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Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup's recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study shows how the delta robot was designed to physically pick-and-place MSW. • Combined with the YOLO-based model, the robot forms the Intelligent MSW Sorter. • IMSWS could automatically sort the multiple MSWs on the conveyor belt. • The mechanical efficiency of the robot dominated IMSWS performance. • Compared with the MSW detection, IMSWS must improve the mechanical efficiency first. Sorting Municipal Solid Waste (MSW) has helped promote the awareness of sustainable development of environment. A robot equipped with an intelligent deep learning (DL) detection algorithm have been proposed to improve the sorting task. But most of the related studies aimed to better the DL algorithms on MSW detection, and few studies integrated the DL algorithms with a robot to identify the dominated factors to Intelligent MSW Sorter (IMSWS). Therefore, this study is to develop IMSWS prototype to better sort MSW, based on the pick-and-place process, and preliminarily evaluate the dominated factors. First, the delta robot prototype was manufactured, and IMSWS was performed with a camera to acquire the RGB image and the height of a MSW in the conveyor belt. The DL algorithm, YOLOv3 or YOLOv4, detected the type and plane location of the MSWs in the conveyor belt. Next, the sequence program transferred the valid MSW data to the delta robot. After the calculation of the absorbed location of the target MSW was made, the arm of this delta robot moved to absorb and then transfer the MSW to the bin. Results showed that the IMSWS prototype could sort the multi-object MSWs in the MSW stream. Both YOLOv3 and YOLOv4 reached high detection accuracy on the MSW image dataset. However, the improvement should be made in the actually moving MSW stream even though the YOLOv4 performed the acceptable detection accuracy. The gripping stability of the arm mainly dominated the performance of IMSWS, and this should be improved first. [ABSTRACT FROM AUTHOR]

The accuracy of contour tracking is an important issue to be addressed for the X‐Y stage in a machining tool. Applying a better control scheme can help to increase the tracking accuracy with the effects of systems' uncertainties, nonlinearities and disturbances. In this paper, to prevent these negative effects and achieve high accuracy tracking control of the two‐axis motion stage, the adaptive fuzzy wavelet neural network (AFWNN) PI sliding mode controller is proposed. The adaptive algorithm is combined with the basis function of the wavelet neural network and fuzzy system to approximate the plant model functions. Then, the controller with PI sliding mode structure could be built based on the estimated system function. In this method, the time domain positioning property of wavelet and the reasoning of the fuzzy system could provide the well‐estimated plant model. Moreover, the adaptive algorithm and sliding mode control scheme help to reduce the effects of parameter variation and uncertainties. The stability is analyzed via Lyapunov stability theorem to prove the asymptotic stability of the system. In the simulation and experimental results, the contours are planned through the nonuniform rational B‐spline (NURBS), which is considered a high accuracy interpolator. The tested curves consist of circle, bow, heart, star and trident. The average and standard deviations of the tracking errors are compared to other methods to illustrate the performance of the proposed method. The results demonstrate that the method can reduce errors more effectively and have better localization for trajectory tracking. [ABSTRACT FROM AUTHOR]

Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications. [ABSTRACT FROM AUTHOR]

Recently, nonlinear system identification has received increasingly more attention due to its promising applications in engineering fields. It has become a challenging task to truly apply this system due to many complex factors especially the nonlinear and dynamical properties. The objective of this paper is to design a nonlinear system identification method using an appropriate learning method. This paper proposes a biogeography-based optimization (BBO)-based multilayer perceptron (MLP) architecture for nonlinear system identification. The BBO algorithm with its habitats imitates the species migration between them. A good solution is featured by an island with a higher High Suitability Index (HSI), and a poor solution by an island with a lower HSI. Higher HSI solutions resist change more effectively than lower HSI solutions. By combining the two schemes, the proposed MLP architecture with BBO learning provides a promising scheme for nonlinear system identification. Three kinds of nonlinear system are adopted for experimental verification, including Mackey–Glass series, Henon system, and nonlinear plant system. Mean squared error (MSE) index is used to calculate the difference between the measured input and output of the systems. By employing the nonlinear cases, the proposed algorithm presents rapid convergence and excellent MSE in nonlinear system identification. [ABSTRACT FROM AUTHOR]

