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With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. [ABSTRACT FROM AUTHOR]

In the inductive wireless power transmission (WPT) designs of consumer electronics and implantable devices, the printed planar coil in standard manufacture is commonly used. Layout optimization of the coils is one of the important ways to make the power transmission system more efficient. Varying the trace width and turn-to-turn spacing together for the coils is proposed to optimize the maximum achievable power transfer efficiency (max). An accurate analytical model for the printed square coils is also established as well to speed up the design process. By virtue of this model, an optimal scaling factor of the trace width and the optimal frequency can be quickly estimated. The proposed model is validated by both the simulations (ANSYS HFSS) and experiments. A WPT link of two planar coils with size of 50 mm × 50 mm × 1 mm, operating at 23 MHz, is optimized by using this methodology. After optimization, the measured max of the WPT system is increased from 22.60% to 32.74% at a 100-mm transmission distance. [ABSTRACT FROM AUTHOR]

Recent progress on human brain science requires developing advanced neural recording system to capture the activity of large neural populations accurately, across a large area of the brain, and over extended periods. Recently proposed distributed neural recording systems with numerous implanted devices require reliably energizing them wirelessly. Random distribution of these mm-sized implants and brain motion place them at different positions and orientations with respect to the power transmitter. Therefore, traditional wireless power transfer techniques fall short of reaching sufficient power for all implants simultaneously, rendering some implants nonfunctional. In this paper, a three-layer power transmitting array with three-phase coil excitation current is introduced, which is capable of producing omnidirectional and homogeneous magnetic field across the volume where the Rx coils are located. The individual coil dimensions in the array is optimized to improve the worst-case scenario in terms of homogeneity, which is further verified by the measurements using a scaled-up prototype system. The measurement results show that the minimum received voltages is improved from 0.34 V for 10-mm side-length hexagonal transmitting coil array to 0.83 V for the optimal case, i.e., 35 mm side-length hexagonal transmitting coil array. [ABSTRACT FROM AUTHOR]

Summary: This article studies the problem of consensus tracking control for second‐order agents in multiagent systems over switching signed digraphs. Compared with the existing consensus tracking works on the structurally balanced signed digraph where the antagonistic relations exist only between two independent subgroups, this article explores a more general case for the first time, in the sense that the antagonistic relation is allowed between any two agents. On the basis of the design of a cooperation‐antagonism environment‐based distributed algorithm, suitable model transformation vectors are utilized to convert the stability of original system into a product convergence problem of time‐varying superstochastic matrices. By analyzing the convergence, algebraic conditions between positive and negative weights are established to ensure that all followers can eventually reach the leader's state under switching signed digraphs with arbitrary antagonistic relations. Simulation examples are provided to demonstrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the distributed scaled consensus problem of multiple agents with high‐order dynamics under the asynchronous setting, where each agent measures the neighbors' information at certain discrete time instants according to its own clock rather than the whole discrete process and all agents' clocks are independent of each other. Assume that the communication topology can be arbitrarily switched and the information transfer between agents has a time‐varying delay. Under the designed asynchronous distributed control protocol, it is shown that the agents with the same scale will reach a common final state, while the agents with different scales will reach different final states. Moreover, an effective parameters selection strategy is presented for a large number of gain parameters in high‐order multiagent systems based on novel model transformation techniques. Simulation examples are provided to demonstrate the high‐order scaled consensus performances for the agents in the presence of asynchronous setting. [ABSTRACT FROM AUTHOR]

In this paper, a Cucker–Smale model with time-varying heterogeneous delays in the communication process is investigated, where the delay on each communication link is time-varying and independent of other communication links. A general update rule based on the distance between agents is used to determine the edge weights of the communication network. The method of constructing augmented system is used so that the system stability is transformed into a product convergence problem of time-varying sub-stochastic matrices equivalently. By analyzing this convergence problem, a sufficient condition is established to achieve flocking, which relates to the initial states, the structure of communication network and the upper bound of time delays. [ABSTRACT FROM AUTHOR]

