This study proposes a cross-coupled dual-beam structure for energy harvesting from vortexinduced vibrations (VIV) induced by wind flows in different directions. A series of wind tunnel tests are conducted to investigate the performance of the proposed energy harvester subjected to the wind load with various speeds and directions. The upper and bottom piezoelectric beams can generate a maximum power output of 6.77 μW and 56.64 μW, respectively. The dominant operation frequencies in different directions are different which indicates a potential broadband capability. A parametric study is performed to reveal the effect of the dimension of the bluff body on the performance of the proposed energy harvester.

This study evaluated the performance of twin adjacent galloping-based piezoelectric wind energy harvesters based on mutual interference. The relative position between the twin harvesters is crucial to their energy generation efficiency. A series of wind tunnel tests were conducted to test energy generation of the two harvesters in tandem or staggered arrangements. The optimal relative position, a streamwise center to center spacing of 1.2B (B is the width of the harvester's square prism) and a transverse center to center spacing of 1.0B, was identified. The total output power of the two harvesters placed in the optimal relative position is up to 2.2 times that of two isolated harvesters. The output power is also much larger than that of the harvesters in tandem arrangements that have been widely tested in previous studies. Therefore, it is recommended to position two adjacent harvesters in optimal relative position(s) to deliver an optimal power output.

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Schools of fish can provide individuals with hydrodynamic advantages, thereby improving the swimming efficiency. Fish schools in nature are mostly spatial configurations, not just limited to the horizontal plane. Through three-dimensional numerical simulations, this paper discusses the hydrodynamic characteristics and flow field structure of fish schools in various vertical patterns. The results show that a school of fish arranged vertically can improve the thrust and swimming efficiency of individuals as well as those arranged horizontally. There are two ways to significantly enhance hydrodynamic advantages. One is to maximize the channeling effect in multiple planes. In a vertical circular pattern, the fish school forms multiple coupled channels, which hinder the free expansion of flow in both vertical and horizontal planes, thereby obtaining higher energy-saving benefits. The other is the combined exploitation of the channeling effect and wake energy, which is illustrated in the vertical rectangular pattern. The following fish can use the channeling effect to increase the thrust due to the presence of parallel companion. Meanwhile, the high speed region of the following fish can merge with the jet flows of the preceding fish, thereby capturing the wake energy and further improving swimming efficiency.

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Recently, sound-based diagnosis systems have been given much attention in many fields due to the advantages of their simple structure, non-touching measurement style, and low-power dissipation. In order to improve the efficiency of coal production and the safety of the coal mining process, accurate information is always essential. It is indicated that the sound signal produced during the cutting process of the coal mining shearer contains much cutting pattern identification information. In this paper, the original acoustic signal is first collected through an industrial microphone. To analyze the signal deeply, an adaptive Hilbert-Huang transform (HHT) was applied to decompose the sound to several intrinsic mode functions (IMFs) to subsequently acquire 1024 Hilbert marginal spectrum points. The 1024 time-frequency nodes were reorganized as a 32 × 32 feature map. Moreover, the LeNet-5 convolutional neural network (CNN), with three convolution layers and two sub-sampling layers, was used as the cutting pattern recognizer. A simulation example, with 10,000 training samples and 2000 testing samples, was conducted to prove the effectiveness of the proposed method. Finally, 1971 testing sound series were recognized accurately through the trained CNN and the proposed method achieved an identification rate of 98.55%. Therefore, it is widely applied in fault diagnosis [7,8], target detection [9], feature extraction [10], and so on.Unfortunately, the original cutting signal acquired from the coal mining field is always nonlinear, nonstationary, and discontinuous. It is an exceedingly difficult problem to extract key information from the signal. Thus, a powerful signal process method is one of the keys to settling the tough matter. However, typical sound signal analysis approaches such as short-time Fourier transform (STFT), wavelet transform (WT), and wavelet packet transform (WPT) are inappropriate to treat the cutting sound. Due to the characteristics of strong nonlinearity and nonstationarity, STFT is unable to play an effective role on the signal due to the Dirichlet condition and Heisenberg uncertainty principle [11,12]. Similarly, the WT and WPT do not work on interval cutting sound signal due to the fixed wavelet basis [13]. In 1998, an adaptive decomposing method, named the Hilbert-Huang transform (HHT), was established by National Aeronautics and Space Administration (NASA) [14]. The HHT is composed of empirical mode decomposition (EMD), proposed by Huang and Wu in 2008, and the Hilbert transform (HT) [15]. As the basis is adaptive, the HHT is not affected by the restrictions of previous approaches and becomes an attractive tool to find faults in diagnosis [16], speech recognition [17], signal denoising [18], pattern recognition [19], forecasting [20], and so on.After decomposing the original sound into a series of time-frequency characteristics, many researchers adopted some feature extractors to reduce the dimension and eliminate redundant information. The effectiveness of feature extracting is a key factor in t...

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