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  • 1. Zhao, J.
    et al.
    Chen, X.
    Duan, X.
    Shen, Tong
    KTH, School of Electrical Engineering (EES).
    Study on demand forecasting and allocation of expressway emergency vehicle resource2014In: Journal of Computational Information Systems, ISSN 1553-9105, Vol. 10, no 10, p. 4205-4215Article in journal (Refereed)
    Abstract [en]

    Emergency vehicles are the key resources of expressway for the rescue mission to traffic accidents. For optimal emergency vehicle resources allocation, an improved Case-Based Reasoning (CBR) and an allocation model are established. First, the effecting factors of traffic safety on expressway are analyzed to establish an accident hazard indicator system. A difference coefficient is introduced to improve Case-Based Reasoning for predicting the demand of emergency vehicle resources. Then, the resource allocation model is established based on the objective constrained optimization method, which tries to find the minimum rescue time with constraints such as road hazard, rescue time, resource demand, and the configuration result is obtained by adopting Particle Swarm Optimization (PSO). Finally, according to the study of a bridge case, it shows that the prediction error of resource demands becomes smaller by using the improved case-based reasoning method and the resource allocation results are reasonable through the calculation of objective constrained optimization model.

  • 2. Zhao, Jiandong
    et al.
    Li, Zhaoxuan
    Zheng, Jun
    Shen, Tong
    KTH, School of Electrical Engineering (EES).
    Walking Stability Compensation Strategy of a Small Humanoid Robot Based on the Error of Swing Foot Height and Impact Force2013In: International Journal of Advanced Robotic Systems, ISSN 1729-8806, E-ISSN 1729-8814, Vol. 10, p. 227-Article in journal (Refereed)
    Abstract [en]

    In order to reduce the impact force of swing legs and improve walking stability when a small humanoid robot is walking, a set of impact dynamics equations based on the second kind Lagrange equation is produced, and an impact compensation control strategy with a BP network optimized by a particle swarm algorithm is designed. The core element of the compensation controller is replacing the error back propagation with a particle swarm algorithm. Due to the regulating joints of the knee, hip and ankle, the walking process is more stable than before. The experiment results show that when the left swing leg lands, the impact force drops by 2N and 1.5N respectively in the moments 4.5s and 10.5s. Therefore, the compensation strategy can reduce the impact force effectively and improve the walking stability.

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