L-type impedance matching circuits (IMCs) can maximize the power transfer efficiency (PTE) by converting the load to the optimum one in the wireless power transfer system. However, their effectiveness is inadequately studied. In this work, the applicability of the L-type IMCs is carefully examined under the restriction of capacitance realization. The sensitivities with respect to the deviation of the capacitance in mass production as well as the variations of the load and the mutual inductance are analyzed in detail and validated by experiments. By comparison with the basic-type IMCs, the L-type IMCs have higher, nearly equal and lower sensitivities with respect to the deviation of the capacitance, the variation of the load, and the variation of the mutual inductance, respectively. [ABSTRACT FROM AUTHOR]

Highlights • Novel Lyapunov functions are constructed to investigate the stability analysis. • The RBF Neural Network can approximate the unknown smooth function. • The dynamic surface control technique is applied to eliminate the "explosion of the complexity". • The adaptive backstepping dynamic surface control is proposed for nonlinear system. Abstract This paper develops an adaptive backstepping dynamic surface control method with RBF Neural Network for a class of nonlinear system under extra disturbances. The considered RBF Neural Network based on adaptive control is applied to approximate the unknown smooth function arbitrarily. The "explosion of the complexity" is eliminated by utilizing the dynamic surface control technique. The Lyapunov function is employed to verify the globally asymptotically stability of the control nonlinear system. Four examples were given to show that the novel control method can not only tracking the expected trajectory very well but also has a better approximation capability for various complex unknown smooth function under disturbances. [ABSTRACT FROM AUTHOR]

Abstract A novel deep learning based eddy current inversion algorithm is proposed and investigated in this paper. Eddy current testing (ECT) for defects detection problem is adopted to demonstrated the proposed algorithms. The proposed model based on a Convolutional Neural Network (CNN) is developed to improve the defect detection performance with uncertainty information. The novelty of our work consists in combining characteristics of ECT data with general deep learning model to improve performance of deep learning in ECT field including a region of interest (ROI) method based on robust principle component analysis, a CNN classification model with weighted loss function and measurement of uncertainties. Experimental dataset obtained from eddy current inspection of heat exchanger tubes is utilized to validate the detection performance improvement. As a result, both the classification accuracy and the percentage of defects correctly identified have been increased to almost 100%. [ABSTRACT FROM AUTHOR]

Abstract Magnetic flux leakage (MFL) techniques are widely used for nondestructive testing of ferromagnetic materials. An analytical model of the MFL field is essential for precise determination and reconstruction of defects. In this paper, we proposed a solenoid model based on magnetization mechanisms of the magnetic medium to explain the MFL principle and simulate the MFL field. By introducing the interaction of solenoids and the Jiles–Atherton model, this model can accurately calculate the MFL field of complex defects, particularly the field distortion caused by the coupling of the defect's components. The solenoid model was experimentally demonstrated with a Z-shaped defect in the specimen and proved to be valid for precise calculation of the magneto-optical image. This work reveals the MFL testing principle from the viewpoint of the magnetic medium and thus helps elucidate and eradicate the simulation errors. The more accurate simulation than the common magnetic dipole model facilitates the inverse calculation of defects, even if field distortion is involved. [ABSTRACT FROM AUTHOR]

Magnetic flux leakage (MFL) testing is a widely applied method for the ferromagnetic specimen defect detection. In this paper, we present a solenoid analytical model to MFL field based on the molecular current model of a magnetic medium. This model applies $B$ – $H$ characteristics of the material to confirm the homogeneous magnetization of the specimen and to obtain the magnetic moment of solenoids, and then it uses a semi-infinite solenoid as the source to simulate the leakage field. Furthermore, this model proves that the edge effect and defect geometry can be represented properly by setting ellipsoid defects at different positions in the specimen. The results of the proposed model are influenced not only by defect shape as that of the magnetic dipole model but also by the relationship between the magnetization and the normal direction of the defect surface, which leads to the more accurate reconstruction of defects. Based on these findings, the error from the assumption of homogeneous magnetic dipole distribution or magnetization can be explained by the solenoid interaction. This explanation provides a new way to eliminate the computation error, and the solenoid model shows higher computational efficiency compared with the numerical model. [ABSTRACT FROM AUTHOR]

