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This paper proposes an adaptive sliding mode control (ASMC) with a modified reduced-order proportional integral observer (MROPIO) to provide speed control for a permanent magnet synchronous motor against unknown disturbances, such as system parameter uncertainties and external load torque variation. The ASMC method, based on a new sliding mode reaching law, improves chattering in the control signal and reduces the time required for the system trajectory to reach the sliding mode surface. The MROPIO estimates unknown disturbances and provides feedforward compensation for the ASMC, thus overcoming incorrect estimations of unknown disturbances during speed jumps can cause overshoots in the speed response. All the proposed methods were implemented on an industrial motor drive. The experimental results confirm the validity of the proposed method for practical applications.

This paper proposes a robot calibration method using an extended Kalman filter (EKF) and an artificial neural network (ANN) based on a butterfly and flower pollination algorithm (ANN-BFPA) to improve the robot's absolute pose (position and orientation) accuracy. After establishing a geometric error model, the EKF, a robust optimization algorithm for a nonlinear system with Gaussian noise, was used to estimate geometric parameter errors and compensate for geometric errors. However, nongeometric errors caused by joint clearance, gear backlash, and link deflection could still affect the pose accuracy and interfere with the correctness of the model. Therefore, the ANN-BFPA was proposed to compensate for these errors. The ANN model was used to establish the complex relationship between joint lengths and pose error. In addition, BFPA was used to optimize weights and bias of the neural network. The efficiency of the proposed calibration method was evaluated using a Stewart platform. Experimental results demonstrated that the proposed method significantly improved the robot's pose accuracy and showed better performance than previous techniques.

Rectification is a standard process in every system that requires multiviews. Existing algorithms largely work on similar field of view (FoV) cases where the two views are mostly identical. Dissimilarities between different FoVs can generate unexpected errors during the optimization process, resulting in a large amount of rectification errors and unwanted geometric distortion. In this study, we present a full pipeline to rectify uncalibrated images captured by cameras that have dissimilar FoVs under the constraints of geometric distortion. The proposed method contains two main parts: Field of View Neutralization and Rectification with Adaptive Geometric Constraints. The Field of View Neutralization module estimates the transformation matrix to compensate for the imbalance between different views. In addition, this module improves the overall quality of correspondences by removing misleading feature matching pairs. Finally, by applying an adaptive optimization process with the geometric constraints involved, our method addresses the overdistortion issue while maintaining small rectification errors. Extensive experiments are conducted to demonstrate the robust performance of the proposed method. Besides the existing datasets, we provide our dissimilar FoVs dataset with multiple baselines to examine the performance. Our method outperforms the existing algorithms in terms of both rectification errors and geometric distortion rates.

As the integration of semiconductors increases, additional diagnosis sensor systems have been developed for the detection of defects. The sensor system for equipment diagnosis, which is added after mass production, performs diagnosis in a standalone format that is operated independently of the equipment. The sensor system mounted in this way cannot deliver the diagnostic data to the system in real-time, but delivers it to the user after a certain period through a separate server. In this paper, we analyzed the data and communication methods of sensors and components used in semiconductors and proposed a standard communication protocol that can be transmitted to the system in real-time. In addition, it is possible to collect equipment data in the protocol so that the sensing trigger signal and dynamic diagnosis algorithm can be utilized through other data in the equipment for intelligent diagnosis. And also, the proposed protocol was added to the data acquisition system and installed and evaluated in mass production equipment.

Stereo vision systems in autonomous vehicles can come with camera sensors with different models and lenses to detect objects at long and short ranges. The intrinsic/extrinsic parameters of the camera sensors, such as the focal length and distortion parameters used for calibration, are not provided. In this article, we present a novel rectification method for uncalibrated stereo cameras based on their corresponding feature points. To obtain reliable key/feature points and descriptors from stereo images, we propose a deep neural network, called a multilevel feature pooling network. The model processes images individually to learn both local and semiglobal features, from which the location and descriptor of each pixel in the image are estimated. We also introduce a method to balance the zoom effect caused by the different focal lengths of the camera lenses and rectify the images by estimating homography matrices for stereo images based on their refined corresponding feature points.

