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Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state-of-the-art methods. With features extraction-based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.

In this paper, a more effective Gaussian denoiser is designed to enhance the resulting image quality. We propose a novel image denoising method using a multiscale dilated residual network, named MDRNet. The proposed method is based on two main strategies. First, we adopt dilated convolutions in our network to enlarge the receptive field while requiring fewer parameters. The hybrid dilation rate pattern (HDP) is implemented such that each pixel in the pattern contributes similarly to the receptive field, allowing our network to learn the image details equally. Second, we employ a contextualized structure to take advantage of the low-level features which are mainly concentrated in the first two layers. Our method achieves competitive denoising performance and requires fewer parameters compared to existing denoising methods that using convolutional network. Through comprehensive experiments, we show that the denoising performance of our method is competitive with the state-of-the-art methods in terms of both quantitative and qualitative evaluation.

The photovoltaic cell pattern and geometry affect the origination, level and distribution of stresses in the modules. The cut cell patterns can be potential alternative of conventional full cell patterns in terms of enhanced stability, reduced hotspot effects, etc. Here, we study the thermo-mechanical behavior of cut cell (half cut and one by three cut cell) and full cell modules during manufacturing and hotspot formation. The 3D geometrical models of modules with maximum details are simulated for these processes. Modules with cut cells are found more resistant to thermo-mechanical loads. The transitory and permanent hotspot cycles with different temperature ends are applied; and it is found that the stress level increases with the increase in temperature and dwell time of hotspots. Due to permanent hotspots with upper extreme end of 110 °C, the busbars experience compressive stresses of −137 MPa, −122 MPa, and −106 MPa in full, half cut, and one by three cut cell modules respectively; and cells experience compressive stresses of −76 MPa, −80 MPa, −72 MPa in full, half cut, and one by three cut cell modules respectively. With the use of cut cells, interconnectors thickness can be reduced and parametric analysis shows that the thinner interconnectors lead to lower stresses in cells. During thermal loading, strain energy is stored in the interconnectors due to deformation and it increases with the increase in thermal loading i.e. for each degree rise in temperature, strain energy increases. In addition, solutions to avoid or reduce hotspot formation are also outlined.

With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.

The uncertainty in the output power of the photovoltaic (PV) power generation station due to variation in meteorological parameters is of serious concern. An accurate output power predictio...

Fault diagnosis in rolling bearings is an indispensable part of maintaining the normal operation of modern machinery, especially under the varying operating conditions. In this paper, an end-to-end adaptive anti-noise neural network framework (AAnNet) is proposed to solve the bearing fault diagnosis problem under heavy noise and varying load conditions, which takes the raw signal as input without requiring manual feature selection or denoising procedures. The proposed AAnNet employs the random sampling strategy and enhanced convolutional neural networks with the exponential linear unit as the activation function to increase the adaptability of the neural network. Moreover, the gated recurrent neural networks with attention mechanism improvement are further adopted to learn and classify the features processed by the convolutional neural networks part. Besides, we try to explain how the network works by visualizing the intrinsic features of the proposed framework. And we explore the effect of the attention mechanism in the proposed framework. Experiments show that the proposed framework achieves state-of-the-art results on two datasets under varying operating conditions.

Defect detection in photovoltaic (PV) modules and their impact assessment is important to enhance the PV system performance and reliability. To identify and analyze the defects, an improved outdoor infrared (IR) thermography scheme is presented in this study. The indoor (dark) and outdoor (illuminated) IR experiments are carried out on normal operating and defective PV modules. The indoor and outdoor measurements for normal operating modules are similar. However, the measurements for defective modules show difference i.e. the outdoor images show fewer or not at all defects in comparison to indoor images. Subsequent to this, outdoor imaging is carried out with our improved outdoor thermography scheme. This scheme is based on modulating the temperature of PV module through altering the electrical behavior of single cell. Therein, a PV cell is shaded in different fractions to attain different current conditions between open circuit and maximum power point, that causes temperature changes in series connected cells leading to different temperature conditions. The images obtained by this scheme provide clearer and detailed information about defects which is much similar to that given by indoor IR images. The severe and mild defective regions show temperature difference of more than 30° and 20° respectively in outdoor. The performance factor (PF) based on translated power output is also calculated for studied two modules that represents the quantitative impact of defects. The PF for PV module 1 and 2 is reduced from 97% to 31% and from 96% to 88% respectively with induction of defects. The PF values correlate with IR measurements of these modules. Furthermore, an image processing scheme comprising image filtering, color quantization and edge detection operations, is presented, that locate the edges of severe and mild defective regions in IR images.

