Measurement and Control

Cough is a protective mechanism of the proximal respiratory tract. The frequency and severity of cough provide useful information for the diagnosis of upper respiratory diseases and the evaluation of their treatments. Manual cough classification is subjective, labor-intensive, and time-consuming. In this study, a cough classification technique based on a microelectromechanical system microphone is proposed. For the classification process, various tabular and time-series machine learning algorithms were applied, and their results were compared. With respect to the time-series algorithms, the random interval decision tree, random interval spectral forest, and random convolution kernel transform (ROCKET) methods were used. With respect to the tabular algorithms, a convolution neural network (CNN) with 40 Mel-frequency cepstral coefficients (MFCCs) and a recurrent neural network with 40 MFCCs were used. Voluntary cough and noncough (throat clearing, expiration, speaking, and rest) signals were recorded from 10 healthy subjects. The ROCKET method showed the best accuracy (98.40%). In addition, while its training took the longest time (1628.80 s), this algorithm took a reasonably short time for prediction (0.27 s). The CNN showed the second-best accuracy (97.81%) with short training (454.13 s) and prediction (0.40 s) times. Thus, given its accuracy and prediction time, the ROCKET method is recommended for this type of classification over the CNN algorithm. To validate the application of the proposed methods, two methods were applied to a public Coswara data set. To classify one cough class and three non-cough classes, the ROCKET and CNN showed reasonable accuracies of 90.33% and 89.16%, respectively.

This paper presents application of the combination of mapping, image identification, and indoor navigation for the omnidirectional wheeled mobile robot (WMR). The mapping part uses the point clouds converted from the depth map of the LiDAR camera to construct the indoor 3D point cloud map. Combining this map with the You Only Look Once ver.4 (YOLOv4) network, the door number and the doorknob can be displayed on the map by image identification. The navigation part applies 3D point cloud map, image, and depth map to an omnidirectional WMR so that it can know the current position and plan the path to the designated location. The proposed control scheme can make the robot perform indoor navigation and reach designated location.

In this paper, we studied the cooperative control problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty. A neuro-adaptive robust controller with sliding mode variable structure method, with an online-learning RBF-like neural network was proposed to approximate the nonlinear terms. Further, sliding mode variable structure method was used to eliminate the influence of external disturbance and system uncertainty. Lyapunov stability theorem verified the capability of system consensus, and the sufficient conditions for cooperatively uniformly ultimately bounded (CUUB) are also given. At last, two numerical simulations on both homogeneous and heterogeneous MASs demonstrated the effectiveness of our proposed method.

Wireless positioning and tracking technology are key technologies in applying wireless sensor networks and have vital research significance and application value. In this paper, in terms of positioning multiple non-coincident dynamic targets, aiming at the problem of low precision and large computational complexity when locating multiple dynamic targets by the two-step least squares algorithm and the constrained total least squares algorithm, an improved constrained total least squares algorithm is proposed. This algorithm fully considers the constraints, introduces the Lagrangian multiplier technology and the quasi-Newton BFGS iterative formula, avoids the calculation of the Hessian matrix, reduces the amount of calculation, and improves the positioning accuracy. Simulation experiments show that when the measurement error and sensor position error are moderate, the ICTLS positioning algorithm has a smaller RMSE than the TSWLS and CTLS positioning algorithms, showing higher positioning accuracy and stronger robustness. Secondly, aiming at the problem that the target is close to the reference node or any coordinate axis causes the positioning error of the traditional positioning algorithm to increase sharply, an optimized two-step least squares algorithm is proposed. This algorithm corrects the defects of the TSWLS algorithm by selecting the reference station and rotating the coordinate system again, and improves the positioning performance of the algorithm while reducing the amount of calculation.