This article proposes a method to improve the accuracy of a vernier absolute magnetic encoder. The encoder consists of a master and a nonius multipolar magnetic track. Sinusoidal signals from the master and nonius tracks are used to infer the absolute information. Unfortunately, these signals are contaminated by nonideal factors such as different amplitudes, dc-offsets, phase shifts, and random noise. Moreover, harmonics existing in the encoder signals distort the vernier principle and significantly affect the accuracy of the encoder. To address these problems, the present article proposes an efficient method with three main parts. The first is an observer phase-locked loop (OPLL), which is used to estimate the phase and eliminate the nonideal factors. The second is nonlinear phase compensation, which is used to correct the vernier principle that deviated due to the existing harmonics. Finally, a pole pitch compensation method is introduced to modulate the master phase angle from the OPLL to eliminate the harmonic distortion. The proposed method can eliminate the nonideal factors, harmonic distortion and improve the accuracy of the encoder. All the proposed methods were implemented on an ARM STM32F407ZG. The experimental results confirm the validity of the proposed method for practical applications.

This article presents a portable smartphone-based laser measurement system (SLM) for measuring gaps and flushes on a car body. The system is designed to replace conventional gauges that are used by human operators. The developed device consists of a smartphone and an off-the-shelf laser-line projector held together by a three-dimensional–printed structure. It captures images of a laser line scattered on target surfaces, identifies the extreme points on the laser profiles, and obtains gap and flush measurements with minimal uncertainty. Since the smartphone is used as the operational unit, the measurement data can be stored and allows the capability for data transfer with other storage locations. Experiments were performed on the specimens and real cars to validate the application of the developed system. The measurement uncertainty on a real car was reported as $\pm$ 0.201 mm for gap and $\pm$ 0.154 mm for flush.

In recent DRAM processes, obtaining the maximum capacitor presents a difficult challenge. Also, the aspect ratio continually increases during the process. To address these issues, the thickness of the top and bottom layers must be constant. However, the thickness is not constant due to the time difference between the bottom and top processes, and because the source changes in each step. To solve this problem, the same reaction should occur at all times while the density of the gas at the bottom and top is gradually changed. In this study, a new vaporizer is developed and evaluated to ensure a constant rate of density change and a rapid vaporization response of the liquid source. To develop a vaporizer that responds quickly, we experimentally evaluated the structures of the vaporizer's showerhead and vaporizing part. To keesp the density of the liquid source for evaporation constant, several holes were made in the showerhead from which the evaporation source is discharged. Also, to ensure a constant flow of liquid to the chamber where the source droplet meets the carrier gas, a helical structure was proposed and evaluated. In addition, using an orifice, the pressure was decreased to increase the vaporization efficiency. As a result, the settling time decreased from 10 to 3 s, overshooting decreased from 25 to 1%, and flow fluctuation decreased from 2.6 to 1.8%. Therefore, this new vaporizer is well-adapted to the process of new devices and provides greater capacitance design margins.

This paper proposes a linear compensator algorithm to improve the performance of an absolute multipolar magnetic encoder (AMPME). An AMPME is an absolute magnetic rotary encoder that uses a multipolar magnet (MPM) to increase the resolution. The resolution can be dramatically increased in proportion to the number of poles in the MPM. However, various hardware problems that occur during the encoder manufacturing process degrade the AMPME performance. Also, harmonic components occur in the raw data due to various problems, such as the resistance error of the analog circuit, magnetic field overlap between the magnets, position error between the sensor and magnet, and pole-pitch difference. In particular, during the magnetization process of the MPM, the pole-pitch difference becomes a problem when the sizes of each pole are not uniform. This problem causes harmonic components that reduce the absolute position accuracy. To solve these problems, this paper proposes a linear compensation method. The proposed linear compensator consists of two parts. The first part is the enhanced ratiometric linearization for phase calculation and calibration. The second is the phase compensator for removing the phase difference via the pole-pitch difference of the MPM. The linear compensator improves various parameters by precomputing the offset, amplitude, and phase corrections. After compensating for sinusoidal signals, the linear compensator applies appropriate parameters at the appropriate times. This method is faster, easier to set up, and more accurate than the conventional method. Furthermore, this method is experimentally verified against the existing harmonic rejection method. Experimental results are provided to verify the effectiveness of the proposed method.

As multiple functions have been added to single-core-based engine electronic control units (ECUs) in vehicles, automotive researchers and manufacturers have actively studied multi-core architecture for engine ECUs. Multi-core architecture can provide load balancing and parallelism that can meet the requirements of international organization standard (ISO) 26262. However, since real-world engine ECUs have the most complex automotive open system architecture (AUTOSAR)-based control logic and datasets among automotive ECUs, developing multi-core-based engine ECUs is a substantial amount of work. Thus, automotive researchers and manufacturers will need new methodologies for multi-core-based engine ECUs. In this paper, we focus on designing a multi-core migration methodology and applying it to a real-world AUTOSAR-based engine ECU from HYUNDAI. We verify its practicability and enhanced performance. In conclusion, through connection with other automotive domain ECUs, it is demonstrated that a multi-core engine ECU using our migration technology can be applied in real-world automotive vehicles, leading to a significant improvement in performance.