Thermo-mechanical loads induce stresses in photovoltaic (PV) modules, leading to crack formation. In this context, the understanding of module's thermo-mechanical behavior is important. To investigate the thermo-mechanical behavior of smart wire connected technology (SWCT) and busbarPV modules throughout their entire life, the present study is conducted that probes the stress distribution and deformation during production, transportation, and subsequent mechanical and thermal loading stages in a consecutive step-by-step manner using finite element modellingapproach. Pre-stresses and non-linearitiesare considered in simulation models. Stresses and displacements experienced by different parts/layers are examined, and crack sensitive regions are identified. In addition, the SWCTand busbarmodules are compared, and it is found that SWCTinterconnection is relatively a less stress inducing process and less susceptible to thermal and dynamic affects. During production stage, stresses of 39.3 MPaand 40.4 MPaare generated in SWCTcells and copper wires respectively; while, stresses of 60 MPaand 87 MPaare generated in busbarcells and busbarrespectively. Similarly, lower stresses are induced in SWCTPV modules during subsequent stages. The comparison results show advantages of SWCTmodule in terms of mechanical stability which can lead to improve the performance and reliability of PV modules.

Feature extraction and feature selection are vital steps to construct an intelligent diagnosis system for classifying the weld flaws from an X-ray image. Deep learning has been successfully used in image analysis and automatic object recognition. It has good performance for learning more representative hierarchical features that are more sensitive to classification. However, there are still few applications of deep learning in feature learning for classifying different weld flaws, and few studies have been performed to compare the feature classification ability of different feature extraction methods. In this paper, we developed a model based on a deep convolutional neural network (DCNN) to extract the deep features directly from X-ray images. To validate the effectiveness of deep features, we cropped patches from the X-ray images as the learning dataset. Furthermore, considering the imbalance of the number of patches with different weld flaws, we used 3 types of resampling methods for 3 balanced datasets. Using the datasets, we compared the classification ability of 5 types of features extracted using traditional methods and deep learning. The best results were obtained for the deep features from the proposed DCNN model, achieving an accuracy of 97.2%, which is considerably higher than that obtained using the traditional feature extraction methods. We believe that the proposed model could be used to help workers evaluate X-ray images more intelligently.

Absolute phase unwrapping in the phase-shifting profilometry (PSP) is significant for dynamic 3-D measurements over a large depth range. Among traditional phase unwrapping methods, spatial phase unwrapping can only retrieve a relative phase map, and temporal phase unwrapping requires auxiliary projection sequences. We propose a shading-based absolute phase unwrapping (SAPU) framework for in situ 3-D measurements without additional projection patterns. First, the wrapped phase map is calculated from three captured images. Then, the continuous relative phase map is obtained using the phase histogram check (PHC), from which the absolute phase map candidates are derived with different fringe orders. Finally, the correct absolute phase map candidate can be determined without additional patterns or spatial references by applying the shading matching check (SMC). The experimental results demonstrate the validity of the proposed method.