Highlights • This paper studies the consensus problem of discrete-time heterogeneous multi-agent systems with random link failures and switching topologies. • The properties of the infinite products of time-varying random row-stochastic matrices and graph theory are explored to analyze this problem. • A sufficient condition that only depends on the topology structures is obtained. Abstract In this paper, we consider the issue of consensus among the agents with heterogeneous dynamics under random link failures that obey Bernoulli distribution. The heterogeneous multi-agent systems (MASs) are composed of hybrid dynamic agents including first-order dynamic agents and second-order dynamic agents, and the communication topologies are assumed to be directed and time-varying. The failure control protocol is designed by randomly utilizing the previous one- or two-step states information sent by the neighbors. A criterion for the heterogeneous consensus is established by constructing augmented systems with nonnegative random coefficient matrices for the switching topologies. With the help of graph theory and stochastic matrix theory, a sufficient condition related to switching topologies is established to ensure the realization of heterogeneous consensus. Numerical examples are finally provided to validate the theoretical result. [ABSTRACT FROM AUTHOR]

A crystal oscillator is a typical frequency generating unit that is widely used in computers, neural chips, biosensors and other applications; thus, it is very important to estimate and predict its remaining useful life (RUL) precisely. However, there are few existing RUL prediction methods because the observed data involve various uncertainties, leading to the great limitation of RUL prediction in practical application. In this work, we propose an uncertainty RUL prediction method based on the exponential stochastic degradation model that considers the multiple uncertainty sources of oscillator stochastic degradation processes simultaneously. Next, based on Bayesian theory, a novel Bayesian-Extreme Learning Machine parameter-updating algorithm that combines the local and global similarity methods is presented and used to eliminate the effects of multiple uncertainty sources and predict the RUL accurately. The effectiveness of the method is demonstrated using the accelerated degradation tests of crystal oscillators. Through comparisons with the predicted results without uncertainty, the proposed method demonstrates its superiority in describing the stochastic degradation processes and predicting the oscillator's RUL. [ABSTRACT FROM AUTHOR]

Abstract: Improving the efficiency of the inductive wireless power transfer (WPT) is an effective way to increase the power transfer distance, reduce the power exposure, and popularize the WPT application. If the geometrical sizes of the transmitting and receiving coils and the distance between them have been determined, an operating frequency exists to maximize the power transfer efficiency. In this work, a method to find this optimal frequency is introduced by virtue of analytical models of self‐ and mutual‐inductances and parasitic resistances and capacitances of the coils, and verified via simulations and measurements. [ABSTRACT FROM AUTHOR]

On the basis of the Faraday Magneto-optical Effect (FMoE) method, that the polarized light would rotate its polarizing direction when there is the magnetic in its moving direction, a NDT method based on Magnetic optic imaging (MOI) is proposed to detect and identify the crack. In order to identify the crack, the pulsed-couple neural network (PCNN) model based this method is developed and improved to select the threshold dynamically. The output image of the PCNN model is pulsed image and this kind of image is processed by the magnetic domain spots filter. This kind of filter is based on the connection law which can detect the crack in the pulsed image. The detecting system could be used to identify the crack accurate by the above two steps, which would be confirmed by the results. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power is volatile in nature and raises the level of uncertainty in power systems. PV power forecasting is an important measure to solve this problem. It helps to improve the reliability and reduces the generation cost. Advances in computer technology and sensors make the numeric modeling methods a hotspot in the field of PV power forecasting. However, data modeling methods strongly rely on the accuracy of measurement data. Unavoidable outliers in the measured meteorological data have an adverse effect on the model due to their heteroscedasticity. Although many studies can be found focusing on outlier detection, only a few have incorporated outlier detection with regression models. In this study, an innovative method employing the weighted Gaussian process regression approach is proposed, such that data samples with higher outlier potential have a low weight. A density-based local outlier detection approach is introduced to compensate the deterioration of Euclidean distance for high-dimensional data. A novel concept of the degree of nonlinear correlation is incorporated to compute the contribution of every individual data attribute. Effectiveness of the proposed method is demonstrated by performing an experimental analysis and making comparisons with other typical data-based approaches, and the results exhibit higher estimation accuracy. [ABSTRACT FROM PUBLISHER]

