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In this paper, we present an application of the renormalization group method developed by Chen, Goldenfeld and Oono for a stochastic differential equation in a space of Hilbert space-valued generalized random variables with multiplicative noise. The driving process is a real-valued fractional white noise with a Hurst parameter greater than 1 / 2 . The stochastic integration is understood in the Wick–Itô–Skorohod sense. This article is a generalization of results of Glatt-Holtz and Ziane, which were for the systems with white noise. We firstly demonstrate the process of formulating the renormalization group equation and the asymptotic solution. Then, we give rigorous proof of the consistency of the approximate solution. In addition, some numerical comparisons are given to illustrate the validity of our results. [ABSTRACT FROM AUTHOR]

A 0.005% and 0.01% morin treatment was applied to treat mango fruits stored under ambient conditions (25 ± 1 °C) with 85–90% relative humidity, and the effects on quality indexes, enzyme activity related to antioxidation and cell wall degradation, and gene expressions involved in ripening and senescence were explored. The results indicate that a 0.01% morin application effectively delayed fruit softening and yellowing and sustained the nutritional quality. After 12 days of storage, the contents of soluble sugar and carotenoid in the treatment groups were 68.54 mg/g and 11.20 mg/100 g, respectively, lower than those in control, while the vitamin C content in the treatment groups was 0.58 mg/g, higher than that in control. Moreover, a morin application successively enhanced the activity of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), but reduced the activity of polygalacturonase (PG) and pectin lyase (PL). Finally, real-time PCR and correlation analysis suggested that morin downregulated the ethylene biosynthesis (ACS and, ACO) and signal transduction (ETR1, ERS1, EIN2, and ERF1) genes, which is positively associated with softening enzymes (LOX, EXP, βGal, and EG), carotenoid synthesis enzymes (PSY and, LCYB), sucrose phosphate synthase (SPS), and uncoupling protein (UCP) gene expressions. Therefore, a 0.01% morin treatment might efficiently retard mango fruit ripening and senescence to sustain external and nutritional quality through ethylene-related pathways, which indicates its preservation application. [ABSTRACT FROM AUTHOR]

Images captured in low-brightness environments typically have low brightness, low contrast, and high noise levels, which significantly affect the overall image quality. To improve the image quality, a low-brightness image enhancement algorithm based on multi-scale fusion is proposed. First, a novel brightness transformation function is used for the generation of two images with different brightnesses. Then, the illumination estimation technique is used to construct a weight matrix, which facilitates the extraction of advantageous features from each image. Finally, the enhanced image is obtained by the fusion of two images using the weight matrix and the pyramid reconstruction algorithm. The proposed method has a better enhancement effect as shown by the experimental results. Compared to other image enhancement algorithms, it has lower evaluation values in the natural image quality evaluator (NIQE) and lightness order error (LOE) indices. The lowest average NIQE value of the proposed algorithm in each dataset is 2.836. This further demonstrates its superior performance. [ABSTRACT FROM AUTHOR]

In this study, a dynamic model of a solar array drive system that includes a pair of flexible solar arrays with a central rigid shaft and a permanent-magnet synchronous motor (PMSM) was developed, and a disturbance compensation sliding mode control (DCSMC) strategy was proposed to realize the speed smoothing and vibration suppression control of the system. The continuous nonlinear dynamic equation of the system was derived from Hamilton's principle, and its linearized form was combined with the boundary conditions to obtain its natural frequency and global mode. The design of the DCSMC strategy was based on the solar array drive assembly (SADA) electromechanical dynamics model of the PMSM direct drive. An extended state observer (ESO) was used to estimate any system disturbances, and the signal was fed forward to sliding mode control (SMC) based on the varying gain saturation reaching law (VGSRL). To verify the validity of the model, its results were compared with those obtained using commercial finite element software. The numerical results showed that the SADA system with the DCSMC strategy outperformed the traditional proportional–integral (PI) control and SMC systems. [ABSTRACT FROM AUTHOR]

Stochastic parallel gradient descent (SPGD) algorithm is one of the most promising methods for effective coherent beam combination. However, the algorithm also has some disadvantages, such as slow convergence speed and local extremum. This paper proposes an AdaDelSPGD algorithm, which combines an AdaDelta algorithm with a SPGD algorithm, and improves the traditional AdaDelta algorithm with adaptive gain coefficient. It is worth noting that the adaptive gain coefficient can be adjusted in real time to improve the convergence rate. The effectiveness of the proposed algorithm is verified by relevant simulation experiments, and the results show that the proposed algorithm can significantly improve the convergence speed. Following the experiments with the fiber laser-phased array multiplex communication system, we can draw the conclusion that the addition of communication modulation reduces the beam quality, and the higher the modulation frequency, the worse the beam quality. However, adding the SPGD algorithm can improve the beam quality. The AdaDelSPGD algorithm proposed in this paper can further improve the beam quality, and the bit error rate of communication is also decreased after testing. This provides a foundation for further research on the fiber laser-phased array multiplex communication system. [ABSTRACT FROM AUTHOR]

