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Weighted affordance-based agent modeling and simulation in emergency evacuation
Busogi, Moise,Shin, Dongmin,Ryu, Hokyoung,Oh, Yeong Gwang,Kim, Namhun Elsevier 2017 Safety science Vol.96 No.-
<P><B>Abstract</B></P> <P>This paper presents an agent-based human behavioral modeling framework to analyze probable human actions, in emergency situations, considering both physical and psychological dimensions, in emergency situations. Human’s prospective controls suggest that the environment can offer certain physical and psychological conditions to opt for a finite number of feasible human actions that lead to desired system states. A set of possible human actions is then generated and updated from the affordance-effectivity duals in a spatial-temporal dimension. In this paper, a reward and cost-based dynamic affordance-based agent model is built upon physical and psychological constraints that are inserted for the agents’ decision-making processes. The model employs Markov Decision Process (MDP), and NASA-TLX (Task Load Index) is used as cost and reward estimates. The action selection process of human agents, i.e., triggering of state transitions, is stochastically modeled in accordance with the action-state cost (load) values. A series of affordance-based numerical values are calculated for predicting prospective actions in the system. Finally, an evacuation simulation example based on the proposed model is illustrated to verify the proposed human behavioral modeling framework.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We present an agent-based simulation model for emergency evacuations. </LI> <LI> The model uses Markov Decision Process whose rewards are expressed using NASA – TLX. </LI> <LI> The model considers agent’s dynamic behaviors under different levels of emergency. </LI> <LI> The framework can be used to evaluate the dynamics of human-included safety systems. </LI> </UL> </P>
오영광,Moise Busogi,Kasin Ransikarbum,신동민,권대일,김남훈 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.12
The quality monitoring and control (QMC) has been an essential process in the manufacturing industries. With the advancements in data analytics, machine-learning based QMC has become popular in various manufacturing industries. At the same time, the cost effectiveness (CE) of the QMC is perceived as a main decision criterion that explicitly accounts for inspection efforts and has a direct relationship with the QMC capability. In this paper, the cost-effective support vector machine (CESVM)-based automated QMC system (QMCS) is proposed. Unlike existing models, the proposed CESVM explicitly incorporates inspection-related expenses and error types in the SVM algorithm. The proposed automated QMCS is verified and validated using an automotive door-trim manufacturing process. Next, we perform a design of experiment to assess the sensitivity analysis of the proposed framework. The proposed model is found to be effective and could be viewed as an alternative or complementary tool for the traditional quality inspection system.