The new trend towards minimally invasive millimeter-sized and free-floating distributed implants promises to enable emerging applications, such as chronic neural recording with minimal damage to the surrounding tissue. However, wireless power transmission (WPT) to these medical devices is quite challenging. The magnetic field produced by external transmitter (Tx) coils at the position of small implants can be considered homogeneous to separate the optimization of Tx and receiver (Rx) coils for efficient WPT. This paper focuses on the optimization of the solenoid-type Rx coils, which are suitable for this application. We have developed an analytical model of solenoid coils that includes the impact of tissue and coating around the coils, verified through simulations and measurements. Using the proposed model, under a given size restriction and a specific load, we find the optimal operating frequency and coil geometry to maximize a figure of merit (FoM) for the Rx that includes the loaded quality factor and its internal efficiency as well as a factor related to the coupling coefficient. For a millimeter-sized coil, the optimal operating frequency for the Rx and the number of turns are found to be 500 MHz and six, respectively, if the coil is closely wound using AWG36 copper wires. If the pitch is also optimized, then 700 MHz and four turns provide the best FoM for the solenoid Rx. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method to detect crack in metals by utilizing the magnetic optical image (MOI) method. MOI detection is one of a nondestructive testing method, which is based on the Faraday magneto-optical effect. The artificial neural network (ANN) approach is applied to identify the crack, according to the dynamically threshold selection. There are two kinds of threshold vectors that are proposed by the ANN algorithm. A good threshold can be obtained from the vectors using a law. The new image acquired is processed by a magnetic domain spots filter. This filtering method is based on the connection law who holds good ability to process the spots in the MOI image. Based on these methods, the detecting system can recognize the crack clearly and the results would confirm it. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of stability analysis for uncertain neutral-type neural networks with Markovian jumping parameters and interval time-varying delays. By separating the delay interval into multiple subintervals, a Lyapunov–Krasovskii methodology is established, which contains triple and quadruple integrals. The time-varying delay is considered to locate into any subintervals, which is different from existing delay-partitioning methods. Based on the proposed delay-partitioning approach, a stability criterion is derived to reduce the conservatism. Numerical examples show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The mutual inductance between the transmitted and received coils is an important parameter in the inductively coupled wireless power transmission system. The relative position between the linked coils may be arbitrary according to the applications. In this paper the analytical calculation of the mutual inductance between two arbitrarily positioned rectangular filaments is proposed based on a more general expression of mutual inductance calculation of two arbitrarily positioned straight filaments than Grover's. The calculation results are validated by using the finite element simulation tools. The previous published approximation methods for square coils in the coaxial, lateral misalignment or angular misalignment situations are verified that they are accurate when the two coils' relative distances are large. [ABSTRACT FROM AUTHOR]

The mutual inductance between two coils is a key parameter in the inductively coupled wireless power transmission. The spiral rectangular coils are easier to implement than the circular ones, but the study of the mutual inductance of the spiral rectangular coils is not enough. In this paper, a new analytical calculation of the coaxial spiral rectangular coils is presented, which is simpler than the greenhouse method. The impact of the track thickness and width is also analyzed. The calculation results are verified by the existing methods and the experimental results. Based on these results, some guidelines for optimizing the coupling coefficient are given, when the received coil's size is limited by space constraints. [ABSTRACT FROM AUTHOR]

In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal Id, which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal Id the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal Id and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results. [ABSTRACT FROM AUTHOR]

This paper offers a novel distributed control scheme for the Cucker-Smale model to examine the leader-follower flocking behavior on networks with both cooperative and competitive interactions among agents. The core idea of the proposed scheme is to characterize the influence of cooperation and competition between agents through positive and negative weight functions with respect to the interaction distance, respectively. Based on the tool of infinite products of super-stochastic matrices, we establish mathematical inequalities about the intensities of cooperation and competition, and further obtain the algebraic conditions that depend on factors such as the initial states of agents, topological structure and weight functions. Moreover, we also discuss the influence of these factors on the least exponential convergence rate and the final position errors among agents. The obtained theoretical results are illustrated through simulation examples. [ABSTRACT FROM AUTHOR]

An enhanced and innovative laser-based magneto-optic (MO) imaging (LMOI) system is presented in this paper to detect buried subsurface flaws in metallic structures for structural nondestructive evaluation. Several key improvements for the new imaging device have been discussed, including the following: the optimization of the MO sensor, the design of the magnetic excitation device, and the development of the image processing approaches, which result in the enhanced MO image quality comparing to the first generation of LMOI. Experimental data have also been obtained that clearly demonstrated the improvement in imaging results. [ABSTRACT FROM AUTHOR]