Low-light image is easily degraded by real noise, which brings great challenges for image enhancement task because the enhancement process will amplify the noise. To address this problem, we propose an amplified noise map guided network (AMG-Net), which simultaneously achieves the low-light enhancement and noise removal by extracting amplified noise map to guide the network training. Specifically, we build an encoder-decoder network as the basic enhancement model to get a preliminary enhanced image that usually includes amplified noise. Subsequently, we fed the preliminary enhanced image into a noise map estimator to continuously estimating the amplified noise map during the enhancement process by adopting residual connection. Finally, a residual block with adaptive instance normalization (AIN) is used to build a denoising model, which is guided by the noise map estimator to remove the amplified noise. Extensive experimental results demonstrate that the proposed AMG-Net can achieve competitive results compared with the existing state-of-the-art methods.

This study proposes an improved extended state observer (ESO) to eliminate the effects of disturbances on the speed control system of an aerial camera lateral swing. First, a mechatronics model of ...

The presented dual-drive long-travel precise positioning stage is employed in grating ruling engine and capable of extended travel and high precision. Based on the characteristic of grating ruling engine, a dual-drive positioning system is developed. The primary positioning stage is an open-loop system and designed to obtain long-travel range. The secondary positioning stage with a control system can guarantee the positioning precision being less than 10 nm. After modeling the fine positioning system, a single-neuron-based PID controller is employed to implement the control system. The experiment results demonstrate that in the long-travel mode, the stage is capable of performing positioning with an extended move range and accuracy being less than 10 nm. Meanwhile, in the short-travel mode, the stage performs positioning with a limited range of 120 μm and accuracy being less than 7 nm. The main sources of the positioning deviation including clearance, vibration, and the measurement error are analyzed in detail.

Based on the study of RBS algorithm in wireless sensor network, an improved algorithm named RBRS is presented, to solve the time synchronization problem of multi hop networks. On the basis of RBS, the broadcast group and least square linear regression methods were used to realize entire network time synchronization in the algorithm. Synchronization error and overhead with the existing improved BRS are compared in the article. The result shows that the algorithm has certain superiority in many algorithms, which is suitable for light and low power consumption network load. Through an emulation with the matlab software, the result indicates that error accumulation is lower in the optimized algorithm, the synchronization overhead is significantly reduced and can realize the time synchronization of the whole network. A time synchronization algorithm based on RBRS algorithm is proposed for multi-hop and the low power consumption. A variable period synchronization method is introduces in the algorithm: in line with Bias maximum a posterior estimation principle, the maximum phase offset is estimated to determine the synchronization period, which can reduce the number of node synchronization, and with the least square linear regression method, the periodic fitting clock offset. The Simulation in matlab indicate that RBRS algorithm can improve the synchronization accuracy and reduce energy consumption significantly, which is conducive to extend the life of the network.

Arbitrary two-dimensional (2-D) motion introduces coordinate errors and phase errors to three-dimensional (3-D) shape measurement of objects in phase-shifting profilometry (PSP). This paper presents a new robust 3-D reconstruction method for arbitrary 2-D moving objects by introducing an adaptive reference phase map and the motion estimation based on fence image. First, a composite fence image is used to track object motion. Second, to obtain the transformation matrixes and remove the coordinate errors among object images, the angle extraction technique and the 1-D hybrid phase correlation method (1-D HPCM) are integrated to automatically estimate the sub-pixel motion of objects. Third, the phase errors are compensated to obtain the rough absolute phase map of objects by combining the transformation matrixes with the reference phase map. Finally, the absolute phase map is refined to reconstruct the 3-D surfaces of moving objects with adaptive reference phase map. The proposed computational framework can accurately and automatically realize 3-D shape measurement of arbitrary objects with 2-D movement. The results of experiment verify the effectiveness of our computational framework.