Tunable mid-infrared lasers are widely used in laser spectroscopy, gas sensing and many other related areas. In order to solve heat dissipation problems and improve the environmental temperature adaptability of solid-state laser sources, a tunable all-fiber laser pumped optical parametric oscillator (OPO) was established, and a compact thermal control system based on thermoelectric coolers, an automatic temperature control circuit, cooling fins, fans and heat pipes was integrated and designed for the laser. This system is compact, light and air-cooling which satisfies the demand for miniaturization of lasers. A mathematical model and method was established to estimate the cooling capacity of this thermal control system under different ambient environments. A finite-element model was built and simulated to analyze the thermal transfer process. Experiments in room and high temperature environments were carried out and showed that the substrate temperature of a pump module could be maintained at a stable value with controlled precision to 0.2 degrees, while the output power stability of the laser was within ±1%. The experimental results indicate that this compact air-cooling thermal control system could effectively solve the heat dissipation problem of mid-infrared solid-state lasers with a one hundred watts level pump module in room and high temperature environments. [ABSTRACT FROM AUTHOR]

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment. [ABSTRACT FROM AUTHOR]

Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image. [ABSTRACT FROM AUTHOR]

Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability. [ABSTRACT FROM AUTHOR]

Due to the dense deployment of base stations (BSs) in heterogeneous cellular networks (HCNs), the energy efficiency (EE) of HCN has attracted the attention of academia and industry. Considering its mathematical tractability, the Poisson point process (PPP) has been employed to model HCNs and analyze their performance widely. The PPP falls short in modeling the effect of interference management techniques, which typically introduces some form of spatial mutual exclusion among BSs. In PPP, all the nodes are independent from each other. As such, PPP may not be suitable to model networks with interference management techniques, where there exists repulsion among the nodes. Considering this, we adopt the Matérn hard-core process (MHCP) instead of PPP, in which no two nodes can be closer than a repulsion radius from one another. In this paper, we study the coverage performance and EE of a two-tier HCN modelled by Matérn hard-core process (MHCP); we abbreviate this kind of two-tier HCN as MHCP-MHCP. We first derive the approximate expression of coverage probability of MHCP-MHCP by extending the approximate signal to interference ratio analysis based on the PPP (ASAPPP) method to multi-tier HCN. The concrete SIR gain of the MHCP model relative to the PPP model is derived through simulation and data fitting. On the basis of coverage analysis, we derive and formulate the EE of MHCP-MHCP network. Simulation results verify the correctness of our theoretical analysis and show the performance difference between the MHCP-MHCP and PPP modelled network. [ABSTRACT FROM AUTHOR]

Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the useful information in the previous individuals were fully exploited and used in the later optimization process, the quality of solutions may be improved significantly. In this paper, we propose several new updating strategies for EHO, in which one, two, or three individuals are selected from the previous iterations, and their useful information is incorporated into the updating process. Accordingly, the final individual at this iteration is generated according to the elephant generated by the basic EHO, and the selected previous elephants through a weighted sum. The weights are determined by a random number and the fitness of the elephant individuals at the previous iteration. We incorporated each of the six individual updating strategies individually into the basic EHO, creating six improved variants of EHO. We benchmarked these proposed methods using sixteen test functions. Our experimental results demonstrated that the proposed improved methods significantly outperformed the basic EHO. [ABSTRACT FROM AUTHOR]

Electromechanical actuator (EMA) systems are widely employed in missiles. Due to the influence of the nonlinearities, there is a flat-top of about 64 ms when tracking the small-angle sinusoidal signals, which significantly reduces the performance of the EMA system and even causes the missile trajectory to oscillate. Aiming to solve these problems, this paper presents a hybrid control for flat-top situations. In contrast to the traditional PID or sliding mode controllers that missiles usually use, this paper utilizes improved sliding mode control based on a novel reaching law to eliminate the flat-top during the steering of the input signal, and utilizes the PID control to replace discontinuous control and improve the performance of EMA system. In addition, boundary layer and switching function are employed to solve the high-frequency chattering problem caused by traditional sliding mode control. Experiments indicate that the hybrid control can evidently reduce the flat-top time from 64 ms to 12 ms and eliminate the trajectory limit cycle oscillation. Compared with PID controllers, the proposed controller provides better performance—less chattering, less flat-top, higher precision, and no oscillation. [ABSTRACT FROM AUTHOR]

Electromechanical actuator (EMA) systems are widely employed in missiles. Due to the influence of the nonlinearities, there is a flat-top of about 64 ms when tracking the small-angle sinusoidal signals, which significantly reduces the performance of the EMA system and even causes the missile trajectory to oscillate. Aiming to solve these problems, this paper presents a hybrid control for flat-top situations. In contrast to the traditional PID or sliding mode controllers that missiles usually use, this paper utilizes improved sliding mode control based on a novel reaching law to eliminate the flat-top during the steering of the input signal, and utilizes the PID control to replace discontinuous control and improve the performance of EMA system. In addition, boundary layer and switching function are employed to solve the high-frequency chattering problem caused by traditional sliding mode control. Experiments indicate that the hybrid control can evidently reduce the flat-top time from 64 ms to 12 ms and eliminate the trajectory limit cycle oscillation. Compared with PID controllers, the proposed controller provides better performance-less chattering, less flat-top, higher precision, and no oscillation., Competing Interests: The authors declare no conflict of interest.