Global interconnects play an increasingly important role in nanometer-scale integrated technologies. The optimization of global wire size (width and/or spacing) has been well studied. However, many optimization methodologies do not consider thermal effects. As technology scales, decreasing interconnect pitch and the introduction of low-k dielectrics have made self-heating of global interconnects an important issue. In this paper, we present a temperature-aware methodology for systematically optimizing the size of global interconnects with optimal repeater for maximizing the circuit performance. We develop techniques to calculate the full-chip temperature as a function of global interconnect width and spacing and analytically analyze the impacts of wire size on the substrate and self-heating temperature. We then reinvestigate the temperature -and size-dependent interconnect delay, bandwidth, and power consumption, and define a product of the delay per unit length, power dissipation per unit length, and reciprocal bandwidth per unit chip edge as an appropriate figure of merit for optimum wire size for various International Technology Roadmap for Semiconductors technology nodes. [ABSTRACT FROM PUBLISHER]

A hybrid translinear (TL) circuit constituted by two kinds of transistors, bipolar and CMOS transistors, was proposed to control its quiescent current. And a new method was introduced to analyze the hybrid TL circuit, which converted it into an uniform equivalent bipolar TL circuit. It simplified its analysis and design. This hybrid TL circuit is applied to the output stage of a class AB amplifier. The simulation results in 1.5µm BCD (Bipolar-CMOS-DMOS) technology were consistent with expectations well. [ABSTRACT FROM AUTHOR]

High frequency (HF) AC and noise modeling of MOSFETs for radio frequency (RF) integrated circuit (IC) design is discussed. Equivalent circuits representing both intrinsic and extrinsic components in a MOSFET are analyzed to obtain a physics-based RF model. Modeling of the intrinsic device and the extrinsic components is discussed by accounting for the important physical effects at both DC and HF. Based m the Y-parameter analysis of the equivalent circuit model, procedures of the HF model parameter extraction are also developed. With the discussed approaches, a sub-circuit RF model incorporating the modeling of parasitics is presented. This model is compared with the measured data for both y parameter and f[sub T] characteristics. Good model accuracy is achieved against the measurements for a 0.25µm RF CMOS technology. The non-quasi-static (NQS) modeling issue has also been discussed by using the BS1M3v3 based RF model to predict the HF characteristics of devices with serious NQS effects. Further, noise modeling issues are discussed by analyzing the theoretical and experimental results in both the flicker noise and thermal noise modeling. Modeling efforts to incorporate new physical effects are needed to predict better the flicker noise characteristics in today's MOSFETs. A detailed analysis of the HF noise parameters has been conducted to establish the relationship between the noise parameters preferred by circuit designers and obtained by HF noise measurement. Analytical calculation of the noise parameters has also been discussed to understand the noise characteristics with/without some parasitic components such as gate and substrate resistances as well as the influence of the induced gate noise. The HF noise predictivities of several HF noise models are also examined with the measured data. The results show that the BSIM3v3 based RF model can predict the channel thermal noise better than the other models. [ABSTRACT FROM AUTHOR]

Proposes an optimization method for the geometry of large van der Waals system by discussing the parallel dimer of benzene. Key to the method; Functions of the optimization method for large supramolecular systems.

In this article, the issue of flocking control for Cucker–Smale model under denial‐of‐service (DoS) attacks is discussed. It is assumed that there is an active stage and an asleep stage for each period of the DoS. Malicious attacks partially or completely destroy the network communication links in the active stages, and replenish energy without any attacks in the dormant stages. By constructing the agents' position and velocity errors, the C–S model under DoS attacks is equivalently transformed into error systems. By applying the approach of products convergence for infinite sub‐stochastic matrices, a sufficient condition for producing the flocking behavior is obtained. An upper bound of relative errors for agents' final positions is established, which depends on initial states, the topology structure and the weight function. Finally, simulation experiments verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Provides information on a study that presents a simple and accurate MOSFET channel charge model for device modeling in circuit simulation. Model presentation; Comparison and discussion between the model and the measured data from devices at different bias and design conditions; Summary.

Discusses the need for new models for digital circuit simulation in the development of very large scale integration of circuits (VLSI) technology. Focus on the Berkeley Short-Channel IGFET Model; Features of the model; Applications of the model.

Presents a physically based analytical model for fully depleted silicon-on-insulator (SOI) metal oxide semiconductor field-effect transistors (MOSFET) Inclusion of the self-heating effect (SHE); Suitability of the model for use in circuit simulators; Other actors taken into account by the model.