In recent years, sketch face recognition has a wide application in law enforcement agencies and criminals. Deep learning plays a crucial role in the recent developments of face recognition, however, it is challenging to employ deep learning methods for sketch face recognition due to insufficient face photo–sketch data. Moreover, compared to photos, sketches lack detailed texture, and there exists a domain gap between photos and sketches, hence, traditional homogeneous face recognition methods perform poorly in sketch face recognition. In this paper, a novel deep learning method termed Domain Adaptation Scaled Entropy Meta-Network (DASEMN) is proposed to tackle sketch face recognition tasks. Specifically, a meta-learning training strategy is designed to tackle the few-shot problem and improve the generalization ability of the network. Then, a generalized entropy loss termed scaled mean entropy loss is proposed to guide the network to extract discriminate features. Finally, a domain adaptation module is introduced in the training set to reduce the domain gap between the sketch domain and the photo domain. Experiments on UoM-SGFS and CUFSF sketch face databases show that the proposed method is superior to other sketch face recognition methods.

Aiming at the problems of low efficiency of the traditional method for detecting smooth plug gauges and poor repeat accuracy of measurement parameters, a measuring system for the diameter of a smooth plug gauge based on machine vision technology is designed. This system drives the work-piece to move by two-dimensional work platform and collects image data through CCD, and the diameter parameters of the smooth plug gauge are obtained by processing the image which uses the threshold neighborhood location and segmentation method. In this paper, the mechanical structure and control structure of the measuring system are designed, the measurement principle and calibration method are explained, the relationship between the diaphragm and the threshold value under a fixed light intensity, and the relationship between the light intensity and the threshold value under a fixed diaphragm are studied. Finally, experiments have verified that the measuring system can complete the measurement efficiently, automatically, and with high precision within 20 s, and the repeatability of the measurement does not exceed 1 μm.

As the core component of rotating machinery, the fault diagnosis of rolling bearing has important engineering practical significance. Most of the current intelligent fault diagnosis methods are based on the premise that the training data and test data have similar probability distributions. However, in practical scenarios, there will inevitably be discrepancies in the distribution of vibration signals due to internal and external factors such as changes in working conditions, which will significantly affect the diagnostic performance of the intelligent diagnostic model. Aiming at problems that the vibration signal characteristic distribution of rolling bearings is inconsistent under different working conditions and the labels of the samples to be diagnosed are difficult to obtain, a new domain-adaptive fault diagnosis method is proposed in this paper. Firstly, the multi-scale feature extraction module is used to extract the features of the input signals, and the residual network structure is used to avoid the degradation of the model performance. Then, the APReLU activation function is used to make the vibration signals perform different nonlinear transformations according to their own characteristics through adaptive learning. Finally, the Joint Maximum Mean Discrepancy (JMMD) is used to reduce the displacement of both conditional and edge distributions between different domains. Therefore, this method can extract domain-invariant feature information and align the source and target domains, which can be used for cross-domain intelligent fault diagnosis. Six transfer fault diagnosis tasks based on the rolling bearing experimental platform are designed to evaluate the performance and effectiveness of the proposed method. At the same time, four popular methods are selected for comprehensive analysis and comparison. The results show that the method has good robustness and superiority under various diagnostic tasks.

The input and output torque testing is crucial for improving the quality of Industry robot reducers. In this study, the TMMISR and TMMOSR for a vertical-type robot reducer detector were designed. The length of measurement chain between the torque transducer and the tested reducer was shorten. The overall stiffness of this instrument has been improved through structural optimization. The characteristics of the two main parts of the torque-measurement errors were also analyzed. A high precision torque calibrator with a standard torque output is used to handle the torque calibration process. An error compensation method based on a backpropagation neural network was adopted for the error compensation process in this study. After error compensation, torque-measurement precision of 0.1% can be achieved by the reducer detector over the full torque measurement scale, and the instrument can be used for both static and dynamic measurements.

The six-degree-of-freedom (6 DOF) motion of the mother-ship, which is caused by wave fluctuation, makes offshore operations difficult. To solve this problem, various heave compensation systems were proposed, as well as the law of wave motion. These heave compensation systems included passive heave compensation system, semi-active heave compensation system, and active heave compensation (AHC) system. However, the empirical formula of wave motion is not suitable for practical offshore operations, and rare AHC systems take the characteristic of compensation into consideration. To study the compensation characteristic of the AHC system based on electric-driven marine winch in the actual sea condition and improve the accuracy of compensation, this work uses the measured data of wave motion, and deduces the motion equations of multi-axial driving system. By substituting the above elements into the AHC system and designing the sliding mode controller (SMC), the simulation results show the ideal compensation effect of the system. By comparing with experimental results, it is concluded that the driving system operates in according with the specified states, and the compensation performance of system is effective. The overall work is conductive to the research on AHC system, as well as the exploitation of marine resources.

