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Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles. [ABSTRACT FROM AUTHOR]

Excessive vibration has always been a serious problem for conical shell structures, while the application of the graphene-based free-constrained layer (GFCL) based on carbon fiber-reinforced composite (CFRC) structure is a novel way to improve structural performance. An analytical model for vibration and dynamic characteristics of the GFCL-CFRC conical shell resting on the Winkler–Pasternak elastic foundation with arbitrary boundary conditions is constructed, and four types of GFCL porosity distribution and GFCL dispersion pattern are considered in this model. The multi-segment technique and virtual spring technique are utilized to simulate arbitrary boundary conditions. Then, the first-order shear deformation theory (FSDT) and Hamilton's principle are employed to obtain the motion equation of the GFCL-CFRC conical shell, and the motion equation of the GFCL-CFRC conical shell is solved by the Ritz method. In conclusion, the dispersion mode of GFCL, thickness ratio of GFCL, and fiber angle have influence on the dynamic performance. With a reasonable design, the dynamic performance of the GFCL-CFRC conical shell can be further improved. [ABSTRACT FROM AUTHOR]

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system. [ABSTRACT FROM AUTHOR]

Summary: This article is concerned with the problem of robust fuzzy fault tolerant control for the nonlinear active suspension system via adaptive hybrid triggered scheme. To describe the system with high nonlinearity, a Takagi‐Sugeno fuzzy approach is applied by weighting a series of linear subsystems. During the design of the controller, the actuator failure is taken into consideration. To ease the network communication pressure, a novel adaptive hybrid triggered scheme is proposed for the suspension system. By applying a Bessel‐Legendre inequality and a Jensen's inequality together, the control algorithm can be derived in the form of linear matrix inequalities with less conservatism. Finally, compared with the existing sampled‐data H∞$$ {H}_{\infty } $$ control and adaptive event triggered H∞$$ {H}_{\infty } $$ control, numerical simulations are performed to demonstrate the effectiveness and superiority of the proposed controller. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have received increased attention in endoscopic images due to their outstanding advantages. Clinically, some gastric polyps are related to gastric cancer, and accurate identification and timely removal are critical. CNN-based semantic segmentation can delineate each polyp region precisely, which is beneficial to endoscopists in the diagnosis and treatment of gastric polyps. At present, just a few studies have used CNN to automatically diagnose gastric polyps, and studies on their semantic segmentation are lacking. Therefore, we contribute pioneering research on gastric polyp segmentation in endoscopic images based on CNN. Seven classical semantic segmentation models, including U-Net, UNet++, DeepLabv3, DeepLabv3+, Pyramid Attention Network (PAN), LinkNet, and Muti-scale Attention Net (MA-Net), with the encoders of ResNet50, MobineNetV2, or EfficientNet-B1, are constructed and compared based on the collected dataset. The integrated evaluation approach to ascertaining the optimal CNN model combining both subjective considerations and objective information is proposed since the selection from several CNN models is difficult in a complex problem with conflicting multiple criteria. UNet++ with the MobineNet v2 encoder obtains the best scores in the proposed integrated evaluation method and is selected to build the automated polyp-segmentation system. This study discovered that the semantic segmentation model has a high clinical value in the diagnosis of gastric polyps, and the integrated evaluation approach can provide an impartial and objective tool for the selection of numerous models. Our study can further advance the development of endoscopic gastrointestinal disease identification techniques, and the proposed evaluation technique has implications for mathematical model-based selection methods for clinical technologies. [ABSTRACT FROM AUTHOR]

In this paper, a robust control algorithm is designed to achieve a finite-time vehicle height and posture control through electronically controlled air suspension (ECAS) system subject to actuator faults, uncertainties under non-stationary condition. To achieve simultaneous position control of four corners of vehicle, synchronization errors between corners are taken to form a synchronization control strategy. Furthermore, to improve the system convergence speed and robustness, finite-time stability constrain is applied and H ∞ index is designed strategically in order to develop a novel robust finite-time controller. Since the solenoid valves in the ECAS system may degrade with the frequent switching, actuator fault and uncertain parameters are considered in this study to design the proposed fault-tolerant control methodology. Meanwhile, road disturbance is applied to the vehicle with the ECAS system to provide a non-stationary condition. Several software-in-the-loop tests and hardware-in-the-loop test are conducted to illustrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The vehicle active suspension has attracted considerable attention owing to its great contributions to the vertical dynamics of vehicle. This paper investigates the non-fragile output feedback control problem of the uncertain vehicle active suspension with stochastic network-induced delay. Firstly, taking the variation of sprung and unsprung masses into consideration, an interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model is introduced to describe the nonlinear characteristics of active suspension systems (ASSs). Secondly, to ensure that the control strategy is practicable when some states are unmeasured, a novel output feedback method is proposed by employing a variable substitution approach. Meanwhile, in order to approximate the real physical conditions of the control system, the gain perturbations are taken into account. Thirdly, with regard to the complexity of signal transmission delay in network control process, a more generalized lumped delay form is employed to represent the network-induced delay. Moreover, to describe the stochasticity of lumped delay, a Markovian process is introduced. Finally, both numerical simulations and experimental tests are carried out to examine the effectiveness and practicability of the proposed controller. [ABSTRACT FROM AUTHOR]

