As quadruped robot is well-balanced, flexible and adaptable to environment, it has wide applications and has attracted the attention of numerous institutes. This paper combines theories of bionics and kinematics to design a quadruped robot and establish a gait control model for it. Firstly, it studies the bone structure of horse to analyze its structure advantages which are then applied to the design of the quadruped robot with the segment ratio of 1:1. Then the Kinematics formula and the traditional CPG gait control strategy are combined to establish the gait control model for quadruped robot which could output the joint control signal curves under stable gait.
Intelligent transportation system (ITS) plays an important role in solving today's transportation problems, and short-term traffic flow prediction is at its core. Deep learning can extract and capture abstract high-order features, and introducing attention mechanism to improve the performance of deep learning algorithm has been verified in many fields. Due to the complexity and randomness of traffic flow, accurate traffic flow prediction is not a simple task. Reasonable use of deep learning to predict traffic flow is of great significance to the whole transportation system. In this paper, the reason of choosing recurrent neural network (RNN) as the basic network for traffic flow prediction is explained. Aiming at the problem of gradient disappearance in practical application, the long short-term memory network (LSTM) is introduced to improve the model, and the model framework, algorithm and training process are described in detail. Attention mechanism is introduced into LSTM-RNN to build a short-term traffic flow prediction model. Applying the proposed model to observed traffic flow data, we found that the proposed model has higher prediction accuracy and model efficiency.
The problem of reservoir flood control operation is a multi-objective optimization problem. In order to solve this problem, a new algorithm based on graph convolution neural network and fast non-dominated sorting genetic algorithm II (GCN_NSGA-II) is proposed in this paper. When NSGA-II algorithm is used to solve multi-objective optimization problems, its convergence speed will slow down when the iteration reaches a certain algebra. In order to speed up the convergence speed, we use genetic algorithm to simulate the reproduction process of biological population. There is a relationship between parents and offspring. By means of group coding, the tree structure of the parents and children is transformed into a graph structure, and the GCN is trained and the graph nodes are classified, and the Pareto solution set can be obtained more quickly. In order to further ensure the integrity and uniformity, the NSGA-IIalgorithm is used to adjust it. The performance index IGD is used to measure the algorithm in the process. This algorithm speeds up the convergence speed and ensures the uniformity of Pareto solution set. The effectiveness of the algorithm in this paper is verified on the Xiaolangdi Reservoir flood control and dispatching problem. Compared with the NSGA-II and NSGA-DE algorithms, for the same index value, the number of iterations of the GCN_NSGA-II algorithm is significantly reduced and the convergence speed is significantly accelerated.
The evaluation and optimization of reservoir operation schemes belong to a multi-objective, multi-level and multi-attribute decision-making problem. The reservoir multi-objective dispatching model generates many Pareto feasible solutions, and decision-makers often make decisions difficult. The traditional scheme selection method has the problems that the index weight is greatly affected by subjectivity, the single weight determination method is one-sided, the model calculation is complicated, and the characteristics of the evaluation index cannot be fully extracted. Therefore, this paper proposes a new method for reservoir operation plan optimization based on fuzzy optimization and convolutional neural network. First, establish the evaluation index system of the reservoir operation plan based on the fuzzy optimization theory, select the analytic hierarchy process to determine the subjective weight of the index, the entropy weight method to determine the objective weight, use the game theory to couple the subjective and objective weights, and calculate the comprehensive evaluation value of the plan through fuzzy comprehensive evaluation. Secondly, the evaluation index and comprehensive evaluation value are used as the input and output of the convolutional neural network to establish the optimal model of the reservoir operation plan. The results of the case analysis show that the research method has high accuracy and reliability, and can provide a scientific basis for reservoir operation decision-making.
We present results on light weakly interacting massive particles (WIMPs) searches with annual modulation (AM) analysis on data from a 1-kg mass $p$-type point-contact germanium detector of the CDEX-1B experiment at the China Jinping Underground Laboratory. Data sets with a total live-time of 3.2 years within a 4.2-year span are analyzed with physics threshold of 250 eVee. Limits on WIMP-nucleus (${\chi}$N) spin-independent cross-sections as function of WIMP mass ($m_{\chi}$) at 90\% confidence level (C.L.) are derived using the Halo Dark Matter model. The 90\% C.L. allowed regions implied by the DAMA/LIBRA and CoGeNT AM-based analysis are excluded at $>$99.99\% and 98\% C.L., respectively. These results correspond to the best sensitivity at $m_{\chi}$$<$6$~{\rm GeV/c^2}$ among WIMP AM measurements to date.
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