Rectification is a core programming process that involves multiple views. In this study, we focus on uncalibrated cases that neglect intrinsic and extrinsic camera information. Existing uncalibrated rectification methods use feature-matching techniques to form relationships between different views and use those features to estimate optimized homography matrices. However, outliers are inevitable in feature matching. Using these outliers in a rectification process produces vertical disparity errors and unwanted geometric distortion. To tackle the problem, we propose a novel method that can learn from the rectification results, re-select the matching pairs, and find superior solutions. The proposed method introduces a novel workflow for uncalibrated rectification that incorporates three cores: field of view (FoV) neutralization, rectification, and feature matching re-selection (FMR). While the FoV neutralization module handles FoV differences among views, a combination of the rectification module and FMR module results in the optimal homography matrices. The rectification module takes neutral correspondences and estimates the optimized rectified matrices. Applying the results from the rectification module, the FMR module optimizes the correspondences and return them to the rectification module. For the rectification module, we apply adaptive geometric constraints and our updated optimization strategy to secure satisfied vertical disparity errors while maintaining low distortion levels. Multidisciplinary experiments are performed to analyze the capabilities of the proposed method. We combine existing datasets with extra samples, including various outdoor environments, to gauge the performance better. Compared with existing methods, the proposed method produces fewer rectification errors and keeps the rectified images under low distortion rates.

This paper combines the extended Kalman filter (EKF), dual quantum-behaved particle swarm optimization (DQPSO), and adaptive neuro-fuzzy inference system (ANFIS) to propose a novel robot calibration method. Robot precision is influenced by kinematic and non-kinematic error sources. The EKF algorithm is robust for the nonlinear system with Gaussian noise to identify kinematic parameter errors for kinematic calibration. However, if inappropriate covariance matrices are selected, the EKF algorithm may converge to an incorrect solution. To increase the effectiveness of the EKF algorithm, we propose a DQPSO algorithm that consists of the QPSO-1 and QPSO-2 algorithms. The QPSO-1 algorithm adapts the covariance matrices of measurement noise and process noise, while the QPSO-2 algorithm optimizes the kinematic parameter errors estimated by the EKF algorithm. In addition, the used ANFIS predicts and compensates the non-kinematic error for non-kinematic calibration. Experiments have been performed on a five-bar parallel robot to confirm the effectiveness of the proposed method. The experimental results demonstrate that the proposed method significantly improves the positional accuracy, and is better than the previous methods.

This paper combines the extended Kalman filter (EKF), dual quantum-behaved particle swarm optimization (DQPSO), and adaptive neuro-fuzzy inference system (ANFIS) to propose a novel robot calibration method. Robot precision is influenced by kinematic and non-kinematic error sources. The EKF algorithm is robust for the nonlinear system with Gaussian noise to identify kinematic parameter errors for kinematic calibration. However, if inappropriate covariance matrices are selected, the EKF algorithm may converge to an incorrect solution. To increase the effectiveness of the EKF algorithm, we propose a DQPSO algorithm that consists of the QPSO-1 and QPSO-2 algorithms. The QPSO-1 algorithm adapts the covariance matrices of measurement noise and process noise, while the QPSO-2 algorithm optimizes the kinematic parameter errors estimated by the EKF algorithm. In addition, the used ANFIS predicts and compensates the non-kinematic error for non-kinematic calibration. Experiments have been performed on a five-bar parallel robot to confirm the effectiveness of the proposed method. The experimental results demonstrate that the proposed method significantly improves the positional accuracy, and is better than the previous methods.

This paper proposes a robot calibration method that uses an extended Kalman filter (EKF) and a neural network based on Levenberg–Marquardt combined accelerated particle swarm optimization (LMAPSO) to improve the accuracy of the robot’s absolute position. After the EKF optimizes all geometric parameters, the robot position still contains non-geometric errors due to joint clearance, gear backlash, and link deflection that are impossible to model. Therefore, an artificial neural network model (ANN) is designed to compensate for these un-modeled errors. The Levenberg–Marquardt combined accelerated particle swarm optimization (LMAPSO) provides a robust optimization search algorithm to optimize the weight and bias of the neural network based on the training set. An experiment on a five-bar parallel robot shows that geometric and non-geometric calibration reduced the maximum absolute position error from (1.548 to 0.045) mm. The experimental results demonstrate the proposed calibration method’s effectiveness with the robot’s absolute position accuracy improving by 98%.