The vehicle active suspension has been widely used as it could effectively improve the vehicle ride comfort. To solve the issues of actuator uncertainty and time delay existing in the active suspension system, a multi-objective non-fragile H ∞ control algorithm is proposed in this work. First, to effectively describe the vehicle suspension dynamics, a quarter-car suspension model is constructed first in this study. Second, aiming at improving the ride quality and guarantee the hard constraints of the suspension system, a multi-objective H 2/ H∞ performance index is proposed. Subsequently, the issues of external disturbances, actuator uncertainty and delay are considered in the design process of the suspension controller. Then, a multi-objective non-fragile control algorithm is proposed based on an effective Lyapunov functional. The control gain can be obtained by solving a convex optimization problem in terms of linear matrix inequalities. Finally, numerical simulations are implemented on MATLAB/Simulink platform to show the robustness and superiority of the proposed controller. Experiments are implemented on a quarter-car test rig to validate the practical performance of the designed controller. [ABSTRACT FROM AUTHOR]

This research proposes a novel hydro-pneumatic suspension structure based on high-speed on-off solenoid valves, which can realize two spring force modes and four damping force modes. The basic working principles of the HPS with multiple output force modes are analyzed to establish the damping and stiffness features mathematical models. Based on the parameters taken from a mine dump truck, the stiffness coefficient and damping coefficient of the proposed HPS are matched firstly, then the initial charging pressure of accumulators and the diameter of throttle valves are determined. Finally, the validity of the proposed model is demonstrated by comparing the force-displacement and the force-velocity responses of the model with the corresponding measured experimental data under a broad range of excitations. [ABSTRACT FROM AUTHOR]

This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective. [ABSTRACT FROM AUTHOR]

This paper is concerned with the human-machine shared control issue for vehicle lane keeping systems via a new fuzzy observer-based event-triggered method. In order to capture system nonlinear and uncertain characteristics such as nonlinear tire dynamics, varying velocity and driver behavioral uncertainties, Takagi-Sugeno fuzzy approach is employed to model the global driver-vehicle-road system. After system modeling, the fuzzy observer-based control structure is considered because a full states information is not available in practical driving environment. Then, most existing human-machine shared control methods are based on the periodic sampling communication mechanism. However, since the network bandwidth is limited, the above mechanism may cause oversampling and communication congestion. Thus, an adaptive event-triggered mechanism is introduced between the observer and the controller to mitigate the communication burden and improve the bandwidth utilization. Based on Lyapunov functional theory, a set of sufficient conditions are given to calculate desired human-machine shared controllers. Finally, simulation tests are implemented on Matlab/Simulink-CarSim platform and simulation results illustrate that the proposed method can achieve a favorable improvement in the lane keeping capability, the driver handling comfort and the network bandwidth utilization. [ABSTRACT FROM AUTHOR]

This article investigates the event-triggered fuzzy filtering issue for vehicle sideslip angle estimation with consideration of data quantization and dropouts. First, an uncertain Takagi–Sugeno fuzzy model is developed to describe vehicle nonlinear dynamics resulted from nonlinear tire dynamics, varying velocity, uncertain mass, and yaw moment inertia. Then, an adaptive event-triggered scheme is introduced between the sensor and the filter for the decision of releasing sampled data to economize limited network resource. Moreover, the network-induced constraints, such as delay, data quantization, and dropouts, are taken into account to improve the robustness of the filtering method. Based on the Lyapunov stability theory, a new event-triggered asynchronous fuzzy filtering method is proposed by establishing an augmented Lyapunov–Krasovskii functional candidate and applying integral inequalities in the derivation. Finally, simulation results are presented to verify the advantages of the proposed method in comparison with the existing results. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of ride height tracking control of the automotive active air suspension (AAS) system with uncertain sprung mass and external time‐varying disturbances. The proposed adaptive robust control approach guarantees that (i) the ride height converges towards, and stays within, an arbitrarily small neighborhood of the desired height, attaining uniform ultimate boundedness; (ii) the closed‐loop AAS system is (1) adaptive to uncertain sprung mass caused by the change of payload through the design of a projector‐based mass estimator; and, (2) robust to external time‐varying disturbance by developing a nonlinear disturbance observer. At last, co‐simulation (AMESim‐Matlab/Simulink) and experimental tests on a self‐fabricated quarter‐car test rig are conducted to demonstrate the effectiveness and performance of the proposed method. [ABSTRACT FROM AUTHOR]

Identifying nonhormonal contraceptives will have profound impacts on avoiding side effects of hormonal birth control methods, minimizing pregnancy complications and infant mortality rates, and promoting family planning. However, phenotypic screening of contraceptives is challenging due to the diverse procedures associated with oocyte culture, biochemical assays, and molecular imaging. This study reports a multifunctional microfluidic platform comprising reconfigurable building blocks and interfaces to implement various cell-based drug screening protocols. This versatile platform has three major layers. The top layer consists of interchangeable 3D microfluidic building blocks (e.g., branching microchannels, chemical gradient generators, pumpless flow controllers, and emulsion generators) or an open interface. The middle layer incorporates a multiwell array with embedded membrane filters for live cell culture, medium exchange, enzymatic cumulus cell removal, washing, and fluorescence staining. The bottom layer is also reconfigurable for waste collection, oocyte culture, plate reader measurement, and high-resolution microscopy. We demonstrate an 8 by 16 (128 wells) system for performing the cumulus–oocyte complex (COC) expansion and oocyte maturation assays for screening nonhormonal contraceptives. The microfluidic building block platform is scalable and can be reconfigured for a variety of drug screening applications in the future. [ABSTRACT FROM AUTHOR]

