Adaptive Weight Coefficients Research Articles

A Spectral Enhancement Method Based on Remote-Sensing Images for High-Speed Railways

This paper proposes a pansharpening model in order to obtain remote-sensing images with high spatial resolution and high spectral resolution. Based on a generic component substitution (CS) fusion framework, the model utilizes the difference between the high-frequency component of the panchromatic (PAN) image and the high-frequency component of the luminance (L) image to express the missing spatial detail information of the ideal high-resolution multispectral (HRMS) image. A rolling guidance filter (RGF) is used in this framework to achieve the effective extraction of high-frequency information from remote-sensing images while reducing the spectral distortion of subsequent operations. The modulation transfer function (MTF) values of the sensor are also applied to the selection of adaptive weighting coefficients to further improve the spectral fidelity of the fused images. At the same time, the choice of suitable interpolation and gain coefficients improves the generalizability of the model while reducing spectral and spatial distortions. Finally, the use of a guided filter (GF) also greatly improves the quality of the fused image. The experimental results show that the model can effectively improve the spatial resolution for foreign objects at the perimeter of high-speed railways, while also ensuring the color fidelity of foreign objects such as colored steel tiles.

Stability control of distributed drive electric vehicle based on adaptive fuzzy sliding mode

To improve the driving stability of a distributed drive electric vehicle (DDEV), we propose a hierarchical direct yaw moment control strategy. In the upper-level controller, an adaptive fuzzy sliding mode controller (AFSMC) is designed with joint control objectives of sideslip angle and yaw rate. In the conventional controller, the weight coefficients of the joint control objectives are fixed values, which reduces the adaptability of the system. To improve this problem and achieve adaptive adjustment of the weight coefficients of the control objectives with the vehicle motion state, an adaptive fuzzy control-based target weight coefficient controller is designed. In the lower controller, a quadratic programing method is used to assign torque with the optimization objective of minimizing tire attachment utilization. A joint simulation with Carsim and Simulink software was performed to compare and analyze the AFSMC vehicle and the traditional SMC vehicle under the steering wheel turning angle sinusoidal input and double shift line conditions. The results show that AFSMC effectively reduces the magnitude of vehicle sideslip angle, yaw rate, and lateral acceleration, enhances the driving stability of the vehicle, and enables adaptive adjustment of the weight coefficients of the joint control objectives, improving the adaptability of the controller. Finally, the effectiveness of the control strategy is verified by hardware-in-the-loop (HIL) test.

Distributed sparse identification for stochastic dynamic systems under cooperative non-persistent excitation condition

This paper considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic systems by using the local information from neighbors. A distributed sparse least squares algorithm is proposed by minimizing a local information criterion formulated as a linear combination of accumulative local estimation error and L1-regularization term. The upper bound of the estimation error of the proposed algorithm is presented. Furthermore, by designing a suitable adaptive weighting coefficient based on the local observation data, the set convergence of zero elements with a finite number of observations is obtained under a cooperative non-persistent excitation condition. It is shown that the proposed distributed algorithm can work well in a cooperative way even though none of the individual sensors can fulfill the estimation task. Our theoretical results are obtained without relying on the independency assumptions of regression signals that have been commonly used in the existing literature. Thus, our results are expected to be applied to stochastic feedback systems. Finally, the numerical simulations are provided to demonstrate the effectiveness of our theoretical results.

Regularization Solver Guided FISTA for Electrical Impedance Tomography.

Electrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton-Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.

Modulation of high-quality internal multifoci based on modified three-dimensional Fourier transform.

We report an efficient method to generate arbitrary three-dimensional (3D) parallel multifoci inside a material. Taking into account the numerical aperture of the objective lens and the refractive index of the material, the Ewald cap was modified with a longer radius, then the whole 3D intensity distribution inside the material could be calculated using only a single Fourier transform (FT). By introducing the adaptive weight coefficient, the uniformity of the 3D multifoci improves from 81.3% to 98.9%. By adjusting the axial resolution of the Ewald cap, the uniformity of the axial multifoci improves from 85.9% to 99.7%. In the experiment, we have realized one-dimensional (1D), 2D, and 3D structures inside the fused silica, which are in excellent agreement with the simulation results. The experimental results of the "H-U-S-T" structure demonstrate that customized arbitrary intensity distribution inside the material can be realized.