The quality of ground surface pregrouting (GSPG) is commonly qualitatively evaluated using single factor; however, the quality evaluation involves numerous facets, and a quantitative evaluation is rare. The aim of this study was to quantitatively evaluate the quality of a GSPG based on three aspects. The fuzzy analytic hierarchy process (FAHP) was adopted to obtain the final score and quality classification of the GSPG. Based on three aspects, namely, integrity, continuity, and sturdiness, a series of field tests was also conducted, qualitatively evaluating the quality of the GSPG preliminary, to obtain the required test data and verify the FAHP results. The results of the FAHP showed that the quality of the GSPG was 86.42, which could be classified as “Good”, whereas the field tests exhibited that the GSPG was effective, thereby verifying that the FAHP was reliable. In addition, the proposed method provided a detailed comprehension of the quality evaluation of GSPG and a frame of reference for analogous engineering.

It is essential to enhance the ability of wind speeds forecasting for wind energy and wind resource planning. For this purpose, a hybrid strategy has been proposed based on spatio-temporal covariance model which combined the spatio-temporal ordinary kriging (STOK) technology with autoregressive integrated moving average (ARIMA) regression smoothing method. This is because wind speed time series exhibits a long-term dependency. In the case study, both STOK method and ARIMA method are employed and their performances are compared. The ARIMA model can obtain a necessary and sufficient smoothing condition for them to be smoothed. Meanwhile, further theoretical analysis is provided to discuss why the STOK method is potentially more accurate than the ARIMA method for wind speed time series prediction. Results show that the proposed method outperforms the Non-Sep-Gneiting model by 9% and 7.2% in terms of mean absolute error (MAE) and root-mean-square error (RMSE).

Short-term wind speed prediction is an essential task for wind resource and wind energy planning. However, most of this literature does not take into account the spatio-termporal correlation of wind data from the geographical field. For this reason, we propose an integrated spatio-temporal kriging and functional kriging strategy to exploit such spatio-temporal correlation into the wind speed prediction. First, the deterministic trend component in wind data is estimated to be removed. The residuals are used for spatio-temporal modeling and prediction. Based on the spatio-temporal kriging framework, four spatio-temporal covariance models (product-sum model, separable exponential product model, separable and nonseparable Gneiting models) are considered which describe the spatio-temporal correlation of wind data. In particular, the flexibility of using the nonseparable Gneiting model is highlighted. More specifically, four spatio-temporal random fields are modeled from the 12 wind monitoring stations over Ireland. We also use an involved weighted least squares method for estimating parameters of the four covariance models involved in the spatio-temporal kriging strategy. We apply the fitted covariance models to generate day-ahead wind speed predictions at both observed and nonobserved locations where wind station already exist but also to nearby locations. Leave-one-out cross-validation is applied to check the significance of the difference among the four models, these spatio-temporal ordinary kriging (STOK), functional ordinary kriging (FOK) and autoregressive integrated moving average (ARIMA) methods are compared for day-ahead wind speed predictions. Forecasting results indicate that the predicting accuracy is improved almost 33.5% using FOK compared with three approaches which confirm the effectiveness of the functional kriging method in the paper.

Sparse component analysis (SCA) has been extensively studied to solve undetermined blind source separation problem in various fields over the last decades. This paper proposes a SCA algorithm based on hierarchical Hough transform. The hyperplanes clustering within the mixture space are revealed as local maxima in the parameter space after Hough transform is performed. Then, the local maxima are picked up, and the mixing matrix is calculated. The grid resolution in which the parameter space is divided plays an important role on the estimation error and the computational load. Therefore, the parameter space is divided into hypercubes recursively from low to high resolution, and Hough transform is performed only on the hypercubes with votes exceeding a selected threshold. The grid resolution selection problem is solved, and the computational load is reduced a lot in the meantime. After the mixing matrix is obtained, the sources are recovered with $$\ell _1$$l1-norm optimization. Numerical simulation and a speech separation application illustrate the superior performance of the proposed algorithm.