With high demands for novel technologies in the marine mechatronic instrumentality and the limitations of the present hydraulic pumps, a novel bidirectional seawater hydraulic internal ball gear pump is proposed to accomplish the function of fluid power transmission and control. The proposed hydraulic pump can be used for hydraulic buoyancy control systems of underwater vehicles and different marine mechatronic equipment. In this paper, the model, the meshing and the flow characteristics of the internal ball gear pump are developed. The radial unbalance force of the pump, leakage, and flow pulsation are also analyzed. The new type of eccentric shaft-hole rotary sealing designed in this paper can eliminate axial leakage and is expected to improve volumetric efficiency, working pressure, and power density. Simulation and experimental results show that: (1) by using the concave/convex ball gears rather than the existing gears, the concave ball gear drives the convex ball ring to rotate, realizing energy transfer and conversion; (2) compared with the water hydraulic internal gear pump, the internal ball gear pump has excellent performance. Experimental results also show that when the speed is 1500 rpm, the maximum operating pressure can reach 7 MPa. At 2 MPa, its volumetric efficiency and flow can reach 75% and 0.189L/min, respectively. The outcomes of this study show that the proposed seawater hydraulic internal ball gear pump is effective and has a lot of potential. [ABSTRACT FROM AUTHOR]

Due to its strong performance in handling uncertain and ambiguous data, the fuzzy ${k}$ -nearest-neighbor method (FKNN) has realized substantial success in a wide variety of applications. However, its classification performance would be heavily deteriorated if the number k of nearest neighbors was unsuitably fixed for each testing sample. This study examines the feasibility of using only one fixed k value for FKNN on each testing sample. A novel FKNN-based classification method, namely, fuzzy KNN method with adaptive nearest neighbors (A-FKNN), is devised for learning a distinct optimal k value for each testing sample. In the training stage, after applying a sparse representation method on all training samples for reconstruction, A-FKNN learns the optimal ${k}$ value for each training sample and builds a decision tree (namely, A-FKNN tree) from all training samples with new labels (the learned optimal ${k}$ values instead of the original labels), in which each leaf node stores the corresponding optimal ${k}$ value. In the testing stage, A-FKNN identifies the optimal ${k}$ value for each testing sample by searching the A-FKNN tree and runs FKNN with the optimal ${k}$ value for each testing sample. Moreover, a fast version of A-FKNN, namely, FA-FKNN, is designed by building the FA-FKNN decision tree, which stores the optimal k value with only a subset of training samples in each leaf node. Experimental results on 32 UCI datasets demonstrate that both A-FKNN and FA-FKNN outperform the compared methods in terms of classification accuracy, and FA-FKNN has a shorter running time. [ABSTRACT FROM AUTHOR]

Road traffic accidents, especially those accidents with multiple-vehicle collision usually cause injuries and mortalities. Currently, studies on collision avoidance mainly focus on the control strategies for adjacent two vehicles or multiple vehicles in a single platoon direction. This paper proposes a bi-directional collision avoidance system for multiple vehicles to minimize the collision risk under the model predictive control (MPC) framework through switching the vehicle-following mode based on the inter-vehicular states. A hierarchical structure with an upper layer and a lower layer is designed. A dual-operational mode switching strategy and the vehicle-following model are formulated in the upper layer, together with the development of the acceleration redistribution cooperative strategy for vehicle platoon. While the lower layer is designed to track the desired acceleration received from the upper layer by considering the practical situation of the control commands. To tackle complex transitional operation, a dynamic weighted tuning strategy is proposed and integrated it with the MPC. The numerical results show that the proposed system outperforms the conventional collision avoidance system and is effective to avoid a collision or minimize the total impact of the vehicle platoon when the collision is unavoidable. [ABSTRACT FROM AUTHOR]

While our recent deep fuzzy classifier DSA-FC, which stacks adversarial interpretable Takagi–Sugeno–Kang fuzzy subclassifiers, shares its promising classification, its training speed will become very slow and even intolerable for large-scale datasets, due to successive training on all training samples with their random gradient based updates along each layer of its stacked structure. In order to circumvent this bottleneck issue, a fast training algorithm FTA is developed in this study by downsizing fuzzy rules with the proposed gradient guided learning for each subclassifier at each layer of DSA-FC on large-scale datasets. The core of FTA is to assure fast training of each subclassifier at each layer of DSA-FC, which first generates first-order smooth gradient guided information by means of the proposed top-k fuzzy rules selected from all fuzzy rules in each subclassifier, and then quickly updates the current inputs in terms of such information, which will be taken as the inputs of the subclassifier at the next layer. Our theoretical analysis reveals that the proposed gradient guided learning indeed enhances the generalization capability of a deep fuzzy classifier with or without adversarial attacks on outputs. Experimental results on large datasets demonstrate that FTA indeed trains the deep fuzzy classifier DSA-FC quickly with enhanced generalization capability. [ABSTRACT FROM AUTHOR]