Multisource-Domain Adaptation-Based Damage Detection Method for Air-Conditioning Condenser Using Laser Ultrasonic Guided Wave

Air conditioning condenser (ACC) is a key component of air-conditioning equipment. ACC tube leakage damage will cause huge economic losses and energy waste. The laser ultrasonic guided wave detection method is suitable for ACC damage detection due to its high sensitivity and non-contact advantages. However, the ACC tube size is small and the weld joint is not standardized, which makes it difficult for the laser to aim at the tube. And the detection signals generated by laser excitation at different laser incident angles are different. In this paper, the Unsupervised Multi-source Adaptation Adversarial Network (UMAAN) is proposed to improve the generalization ability of the ACC detection model. Firstly, the feature diagrams of the signal are obtained by time-frequency analysis, and the diagrams are set to different source and target domains according to the different incident angles of laser excitation. Then, the shared feature extractor is used to obtain features of all samples. And the model parameters are optimized by adversarial network to make the features distance of the target and source domains closer. Among them, the adaptive weight coefficients are designed to adjust the loss value weights of the different source domains. And the label error weight factor is designed to improve the damaged signals detection accuracy by adjusting the loss weight of damaged samples and normal samples. Finally, the damage detection experiments of the ACC are carried out. The experimental results illustrate that the proposed UMAAN can achieve high-precision detection of ACC damages.

Fault Assessment for Mechanical Equipment with Adaptive Weights Incorporating Sensitivity and Monotonicity

Driven by the increasing needs in industrial processes for condition-based maintenance, this paper present an adaptive weighting strategy based on sensitivity and monotonicity for fault assessment. Since the sensitivities of features vary with the changes of fault severity, adaptive weight coefficients are designed based on sensitivities to strengthen the feature information. Meanwhile, considering the irreversibility of fault evolution and the difference of sensitive features to different faults, unique monotonic multi-domain feature set with high sensitivity can be selected. Finally, a monotonic health index (HI) is fused based on adaptive weight coefficients for fault assessment which satisfies the needs for intuitiveness in industrial sites. Moreover, the effectiveness of the proposed method is demonstrated by rolling bearing test rig. Results show that the average assessment accuracy can reach 87.5%, 95.375% and 95.375%.

Novel weight-adaptive fusion grey prediction model based on interval sequences and its applications

With the increasing complexity of systems, it is difficult to describe a huge amount of information accurately with real numbers. To make predictions using uncertain information, this study uses interval numbers as descriptors and proposes a weight-adaptive fusion grey prediction model based on interval sequences (WAFGM(1,N) model). Unlike most interval prediction models; the proposed model uses the adaptive weight and compensation coefficient to directly predict interval grey numbers without converting interval sequences into real number sequences. Moreover, it improves the four errors of the traditional GM(1,N) model, thereby further improving model accuracy. First, by fully considering the integrity and interaction of the upper and lower boundaries of the interval, this study introduced a weight parameter to comprehensively characterize the development trend of the interval, and by optimizing the background value and adjusting the compensation coefficient, the univariate and multivariate weight-adaptive grey prediction models for interval prediction were constructed, respectively. Second, the interval development and driving coefficients, compensation coefficient, and weight parameter were estimated and optimized using the least squares method, accumulation method, and particle swarm algorithm, respectively. Finally, the WAFGM(1,N) model was constructed by combining the two models. The validity and applicability of the WAFGM(1,N) model were proved using two examples: the gross agricultural output value of Northeast China and the production of dairy products in the golden milk source areas.

RETRACTED: Renewable source uncertainties effects in multi-carrier microgrids based on an intelligent algorithm

High volatility and unpredictable behavior of renewable energy sources provide various challenges in the operation of power systems with maintaining the system's reliability. To solve the challenges of grid-connected renewable energy sources, microgrids (MGs) can play an essential role due to their tuning capability and flexibility. The fast growth of power-to-gas technology causes to support renewable energy integration and transforms the extra renewable generation to combined normal gas at a suitable time. The optimal type, size, and commissioning year of the accessible distributed energy resources through the economic application of nominated resources are evaluated to be applied in the suggested multi-carrier microgrid. This study focuses on the simultaneous operation of multi-carrier MGs and their equipment as an optimization problem. Small-scale energy sources and load demand in MGs are modeled to reduce costs of exchange between the main grid and MGs, as well as the load balance between MGs and load demand. Given that it is difficult to solve the desired problem considering various constraints, the developed meta-heuristic whale optimization algorithm (WOA) is used along with adaptive weighting coefficients and a local search operator. Moreover, this model investigates the unstable behavior of loads, renewables, and electricity prices using the probabilistic load flow method (PLF), considering multiple carriers simultaneously to match the real world. In this paper, MGs with multiple-carriers exchange energy with the upstream grid. The energy exchange between MGs is also considered. The results of simulations performed on the grid consisting of three interconnected multi-carrier MGs confirmed the effectiveness of the suggested model. Obtained numerical results prove the validity of the suggested multi-carrier microgrid expansion planning issue by economic, environmental, and technical points.