Sparse component analysis (SCA) has been introduced to the output-only modal identification for several years. This paper proposes a new method based on hierarchical Hough transform to extract the modal parameters of mechanical structures. First, the measured system responses are transformed to Time-frequency (TF) domain using Short time Fourier transform (STFT) to get a sparse representation. Then, Hough transform is applied to the TF coefficients hierarchically to identify the hyperplanes and the mixing matrix is calculated. Finally, the modal responses are recovered by using l 1 -optimization and inverse STFT. From the recovered modal responses, natural frequencies and damping ratios are extracted. Numerical simulation of a 4 Degree-of-freedom (DOF) spring-mass system verifies the validity of the method. Free vibration of a steel cantilever beam is captured by a high-speed camera and then analyzed by the proposed method. The comparison of the estimated natural frequencies and damping ratios illustrates the good performance of the proposed algorithm.

This paper presents a theoretical analysis for a graphene-based FET real-time detector of the target bacteriaE. coliK12. The motivation for this study is to design a sensor device for detection of bacteria in food and water in order to guarantee food safety. Graphene is chosen as our material for sensor design, which has outstanding electrical, physical, and optical performance. In our sensor structure, graphene-based solution gate field effect transistor (FET) is the device model; fabrication and functionalization protocol are presented together in this paper. What is more, a real-time signal display system is the accompanied equipment for our designed biosensor device. In this system, the sensor bias current signalIdswould change obviously when the target bacteria are attached to the sensor surface. And the bias currentIdsincreases when theE. coliconcentration increases. In the latter part, a theoretical interpretation of the sensor signal is to explain the bias currentIdsincreasing after theE. coliK12 attachment.

Pixel-by-pixel phase unwrapping (PPU) has been employed to rapidly achieve three-dimensional (3-D) shape measurement without additional projection patterns. However, the maximum measurement depth range that traditional PPU can handle is within 2π in phase domain; thus PPU fails to measure the dynamic object surface when the object moves in a large depth range. In this paper, we propose a novel adaptive pixel-by-pixel phase unwrapping (APPU), which extends PPU to an unlimited depth range. First, with PPU, temporary phase maps of objects are obtained referring to the absolute phase map of a background plane. Second, we quantify the difference between the image edges of the temporary phase maps and the practical depth edges of dynamic objects. Moreover, according to the degree of the edge difference, the temporary phase maps are categorized into two classes: failed phase maps and relative phase maps. Third, by combining a mobile reference phase map and the edge difference quantization technique, the failed phase maps are correspondently converted into relative phase maps. Finally, the relative phase maps are innovatively transformed into the absolute phase maps using a new shadow-informed depth estimation method (SDEM). The proposed approach is suitable for high-speed 3-D shape measurement without depth limitations or additional projection patterns.

Transmission line failure is a serious problem in developing countries. The major cause of high voltage overhead transmission line failure in power system is prolonged excessive current. The determination of line segments where high tension is generated due to prolonged excessive current under the different arrangements of line spacing and current carrying conditions can lead us to analyze high tension zones for efficient design of transmission lines. In this context, the mechanical tension on an overhead high voltage electrical power transmission line resulting from excessive or normal currents was computed using Finite Element Method. Multiple segments of a long transmission line with symmetrical and unsymmetrical spacing under accidental and normal line current are examined. Therein, the line segments with high tension are determined. Maximum integrated tension is found in middle segments of both symmetrical and unsymmetrical spaced transmission line. While, maximum per unit tension is found in initial and bottom segments. However, variation in magnitude is found for symmetrical and unsymmetrical spacing. The present study may provide technical guidance to the transmission line design engineers and technicians regarding the risk and solutions related to electrical power transmission line systems.