This paper presents a novel observer-based gain-scheduling path following control algorithm for autonomous electric vehicles subject to time delay. Firstly, the lateral dynamic model of the autonomous electric vehicle is constructed by a polytope with four vertices, in which the issues of the time-varying longitudinal velocity and nonlinear tyre dynamics are accurately described. Secondly, taking the time delay encountered in the process of signal transmission into consideration, the observer-based path following controller is proposed by using easily measured vehicle states. In the algorithm, the observer and controller gains are gain scheduled according to the actual longitudinal velocity. Thirdly, based on Lyapunov stability theory, an appropriate Lyapunov–Krasovskii functional is constructed to derive sufficient conditions of the controller, which is effective to ensure the asymptotical stability of the closed-loop path following error system with a guaranteed H ∞ performance. Specially, for ease of computation, the sufficient conditions of controller design are developed in terms of a set of linear matrix inequalities. Finally, numerical simulations are implemented to illustrate the efficiency and superiority of the proposed method in comparison with the existing method. [ABSTRACT FROM AUTHOR]

Bloodstream infections (BSI) are a leading cause of death worldwide. The lack of timely and reliable diagnostic practices is an ongoing issue for managing BSI. The current gold standard blood culture practice for pathogen identification and antibiotic susceptibility testing is time-consuming. Delayed diagnosis warrants the use of empirical antibiotics, which could lead to poor patient outcomes, and risks the development of antibiotic resistance. Hence, novel techniques that could offer accurate and timely diagnosis and susceptibility testing are urgently needed. This review focuses on BSI and highlights both the progress and shortcomings of its current diagnosis. We surveyed clinical workflows that employ recently approved technologies and showed that, while offering improved sensitivity and selectivity, these techniques are still unable to deliver a timely result. We then discuss a number of emerging technologies that have the potential to shorten the overall turnaround time of BSI diagnosis through direct testing from whole blood—while maintaining, if not improving—the current assay's sensitivity and pathogen coverage. We concluded by providing our assessment of potential future directions for accelerating BSI pathogen identification and the antibiotic susceptibility test. While engineering solutions have enabled faster assay turnaround, further progress is still needed to supplant blood culture practice and guide appropriate antibiotic administration for BSI patients. [ABSTRACT FROM AUTHOR]

This article proposes a novel improved adaptive event-triggered (AET) control algorithm for networked Takagi–Sugeno (T–S) fuzzy systems with asynchronous constraints. First, taking the limited bandwidth of the network into consideration, an improved AET mechanism is proposed to save the communication resource. Superior to the existing event-triggered mechanism, the improved AET scheme introduces two adjusting parameters, which further contribute to the economization of the communication resource. Second, with consideration of asynchronous premise variables, a reconstructed approach is applied to synchronize the time scales of membership functions of the fuzzy system and the fuzzy controller. Third, to derive a less conservative sufficient condition for the controller design, a new augmented Lyapunov–Krasovskii functional with event-triggered information and triple integral terms is constructed. Meanwhile, by applying a Bessel–Legendre inequality and extended reciprocally convex matrix inequality together, a new control algorithm is derived with less conservatism. Finally, simulations on a cart–damper–spring system are implemented to evaluate and verify the performance and advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Automotive engine knock is an abnormal combustion phenomenon that affects engine performance and lifetime expectancy, but it is difficult to detect. Collecting engine vibration signals from an engine cylinder block is an effective way to detect engine knock. This paper proposes an intelligent engine knock detection system based on engine vibration signals. First, filtered signals are obtained by utilizing variational mode decomposition (VMD), which decomposes the original time domain signals into a series of intrinsic mode functions (IMFs). Moreover, the values of the balancing parameter and the number of IMF modes are optimized using genetic algorithm (GA). IMFs with sample entropy higher than the mean are then selected as sensitive subcomponents for signal reconstruction and subsequently removed. A multiple feature learning approach that considers time domain statistical analysis (TDSA), multi-fractal detrended fluctuation analysis (MFDFA) and alpha stable distribution (ASD) simultaneously, is utilized to extract features from the denoised signals. Finally, the extracted features are trained by sparse Bayesian extreme learning machine (SBELM) to overcome the sensitivity of hyperparameters in conventional machine learning algorithms. A test rig is designed to collect the raw engine data. Compared with other technology combinations, the optimal scheme from signal processing to feature classification is obtained, and the classification accuracy of the proposed integrated engine knock detection method can achieve 98.27%. We successfully propose and test a universal intelligence solution for the detection task. [ABSTRACT FROM AUTHOR]

Double-stranded (ds) biosensors are homogeneous oligonucleotide probes for detection of nucleic acid sequences in biochemical assays and live cell imaging. Locked nucleic acid (LNA) modification can be incorporated in the biosensors to enhance the binding affinity, specificity, and resistance to nuclease degradation. However, LNA monomers in the quencher sequence can also prevent the target-fluorophore probe binding, which reduces the signal of the dsLNA biosensor. This study investigates the influence of LNA modification on dsLNA biosensors by altering the position and amount of LNA monomers present in the quencher sequence. We characterize the fluorophore–quencher interaction, target detection, and specificity of the biosensor in free solution and evaluate the performance of the dsLNA biosensor in 2D monolayers and 3D spheroids. The data indicate that a large amount of LNA monomers in the quencher sequence can enhance the specificity of the biosensor, but prevents effective target binding. Together, our results provide guidelines for improving the performance of dsLNA biosensors in nucleic acid detection and gene expression analysis in live cells. [ABSTRACT FROM AUTHOR]