This study studies a critical issue that linear velocity and steering angular velocity of the wheels of omni-directional mobile robot are easily affected by the unknown external disturbances such as wind direction and road flatness during the visual servoing stabilization task. To overcome such challenging problems, this study proposes a disturbance observer based visual servoing control strategy. In configuration, the controller is divided into two parts: the quasi-min-max model predictive controller is designed to accommodate the state constraint and input constraint of the system, and complete the visual stabilization task without disturbance. A nonlinear disturbance observer is designed to estimate the external disturbance and feedback compensate the effect of the actual disturbance on the wheel speed. The simulation results and real experiments show that the proposed control strategy can stably accomplish the visual servoing stabilization task even with a large external disturbance.

The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.

In this paper, an event-triggered mechanism for a class of multi-thread control systems is designed by the relationship between the threshold line and the Lyapunov function and an algorithm is proposed with update delays considered. In order to solve the problem of resource waste in complex systems with mismatched characteristics of the number of controllers and controlled plants, a stable multi-thread control algorithm considering the delay is designed by adopting the event-triggered mechanism. In the algorithm, the concept of a novel multi-thread control system is utilized to eliminate the need for a complex description of this class of systems. By reducing the constraints on the Lyapunov function in the traditional switching control scheme, a novel event-triggered rule is designed to save resources and ensure that the system is asymptotically stable. Furthermore, it is proved that the proposed multi-thread control algorithm based on event-triggered mechanism is feasible when the system has a non-zero update delay. Finally, the system is guaranteed to avoid the Zeno phenomenon and the proposed algorithm is investigated by simulation.

The existence of a Lyapunov function is known to ensure either local or global stability of a system’s equilibrium state. Inspired by the control-Lyapunov method, we here construct a Lyapunov candidate function by analyzing a pendulum system’s total energy and then applying appropriate control actions such that the conditions of a Lyapunov function are met. More specifically, our controller evaluates the Lyapunov function’s time derivative at each time step, and applies control torque such as to ensure that the Lyapunov function decreases for each step toward the goal upright state. Unlike the control-Lyapunov method, which aims to select control input as to minimize the Lyapunov function’s time derivative, our method provides up front the satisfactory conditions that yield a globally stable controller by using a rigorously designed proportional bang-bang control strategy. We show how to derive the controllers evaluation function, and how the controller is implemented in code. We further demonstrate the effectiveness of our method through numerical simulations. The result of our approach is a globally stable upright pendulum using a single-controller strategy.

Most aspect-level sentiment classification networks include the long short-term memory (LSTM) network, coupled with attention mechanism and memory module, is becoming widely applied in aspect-level sentiment classification. Although it has achieved good results, it cannot extract the global and local information of the context at the same time, and it is only based on the semantic relatedness between an aspect and its corresponding context words to model, while neglecting their syntactic dependencies. This paper proposes the aspect-level sentiment classification by combining convolutional neural network (CNN) and proximity-weighted convolution network (PWCN), as well as a new method to calculate the proximity weight. To obtain contextualized word vectors, corpora has been trained by the model of bidirectional encoder representations from transformers (BERT), which can be taken as text features. The CNN is able to extract sequence features from the text and to take the sequence information from the text into account. In addition, the PWCN can consider the syntactic dependencies inside the sentences. The BERT model also has the ability to model complex features of words, such as their syntactic and semantic changes in a linguistic context. Experiments conducted on the SemEval 2014 benchmark demonstrate compared to the well-established ones, the proposed approach had bigger effectiveness.

An aggregative game of disturbed Euler-Lagrange systems is studied in this paper. The cost function of each player depends on its own decision and the aggregate of all decisions. Different from the well-known aggregative games, the second-order nonlinear dynamic of every player is considered in our problem, and every player is influenced by exogenous disturbances. To seek the Nash equilibrium, a distributed algorithm is developed via state feedback, gradient descent, and internal model. The convergence of the algorithm is analyzed with the help of variational analysis and Lyapunov stability theory. It shows that the Euler-Lagrange players with the proposed algorithm, can asymptotically converge to the Nash equilibrium, even though exogenous disturbances have impact on the behaviors of the players. Finally, a numerical simulation is given to illustrate the effectiveness of our algorithm.