It is common for communities in real-world networks to possess hierarchical and overlapping structures, which make community detection even more challenging. In this paper, by investigating consensus process of the classical DeGroot model in opinion dynamics, we propose a novel method based on the cumulative opinion distance (COD) to discover hierarchical and overlapping communities. It is shown that this method is different from those classical algorithms relying on static fitness metrics that depict the inhomogeneous connectivity across the network. The proposed method is validated from two aspects. First, by estimating the eigenvectors of adjacency matrices, we investigate the detectability limit of our algorithms on random networks, which together with the results concerning the convergence speed of consensus guarantees the performance of our method theoretically. Second, experiments on both large scale real-world networks and artificial benchmarks show that our method is very effective and competitive on hierarchical modular graphs. In particular, it outperforms the state-of-the-art algorithms on overlapping community detection. [ABSTRACT FROM AUTHOR]

This paper studies the expected-time tracking control problem for multiagent systems with disturbances and time delays. The expected-time control scheme can ensure that control objectives can be completed within the desired time. In the other words, the output signal can track the reference signal in an expected time. Based on a special error and the adaptive technology, the expected-time tracking consensus control scheme is achieved. Moreover, by using the neural networks technique, the unknown nonlinear functions which often exist in the dynamic of the system are handled. According to the Lyapunov stability theorem and the Lyapunov–Krasovskii function, it is proved that the consensus errors are semi-globally uniformly ultimately bounded. At last, the feasibility of the control method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system's ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles' operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model's performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms. [ABSTRACT FROM AUTHOR]

As a core functional material in acoustic resonators and filters, the elasticity of aluminum nitride (AlN) piezoelectric thin film and its correlated intrinsic properties deserve priority attention. This paper presents an in‐depth investigation of elastic softening and stiffening effect induced by stress loading in AlN piezoelectric thin film. Based on the Born‐Lande equation, the physical mechanism of such new intrinsic elastic property is revealed and predicted quantitatively for the first time from the perspective of atomic interaction forces. It is found that a maximum 6.7% modulation range of elasticity coefficient can be achieved under ultimate stress loading conditions from (−2.5, −2.5) to (2.5, 2.5) GPa. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Managing failure dependence of complex systems with hybrid uncertainty is one of the hot problems in reliability assessment. Epistemic uncertainty is attributed to complex working environment, system structure, human factors, imperfect knowledge, etc. Probability‐box has powerful characteristics for uncertainty analysis and can be effectively adopted to represent epistemic uncertainty. However, arithmetic rules on probability‐box structures are mostly used among structures representing independent random variables. In most practical engineering applications, failure dependence is always introduced in system reliability analysis. Therefore, this paper proposes a developed Bayesian network combining copula method with probability‐box for system reliability assessment. There are four main steps involved in the reliability computation process: marginal distribution identification and estimation, copula function selection and parameter estimation, reliability analysis of components with correlations and Bayesian forward analysis. The benefits derived from the proposed approach are used to overcome the computational limitations of n‐dimensional integral operation, and the advantages of useful properties of copula function in reliability analysis of systems with correlations are adopted. To demonstrate the effectiveness of the developed Bayesian network, the proposed method is applied to a real large piston compressor. [ABSTRACT FROM AUTHOR]

Optimizing deployment of charging base stations in wireless rechargeable sensor networks can considerably reduce the cost. Previously, the charging base stations are simply installed at some fixed special points (e.g., the grid points) after partitioning the area distributed by sensor nodes. In this paper, a new algorithm of planning the charging base stations is proposed based on the greedy algorithm and the location relationship of the sensor nodes. The proposed algorithm exploits the local search ability and avoids falling into an exponential increase of the number of the charging base stations (i.e., combinatorial explosion). The simulated results show that the proposed algorithm can result in less number and flexible deployed locations of the charging base stations. In addition, this algorithm provides a novel solution for the point coverage problems. [ABSTRACT FROM AUTHOR]

