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Herein, in situ water reflectance and TSM data obtained from several experiments on the Nanyi Lake from 2018 to 2022 and the Sentinel-3 Ocean and Land Colour Instrument (OLCI) satellite synchronization data were used to compare four atmospheric correction methods (FLAASH, 6S, ACOLITE, and C2RCC) and construct an empirical model for TSM inversion in the Nanyi Lake to analyze the spatial and temporal changes in the water quality in the Nanyi Lake from 2018 to 2023. On the Sentinel-3 OLCI data, the C2RCC algorithm showed the highest accuracy and overall performance stability (RMSE: 0.0014–0.0051 Sr−1, MAPE: 18.44%–68.47%, and BIAS: from −3.68% to 23.63%). The highest correlation was observed between the three-band ratio (B9 + B18)/B10 and the in situ TSM; the TSM inversion model constructed based on this inversion factor showed the best accuracy for the Nanyi Lake (R2: 0.76, RMSE: 5.01 mg/L, and MAPE: 28.46%). The spatial and temporal changes in TSM in the Nanyi Lake exhibited significant regularity. Specifically, the TSM was higher in 2018–2019, significantly decreased in 2020, and stabilized in 2021–2023. Owing to the effects of human activities, precipitation, and illumination, seasonal variation in the TSM in the Nanyi Lake was detected, with TSM decreasing in the following order: summer > autumn > spring > winter. Concerning spatial variations, high TSM was observed in the northwest, northeast, and southeast of the Nanyi Lake reclamation area and its surrounding lake area. River confluence and human activities affected the area, leading to significant fluctuations in TSM in 2018–2023.

Numerous spatiotemporal fusion (STF) methods have been developed to generate surface reflectance data with high spatial and temporal resolutions for dynamic monitoring. Although comparative studies have been conducted to assess various fusion methods, selecting the most suitable fusion method for acquiring long-term time series data remains a challenge. This article compared four representative STF methods based on the effect of 8 × n-day (n = 1, 2, …, 7) time scales between base and predicted data. These methods included the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), enhanced STARFM (ESTARFM), and sensor-bias driven spatio-temporal fusion model (BiaSTF). Accuracy was assessed using metrics such as the root-mean-square error, correlation coefficients, erreur relative globale adimensionnelle de synthèse, and spectral angle mapper. The results indicate that as the time scale increases, fusion accuracy decreases, with a significant drop observed at the 40-day mark. Compared with the 8-day scale, at the 40-day scale, the ERGAS of STARFM decreased by 20.66%, that of FSDAF by 17.00%, that of ESTARFM by 14.37%, and that of BiaSTF by 11.48%. Furthermore, STF methods based on two pairs of images demonstrate a notable advantage in capturing data from distant temporal phases. In regions with pronounced phenological changes and longer time scales, BiaSTF consistently exhibits the best fusion performance (ERGAS = 1.67), followed by ESTARFM (ERGAS = 1.85). These findings can aid in determining the most suitable STF methods and provide guidelines for the development of new methods.

Unmanned aerial vehicles have a wide range of applications. An intelligent optimization algorithm based on the traditional bat algorithm (BA) is investigated in this paper for UAV flight path planning in a static complex environment. The primary goal of this work is to develop a safer flight path while considering the feasibility of the UAV and the requirements for safe operation. This research proposes an improved spherical coordinate and truncated average stable strategy-based bat optimization algorithm (TMS-SBA). The algorithm uses the UAV’s motion space to encode the operator, and by substituting a new bat for the worst of the old one after each iteration to increase population diversity, the algorithm can converge quickly in a complex environment while maintaining stable operation. In addition, the flight path is smoothly generated by using B-spline curves to make the planned path suitable for UAV. MATLAB simulation experiments show that, compared with other traditional swarm intelligent algorithms, TMS-SBA can successfully generate feasible and effective optimal solutions in complex environments and plan shorter, safer, and more accessible flight paths for UAV.