We report an on-chip platform for low-intensity pulsed ultrasound (LIPUS) stimulation of cells directly cultured on a biocompatible surface of a transparent ultrasound transducer (TUT) fabricated using lithium niobate. The high light transmittance (>80%) and compact size (3 mm × 3 mm × 2 mm) of TUTs allowed easy integration with powerful optical microscopy techniques with no additional acoustic coupling and risk for contamination. TUTs were excited with varying acoustic excitation parameters (voltage amplitude and duty cycle) and resulting live cell calcium signaling was simultaneously imaged using time-lapse confocal microscopy, while the temperature change was measured by a thermocouple. Quantitative single-cell fluorescence analysis revealed the dynamic calcium signaling responses and together with the temperature measurements elucidated the optimal stimulation parameters for non-thermal and thermal effects. The fluorescence change profile was distinct from the recorded temperature change (<1 degree Celsius) profile under LIPUS treatment conditions. Cell dead assay results confirmed cells remain viable after the LIPUS treatment. These results confirmed that the TUT platform enables controllable, safe, high-throughput, and uniform mechanical stimulation of all plated cells. The on-chip LIPUS stimulation using TUTs has the potential to attract several in vitro and in vivo biomedical applications such as controlling stem cell differentiation and proliferation, studying biomechanical properties of cancer cells, and gaining fundamental insights into mechanotransduction pathways when integrated with state-of-the-art high-speed and high-resolution microscopy techniques. [ABSTRACT FROM AUTHOR]

As a key environment perception technology of autonomous driving or driver assistance systems, lane detection is to ensure vehicles to drive safely in corresponding lane. However, existing lane detection algorithms for two-lane detection focus on using various filtering methods to reduce the impact of useless information, resulting in low accuracy and low efficiency. In this paper, a novel Adaptive Region of Interest (A-ROI) extraction method is proposed to improve the accuracy and real-time performance of the two-lane detection algorithm. Three key technologies are introduced to solve the problems. First, A-ROI, which only focuses on the lane where the vehicle is located, is applied to the Bird's-Eye-View image obtained by using Inverse Perspective Mapping (IPM). Next, based on Bayesian framework and Likelihood models, a lane feature extraction method with a lane-like feature filter is used for edge detection. Finally, an improved Random Sample Consensus (RANSAC) algorithm is introduced by using a filter that can remove noisy lane data. The performance of the proposed A-ROI method together with the improved lane detection method is evaluated via simulation of various scenarios. Experimental results show the proposed method has better accuracy and real-time performance than the traditional lane detection methods. [ABSTRACT FROM AUTHOR]

Due to the bus characteristics of large quality, high center of gravity and narrow wheelbase, the research of its yaw stability control (YSC) system has become the focus in the field of vehicle system dynamics. However, the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research. In this paper, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads. Firstly, the STI (Systems Technologies Inc.) tire model, which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force, is established based on experimental data and firstly adopted in the bus YSC system design. On this basis, a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM, which has the merits of fast transient response, finite time convergence and high robustness against uncertainties and external disturbances, is designed. Thirdly, to solve the optimal allocation problem of the tire forces, whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire, the robust least-squares allocation method is adopted. To verify the feasibility, effectiveness and practicality of the proposed bus YSC approach, the TruckSim-Simulink co-simulation results are finally provided. The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved. This research proposes a more effective design method for bus YSC system based on a more accurate tire model. [ABSTRACT FROM AUTHOR]

Aiming at enhancing the vehicle comfort and handling performances, this article concerns with the development of the vibration control method for the semiactive suspension systems installed with electrohydraulic dampers. To reject the external disturbances induced by the irregular road profile, sliding mode was intensively investigated for vibration control owing to its robust feature of insensitivity to the parametric uncertainty and external disturbances. However, the unknown nonlinearity of damper behaviors leads to model mismatch to cause the high-frequency switching of sliding-mode controllers. Consequently, the severe chattering phenomenon produces. Although saturated function is available to alleviate the chattering problem of sliding-mode control, there is always a tradeoff problem between tracking accuracy and chattering suppression. To solve this problem, this study provides a new intelligent robust control method for simultaneous improvements of tracking accuracy and chattering suppression. Given the computational efficiency, an improved extreme learning machine (ELM) is proposed to intelligently approximate and compensate the unmodeled dynamics with unknown nonlinearity to restrain the chattering problem, where a new adaptive learning law is designed in the premise of Lyapunov stability. To validate the effectiveness and efficiency of the proposed ELM-based robust control, a quarter-car test rig was set up for the hardware-in-the-loop test. Experimental results show that the proposed controller outperforms the sliding-mode controller with saturated function in depressing the sprung mass acceleration and tire deflection, showing its significance in both control performance enhancement and chattering elimination. [ABSTRACT FROM AUTHOR]

