In this paper, multiple actively-controlled fans generate outdoor near-surface airflow in a wind tunnel with once-through openings. The wind tunnel system consists of 6 groups of fans installed on rotatable plates in a rectangular inlet. We use a stochastic strategy to control the fans and the rotatable plates to regulate wind speed and direction that fluctuate according to the pattern of outdoor near-surface airflow. We utilize the statistics and multi-scale methods to analyze the effectiveness of the strategy for simulating the outdoor near-surface airflow. We provide comparison studies on the multi-scale entropy of wind speed, wind stability, and the standard deviation of directions between out-door wind and the tunnel generated wind. Results show that a flow field akin to the near-surface airflow in outdoor environments can be produced by the wind tunnel using the stochastic control strategy, which can be considered as a reliable experiment environment for gas pollution source localization research in outdoor near-surface breeze conditions.
The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.
In traditional biomechanical analysis of upper limb, the high-precision motion data and lifelike human models are needed. It is obvious that those processes are costly and time-consuming. In this paper, a novel and simple combination method based on Kinect-LifeMOD is proposed. Firstly, the Microsoft Kinect (a latest depth sensor) is used to build a cheap and precise motion capture platform. Real-time and reliable key-node rotation data of human skeletons can be acquired by this motion capture system. Next, rotation data is converted into position data as the input of the LifeMOD software which can establish mathematical model of upper limb and execute biomechanical analysis automatically. The experimental results show that the proposed method could achieve the satisfactory performance.
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