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This study investigates singularly perturbed differential equations through advanced convection-diffusion techniques. We employ a finite difference approach utilizing a piecewise uniform Shishkin-type mesh to tackle this problem. Our analysis demonstrates that the approach achieves virtually first-order convergence. Error estimates are computed using discrete norms, and numerical experiments are conducted to validate these theoretical results.

The challenge in developing an AI deep learning model for motor health diagnosis is hampered by the lack of sufficient and representative datasets, leading to considerable time and resource consumption in research. Therefore, this paper focuses on the analysis of the second harmonic component fault characteristic induced by inter-turn short circuits (ITSCs) in phase voltages. First, it establishes a coil inter-turn short-circuit fault (ITSCF) model of the motor to identify the twice-frequency q-axis voltage error characteristics. Subsequently, it develops simulation programs by integrating control and fault models in MATLAB/Simulink/Simscape to observe and analyze the q-axis voltage and circulating current errors caused by the short circuit. Finally, a discrete wavelet transform method is established to analyze the q-axis synchronous reference frame voltage. By applying the energy-based method to extract the twice-frequency voltage error characteristics, the approach successfully detects the error features and confirms ITSCF in the motor. The contributions of this paper include not only the development of an ITSCF characteristic model for the motor but also the successful application of wavelet transform to effectively analyze the time-frequency characteristics of its signals. This approach can serve as a valuable reference for the design of deep learning models in future AI applications.

Fear and prey refuges are two significant topics in the ecological community because they are closely associated with the connectivity of natural resources. The effect of fear on prey populations and prey refuges (proportional to both the prey and predator) is investigated in the nonlinear-type predator-harvested Leslie–Gower model. This type of prey refuge is much more sensible and realistic than the constant prey refuge model. Because there is less research on the dynamics of this type of prey refuge, the current study has been considered to strengthen the existing literature. The number and stability properties of all positive equilibria are examined. Since the calculations for the determinant and trace of the Jacobian matrix are quite complicated at these equilibria, the stability of certain positive equilibria is evaluated using a numerical simulation process. Sotomayor’s theorem is used to derive a precise mathematical confirmation of the appearance of saddle-node bifurcation and transcritical bifurcation. Furthermore, numerical simulations are provided to visually demonstrate the dynamics of the system and the stability of the limit cycle is discussed with the help of the first Lyapunov number. We perform some sensitivity investigations on our model solutions in relation to three key model parameters: the fear impact, prey refuges, and harvesting. Our findings could facilitate some biological understanding of the interactions between predators and prey.

In recent years, multi-robot control systems and service robots equipped with graphical computing have been introduced in various application scenarios. However, the long-term operation of VSLAM calculation leads to reduced energy efficiency of the robot, and accidental localization failure still persists in large-scale fields with dynamic crowds and obstacles. This study proposes an EnergyWise multi-robot system based on ROS that actively determines the activation of VSLAM using real-time fused localization poses by an innovative energy-saving selector algorithm. The service robot is equipped with multiple sensors and utilizes the novel 2-level EKF method and incorporates the UWB global localization mechanism to adapt to complex environments. During the COVID-19 pandemic, three disinfection service robots were deployed to disinfect a large, open, and complex experimental site for 10 days. The results demonstrated that the proposed EnergyWise multi-robot control system successfully achieved a 54% reduction in computing energy consumption during long-term operations while maintaining a localization accuracy of 3 cm.

Over the past few years, due to an aging population and a longer life expectancy, traditional high-oil, high-calorie, and high-temperature culinary concepts have been widely replaced by healthier, low-temperature heating methods. However, while the key device of the Sous-Vide heating system ordinarily operates at full power to achieve the target temperature, pump speed control is not currently considered within the water heating and circulating system framework. This study develops a model for a water heating and circulating system and examines the characteristics of the lowest power point and pump speed. Building upon these results, we present the LPPT control method as a means of optimizing input power for heating. The effectiveness of this method is supported by simulations and experiments, which demonstrate a significant reduction in energy consumption. The control concept calculates the real-time input power based on the input voltage and current, and it can achieve the most efficient input power by perturbing the pump speed. It is demonstrated that applying LPPT to daily pot capacity reduces the Sous-Vide Cooker’s input power by up to 17% and achieves efficiency optimization control by removing the need to calculate the foods and other parameters of the water heating and circulating system environment.

This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper.

Bearings are the most commonly used components in rotating machines and the ability to diagnose their faults and predict their remaining useful life (RUL) is critical for system maintenance. This paper proposes a smart system combined with a regression model to predict the RUL of bearings. The method converts the azimuth signal through low-pass filtering (LPF) and a chaotic mapping system, and uses Euclidean feature values (EFVs) to extract features in order to construct useful health indicators (HIs). In fault detection, the iterative cumulative moving average (ICMA) is used to smooth the HIs, and the Euclidean norm is used to find the time-to-start prediction (TSP). In terms of prediction, this paper uses a self-selective regression model to select the most suitable regression model to predict the RUL of the bearing. The dataset provided by the Center for Intelligent Maintenance Systems (IMS) is applied for performance evaluation; in comparison with previous research, better prediction results can be achieved by applying the proposed smart assessment system. The proposed system is also applied to the PRONOSTIA (also called FEMTO-ST) bearing dataset in this paper, demonstrating that acceptable prediction performance can be obtained.

Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human–machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.

A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper. This paper proposes the development and application of gantry robots with MRAC and FNN online compensators. First, we propose a model reference adaptive controller (MRAC) under the cascade control method to make the reference model close to the real model and reduce tracking errors for the single axis. Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement. In addition, an online parameter training method is proposed to adjust the parameters of the FNN. Finally, the experimental results show that the proposed method improves the synchronous errors of the gantry robot and demonstrates the methodology in this paper. This study also successfully integrates the hardware and successfully verifies the proposed methods.

The increasing complexity of the manufacturing industry demands computer numerical control (CNC) machining, a massively used process that is equipped with an intelligent feature. Not only does its control function revitalize competition in a sustainable manufacturing ecosystem but also advances the Industry 4.0 readiness. This paper develops an intelligent computer-aided process planning (i-CAPP) based on two widely accepted performance measures: manufacturability and efficiency. Machine tapping is a very popular CNC machining process making threaded holes, from which there is an investigation of two manufacturability scenarios. The first scenario deals with all holes with the same size that undergo the same operation (OP). The second one handles the groups of holes with different sizes machined by various OPs. Meanwhile, the study of efficiency focuses on point-to-point drilling, tapping, and chamfering. This research work verifies such indicators by proposing hybrid-two-stage optimization algorithms: 1) traveling salesman problem and 2) innovative Tabu Search. The i-CAPP verification of both indicators was confirmed in an application with a five-axis CNC tapping machine to realize the Industry 4.0 readiness. Extensions of the research in the pursuit of higher intelligent manufacturing platforms are discussed.

This paper mathematically explains how state derivative space (SDS) system form with state derivative related feedback can supplement standard state space system with state related feedback in control designs. Practically, inverse optimal control is attractive because it can construct a stable closed-loop system while optimal control may not have exact solution. Unlike the previous algorithms which mainly applied state feedback, in this paper inverse optimal control are carried out utilizing state derivative alone in SDS system. The effectiveness of proposed algorithms are verified by design examples of DC motor tracking control without tachometer and very challenging control problem of singular system with impulse mode. Feedback of direct measurement of state derivatives without integrations can simplify implementation and reduce cost. In addition, the proposed design methods in SDS system with state derivative feedback are analogous to those in state space system with state feedback. Furthermore, with state derivative feedback control in SDS system, wider range of problems such as singular system control can be handled effectively. These are main advantages of carrying out control designs in SDS system.

In this article, a set of fuzzy logic controller is alternatively designed for control of nonlinear systems in Lyapunov sense. Through referencing the statuses of error states and error dynamics, the corresponding fuzzy if–then rules can be constructed based on Lyapunov Stability Theory following the proposed standard design procedure, where simple quadratic form is decided for candidate Lyapunov function. Different from previous research achievements, the resulting controllers are simple constant forces, and for control of two nonlinear systems with different structures, the design procedure is still the same without any further information. Furthermore, appropriate and adjustable control forces with less maximum magnitude are stably supplied through exciting the weighting through the constructed fuzzy if–then rules, which leads the proposed fuzzy logic controllers compensate control system more flexible and in real time. Two examples, control of two identical and different nonlinear systems, are illustrated for simulation examples. In comparison to the previous work, the simulation results reveal the effectiveness, flexibility, as well as convenient design of the proposed method.

In this article, a neural network corrector is proposed to correct the image shift, yielding the degradation of three-dimensional image reconstruction, for each slice captured by cone-beam computed tomography simulator. There are 3 degrees of freedom in tube module of simulator; the central point of tube module should be aligned with the central point of detector module to guarantee the accurate image projection. However, the mechanism manufacturing and assembling tolerance will let the above aim cannot be met. Here, a standard kit is made to measure the image shift by 1° step from −10° to 10°. The measure data will be the input training data of proposed neural network corrector, and the corrected translation position will be the output of neural network corrector. The Levenberg–Marquardt learning algorithm adjusts the connected weights and biases of the neural network using a supervised gradient descent method, such that the defined error function can be minimized. To avoid the problem of overfitting and improve the generalized ability of the neural network, Bayesian regularization is added to the Levenberg–Marquardt learning algorithm. After the training of neural network corrector, the different target position commands are fed into the neural network corrector. Then, the corrected data from neural network corrector are fed to be the new position command to verify the image correction performance. Moreover, a phantom kit is made to check the corrected performance of the neural network corrector. Finally, the experimental results verify that the image shift can be reduced by the neural network corrector.

In this paper, a novel fuzzy-model-based adaptive synchronization scheme and its fuzzy update laws of parameters are proposed to address the adaptive synchronization problem. The proposed fuzzy controller does not share the same premise of fuzzy system, and the numbers of fuzzy controllers is reduced effectively through the novel modeling strategy. In addition, based on the adaptive synchronization scheme, the error dynamic system can be guaranteed to be asymptotically stable and the true values of unknown parameters can be obtained. Two identical complicated dynamic systems, Mathieu-Van der pol system (M-V system) with uncertainties, are illustrated for numerical simulation example to show the effectiveness and feasibility of the proposed novel adaptive control strategy.