Online remaining charging-discharging cycle (RCDC) prognosis is of great significance for lithium-ion batteries. The conventional method is usually based on whether the state-of-health (SOH) of capacity reaches the end-of-life (EoL) threshold. However, the most available prediction methods have two problems that need to be solved. First, the SOH degradation curve of the lithium-ion battery is nonlinear and non-Gaussian, and the battery capacity regeneration phenomena (CRP) has a direct impact on RCDC estimation efficiency. These factors challenge the precise forecast of RCDC and increase the risk of prediction failure. Second, existing methods have insufficient early-stage prediction ability for capacity degradation because too little data are available to facilitate establishing and optimizing the prediction models. To overcome the above-mentioned drawbacks, this study introduces the Mann-Kendall trend analysis to generate an equivalent degradation indicator (EDI), and to replace the capacity-based SOH. The proposed EDI has good linearity and monotonicity, and is conducive to adopt a simple structured prediction model to determine the RCDC. Besides, this study is based on the “SOH-EDI” synchronization mapping relationship and applies an one-degree polynomial regression model to estimate the EoL threshold on the EDI curve. From the perspective of computational complexity, the proposed framework uses two polynomial prediction models with simple structures, which realizes a low computational burden and online RCDC prediction. To verify the efficiency of the proposed method, this paper introduces three methods for comparison. Experimental results show that the proposed framework has satisfied early-stage prediction ability of RCDC and has a superior prognosis efficiency. [ABSTRACT FROM AUTHOR]

The piecewise affine (PWA) model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the PWA model of the tire nonlinear cornering properties is introduced. In this approach, the highly nonlinear dynamic of the tire cornering properties is well approximated by a set of affine maps which relate inputs and outputs. These maps are defined in disjunctive regions in the regression space, itself composed of system inputs and outputs. The tire cornering properties tests are firstly carried out through a high-performance flat-plate test bench, thus the experimental data which can accurately reflect the tire cornering properties is obtained. On this basis, the PWA identification of the tire cornering properties is composed of the data clustering, the parameter estimation of the affine submodels and the calculation of the hyperplane coefficient matrices, which are respectively achieved by means of fuzzy c-means clustering, weighted least-squares and support vector machines. Finally, to verify the PWA model accuracy in approximating the tire nonlinear cornering properties, the comparison between the simulation results of the PWA identification model and the experimental data is conducted and the comparison results are analyzed. [ABSTRACT FROM AUTHOR]

In this article, a novel self-evolving data cloud-based proportional integral derivative (PID) (SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using fuzzy rules with nonparametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameter concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like subcontrollers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems. [ABSTRACT FROM AUTHOR]

Single-molecule and single-cell analysis techniques have opened new opportunities for characterizing and analyzing heterogeneity within biological samples. These detection methods are often referred to as digital assays because the biological sample is partitioned into many small compartments and each compartment contains a discrete number of targets (e.g. cells). Using digital assays, researchers can precisely detect and quantify individual targets, and this capability has made digital techniques the basis for many modern bioanalytical tools (including digital PCR, single cell RNA sequencing, and digital ELISA). However, digital assays are dominated by optical analysis systems that typically utilize microscopy to analyze partitioned samples. The utility of digital assays may be dramatically enhanced by implementing cost-efficient and portable electrical detection capabilities. Herein, we describe a digital electrical impedance sensing platform that enables direct multiplexed measurement of single cell bacterial cells. We outline our solutions to the challenge of multiplexing impedance sensing across many culture compartments and demonstrate the potential for rapidly differentiating antimicrobial resistant versus susceptible strains of bacteria. [ABSTRACT FROM AUTHOR]

This study aims to explore the impacts of the blending, fumigating, and combined fumigating+blending (F + B) methods, compared to the diesel method, on the engine combustion and cyclic variability parameters under five engine loads and speeds. The diesel and blending methods created heterogeneous combustion, while the fumigating and F + B methods created homogeneous+heterogeneous combustion. A fixed fuel composition of diesel-biodiesel-ethanol (D80B5E15, volume %) was used for the blending, fumigating, and F + B methods to maintain the same overall fuel composition for the comparison. It is found that all examined fueling methods have no pre-ignition and a similar trend for cumulative heat release fractions under all tested conditions. The blending method has similar, while the fumigating method has slightly longer overall combustion progression (from ignition delay to 95% of heat release) up to about 3 °CA in contrast to the diesel method. Compared to the diesel method, the blending (47.2 and 43.6%) and fumigating (22.3 and 22.2%) methods have longer premixed combustion phase, however a shorter diffusion combustion phase is achieved only by the blending method (−19.1 and −19.5%) and the fumigating method has no effect (1.7 and −1.3%) on it, according to the average results from five loads and speeds, respectively. Also, the blending (6.6 and 8.8%) and fumigating (26.3 and 53.5%) methods cause increase in COVIMEP. While, the blending method (9.2 and 6%) causes increase in COVMax(dP/dθ), and the fumigating method (−2.7 and −2.9%) leads to drop in COVMax(dP/dθ). The F + B method gets the results in between those of the fumigating and the blending methods. [ABSTRACT FROM AUTHOR]