Automatic Registration Algorithm for the Point Clouds Based on the Optimized RANSAC and IWOA Algorithms for Robotic Manufacturing

In order to solve the problems of low accuracy and low efficiency of point cloud registration for stereo camera systems, we propose a binocular stereo camera point cloud registration method based on IWOA and Improved ICP. We propose the following approaches in this paper—the registration process is divided into two steps to complete the initial coarse registration and the exact registration. In the initial registration stage, an improved Whale Optimization Algorithm (IWOA) based on nonlinear convergence factor and adaptive weight coefficients was proposed to realize the initial registration in combination with the RANSAC algorithm, and the obtained transformation matrix was used as the initial estimate of the subsequent exact registration algorithm. In the second step of the exact registration stage, an IICP algorithm with the introduction of normal vector weighting constraints at key points was proposed for achieving point cloud exact registration. This algorithm was verified by using Stanford point clouds (bunnies and monkeys) and our own point clouds algorithm, and the proposed algorithm in this paper has high registration accuracy, improved registration speed, and convergence speed.

In-wheel motor control system used by four-wheel drive electric vehicle based on whale optimization algorithm-proportional–integral–derivative control

Aiming to more accurately control the wheel speed of an electric vehicle (EV) driven by four in-wheel motors, a developed whale optimization algorithm-proportional–integral–derivative (KW-WOA-PID) control algorithm is proposed herein. In this study, mathematical and simulation models are built for EVs by analyzing the mechanical structures of EVs driven by four in-wheel motors. Simulations are conducted, and the driving and control requirements for the in-wheel motors are obtained. Then, mathematical and simulation models are built for a specific in-wheel motor. The whale optimization algorithm (WOA) is optimized by kent mapping and the adaptive weight coefficient to improve the ability of the algorithm to jump out of the local optimum and the convergence speed and convergence accuracy of WOA. Then the further simulations are conducted. The simulation results display that the maximum overshoot and adjustment time of the motor under KW-WOA-PID control are significantly optimized. Then, a speed-control bench test system is built for the in-wheel motor, and real-life experiments were conducted. The experimental results verify that KW-WOA-PID has higher control accuracy and a better response performance; accordingly, the developed control algorithm can meet driving requirements. The handling stability of EVs is effectively improved by controlling the motor speed.

Research on multi-energy complementary microgrid scheduling strategy based on improved bat algorithm

In order to improve the accuracy of integrated energy microgrid cluster scheduling operation in existing technologies, a scheduling optimization measure based on an improved bat algorithm was proposed in integrated energy system in this study. Firstly, the integrated demand response (IDR) was used to enhance the load-side energy utilization link. Secondly, the front-end data fragments of the integrated energy system are optimized and integrated based on fuzzy clustering and Mahalanobis distance. Finally, the bat algorithm is improved and substituted by increasing the adaptive inertia weight coefficient. In the simulation stage, the operation simulation test of the system was realized by MATLAB. Experiments show that the optimized integration method based on fuzzy clustering and Mahalanobis distance can make the front-end data achieve reliable clustering, and reduce the redundancy by more than 80%. After improving the bat algorithm, not only the MAPE and RMSE error of scheduling prediction can be reduced to 1.52% and 17.45%, but also the total cost of system scheduling operation can be reduced by about 2.5%. This study can improve the accuracy, economy and prediction speed of scheduling prediction, and has a good application prospect in the field of integrated energy scheduling optimization.

