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During the cold-start process of a PEMFC, the supply of air and hydrogen in the gas supply system has a great influence on the cold-start performance. The cold-start of a PEMFC is a complex nonlinear coupling process, and the traditional control strategy is not sensitive to the real-time characteristics of the system. Inspired by the strong perception and decision-making abilities of deep reinforcement learning, this paper proposes a cold-start control strategy for a gas supply system based on the MADDPG algorithm, and designs an air supply controller and a hydrogen supply controller based on this algorithm. The proposed strategy can optimize the control parameters of the gas supply system in real time according to the temperature rise rate of the stack during the cold-start process, the fluctuation of the OER, and the voltage output characteristics. After the strategy is trained offline according to the designed reward function, the detailed in-loop simulation experiment results are given and compared with the traditional control strategy for the gas supply system. From the results, it can be seen that the proposed MADDPG control strategy has a more effective coordination control effect. [ABSTRACT FROM AUTHOR]

In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo image pairs also have an important impact on the reconstruction accuracy in the process of network learning. Therefore, aiming at the information interaction and stereo consistency of stereo image pairs, a cross-view attention interaction fusion stereo super-resolution algorithm is proposed. Firstly, based on parallax attention mechanism and triple attention mechanism, an attention stereo fusion module is constructed. The attention stereo fusion module is inserted between different levels of two single image super-resolution network branches, and the attention weight is calculated through the cross dimensional interaction of the three branches. It makes full use of the ability of single image super-resolution network to extract single view information and further maintaining the stereo consistency between stereo image pairs. Then, an enhanced cross-view interaction strategy including three fusion methods is proposed. Specifically, the vertical sparse fusion method is used to integrate the interior view information of different levels in the two single image super-resolution sub branches, the horizontal dense fusion method is used to connect the adjacent attention stereo fusion modules and the constraint between stereo image consistency is further strengthened in combination with the feature fusion method. Finally, the experimental results on Flickr 1024, Middlebury and KITTI benchmark datasets show that the proposed algorithm is superior to the existing stereo image super-resolution methods in quantitative measurement and qualitative visual quality while maintaining the stereo consistency of image pairs. [ABSTRACT FROM AUTHOR]

In this paper, an improved self-organizing fuzzy neural network (SOFNN-CA) based on a clustering algorithm is proposed for nonlinear systems modeling in industrial processes. In order to reduce training time and increase training speed, we combine offline learning and online identification. The unsupervised clustering algorithm is used to generate the initial centers of the network in the offline learning phase, and, in the self-organizing phase of the system, the Mahalanobis distance (MD) index and error criterion are adopted to add neurons to learn new features. A new density potential index (DPI) combined with neuron local field potential (LFP) is designed to adjust the neuron width, which further improves the network generalization. The similarity index calculated by the Gaussian error function is used to merge neurons to reduce redundancy. Meanwhile, the convergence of SOFNN-CA in the case of structural self-organization is demonstrated. Simulations and experiments results show that the proposed SOFNN-CA has a more desirable modeling accuracy and convergence speed compared with SOFNN-ALA and SOFNN-AGA. [ABSTRACT FROM AUTHOR]

Microneedle systems have been widely used in health monitoring, painless drug delivery, and medical cosmetology. Although many studies on microneedle materials, structures, and applications have been conducted, the applications of microneedles often suffered from issues of inconsistent penetration rates due to the complication of skin-microneedle interface. In this study, we demonstrated a methodology of determination of transdermal rate of metallic microneedle array through impedance measurements-based numerical check screening algorithm. Metallic sheet microneedle array sensors with different sizes were fabricated to evaluate different transdermal rates. In vitro sensing of hydrogen peroxide confirmed the effect of transdermal rate on the sensing outcomes. An FEM simulation model of a microneedle array revealed the monotonous relation between the transdermal state and test current. Accordingly, two methods were primely derived to calculate the transdermal rate from the test current. First, an exact logic method provided the number of unpenetrated tips per sheet, but it required more rigorous testing results. Second, a fuzzy logic method provided an approximate transdermal rate on adjacent areas, being more applicable and robust to errors. Real-time transdermal rate estimation may be essential for improving the performance of microneedle systems, and this study provides various fundaments toward that goal. [ABSTRACT FROM AUTHOR]

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints. [ABSTRACT FROM AUTHOR]

Infrared maritime target detection is the key technology of maritime target search systems. However, infrared images generally have the defects of low signal-to-noise ratio and low resolution. At the same time, the maritime environment is complicated and changeable. Under the interference of islands, waves and other disturbances, the brightness of small dim targets is easily obscured, which makes them difficult to distinguish. This is difficult for traditional target detection algorithms to deal with. In order to solve these problems, through the analysis of infrared maritime images under a variety of sea conditions including small dim targets, this paper concludes that in infrared maritime images, small targets occupy very few pixels, often do not have any edge contour information, and the gray value and contrast values are very low. The background such as island and strong sea wave occupies a large number of pixels, with obvious texture features, and often has a high gray value. By deeply analyzing the difference between the target and the background, this paper proposes a detection algorithm (SRGM) for infrared small dim targets under different maritime background. Firstly, this algorithm proposes an efficient maritime background filter for the common background in the infrared maritime image. Firstly, the median filter based on the sensitive region selection is used to extract the image background accurately, and then the background is eliminated by image difference with the original image. In addition, this article analyzes the differences in gradient features between strong interference caused by the background and targets, proposes a small dim target extraction operator with two analysis factors that fit the target features perfectly and combines the adaptive threshold segmentation to realize the accurate extraction of the small dim target. The experimental results show that compared with the current popular small dim target detection algorithms, this paper has better performance for target detection in various maritime environments. [ABSTRACT FROM AUTHOR]