Active front steering (AFS) can enhance the vehicle yaw stability. However, the control of vehicle yaw rate is very challenging due to (1) the unmodelled nonlinearity and uncertainties in vehicle dynamics; (2) timely response in control scheme. These two issues can be simultaneously alleviated through a random projection neural network (RPNN) for its high model generalisation and fast computational speed. However, typical RPNN cannot be directly applied to adaptive control applications. Therefore, a new RPNN-based adaptive neural control method is proposed, which is equipped with a newly designed adaptation law based on the theorem of Lyapunov stability. To test the performance of the proposed control method, simulations were carried out using a validated vehicle model. The simulation results show that, compared to conventional backpropagation neural network (BPNN) based controller, the proposed RPNN-based adaptive controller can reduce the response time and attenuate oscillatory steering in the case of cornering manoeuvre under fast variant vehicle speed. The results also demonstrate that the proposed RPNN-based adaptive controller outperforms the state-of-the-art fuzzy logic controller and the error feedback controller in multiple aspects including tracking nominal vehicle yaw rate, desired sideslip angle and intended path, showing its significance in vehicle yaw stability control. [ABSTRACT FROM AUTHOR]

This article presents a novel adaptive-event-trigger-based fuzzy nonlinear lateral dynamic control algorithm for autonomous electric vehicles under insecure communication networks. The integration of path following control and direct yaw moment control is considered to acquire a better lateral dynamic performance. To effectively tackle inherent nonlinearities of lateral dynamics, the Takagi–Sugeno fuzzy model approach is employed to approximate the nonlinear lateral dynamics. To avoid the infeasibility of state-feedback control due to the immeasurability of sideslip angle, a fuzzy output feedback control strategy is proposed via a useful transformation. To improve resource utilization of the band-limited communication networks, an adaptive event-triggered communication mechanism is proposed to save communication resource. Moreover, to enhance the robustness of the proposed controller, the insecure communication link is considered, which is described by the Bernoulli random distributed process. Finally, simulation results show the proposed adaptive-event-trigger-based fuzzy controller not only can save communication resource, but also achieve the perfect control performance. [ABSTRACT FROM AUTHOR]

In this research, a multi-objective frequency domain-constrained static output feedback control is developed for the delayed active suspension system with wheelbase preview information. Firstly, the model of the half-vehicle active suspension system with wheelbase preview information and time delay is constructed by employing the augmented method. Secondly, in vertical vibration, the human body senses are more intense when the frequency of the external disturbance is in the range of 4–8 Hz. Therefore, a finite frequency control method is proposed for the controller design with robust H ∞ and generalized H 2 performance. Thirdly, considering that a full online knowledge of states is not available in practical suspension systems, a static output feedback strategy is proposed to guarantee the feasibility of the controller by using a two-stage method. Finally, simulation study is given to explain the effectiveness and superiority of the presented controller. [ABSTRACT FROM AUTHOR]

The assessment of vehicle dynamic comfort to human body vibration varies from person to person. Even though the dynamic comfort rating can be obtained by placing measurement devices over the cabin, passengers may have dissimilar sensations. For many car manufacturers, both subject and objective measurements are used together at development of new cars. To effectively assess the dynamic comfort of vehicle, this research explores the correlation between the objective measurement of the whole body vibration, hand-transmitted vibration and subjective assessment. The whole body vibration is measured from the backrest, seat surface and vehicle floor, whereas the hand-transmitted vibration is measured from the steering wheel. Four different vehicles are driven at various speeds on five types of pavements. Both simple linear regression and polynomial regression models based on Stevens's power law from psychophysics are built to correlate the subjective and objective data. The regression results show that (1) the seat surface is more reliable than other positions for assessing the human body vibration; (2) the hand transmitted vibration on rough roads is more trustful for dynamic comfort evaluation than on smooth roads; (3) the overall dynamic comfort value is the most reliable index for dynamic comfort evaluation. [ABSTRACT FROM AUTHOR]

Wind speed interval prediction plays an important role in wind power generation. In this article, a new interval construction model based on error prediction is proposed. The variational mode decomposition is used to decompose the complex wind speed time series into simplified modes. Two types of GRU models are built for wind speed prediction and error prediction. Prediction error for each mode is given a weight and accumulated to obtain the width of the prediction interval. The particle swarm optimization algorithm is applied to search for the optimal weights of the prediction errors. Experiments considering eight cases from two wind fields are conducted by using methods of interval construction in the literature for comparison with the proposed model. The result shows that the proposed model can obtain prediction intervals with higher quality. [ABSTRACT FROM AUTHOR]

The automotive engine idle speed control problem is a compromise among low engine speed for fuel saving, minimum emissions and disturbance rejection ability to prevent engine stall. However, idle speed regulation is very challenging due to the presence of high nonlinearity and aging-caused uncertainties in the engine dynamics. Therefore, the engine idle speed system is a typical uncertain nonlinear system. To address the problems of inherent nonlinearity and uncertainties in idle speed regulation, an extreme learning machine (ELM)-based adaptive neural control algorithm is proposed for tracking the target idle speed adaptively. The purpose of ELM is to rapidly deal with the uncertain nonlinear engine system. Since the original ELM is not designed for adaptive control, a new adaptation law is designed to update the weights of ELM in the sense of Lyapunov stability. Experiment is conducted to validate the performance of the proposed control method. Experimental result indicates that the ELM-based adaptive neural control outperforms the classical proportional–integral–derivative (PID), fuzzy-PID and back-propagation-neural-network-based controllers in terms of tracking performance under the variation of engine load. [ABSTRACT FROM AUTHOR]