This article presents a self-referencing lookup table (LUT) algorithm to enhance the multiturn accuracy of a battery-free multiturn absolute magnetic encoder. The encoder consists of a single-turn angle for the measurement of the degree of rotation within $2\pi $ using a multipolar magnet to achieve high resolution and a multiturn counter to monitor the total number of revolutions made by the encoder’s shaft through the different angles of the four bipolar magnets (BPMs) based on four-shaft gears. However, the signals from the BPMs are affected by factors, such as amplitude mismatch, dc offsets, random noises, and harmonic components that are as a result of electrical and mechanical distortions. To solve this problem, this article suggested a method derived from the single-turn angle and the raw signals from the BPMs to generate the LUTs for multiturn angles. These angles eliminate the effects of the disturbances. Additionally, a multiturn compensation algorithm is suggested to compensate for the number of revolutions made by the encoder. The proposed methods are simulated in MATLAB software and implemented in ARM STM32F407ZG. The effectiveness of the proposed method is demonstrated by the obtained results.

To improve the accuracy of permanent magnet (PM) rotor position estimation for a hybrid stepper motor (HSM), the authors propose a sensorless control design based on the back electromagnetic force (back-EMF) caused by magnetic induction and harmonic rejection via an orthogonal third-order phase-locked loop (PLL 3 rd) and an integral harmonic filter (IHF). The accuracy of estimation is analysed considering PM rotor position estimation errors. The harmonics of the back-EMF signal are eliminated by the IHF before entering the PLL 3 rd, which synchronises and decreases position estimation error. This technique is a simple, reliable, and effective method for implementing sensorless control of an HSM. An industrial test-bench was used to implement the proposed scheme. The experimental results are presented to validate the effectiveness of the proposed estimation method.

This paper proposes a stable adaptive gradient descent for harmonic-disturbance rejection as a tool for grid-power signal processing, magnetic rotary encoders, and permanent-magnet synchronous motors. The method can be widely applied to increase the accuracy of phase or position estimation in various systems in which harmonic disturbances exist. The proposed technique is based on learning the harmonic amplitudes by means of gradient descent on the feedback phase error. To compensate for the disadvantages of existing gradient-descent methods, the derivative expansion is fully developed with system transfer function integration for stable weighting update. In addition, an adaptive learning rate is also proposed based on discrete Lyapunov standard to achieve stability, and theoretical analysis is discussed. The performance of the method is evaluated by numerical simulation in MATLAB with various scenarios, and by the experiment with the rotary magnetic encoders. The results show that the proposed method achieves stability and high performance in harmonic rejection.

A network-based control system includes many connected devices, and time synchronization plays an important role in the unified operation of those systems. A clock servo needs a guarantee time offset and a clock rate to minimize the time difference between devices. This paper proposes an adaptive clock servo to improve time synchronization. This proposed algorithm uses a fuzzy-based proportional-integral clock servo (fuzzy-PI) based on IEEE 1588 to reduce the frequency of clock compensation. This method adapts the bandwidth and enhances noise reduction, improving both the time offset and rate difference between the slave and master. The node time can synchronize with the master time after just one cycle of synchronization. Experiments validated the effectiveness of the algorithm and demonstrated that the slave can track the master with the mean and standard deviation of the time offset are 0.432ns and 4.402ns. A cycle time of one second is used to ensure a low-bandwidth network. With these results, the number of nodes over a real-time network can be increased.

Traditional deep learning-based vehicle detection methods are often designed using a pyramid of filters with multiple scales and sizes; therefore, the processing time is slow due to the large number of scales used and because the classifier runs at all scales. Recently, a deep learning-based region proposal network was introduced to detect vehicles that only employ the network one time regardless of the size of the input image. In object detection, deep learning-based region proposal networks have achieved state-of-the-art performance in terms of accuracy. These systems achieve a very high accuracy under normal driving conditions; however, their performance decreases under difficult driving conditions such as in snow, rain, or fog. In addition, the current state-of-the-art system-based region proposal networks still fail to satisfy the real-time requirements of the driving assistant systems. More recently, the identification of local patterns has been shown to improve the performance of the traditional deep-learning systems; hence, this paper investigates local patterns in region proposal networks to improve their accuracy. Depth information is also investigated to improve the processing time of current region proposal networks. Our experimental results show that the proposed system obtains better performance than the state-of-the-art object region detection systems in terms of both accuracy and processing time.

