Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events

Agent based simulation method has become a prominent approach in computational modeling and analysis of public emergency management in social science research. The group emotions evolution, information diffusion, and collective behavior selection make extreme incidents studies a complex system problem, which requires new methods for incidents management and strategy evaluation. This paper studies the group emotion evolution and intervention strategy effectiveness using agent based simulation method. By employing a computational experimentation methodology, we construct the group emotion evolution as a complex system and test the effects of three strategies. In addition, the events-chain model is proposed to model the accumulation influence of the temporal successive events. Each strategy is examined through three simulation experiments, including two make-up scenarios and a real case study. We show how various strategies could impact the group emotion evolution in terms of the complex emergence and emotion accumulation influence in extreme events. This paper also provides an effective method of how to use agent-based simulation for the study of complex collective behavior evolution problem in extreme incidents, emergency, and security study domains

Parameter Optimization of Pure Electric Vehicle Power System Based on Genetic Algorithm

In this paper, the ADVISOR software was used to establish a complete vehicle model of an electric vehicle, and the model was verified by CYC_NEDC under European urban conditions to meet the requirements. The maximum power of the driving motor, the speed ratio of the transmission system and the capacity of the storage battery are taken as the optimization objectives to carry out multi-objective optimization. Connect the model built by genetic algorithm and ADVISOR, run the program to simulate the two together, and get the result of parameter optimization of dynamic system. Through the simulation analysis and comparison under CYC_NEDC cycle conditions, the maximum speed, maximum climb slope, acceleration time and other dynamic performance parameters of this electric vehicle are effectively improved after optimization

Damage Effect of Terrorist Attack Explosion-induced Shock Wave in a Double-deck Island Platform Metro Station

The objective of this research was to reasonably assess the damage to people and station structures caused by terrorist attack explosion at metro stations, taking the Liyuan station of Wuhan metro which adopts double-deck island platform as an typical example. The TNT explosion process inside the metro station was calculated and analyzed using the dynamic finite element numerical simulation software LS-DYNA. First, the peak overpressure curve and the positive pressure time curve of the shock wave of explosive under the condition of confined space in the metro station were obtained. Then, through the comparison and analysis of the theoretical formulas of explosive shock wave propagation characteristics, the accuracy and reliability of numerical calculation methods and model parameters were verified. At last, combining with the overpressure criterion of shock wave in explosive air, the distribution characteristics of the casualties in the metro station under the explosion shock wave are analyzed, and the dynamic response and damage effect of the pillar structure of the metro station under the explosion shock wave are studied

Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems

Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data. In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems. It leverages very few task-related parallel word pairs to generate code-switching sentences for learning the inter-lingual semantics across languages. Instead of manually selecting the word pairs, we propose to extract source words based on the scores computed by the attention layer of a trained English task-related model and then generate word pairs using existing bilingual dictionaries. Furthermore, intensive experiments with different cross-lingual embeddings demonstrate the effectiveness of our approach. Finally, with very few word pairs, our model achieves significant zero-shot adaptation performance improvements in both cross-lingual dialogue state tracking and natural language understanding (i.e., intent detection and slot filling) tasks compared to the current state-of-the-art approaches, which utilize a much larger amount of bilingual data.Comment: Accepted as an oral presentation in AAAI 202