Rotor unbalance faults are one of the high-frequency faults in rotating machinery. As such, their accurate and timely diagnosis is important. In contrast to traditional methods based on static features, the dynamics features and support vector machines (SVM) are combined for the accurate detection and classification of rotor unbalance faults. First, the dynamical trajectories of the rotor system associated with unbalance faults are accurately identified locally based on the deterministic learning theory, which is more sensitive to abnormal changes in the rotor system. Second, entropy dynamics features, including the sample entropy, fuzzy entropy, and permutation entropy, are extracted based on the obtained dynamical trajectory data. Finally, the dynamics features are used to train the fault classifier based on the SVM with a Gaussian kernel function. Experiments on a rotor unbalance fault test rig demonstrate the effectiveness of the proposed method. The accurate detection and classification of rotor unbalance faults were also achieved compared with the results of employing static time or frequency features.

A new way to measure the penetration efficiency of a face mask is introduced. It has a number of advantages over the currently used measurement systems in which a stationary mask is impacted with a moving aerosol during which the upstream and downstream particle concentrations are compared. The equivalent system described here instead moves a mask through a stationary aerosol in a closed chamber and compares the particle concentrations before and after the movement of the mask. This system provides many advantages, such as greater simplicity, greater accuracy, an exact constant aerosol flow speed through a mask, more easily made concentration measurements, more realistic impacts, more controlled averaging, the absence of the need for preliminary impacts during flow stabilization, and a lower cost. A laboratory prototype of the system has been fabricated and seen to perform as expected. Data obtained from this prototype are displayed, analyzed, and used to demonstrate the consistency and repeatability of the system. A portable fully-automated compact prototype has also been designed and fabricated. This device can be easily operated, and the total time required to test a mask, from material insertion to result display, is only a few minutes.

Micro terminals are often used in every laptop, mobile, and other electrical product. It is challenging to automatically buckle the terminal head to its terminal base during manufacturing because of trouble in accurate positioning and gripping. A double-robots collaborative assembly system is developed to buckle millimeter-scale terminals in three-dimensional space. Robot 1 takes the terminal head horizontally by grasping its flexible line with a customized clamp, including two fingers. Robot 2 presses the aligned terminal head through a force control strategy to ensure that the terminal head and the terminal base can complete buckling accurately, even if there is a certain deviation in the vertical direction. There are two cameras to be used in the system. A horizontally placed camera is used to detect and calculate the angle between the terminal head and the horizontal plane. The angle data will drive robot 1 to make the terminal end face parallel to the horizontal plane to complete the pose correction of the terminal head. Another camera is vertically fixed at the end of industrial robot 1 and used to detect and calculate the position deviation between the terminal head and the terminal base. The position deviation will drive robot 1 to align the terminal head with the terminal base to complete the position correction. The YOLOv3, least square, and feature extraction algorithms are used in image processing. The accuracy of the YOLOv3 target detection model trained by self-made data set can reach more than 95% under different conditions. The detection period is within 65 ms. The experimental results show that the terminal assembly system designed in this paper has excellent reliability and assembly success rate. It also has a significant reference value for other terminals’ automatic buckling assemblies.

A robust iterative learning control algorithm is proposed for continuous-time switched singular systems disturbed by random measurement noise. Firstly, the switched singular systems is transformed into a differential-algebraic equation composed of slow subsystem and fast subsystem by nonsingular transformation. Then, the control input is continuously modified by the state measurement errors and the learning gain with attenuation characteristics. The convergence of each subsystem is strictly proved in theory, and the sufficient conditions for the convergence of this algorithm are given. Theoretical analysis shows that the algorithm can effectively suppress the measurement noise and make the system state completely track the desired trajectory in a finite time interval. Finally, compared with the fixed gain iterative learning control method, the simulation results show that the tracking accuracy of the proposed algorithm is significantly improved.