Nonlinear time series analysis and forecasting are an essential part in a diverse range of physical and natural applications. This paper presents a time-division cerebellar model articulation controller (TDCMAC) network using a modified biogeography-based optimization (modified BBO) learning algorithm for nonlinear time series and measurement data prediction. The TDCMAC method is a time windowing strategy constructed using the CMAC network. BBO algorithm is designed related to the geographical distribution of species over time and space. This study presents two modified migration functions of the essential BBO, i.e. quadratic migration BBO (QBBO) and sinusoidal migration BBO (SBBO) methods, to improve convergence rate and quality of solution. Five nonlinear time series, including Mackey-glass, Lorenz, Rossler, Limber Pine, and Ponderosa Pine data series, are employed to investigate the proposed predictor. The TDCMAC networks using QBBO and SBBO learning algorithms are compared with the gradient descent (GD) method and other existing heuristic learning methods, including particle swarm optimization (PSO), genetic algorithm (GA), and conventional BBO methods, to verify the estimation performance of the proposed method. The performances are evaluated through an extensive simulation by computing the root mean square error (RMSE), mean absolute percentage error (MAPE), and average relative variance (ARV) metrics. Experimental results demonstrate that the proposed predictor indeed achieve more accurate performances and faster learning speed for time series prediction applications. [ABSTRACT FROM AUTHOR]

Real systems with nonlinearity and valuable information always have unpredictable meaning and chaos properties in nature. It is often difficult to obtain the system model based on set of stimulus and response samples due to its complexity. This paper proposes the type-2 fuzzy neural network (T2FNN) and its learning method using grey wolf optimizer (GWO) for system identification. The structure of T2FNN is a combination between a type-2 fuzzy logic system with human-like IF-THEN rule thinking properties and neural networks (NN) with learning and optimization capability. The GWO is a meta-heuristic algorithm inspired from the social hunting behavior of grey wolves as search agents. This method is investigated by strategy, which maintains a proper balance between exploration and exploitation processes and gives more importance to the fittest to find the new position of search agent during the iterations. The GWO algorithm is considered for learning method due to its advantages, including high accuracy, very effective and more competitive. To evaluate the proposed system, the T2FNN with GWO learning method is employed by several nonlinear problems, including single-input single-output systems and multi-input multi-output (MIMO) systems. They are Mackey-Glass chaotic data series, nonlinear plant, nonlinear dynamical time-varying plant, and two MIMO systems. The merits of the proposed learning algorithm can be defined through experiments of the model and compared with existing learning PSO and GA algorithms and previous studies in literature. Based on the experimental results and performance comparisons, the proposed GWO algorithm indeed obtains the enhanced performance and capability in terms of the root mean squared error for nonlinear system identification applications. [ABSTRACT FROM AUTHOR]

In this study, the authors present a control method for the permanent magnet synchronous motor (PMSM) driven X–Y motion system for application in industrial and manufacturing apparatus. Backlash and friction effects in ball‐screw actuated systems are common obstacles to achieve high‐accuracy control. The authors investigate the indirect fuzzy controller to demonstrate the feasibility of reference trajectory tracking and derive an adaptive law in the error state space to deal with parameter uncertainties and external disturbances. The authors adjust the fuzzy parameters online, based on the Lyapunov stability theorem, such that the asymptotic stability of the whole system and error convergence can be guaranteed. In the authors' experiments, this indirect fuzzy control approach compensated for the non‐linear friction and disturbance variation of the biaxial linear stage following the application of four contour shapes, i.e. square, triangular, star, and circular reference contours. Both the authors' simulation and experimental results demonstrate substantial improvement in contour tracking performance in terms of average tracking error and tracking error standard deviation, as well as robustness against platform uncertainties. [ABSTRACT FROM AUTHOR]

