematical study has been undertaken on models of the interaction between it and invading pathogens against a background of antibiotic treatment. To quantify key elements of bacterial behavior in a host, pairs of nonlinear differential equations were used to describe three categories of human health conditions, namely, healthy, latent infection, and active infection. In addition, a cutoff value was proposed to represent the minimum population level required for survival. The recovery of normal flora after antibiotic treatment was also included in the simulation because of its relation to human health recovery. The significance of each simulation parameter for the bacterial growth model was investigated. The devised simulation showed that bacterial proliferation rate, carrying capacity, initial population levels, and competition intensity have a significant effect on bacterial behavior. Consequently, a model was established to describe competition between normal flora and an infiltrating pathogen. Unlike other population models, the recovery process described by the devised model can describe the human health recovery mechanism.

• The Journal of Information Systems
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• v.28 no.2
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• pp.129-152
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• 2019

Purpose The purpose of this study is to empirically identify the risky behavior of mobile device users using the Internet of Things on a situational perspective. Design/methodology/approach This study made a design of the research model based on model of the determinants of risk behavior. Data were collected through a survey including hypothetical scenario. SmartPLS 2.0 was used for the structural model analysis and t-test was conducted to compare the between normal and situational behavior. Findings The results were as follows. First, the central roles of risk propriety and risk perception were verified empirically. Second, we identified the role of locus of control as a new factor of impact on risky behavior. Third, mobile risk propensity has been shown to increase risk perception. Fouth, it has been shown that risk perception does not directly affect risky behavior and reduce the relationship between mobile risk propensity and risk behavior. According to the empirical analysis result, Determinants of risk behavior for mobile users were identified based on a theoretical framework. And it raised the need to pay attention to the impact of locus of control on risk behavior in the IS security field. It provided direction to the approach to risky behavior of mobile device users. In addition, this study confirmed that there was a possibility of taking risky behavior in the actual decision-making.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.209-216
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• 2000

The lateral behavior of the moving web is critical to the quality of the web products. The alignment of the rollers carrying the web is found to be one of important factors to the lateral behavior of the moving web. But, the study on the effect of the tilting roller in the direction of the normal to the moving web on the lateral behavior has not been reported in the literature yet. For example, the contact roller often contacts the winding roll in a tilted fashion and causes the lateral motion of the winding web, which induces the offset on the wound roll. The lateral dynamics of the moving web induced by a tilted roller in normal direction of a web is investigated in this paper. The two-dimensional dynamic model developed by Shelton is extended to investigate the effect of a titled roller in a normal direction of the moving web on the lateral motion of the moving web. New boundary conditions are developed to solve the extended model. Computer simulation study proved that the model developed can be used to predict the lateral motion of the moving web ？ to a tilted roller in normal direction of the moving web. The lateral deflection is increased exponentially a the tilting angle is increased. As the length of web span is increased, the amount of lateral deflection was increased almost linearly for the same tilting angle. The lateral dynamics turned out to be almost independent to the operating tension. The model developed can be used to solve the offset problem of the staggered winding and also to design a new web guiding mechanism.

• Korean Journal of Acupuncture
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• v.38 no.3
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• pp.162-174
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• 2021

Objectives : We aimed to identify the antidepressant effect of liver tonification acupuncture treatment (ACU (LT); KI10, LR8, LU8, LR4) and four gate acupuncture treatment (ACU (FG); LI4, LR3) and its brain neural activity in the normal and chronic restraint stress (CRS)-induced mouse model. Methods : Firstly, normal mice were given ACU (LT) or ACU (FG) and the c-Fos expressions in each brain region were analyzed to examine brain neural activity. Secondly, CRS was administered to mice for 4 weeks, then ACU (LT) or ACU (FG) was performed for 2 weeks. The depression-like behavior was evaluated using open field test (OFT) before and after acupuncture treatment. Then, the c-Fos expressions in each brain region were analyzed to examine brain neural activity. Results : In normal mice, ACU (FG) regulated brain neural activities in the hypothalamus, hippocampus, and periaqueductal gray. ACU (LT) changed more brain regions in the prefrontal cortex, insular cortex, striatum, and hippocampus, including those altered by ACU (FG). In CRS-induced model, ACU (LT) alleviated depression-like behavior more than ACU (FG). Also, brain neural activities in the motor cortex area 2 (M2), agranular ventral part and piriform of insular cortex (AIV and Pir), and cornu ammonis (CA) 1 and CA3 of hippocampus were changed by ACU (LT), and those of AIV and CA3 were also changed by ACU (FG). As in normal mice, ACU (LT) resulted in changes in more brain regions, including those altered by ACU (FG) in CRS model. M2, Pir, and CA1 were only changed by ACU (LT) in depression model, suggesting that these brain regions reflect the specific effect of ACU (LT). Conclusions : ACU (LT) relieved depression-like behavior more than ACU (FG), and this acupuncture effect was associated with modulation of brain neural activities in the motor cortex, insular cortex, and hippocampus.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.141-148
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• 1998

An analytical model which can simulate the post-cracking behavior of reinforced concrete structures subjected to in-plane shear and normal stresses is presented. Based on the force equilibriums, compatibility conditions, and bond stress-slip relationship between steel and concrete, a criterion to simulate consider the tension-stiffening effect is proposed. The material behavior of concrete is described by an orthotropic constitutive model, and focused on the tension-compression region with tension-stiffening and compression softening effects defining equivalent uniaxial relations in the axes of orthotropy. Correlation studies between analytical results and available experimental data are conducted with the objective to establish the validity of the proposed model.

• Korea-Australia Rheology Journal
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• v.11 no.3
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• pp.225-232
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• 1999

This study presents 1-dimensional simple model for sheet casting or rectangular fiber spinning process. In order to achieve this goal, we introduce the concept of force flux balance at the die exit, which assigns for the extensional flow outside the die the initial condition containing the information of shear flow history inside the die. With the Leonov constitutive equation that predicts non-vanishing second normal stress difference in shear flow, we are able to describe the anisotropic swelling behavior of the extrudate at least qualitatively. In other words, the negative value of the second normal stress difference causes thickness swelling much higher than width of extrudate. This result implies the importance of choosing the rheological model in the analysis of polymer processing operations, since the constitutive equation with the vanishing second normal stress difference is shown to exhibit the characteristic of isotropic swelling, that is, the thickness swell ratio always equal to the ratio in width direction.