The asymmetric loads on wind turbines are becoming a fatal constraint factor with the increase of wind turbine size. In order to tackle this challenge, individual pitch control mechanism is introduced into the wind turbines. However, the application scopeof the traditional individual pitch control (IPC) is limited due to the complex dynamic coupling among wind turbine components, the strong nonlinearity of the pitch system and the inevitable unmodeled dynamics. Therefore, a multivariable model-free adaptive control (MIMO-DE-MFAC) strategy with differential characteristic is first proposed in this paper. Then, the proposed method is applied to the individual pitch control of the 5MW wind turbine. The simulation results from FAST demonstrate that the MIMO-DE-MFAC achieves a notable performance improvement on blade root compared with the PI. Furthermore, compared with other existing model-free adaptive control (MFAC) strategies, the proposed method not only achieves the optimal control results, but also has stronger robustness in speed-varying wind fields. Finally, the hardware-in-the-loop (HIL) experiment further verifies the reliability of the above simulation results, which provides a useful reference value for the field test.

The leakage point location of the water supply pipeline (WSP) can be estimated by the time difference of the leakage characteristic signals obtained from both ends of the pipeline. It is very important to extract the characteristic signal in the frequency domain to accurately locate the leakage point. The variational mode decomposition (VMD) has been proven to be effective in extracting leakage characteristic frequency domain signals, but it needs to set parameters in advance to adjust the signal processing effect. Therefore, it is proposed to initialize the center frequency required for the VMD with the frequency corresponding to the peak of the power spectrum of the original signal and then solve the preset problem of VMD decomposition layers by the recursive method. In addition, mutual information is used as a screening criterion for composite pattern functions to select appropriate pattern components to reconstruct signals. The actual leak location results show that the location errors of the proposed algorithm are only 0.20 and 1.66% for 23.3 and 38.9 m pipeline lengths, respectively. This shows that the proposed method can achieve the accurate location of the water leakage point under long measurement distances and complex interference.

Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO₂) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO₂ is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO₂ concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained.

Currently, although semi-supervised image segmentation has achieved significant success in many aspects, further improvement in segmentation accuracy is necessary for practical applications. Additionally, there are fewer networks specifically designed for segmenting 3D images compared to those for 2D images, and their performance is notably inferior. To enhance the efficiency of network training, various attention mechanisms have been integrated into network models. However, these networks have not effectively extracted all the useful spatial or channel information. Particularly for 3D medical images, which contain rich spatial and channel information with tightly interconnected relationships between them, there remains a wealth of spatial and channel-specific information waiting to be explored and utilized. This paper proposes a bidirectional and efficient attention parallel network (BEAP-Net). Specifically, we introduce two modules: Supreme Channel Attention (SCA) and Parallel Spatial Attention (PSA). These modules aim to extract more spatial and channel-specific feature information and effectively utilize it. We combine the principles of consistency training and entropy regularization to enable mutual learning among sub-models. We evaluate the proposed BEAP-Net on two public 3D medical datasets, LA and Pancreas. The network outperforms the current state of the art in eight algorithms and is better suited for 3D medical images. It achieves the new best semi-supervised segmentation performance on the LA database. Ablation studies further validate the effectiveness of each component of the proposed model. Moreover, the SCA and PSA modules proposed can be seamlessly integrated into other 3D medical image segmentation networks to yield significant performance gains.