A droop controlled microgrid with distribution static compensator (DSTATCOM) is developed to improve the power quality in this study. Due to the reactive power/voltage Q-V droop characteristic and the existence of the unbalanced, linear inductive and nonlinear loads, the power quality problems, including the voltage drop, unbalanced currents, lagging power factor (PF) and current harmonics, are very serious in the islanded microgrid. Moreover, owing to the instantaneous power following into or out of the DC-link capacitor of the DSTATCOM under load variation, the performance of the DSTATCOM for power quality improvement is seriously degenerated. Hence, to effectively improve the power quality of the droop controlled microgrid and the transient response of the DC-link voltage of the DSTATCOM under load variation, an online trained polynomial petri fuzzy neural network (PPFNN) controller is firstly proposed as the DC-link voltage controller to supersede the conventional proportional-integral (PI) controller in the DSTATCOM. The network structure and the online learning strategy of the proposed PPFNN are detailedly derived. Finally, the effectiveness of the DSTATCOM using the proposed PPFNN controller to improve the unbalanced currents, the total harmonic distortion (THD) reduction of the current and to compensate the reactive power for the voltage support and PF correction in the droop controlled microgrid is certified.

The digital back-propagation (DBP) is an effective algorithm to compensate for the nonlinear impairments in high-baud-rate and long-haul optical systems. The nonlinear coefficient of the DBP algorithm is optimized based on the mean power at the step position. However, when the number of steps per span of the DBP algorithm is fixed below the optimum step number to constrain computational complexity, the optimum nonlinear coefficient is sensitive to the number of steps per span. In this paper, we used the average value of the mean power over the step size to optimize the nonlinear coefficient of the DBP algorithm. The modified mean-power-distribution-based nonlinear coefficient optimization scheme was applied to constant step-size DBP (MMPD-CS-DBP) and logarithmic step-size DBP (MMPD-LS-DBP) algorithms in 30 × 80 km SSMF single-channel Nyquist 64 GBaud and 5 × 64 GBaud Nyquist WDM polarization multiplexing 16QAM systems, respectively. Simulation results showed that the optimum nonlinear coefficient of the MMPD-CS-DBP and MMPD-LS-DBP algorithms was the nonlinear coefficient of the optical fiber and was robust to the number of steps per span.

This article investigates an inverse problem for variable physical and mechanical comprehensive parameters. A back analysis method based on an improved BFGS (Broyden-Fletcher-Goldfarb Shanno-Newtonian iteration) and AC (adjustment coefficient) is proposed to estimate the physical and mechanical comprehensive parameters of complex concrete arch dam structures. These quasi-viscoelastic variable physical and mechanical comprehensive parameters of concrete arch dam structures are established based on in situ monitoring data and test results. By studying the constitutive model of concrete arch dam structures and the corresponding characterization method of the gradual changes of those parameters, a method utilizing adjustment coefficient is established to determine the initial values of the physical and mechanical comprehensive parameters and an adjoin method combined with BFGS Newtonian iteration method is proposed to inverse the variable physical and mechanical comprehensive parameters of concrete arch dam structures and analyze the variation regulation of these parameters. The proposed inversion analysis method was successfully implemented for the second-highest concrete double curvature arch dam. The results indicate that the proposed method has high precision and strong generalizability.

A fiber nonlinearity compensation scheme based on a complex-valued dimension-reduced neural network is proposed. The proposed scheme performs all calculations in complex values and employs a dimension-reduced triplet feature vector to reduce the size of the input layer. Simulation and experiment results show that the proposed neural network needed only 20% of computational complexity to reach the saturated performance gain of the real-valued triplet-input neural network, and had a similar saturated gain to the one-step-per-span digital backpropagation. In addition, the proposed scheme was 1.7 dB more robust to the noise from training data and required less bit precision for quantizing trained weights, compared with the real-valued triplet-input neural network.

Electromagnetic forming is a novel technology in which a coil is used to generate a pulsed Lorentz force for shaping sheet metal pieces. However, the Lorentz force between the coil and the sheet metal is generally repulsive due to the Lenz's Law, which limits the practical application of this technology. To break this bottleneck, a dual-coil method is proposed to generate attractive Lorentz force, which is then used to form sheet metals. The theoretical principle of this method is presented, and an infinite element model is established to simulate the forming effect. The simulation results show that an A1060-O plate with a diameter of 140 mm and thickness of 1 mm can be attractively shaped under this method. The maximum axial displacement of the plate reaches 2.74 mm. Unfortunately, a dent exists in this situation. Further analysis indicates that this dent can be eliminated by increasing the inner and outer diameters of the coils. Further, it is found that when the capacitance value of the long pulse-width discharge circuit is set to 5.6 mF and the initial discharge voltage is set to 10.690 kV, the sheet metal attains the best shape.