3-D Inversion of Gravity Anomalies by Combining Power-Spectrum Derived Adaptive Weighting and Cross-Gradient Regulation: Application to Molybdenum–Copper Deposit

Gravity measurement is an important geophysical prospecting method for mineral exploration. With most of the shallow ore deposits in China being exploited, the future targets for exploration will be aimed at the deep mineral resources about 4-km underground. To improve the resolving ability in the vertical direction of the 3-D density inversion to find deep-source minerals, we propose to achieve the inversion of gravity anomalies by combining power-spectrum derived adaptive weighting and cross-gradient regularization. First, spectral analysis is utilized to conduct the source-depth separation, and the adaptive weighting function is designed depending on the slope of the radial logarithmic power spectrum of the gravity anomaly, which can enhance the correspondence for the field sources with different depths to improve the vertical resolution relatively. Thus, each separated source with different depths could be given an adaptive weighting coefficient in each separated inversion. Second, the cross-gradient technique is introduced as a structural constraint in the objective function to further constrain the separated inversion process. By performing two-cuboid-source cases, it can be seen that this combination calculated approach has great potential to improve the quality of the inversion results. When examining the inversion of the actual gravity data from certain ore district in the west of Zhen’an, South Qinling, we speculate that a potential deep ore body would exist in the deposit via utilizing the recovered density model obtained by the proposed method.

Multi-Scale Exposure Fusion Based on Multi-Visual Feature Measurement and Detail Enhancement Representation

Multi-exposure image fusion (MEF) is used to generate a high-quality image from a series of images with different exposure levels. The multi-scale-based MEF method achieves better fusion performance than single-scale-based method because it can better preserve the global contrast information. However, this method still has the problem of the loss of details in the fused results. To solve this problem, a novel multi-scale exposure image fusion method based on multi-visual feature measurement and detail enhancement representation in the IHS color space is proposed. First, three visual features of the source multi-exposure images, namely contrast, saturation, and exposure, are measured and are then adopted to construct the initial weight maps with adaptive weighting coefficients. Second, to optimize the initial weight maps and obtain the middle weight maps, a decision map construction method is proposed by comparing the pixel values of the detail maps of the intensity components, which can enhance the representation of detail information. Third, guided filtering is applied to eliminate the noise in the middle weight maps to obtain the final weight maps, which improves the visual effect of the fused image. Finally, image pyramid decomposition and reconstruction are performed on the source images and the final weight maps to achieve the final fused image. Numerical experimental results indicate that the proposed method outperforms state-of-the-art methods in terms of subjective visual and quantitative evaluations.

A Paddy Field Segmentation Method Combining Attention Mechanism and Adaptive Feature Fusion

HighlightsA paddy field segmentation network model, SA-DeepLabv3+, for hilly areas in southern China is proposed.SA-DeepLabv3+ uses attention mechanism and adaptive spatial feature fusion algorithm.The deep model was trained end-to-end using a few annotated image datasets.SA-DeepLabv3+ has good segmentation results for the four common types of paddy fields.Abstract. The hilly areas of southern China have complex feature distributions, and the boundaries of cultivated land are irregular. This kind of environment causes several image segmentation problems such as low accuracy of UAV low-altitude remote sensing arable land image interpretation. This paper proposes a SA-DeepLabv3+ paddy field segmentation model that combines attention mechanism and adaptive spatial feature fusion algorithm based on DeepLabv3+ semantic segmentation model. The sampling areas include Yangwan Village, Duchang County, Jiangxi Province, China, Keli Village, Xinjian County, Nanchang City and Chengxin Farm, Nanchang City. A high-resolution image dataset was found by capturing low-altitude remote sensing images of paddy fields with DJI UAVs, and the dataset images are categorised into four types of paddy fields. By analysing the image features of the dataset, the feature map is remapped by incorporating scSE attention mechanism module in the backbone network of the DeepLabv3+ encoder to enhance the paddy field feature representation. The ASFF algorithm is applied in the decoder, which obtains an adaptive weight coefficient to improve the problem of inadequate fusion of multi-scale features in the upsampling. By comparing the segmentation results of models with different ASPP dilation rates, the optimal set of dilation rates is determined to improve the model's ability to extract fine features of paddy fields. Finally, the performance of this model for paddy fields segmentation is verified by comparison experiments of different models. The data show that the mean values of PA, MIoU, Recall, and F1 score of the SA-DeepLabv3+ paddy field segmentation model are 0.955, 0.875, 0.865, and 0.908, respectively, which are improved by 0.019, 0.032, 0.008, and 0.005, and also has a higher segmentation performance compared with the typical segmentation models of UNet, SegNet, and PSPNet. The results show that the SA-DeepLabv3+ paddy field segmentation model with high accuracy and robustness, which provides an important basis for further acquiring high-precision paddy field boundary positioning information and constructing high-precision maps of several paddy fields in larger areas, and plays a positive role in promoting efficient and accurate information management of paddy fields. Keywords: Attention mechanism, ASFF, DeepLabv3+ network, Paddy field.