In this article, a novel recursive maximum correntropy-based evolving fuzzy system (RMCEFS) is proposed. The proposed system has the capability of reorganizing the structure and adapting itself in a dynamically changing environment with non-Gaussian noises. The system generates a new rule based on the correntropy criterion which represents a robust nonlinear similarity measure between two random variables and avoids recruiting the noises as the rules. Maximizing the cross-correntropy between the system output and the desired response leads to the maximum correntropy criterion for system self-adaptation. In our article, a recursive solution of the maximum correntropy criterion is derived to update the parameters of the evolving rules. This avoids the convergence problem produced by the learning size in the gradient-based learning. Also, the steady-state convergence performance of the proposed RMCEFS is studied, where the analytical solutions of the steady-state excess mean square error for the Gaussian noise and non-Gaussian noises are derived. The simulation studies show that the proposed RMCEFS using the recursive maximum correntropy converges much faster and is more accurate than the existing evolving fuzzy systems in the case of noise-free and noisy conditions. [ABSTRACT FROM AUTHOR]

Tire cornering characteristics have significant influence on vehicle lateral dynamics control. Unlike traditional tire mechanics models which are established based on the research experience or the mechanics mechanism, in this study, a novel experimental data-driven modeling approach is presented to model the tire cornering characteristics based on piecewise affine (PWA) identification method. In this approach, the highly nonlinear dynamic of the tire cornering characteristics is well approximated by several affine submodels acting on different regions. To obtain the experimental data which can accurately reflect the tire cornering characteristics, the tire tests are firstly carried out through a high-performance flat-plate test bench. On this basis, the PWA identification of the tire cornering characteristics is composed of the data clustering, the parameter estimation of the affine submodels and the calculation of the hyperplane coefficient matrices. The simulation results of the PWA identification model are finally compared with the experimental data to illustrate that the identified model has high accuracy in approximating the tire nonlinear cornering characteristics under wide range driving conditions. [ABSTRACT FROM AUTHOR]

Maximum margin clustering (MMC) is a recent approach of applying margin maximization in supervised learning to unsupervised learning, aiming to partition the data into clusters with high discrimination. Recently, extreme learning machine (ELM) has been applied to MMC (called iterative ELM clustering or ELMCIter) which maximizes the data discrimination by iteratively training a weighted extreme learning machine (W-ELM). In this way, ELMCIter achieves a substantial reduction in training time and provides a unified model for both binary and multi-class clusterings. However, there exist two issues in ELMCIter: (1) random feature mappings adopted in ELMCIter are unable to well obtain high-quality discriminative features for clustering and (2) a large model is usually required in ELMCIter because its performance is affected by the number of hidden nodes, and training such model becomes relatively slow. In this paper, the hidden layer in ELMCIter is encoded by an approximate empirical kernel map (AEKM) rather than the random feature mappings, in order to solve these two issues. AEKM is generated from low-rank approximation of the kernel matrix, derived from the input data through a kernel function. Our proposed method is called iterative AEKM for clustering (AEKMCIter), whose contributions are: (1) AEKM can extract discriminative and robust features from the kernel matrix so that better performance is always achieved in AEKMCIter and (2) AEKMCIter produces an extremely small number of hidden nodes for low memory consumption and fast training. Detailed experiments verified the effectiveness and efficiency of our approach. As an illustration, on the MNIST10 dataset, our approach AEKMCIter improves the clustering accuracy over ELMCIter up to 5%, while significantly reducing the training time and the memory consumption (i.e., the number of hidden nodes) up to 1/7 and 1/20, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the tasks of height and posture motion control for an electronically controlled active air suspension (AAS) system. A mathematical model of a vehicle body with AAS system is established to describe the dynamic characteristics and then formulated into a multi-input multi-output nonlinear system by considering parametric uncertainties and unmodelled dynamics. Based on this mathematical model, a synchronization control strategy is proposed to adjust the heights of adjacent AASs simultaneously, driving the pitch and roll angles closely to an arbitrarily neighborhood of zero, achieving global uniform ultimate boundedness. The proposed controller is robust to parametric uncertainties and external disturbances. A projection operator is utilized to limit the estimated parameters to their corresponding prescribed bounds in finite time. A co-simulation is conducted by combining a virtual vehicle plant with ASS system in AMEsim with the proposed synchronization controller in MATLAB/Simulink. Simulation results demonstrate that the proposed synchronization controller is effective and robust. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation. [ABSTRACT FROM AUTHOR]

The active suspension has drawn considerable attention due to its superiority in improving the vehicle vertical dynamics. This paper investigates robust nonfragile H ∞ optimal control for the vehicle active suspension with time-varying actuator delay. Firstly, the dynamic equation of an active suspension system with actuator delay is established in terms of the main performance objectives, that is, ride comfort, handling ability, and road holding. Then, a robust nonfragile H ∞ optimal controller is proposed to deal with the problem of active suspension control with time delay and actuator uncertainty, which is based on Lyapunov theory, convex optimization, and the linear matrix inequality approach. Finally, a quarter-car test rig is used for an experiment to illustrate the effectiveness of the proposed controller. Simulation and experimental results demonstrate that the proposed controller can ensure the asymptotic stability of the closed-loop system with bounded time-varying actuator delay, while managing the tradeoff between the conflicting performances and achieving performance optimization for the active suspension. [ABSTRACT FROM AUTHOR]