The attractive electromagnetic bulging is a novel technology which has unique advantages in the formation of small tube fittings. To generate the attractive electromagnetic force, a dual-frequency current method has been proposed in the existing research on the attraction bulging of the small tube fittings. This method, however, has a flaw that the tube fittings are subjected to a large repulsion electromagnetic force before the attraction, which will lead to a negligible effect on the final forming results. To solve this problem, a non-uniform solenoid coil structure is proposed in this paper to suppress the repulsion to improve the attraction. On this basis, a finite element model is built to analyze the process of an aluminum tube with an inner diameter of 20 mm and an outer diameter of 22 mm and the thickness of 1 mm. The influences of the coil structure on the magnetic induction intensity B, eddy current density Je and the stress of the coil have been studied. The results show that the expansion of the tube can be improved when the non-uniform solenoid coil is used. The maximum expansion in this dissertation is achieved with a 28 turns non-uniform solenoid, and its expansion uniformity standard deviation is only 0.156. These simulations have a significant outcome to the design of the coils and the application of electromagnetic bulging in small tube fittings.

Electromagnetic forming (EMF) is a kind of high-energy-rate forming technology which uses pulsed electromagnetic forces to achieve rapid plastic deformation of workpieces. It can significantly improve the forming limit and formability of light-weight alloys and has been widely used in the field of tube forming. However, in the existing tube forming systems, the electromagnetic force between the tube and the coil is mainly dominated by a repulsive force. When flaring the tube, the coil must be placed inside the tube, which is not suitable for the forming of small tubes. In order to solve this problem, a method of flaring small tube with an outer coil is developed, where the coil current is generated by a dual-power supply system and a significant attractive electromagnetic force between the coil and the tube is produced for flaring forming. The feasibility of the method has been demonstrated by both numerical simulations and experiments. It has been shown that the flaring of an A1060-O aluminum alloy tube with diameter of 20 mm can be successfully realized, and the maximum radial deformation is about 4.88 mm. Meanwhile, it has been pointed out that the performance of tube flaring can be further improved by changing the relative position of tube and coil and the discharge voltage value of the dual-power supply system. The obtained results provide a new flaring tool for small tubes and expand the application of electromagnetic forming technology in tube processing.

Due to severe economic losses caused by forest diseases and pests in China, prediction for the spread of forest diseases and pests has become one of the most challenging and hottest issues. The most previous solutions have at least the following three disadvantages: (1) lacking effective utilization of image data; (2) only supporting one-dimensional prediction value, which provides limited information; (3) limiting to a small scale (e.g., sample-plot), rather than a large scale like a forest zone. Therefore, we propose an algorithm for the spread prediction based on linear regression applied to a large regional spread of forest diseases and pests. Compared to the most conventional numerical prediction, our prediction method works on two dimensions. Specifically, the diseases and pests areas are fitted by a group of cubic B-spline curves and a defined energy function is provided to describe the difference between the contours of the future time and the current time. Then, linear regression is applied to predict the spread parameters (the distance and angle), adhering to prior forestry research. After two-step corrections, the final predicted contour is obtained. Finally, we devise an appropriate 3D interactive visualization. Experimental results indicate that the proposed algorithm can effectively predict the spread of forest diseases and pests, providing forestry workers with visual aids of the future situation.

At present, solenoid (helical) coils are widely used in various researches, especially electromagnetic forming (EMF), due to their advantages of simple manufacture and high magnetic field strength. However, in the study of EMF, there are still some defects in the high precision calculation of solenoid coils in the magnetic field the main reason is that the solenoid coil is of non-axisymmetric structure, which is generally equivalent to the 2-D or 3-D axisymmetric structure in practical research. Not only can it ignore the effect of the pitch effect of the solenoid coil, but also causes a certain deviation in the calculation of the magnetic field. Hence, this paper establishes 5 helical coils and 3-D axisymmetric finite element coils models with different layers for comparative analysis. The simulation results show that the magnetic flux density distribution deviation of helical coil and the 3-D axisymmetric coil is small and can be ignored when the coil layers are 1 or 2 layers. However, the number of linear circles increasing, the deviation between the two increases obviously. Therefore, in the study of EMF, the effect of solenoid coil pitch effect on magnetic field calculation can be ignored when the layers, radius and pitch of helical coils are used less whereas the effect of solenoid coil pitch effect on magnetic field calculation must be considered when the layers, radius and pitch of helical coils are used more.