In a multifrequency phase-shifting (MFPS) algorithm, the temporal phase unwrapping algorithm can extend the unambiguous phase range by transforming the measurement range from a short fringe pitch into an extended synthetic pitch of two different frequencies. However, this undesirably amplifies the uncertainty of measurement, with each single-frequency phase map retaining its measurement uncertainty, which is carried over to the final unwrapped phase maps in fringe-order calculations. This article analyzes possible causes and proposes a new absolute depth measurement algorithm to minimize the propagation of measurement uncertainty. Developed from normalized cross-correlation (NCC), the proposed algorithm can minimize wrong fringe-order calculations in the MFPS algorithm. The experimental results demonstrated that the proposed measurement method could effectively calibrate the wrong fringe order. Moreover, some extremely low signal-to-noise ratio (SNR) regions of a captured image could be correctly reconstructed (for surface profiles). The present findings confirmed measurement precision at one standard deviation below 5.4 µm, with an absolute distance measurement of 16 mm. The measurement accuracy of the absolute depth could be significantly improved from an unacceptable level of measured errors down to 0.5% of the overall measuring range. Additionally, the proposed algorithm was capable of extracting the absolute phase map in other optical measurement applications, such as distance measurements using interferometry.

A new surface profilometry technique is proposed for profiling a wafer surface with both diffuse and specular reflective properties. Most moiré projection scanning techniques using triangulation principle work effectively on diffuse reflective surfaces, on which the reflected light beams are assumed to be well captured by optical sensors. In reality, this assumption is no longer valid when measuring a semiconductor wafer surface having both diffuse and specular reflectivities. To resolve the above problem, the proposed technique uses a dual optical sensing configuration by engaging two optical sensors at two different viewing angles, with one acquiring diffuse reflective light and the other detecting at the same time specular surface light for achieving simultaneous full-field surface profilometry. The deformed fringes measured by both sensors could be further transformed into a 3-D profile and merged seamlessly for full-field surface reconstruction. Several calibration targets and industrial parts were measured to evaluate the feasibility and accuracy of the developed technique. Experimental results showed that the technique can effectively detect diffuse and specular light with repeatability of one standard deviation below 0.3 µm on a specular surface and 2.0 µm on a diffuse wafer surface when the vertical measuring range reaches 1.0 mm. The present findings indicate that the proposed technique is effective for 3-D microscale surface profilometry in in-situ semiconductor automated optical inspection (AOI).

The existing trademark image retrieval (TIR) approaches mostly use complex image features, the integration of multi features, a tree structure, etc. to enable highly accurate retrieval. However, there is the heavy computational burden for complex image features and maximum similarity subtree isomorphism (MSSI) measurement. This paper aims to provide an efficient solution for TIR in real-time applications, especially in measuring the similarity between multi-object trademark images. In particular, we propose a novel algorithm for tree similarity measurement based on the fuzzy inference system (FIS) to improve retrieval efficiency. Furthermore, the integration of global and local geometric descriptors is used to enable accurate retrieval. The global descriptor is computed by employing the Hu moments, while the local descriptors are generated by using a tree structure based on the five geometric features: convexity, eccentricity, compactness, circle variance, and elliptic variance. During the retrieval process, the similarity coefficient between the query and the database image is obtained from the similarity of the global and local descriptors. The proposed technique is evaluated using 1800 trademark images, including 12 different classes and 416 trademark images. Additionally, the three common indices, the precision/recall rate, the Bull’s eye score, and the average normalized modified retrieval rank (ANMRR) are used as the performance indices. The experimental results show that the proposed technique is superior to the other two competitive approaches. It shows 19.43% and 26.78% precision/recall improvement, 19.56% and 30.58% improvement in the average Bull’s eye score, and 0.167 and 0.236 improvement in the ANMRR score, respectively, for the 416 query images. It can be concluded from the experimental analysis that the proposed technique not only provides reliable retrieval results but also improves the retrieval efficiency by 151 times in the retrieval process.

This paper proposes a novel image alignment algorithm based on rotation-discriminating ring-shifted projection for automatic optical inspection. This new algorithm not only identifies the location of the template image within an inspection image but also provides precise rotation information during the template-matching process by using a novel rotation estimation scheme, the so-called ring-shifted technique. We use a two stage framework with an image pyramid searching technique for realizing the proposed image alignment algorithm; in the first stage, the similarity based on hybrid projection transformation with the image pyramid searching technique is employed for quick selection and location of the candidates in the inspection image. In the second stage, the rotation angle of the object is estimated by a novel ring-shifted technique. The estimation is performed only for the most likely candidate which is the one having the highest similarity in the first stage. The experimental results show that the proposed method provides accurate estimation for template matching with arbitrary rotations and is applicable in various environmental conditions.