Waste sorting is highly labor intensive because the wide variety of waste items prohibits automation. More recently, deep learning (DL) and computer vision technology has presented an opportunity to streamline the sorting process, but many important developmental steps remain. If computer vision technology can increase the efficiency of automated waste sorting, this would be beneficial for society and the environment. Accordingly, this study used the You Only Look Once-v3 (Yolo-v3) detection model based on DL to enhance recognition performance of household waste products. TrashNet, a commonly used waste image database, was used to train an initial Yolo-v3 model, however each image used for training only had a single waste object, and this study found that the detection model trained with a single object dataset was not only unsuitable for sorting multiple waste objects, but that this has rarely been addressed in academic literature. It was also discovered that nations and regions will need to develop their own unique databases that reflect the types of waste products found. Samples images need to account for the various appearances and colors and be combined in multiple waste object images when training the system. This paper documents the training and testing of an object detection model suitable for detecting domestic waste specific to Taiwan; however, the approach taken would be of use to other countries seeking to automate waste sorting. To achieve this, it was necessary to compile the Taiwan Recycled Waste Database (TRWD). This was then used to train the Yolo-v3, and the efficiencies of this, versus the standard TrashNet model were compared. Results showed that the TRWD-trained Yolo-v3 achieved mAP @0.5 of 92.12% and could detect waste in real-time. Relative to the TrashNet-trained Yolo-v3, the TRWD counterpart performed better due to the multiple waste objects and more relevant image repository. Further studies are recommended to investigate the effect of combining additional sensors that would enable improved detection of specific wastes. Combining the TRWD-trained Yolo-v3 with a robot system for waste sorting would potentially be another rewarding avenue of research. [Display omitted] • Automatic waste detection improves waste recycling efficiency. • Different nations require customized datasets to train Yolo-v3 detection model. • Taiwan recycled waste dataset (TRWD) was expanded to improve detection rates. • Yolo-v3 trained on the TRWD outperformed the same system using TrashNet. [ABSTRACT FROM AUTHOR]

Abstract: Multipath effects are the major source of error in GPS differential positioning. In this paper, an adaptive multipath mitigation tracking system is presented for GPS applications. It is comprised of four function blocks, those being (1) adaptive path estimator (APE), (2) multipath interference reproducer (MPIR), (3) Rake-based delay locked loop (RB-DLL), and (4) Rake-based phase locked loop (RB-PLL). Only the short delay condition with delay less than 1.5 PN chip is considered here, because GPS pseudorange error envelope decreases to zero for delay time greater than 1.5 PN chip. In order to estimate reflection profile in the correlation domain, the FFT-based circular correlation and block average method (BAM) are utilized to offer significant savings in computational complexity. The APE estimates the delayed profiles and coefficients of the reflection signals. By using the path parameters from APE, the corresponding multipath arms are activated to accomplish the multipath reproduction. These replica profiles are used for subtracting the reflection components from carrier and code discriminators before sending it into the Rake-based carrier/code tracking loops. Simulation results show that our proposed method provides a better performance in terms of multipath error envelope and carrier-phase error. [Copyright &y& Elsevier]

The global positioning system (GPS), which provides accurate positioning and timing information, has become a commonly used navigation instrument for many applications. The application of a new adaptive all-pass based notch filter (ANFA) for narrowband/FM interference suppression and frequency estimation in GPS receivers is proposed. An ANFA structure that achieves better unbiased characteristics with its coefficients is employed to accurately estimate the narrowband interfering signals in online fashion. A variable convergence factor that optimizes the maximal mean square error (MSE) reduction in each iteration is applied in a modified adaptive Gaussian-Newton (MAGN) algorithm. The proposed MAGN algorithm can lead to both faster convergence speed and higher estimation accuracy. Simulation results show that the ANFA offers a better performance than conventional linear predictors in terms of the SNR improvement and the mean output power (MOP) under the interference environments of interest. [ABSTRACT FROM AUTHOR]

Abstract: Global positioning system (GPS) is a system combining code division multiple access (CDMA) and trilateration techniques to obtain precision position and timing information. The main goal of this paper is to present a high-sensitivity receiver for acquisition of weak GPS signals. A new peak-finding algorithm is developed to search the C/A code phase in the FFT-based correlation domain. This method can estimate the peak location accurately and provides a faster performance in software-based signal acquisition. Because the received frequency increases as the satellite advances the receiver and decreases as it recedes from the user in the Doppler circumstance, the code delay compensation (CDC) algorithm is proposed to enhance the capability of waveform search under indoor conditions. Using the carrier-aiding information, the CDC method can find the autocorrelation peak value effectively under both Doppler shift and weak signal environments. A step-by-step acquisition procedure is designed for indoor GPS receivers to detect the C/A code delayed phase effectively and reliable operation in a diversity of weak signal and Doppler environments. Simulation results demonstrate that our proposed scheme can achieve a significant improvement in processing gain over the traditional methods and acquire the GPS signal efficiently under the power levels of in frequency domain processing. [Copyright &y& Elsevier]