Unmanned road roller has become an attractive solution to reduce operators' working intensity and improve compaction efficiency. As a result of working in bumpy environments, sideslip effect is unavoidable during the compacting process. In order to guarantee the reliability and stability of path tracking control, a novel and effective method based on preview control and sideslip compensation is proposed in this paper. The kinematic and dynamic models of articulated road roller considering sideslip angles are obtained first. Then, a preview controller with sideslip compensation (PCSC) is developed to eliminate the path tracking errors between the actual path and the desired path. To evaluate the control performance of the proposed method, simulation under the MATLAB/Simulink and automatic compaction experiments at the construction site were performed with initial errors. Finally, the simulation and experimental results show that the path tracking errors can converge to zero as time goes by. The proposed method has the ability to navigate an articulated road roller to track a desired path in the existence of sideslip, only regarding the steering angle dynamic as control input. Compared with other benchmark method, it not only improves the control accuracy, but also increases the convergence rate, proving its effectiveness and feasibility. All these also demonstrate the practical value and validity of the proposed models with sideslip.

This paper investigates the problem of the filter design for linear switched systems with interval time-varying delays under asynchronous switching. A Lyapunov function is designed by using the Lyapunov stability principle. When there exists asynchronous switching between the filter and the system, we prove the stability with a prescribed weighted L2 - L∞ performance of the designed linear time-delay augmented filtering error switched systems by using the average dwell time approach. In addition, we use the linear matrix inequality (LMI) method to solve L2 - L∞ filter design problem and obtain the filter parameters by solving a set of matrix inequalities. Finally, some numerical simulations are given to illustrate the validity and reliability of the conclusions.

The VLD technique has introduced for the propose of achieving the ideal surface electric field via a non-uniformly doped drift region. Yet, the ideal doping profile is impossible to be fulfilled in practical thus breaking the optimized lateral breakdown characteristic. In addition, the conventional 2-D methods, due to its complexity, are impractical in both explaining its physical nature and providing designing guidance. In this paper, a simple 1-D methodology based on Effective Concentration Profile (ECP) theory is proposed to provide the physical insight of the VLD technique and quantitatively depict its breakdown characteristic. The VLD-ECP concept indicates that the perfectly even surface electric field can be obtained by adjusting the drift region doping dose equals to the Charge Appointment Line (CAL) so that all the charges in drift region contribute to the vertical depletion, thus the lateral structure being an equivalently P-I-N junction. Considering non-ideal doping profile of commercial devices, a designing optimization criterion is proposed to avoid the undesirable lateral breakdown. The analytical results obtained by the proposed model are found to be sufficiently accurate comparing with TCAD simulation results verifying the veracity and effectiveness of the proposed methodology.

In this paper, we propose a new low turn-off loss silicon-on-insulator (SOI) lateral insulated gate bipolar transistor (LIGBT) with buried variation of lateral doping (VLD) layer. The proposed device features a VLD layer inserted in the drift region, which increases the doping dose (Q) and gradient (G) compared with Uniform P-buried (UPB) SOI LIGBT. The larger capacitance effect induced by lager Q and faster depletion leads to the lower rising anode voltage and reduced storage charge in the drift region. Therefore, a considerable low turn-off loss (Eoff) can be obtained. It is worth to note that owing to reshaped electric field in the new structure, the excess carriers of the drift region could be removed more quickly. Furthermore, larger G of the VLD layer improves the tradeoff between breakdown voltage and turn-off loss. The results of 2-D simulation indicate that the Eoff of the proposed device can reduce by 29.4% and 69.7% at 100 A $\cdot$ cm−2 and 200 A $\cdot$ cm−2, respectively, when compared with UPB SOI LIGBT

As a significant technology in the automotive manufacturing industry, weight reduction in vehicle design has attracted much attention. Its effect on energy saving and emission reduction is prominent. The application of lightweight material is commonly adopted as a primary way of weight reduction. However, material selection is often subject to multi-perspective performance characteristics, e.g., mechanical and societal properties, and therefore, an effective multi-criteria decision-making (MCDM) method is needed. This paper presents a systematic hierarchical structure of multi-perspective indices for optimal lightweight material selection, including mechanical, durability, societal, and technical properties. A hybrid evaluation approach (G-TOPSIS) integrating grey relation analysis and technique for order performance by similarity to ideal solution (TOPSIS) is applied to evaluate lightweight material alternatives and obtain an optimal one. A case study, i.e., 17 kinds of lightweight materials, is conducted to verify the hierarchical structure and the MCDM method. In addition, a sensitivity analysis is conducted to monitor the robustness of solution ranking to changes. The results show that this method provides an effective decision-making tool for optimal lightweight material selection for automobile applications.