Automatic defect detection is gaining huge importance in photovoltaic (PV) field due to limited application of manual/visual inspection and rising production quantities of PV modules. This study is conducted for automatic detection of PV module defects in electroluminescence (EL) images. We presented a novel approach using light convolutional neural network architecture for recognizing defects in EL images which achieves state of the art results of 93.02% on solar cell dataset of EL images. It requires less computational power and time. It can work on an ordinary CPU computer while maintaining real time speed. It takes only 8.07 ms for predicting one image. For proposing light architecture, we perform extensive experimentation on series of architectures. Moreover, we evaluate data augmentation operations to deal with data scarcity. Overfitting appears a significant problem; thus, we adopt appropriate strategies to generalize model. The impact of each strategy is presented. In addition, cracking patterns and defects that can appear in EL images are reviewed; which will help to label new images appropriately for predicting specific defect types upon availability of large data. The proposed framework is experimentally applied in lab and can help for automatic defect detection in field and industry.

An improved active disturbance rejection controller (ADRC) is designed to eliminate the influences of the current-loop for the pitching axis control system of an aerial camera. The improved ADRC is composed of a tracking differentiator (TD), an improved extended state observer (ESO), an improved nonlinear state error feedback (NLSEF), and a disturbance compensation device (DCD). The TD is used to arrange transient process. The improved ESO is utilized to observe the state extended by nonlinear dynamics, model uncertainty, and external disturbances. Overtime variation of the current-loop can be predicted by the improved ESO. The improved NLSEF is adopted to restrain the residual errors of the current-loop. The DCD is used to compensate the overtime variation of the current-loop in real time. The improved ADRC is designed based on a new nonlinear functionnewfal(·). This function exhibits enhanced continuity and smoothness compared to previously available nonlinear functions. Thus, the new nonlinear function can effectively decrease the high-frequency flutter phenomenon. The improved ADRC exhibits improved control performance, and disturbances of the current-loop can be eliminated by the improved ADRC. Finally, simulation experiments are performed. Results show that the improved ADRC displayed better performance than the proportional integral (PI) control strategy and traditional ADRC.

In this paper, a Cartesian admittance controller with on-line gravity and friction observer compensation based on passivity theory for elastic joint robots is proposed. For this study, a compliance and position controller for joint and Cartesian level has been described with dual loop construction: an outer-loop admittance control, and an inner-loop motor side position with torque feedback control. In terms of torque feedback, a physical interpretation is addressed and analyzed. Regarding as admittance controller of the elastic joint, two type (stiffness and damping) of the systematic control model can be represented. In addition to an on-line gravity compensation based-on output-side encodes has been proposed. Moreover, in order to achieve high trajectory tracking performance, a friction observer based compensation is applied also. Furthermore, aimed at the control performance of the stiffness, damping, and admittance control algorithm are assessed by a simulation, respectively. Finally, the simulation studies reveal a better effect regarding as the position tracking and torque output in contrast to without friction passivity algorithm and proportional-derivative (PD) control. In addition, the response of proposed compliance control method is effective for external force.

This note reports an efficient singular value decomposition (SVD)-based vibration extraction approach that recovers sound information in silent high-speed video. A high-speed camera of which frame rates are in the range of 2 kHz–10 kHz is applied to film the vibrating objects. Sub-images cut from video frames are transformed into column vectors and then reconstructed to a new matrix. The SVD of the new matrix produces orthonormal image bases (OIBs) and image projections onto specific OIB can be recovered as understandable acoustical signals. Standard frequencies of 256 Hz and 512 Hz tuning forks are extracted offline from their vibrating surfaces and a 3.35 s speech signal is recovered online from a piece of paper that is stimulated by sound waves within 1 min.

In this paper, a passivity-based controller with the link side on-line gravity compensation for elastic joints of robot manipulators is presented. In this study, a joint level position, torque control law with double loop construction: an outer the motor side position control, and an inner torque feedback are considered. In terms of torque feedback, a physical interpretation is addressed and analyzed for the purpose controller via passivity theory. It is proved by a Lyapunov function that the control law is globally asymptotically stabilizes at any reference configuration. Moreover, the control performance of algorithm is assessed by a simulation; the simulation study reveals a better performance regarding as the torque ripple in contrast to a proportional-derivative (PD) control.

In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O) and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP). In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO) algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions.