Object Tracking Based on the Fusion of Roadside LiDAR and Camera Data

Tracking road users with high resolution is important for connected vehicles. Due to the complicated environments, tracking objects with a single sensor could not meet the requirements of high-resolution trajectories due to occlusions. How to acquire accurate and complete trajectories based on multi-source data is a major challenge for researchers and engineers. This paper developed a novel tracking method based on the fusion of roadside LiDAR and camera. According to the relationship between the number of points and distance, the adaptive weight coefficient related to 3D trajectory information was determined. The performance of the proposed method was evaluated at five selected sites. The proposed tracking method had high performance in terms of speed calculation, tracking range, the rate of object loss, and the repairing rate of disconnected trajectories. The proposed method can benefit many transportation areas, such as traffic volume counting, vehicle speed tracking, and traffic safety analysis.

Optimization of CSO algorithm based on adaptive inertia weight coefficient

The traditional CSO algorithm is easy to fall into local extremum in optimization. In this paper, a CSO algorithm based on weight coefficient is proposed. In the CSO algorithm, the inertia weight coefficient is introduced into the hen position formula, and the learning factor influenced by the rooster is added to the chick position formula. Finally, using the idea of heredity, individuals with excellent fitness value are selected for crossover and mutation with a certain probability. Through the simulation comparison of five typical test functions, the simulation results show that the improved CSO algorithm can avoid local optimization, strengthen the global extreme value search ability, and improve the convergence speed and accuracy range of the algorithm.

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

Magnetorheological (MR) dampers play a crucial role in various engineering systems, and how to identify the control parameters of MR damper models without any prior knowledge has become a burning problem. In this study, to identify the control parameters of MR damper models more accurately, an improved manta ray foraging optimization (IMRFO) is proposed. The new algorithm designs a searching control factor according to a weak exploration ability of MRFO, which can effectively increase the global exploration of the algorithm. To prevent the premature convergence of the local optima, an adaptive weight coefficient based on the Levy flight is designed. Moreover, by introducing the Morlet wavelet mutation strategy to the algorithm, the mutation space is adaptively adjusted to enhance the ability of the algorithm to step out of stagnation and the convergence rate. The performance of the IMRFO is evaluated on two sets of benchmark functions and the results confirm the competitiveness of the proposed algorithm. Additionally, the IMRFO is applied in identifying the control parameters of MR dampers, the simulation results reveal the effectiveness and practicality of the IMRFO in the engineering applications.

Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity

Active contour model is one of the most widely used image segmentation tools at present, but the existing methods only utilize single feature information of image to minimize the energy function, which is easy to cause false segmentations in infrared (IR) images. In this paper, we propose a multi-feature driven active contour segmentation model to handle IR images with intensity inhomogeneity. Firstly, an especially-designed signed pressure force (SPF) function is constructed by combining the global information calculated by global average gray information and the local multi-feature information calculated by local entropy, local standard deviation and gradient information. Then, we draw upon adaptive weight coefficient computed by local range to incorporate the afore-mentioned global term and local term. Next, the SPF function is substituted into the level set formulation (LSF) for further evolution. Finally, the LSF converges after a finite number of iterations and the IR image segmentation result is obtained from the corresponding convergence result. Experimental results demonstrate that the presented method outperforms typical models in terms of precision rate and overlapping rate in IR test images. The code is available at: https://github.com/MinjieWan/MFDACM.

Improved Auto Disturbance Rejection Control Based on Moth Flame Optimization for Permanent Magnet Synchronous Motor

AbstractIn this paper, an improved auto disturbance rejection controller (ADRC) is applied to permanent magnet synchronous motor (PMSM) speed control system. Specifically, the improved ADRC is proposed to solve the defect that the nonlinear function of traditional ADRC is not differentiable at the piecewise point. In addition, considering that there are numerous parameters to be optimized in ADRC, an improved moth‐flame optimization (MFO) is proposed to obtain ADRC parameters, and adaptive inertia weight coefficient and optimization mechanism based on the fusion of refraction operation and chaotic search are proposed to improve the convergence speed and optimization accuracy for MFO. Simulation and experimental results show that the improvement strategies proposed in this paper can increase the optimization performance for MFO algorithm effectively so that obtained more appropriative ADRC parameters. The improved ADRC can effectively improve the tracking control performance of PMSM speed control system. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.