Binocular stereo matching is a challenging problem in computer vision. Recently, convolutional neural networks (CNNs) have emerged as a promising approach. However, matching ambiguities on ill-posed regions remain an intractable challenge for current methods. In this paper, we propose a novel network model consisting of two main parts: a wide context learning network and stacked encoder–decoder 2D CNNs with a spatial diffusion module. The first part leverages the power of a dilated convolutional layer and spatial pyramid pooling to extract global context information and constitute a matching cost volume. The second part performs contextual aggregation over this matching cost volume to optimize and smooth the matching cost. Finally, we estimate a disparity map by computing the probability of each disparity from the predicted matching cost. The proposed network model allows us to train end-to-end without any further post-processing or refinement. In the experiments, we evaluate our method using the synthesis Scene Flow and real-world KITTI datasets. Our proposed method achieves performance results that are competitive against state-of-the-art methods while maintaining a fast run time.

In this paper, we propose an advanced approach to improve the accuracy of stepper motors based on an effective phase compensator. The proposed approach includes an adaptive current controller and an adaptive position controller. The proposed adaptive feed-forward proportional-resonant (AFPR) current control technique with a neural network (NN) is an improvement over the conventional proportional-integral algorithm. In which, the NN algorithm is applied to obtain the optimal parameters for the AFPR controller. This results in an enhancement of the stepper motor current tracking. In addition, the advanced position controller is a combination of a damping and a phase-compensated (D&PC) method to reduce steady-state error and variations during low-speed operation. The position tracking performance is further appreciated by applying an advanced proportional integral derivative (PID) controller during high-speed operation. A bandpass filter is designed to switch the motor between low- and high-speed operational regimes. The proposed approach does not require any changes in the structure of the hardware. Therefore, it can be widely applied to a diverse set of industrial applications. The performance of the proposed approach was validated by conducting experiments under practical conditions.

This paper proposes a method to improve the accuracy of an absolute magnetic encoder by using harmonic rejection (HR) and a dual-phase-locked loop (DPLL). The encoder consists of two permanent magnets: an edge-located multipolar magnet (MPM) and a center-located bipolar magnet, in which the signal-processing accuracy of the MPM is crucial for achieving high accuracy of the entire encoder. However, the MPM signals are disturbed by nonidealities such as dc offsets, amplitude mismatch, low- and high-order harmonics, and random noises. In this paper, the HR approach investigates the characteristics of nonidealities of the phase detector and rejects them by using gradient descent. In addition, the DPLL remains robust by maintaining a zero steady-state error during a phase jump, a constant frequency, and a ramp frequency. The proposed method is simulated with MATLAB software and implemented in ARM STM32F407ZG. The obtained results demonstrate efficient performance. This method can be applied to any use of quadrature sinusoidal signals, such as in power grids and in permanent-magnet synchronous motor phase detection.

Absolute magnetic encoders (AMEs) use two magnets: a ring multipolar magnet (MPM) generating high-resolution and improving the accuracy for the encoder, and a bipolar magnet in the center calculating the number cycle of MPM signals. The phase outputs of these AMEs are tracked from the sinusoidal signals of the MPM. However, these sine/cosine signals are disturbed by amplitude differences, offsets, phase-shift, harmonic components, and random noise. In order to solve this problem, this paper presents an adaptive linear neuron based on a third-order phase-locked loop (ALN-PLL) to improve the accuracy of AMEs. The proposed approach consists of two main parts: The first part is an ALN algorithm that uses the phase feedback of the third-order PLL in order to build the mathematical model of input signals, and then reject the disturbances. The second part is a third-order PLL that is designed based on a dominant pole approximation algorithm. The proposed PLL can reduce noise and eliminate dc-error during the phase step, frequency step, and frequency ramp. The simulation and experimental results demonstrate the effectiveness of the proposed approach.

Recently, advanced driver assistance systems (ADAS) have gained prominence in the vehicle industry, with respect to the importance of the stability and reliability of vehicular control technology or communication technology. In such situations, when data is transmitted, communication delays between different networks occur inevitably, and if the electronic control units (ECU) of each sensor are not time-synchronized, they result in control process issues of the ADAS. In this paper, we propose a time synchronization method of each node of the CAN-FD network by applying the end-to-end (E2E) mechanism of applying the precision time protocol (PTP).