This paper proposed a novel in-plane displacement field measurement algorithm using an optical flow strategy. We built a linear illumination model between images before and after deformation to guarantee intensity invariability. We used image upsampling and a reliability-guided strategy to find the matching points accurate to 0.5 pixels in the reference and deformed images. The criterion to determine the reliability is zero-mean normalized cross-correlation coefficient. Afterward, we used the brightness constancy constraint combined with Lucas-Kanade optical flow constraint in a specific image region to obtain an overdetermined linear equation. We applied the noniterative least-squares algorithm to solve the equations and to achieve the displacement offsets. This research utilized multithread calculation to handle the complete cracking applications. We estimated the computing efficiency and calculation precision of the proposed method through a series of experimental speckle patterns. All results demonstrated the correctness, effectiveness, and robustness of the proposed method.

In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

High-speed cameras provide full field measurement of structure motions and have been applied in nondestructive testing and noncontact structure monitoring. Recently, a phase-based method has been proposed to extract sound-induced vibrations from phase variations in videos, and this method provides insights into the study of remote sound surveillance and material analysis. An efficient singular value decomposition (SVD)-based approach is introduced to detect sound-induced subtle motions from pixel intensities in silent high-speed videos. A high-speed camera is initially applied to capture a video of the vibrating objects stimulated by sound fluctuations. Then, subimages collected from a small region on the captured video are reshaped into vectors and reconstructed to form a matrix. Orthonormal image bases (OIBs) are obtained from the SVD of the matrix; available vibration signal can then be obtained by projecting subsequent subimages onto specific OIBs. A simulation test is initiated to validate the effectiveness and efficiency of the proposed method. Two experiments are conducted to demonstrate the potential applications in sound recovery and material analysis. Results show that the proposed method efficiently detects subtle motions from the video.

The pitching axis of a satellite camera is controlled under the weightless environment. A novel active disturbance rejection controller is designed to eliminate the influences of the pitching axis. The novel active disturbance rejection controller is designed based on a new nonlinear function, and thus, this function is first established. The function exhibits better continuity and smoothness than previously available functions, hence, it can effectively improve the high-frequency flutter phenomenon. Therefore, the novel active disturbance rejection controller based on the new nonlinear function can eliminate disturbances of the pitching axis. The novel active disturbance rejection controller is composed of a tracking differentiator, a novel extended state observer, and a novel nonlinear state error feedback. The tracking differentiator is used to arrange the transient process. Nonlinear dynamics, model uncertainty, and external disturbances are extended to a new state. The novel extended state observer is utilized to observe this state. The overtime variation of the system can be predicted and compensated using the novel extended state observer. The novel nonlinear state error feedback is adopted to restrain the residual errors of the system. Finally, simulation experiments are performed, and results show that the novel active disturbance rejection controller exhibits better performance than the traditional active disturbance rejection controller.

Rotational angle and speed are important parameters for condition monitoring and fault diagnosis of rotating machineries, and their measurement is useful in precision machining and early warning of faults. In this study, a novel vision-based measurement algorithm is proposed to complete this task. A high-speed camera is first used to capture the video of the rotational object. To extract the rotational angle, the template-based Lucas-Kanade algorithm is introduced to complete motion tracking by aligning the template image in the video sequence. Given the special case of nonplanar surface of the cylinder object, a nonlinear transformation is designed for modeling the rotation tracking. In spite of the unconventional and complex form, the transformation can realize angle extraction concisely with only one parameter. A simulation is then conducted to verify the tracking effect, and a practical tracking strategy is further proposed to track consecutively the video sequence. Based on the proposed algorithm, instantaneous rotational speed (IRS) can be measured accurately and efficiently. Finally, the effectiveness of the proposed algorithm is verified on a brushless direct current motor test rig through the comparison with results obtained by the microphone. Experimental results demonstrate that the proposed algorithm can extract accurately rotational angles and can measure IRS with the advantage of noncontact and effectiveness.

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.