Abstract: The GPS provides accurate positioning and timing information that is useful in various applications. A new adaptive neural predictor for GPS jamming suppression applications is proposed. The effective and computationally efficient square-root extended Kalman filter (SREKF) algorithm is adopted to adjust the synaptic weights in the nonlinear recurrent architecture and thereby estimate the stationary and non-stationary narrowband/FM waveforms. Cholesky factorization is employed in Riccati recursion to improve numerical stability because of the propagation of round-off errors in conventional KF equations. The main characteristics of the proposed SREKF-based canceller are their rapid convergence and favorable tracking performance. Simulation results reveal that its SNR improvement factor exceeds the factors of conventional LMS, RLS, ENA and TLFN filters in single-tone CWI, multi-tone CWI, pulse CWI and FM jamming environments, respectively. [Copyright &y& Elsevier]

In this paper, the autoencoder learning method is proposed for the system diagnosis of chamfering tool equipment. The autoencoder uses unsupervised learning architecture. The training dataset that requires only a positive sample is quite suitable for industrial production lines. The abnormal tool can be diagnosed by comparing the output and input of the autoencoder neural network. The adjustable threshold can effectively improve accuracy. This method can effectively adapt to the current environment when the data contain multiple signals. In the experimental setup, the main diagnostic signal is the current of the motor. The current reflects the torque change when the tool is abnormal. Four-step conversions are developed to process the current signal, including (1) current-to-voltage conversion, (2) analog-digital conversion, (3) downsampling rate, and (4) discrete Fourier transform. The dataset is used to find the best autoencoder parameters by grid search. In training results, the testing accuracy, true positive rate, and precision approach are 87.5%, 83.33%, and 90.91%, respectively. The best model of the autoencoder is evaluated by online testing. The online test means loading the diagnosis model in the production line and evaluating the model. It is shown that the proposed tool can effectively detect abnormal conditions. The online assessment accuracy, true positive rate, and precision are 75%, 90%, and 69.23% in the original threshold, respectively. The accuracy can be up to 90% after adjusting the threshold, and the true positive rate and precision are up to 80% and 100%, respectively. [ABSTRACT FROM AUTHOR]

The microwave dielectric constants of ceramic dispersions in the polyethylene matrix are studied by five well-known mixture equations in this article. Three ceramic powders of fillers with various dielectric constants at 10 GHz are investigated. The experimental dielectric constant values of the mixtures are compared to the theoretical curves of the five mixing rules. The dielectric constants of pure ceramics are then estimated by these mixing laws from the dielectric constants of composites of various filler concentrations. The theoretical errors of estimations of rules are derived and analyzed. The most adequate mixture law(s) for measuring the dielectric constants of ceramic fillers will be recommended. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:2481-2485, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26357 [ABSTRACT FROM AUTHOR]

Abstract: Powder mixture rules derived from the filler particles with the shapes of sphere, cylinder or rod, and lamella or disk with random distributions are studied in this paper. Three ceramic powders of fillers with dielectric constants of 10, 20, and 36, respectively, are adopted. The experimental dielectric constants of ceramic dispersions in the polyethylene matrix at microwave frequency are compared to those obtained by using different mixing laws. The mixing rules are also adopted to estimate the dielectric constants of pure ceramics from the measured dielectric constants of composites with various concentrations. The theory for the error of estimation is studied. In contrast to the traditional concept of obtaining the best curve fitting of mixture rule with the experimental data, this study conclude a very important concept on the powder mixture rule, that is, the most adequate mixture law for estimating the dielectric constants of pure ceramics requires both good curve fitting and potential of less error. [Copyright &y& Elsevier]