A double-layer fan-shaped configuration for generating twin photonic hooks (t-PHs) is investigated. The t-PHs properties from this configuration are investigated and optimized using Finite-Difference Time-Domain (FDTD) method. The results show that t-PHs properties can be flexibly tuned by adjusting the opening angles, sizes of double-layer fan-shaped microcylinder and background refractive index. Especially, under 632 nm illumination, t-PHs with a maximum of 20.6 λ and a maximum bending angle of 50.3° are obtained. The high-performance t-PHs from our simple scheme offers innovative applications for fields of nanolithography, as well as integrated optics. • A double-layer fan-shaped configuration for generating twin photonic hooks (t-PHs) is investigated. • Various t-PHs bending angles and propagation lengths from the double-layer fan-shaped configuration are demonstrated. • Under 632 nm illumination, t-PHs with a maximum propagation length of 13.04 µm with a bending angle of 50.3° are obtained. [ABSTRACT FROM AUTHOR]

The first step to quantifying complex defects is knowing its geometry, shape, and orientation. This is evidently challenging in electromagnetic nondestructive testing (ENDT), especially for subsurface complex defect detection, in cases where no information about defect or material is known or given. In this article, we propose a heuristic approach for the visualization, verification, and validation of complex defects in magnetic flux leakage (MFL) testing. This method is based on MFL experiment using magneto-optical images (MOIs) that are obtained from four different magnetization patterns. Using the proposed magnetization patterns, images of complex defect are captured with the aid of a charge-coupled device (CCD) camera, based on the interaction of magnetic field distribution and detected defects, from different angles and directions. An enhanced ant colony algorithm (EACA) is then used to reconstruct complex defect shapes from captured images using a mean image approach. The reconstructed image (mean image) reveals the defect shape with high precision for which the EACA is able to extract important defect features such as specific edges that might be hidden or blurred. This approach based on results has proved to provide promising solution to visually verifying complex defects in MFL, leading to a more simplified and faster way to characterize these defects as compared with conventional methods, using their shapes and orientation. [ABSTRACT FROM AUTHOR]

Ionospheric heights provided by ionosondes are vital for over-the-horizon radar (OTHR) target tracking. However, biases contained in the provided ionospheric heights definitely cause degradation of the tracking performance. This paper is concerned with the joint estimation problem of the target state and the ionospheric height bias for OTHR target tracking subject to multipath and cluttered measurements. A Markovian jump ionospheric height bias model is presented by simultaneously considering the intermittent and abrupt ionosphere changes. Meanwhile, a set of stochastic variables are adopted to depict association uncertainties among measurements, clutters, and propagation modes so that association is embedded in the resultant measurement model with random coefficients. Through such modeling transformation from association uncertainties to parameter randomness, the coupling processing of data association and state estimate is equivalent to pure state estimate subject to stochastic parameters. Furthermore, an optimal linear joint estimator containing causality constraints is developed in the minimum mean-squared error sense, and further extended to the case of nonlinear measurement model via iterative optimization. A target tracking example with four resolvable propagation modes illustrates the effectiveness of the proposed estimation scheme. [ABSTRACT FROM AUTHOR]

This paper provides a symbolic quality characteristics prediction method of complex mechatronics system based on gray model. The time series data that are collected from the design, manufacture and service process are transformed to corresponding symbolic sequence data by using the symbolic modeling analysis method. The Shannon entropy that represents the variation of symbolic quality characteristics can then be calculated, and the histogram and main modes of symbolic sequences are obtained. A preliminary prediction of system quality characteristics is conducted based on the gotten main modes. By using the fitting capability of gray theory for the trend of data sequences, the GM(1,1) model is employed for the prediction modeling of symbolic quality sequences in this paper, and the prediction of corresponding quality characteristics of whole complex mechatronics system are implemented. Furthermore, the correlation of multiple quality characteristics is analyzed by relative entropy and gray correlation analysis method. The key quality fluctuation prediction of electric control and drive system of heavy machine tool by this method has shown that the presented method has high computational efficiency and large practical value. [ABSTRACT FROM AUTHOR]

With the increasing complexity of engineering systems, reliability analysis and evaluation of systems with traditional methods can't meet practical engineering requirements. Based on limited experimental conditions, lack of data, complex structure models, insufficient cognitive abilities, and many other issues, people have to consider many uncertain factors in system reliability research. Besides, common cause failure (CCF) has become an important factor of system failure. In this paper, a discrete‐time Bayesian network (DTBN) associated with an eight‐rotor unmanned aerial vehicle (UAV) system is presented to discuss above problems. In this approach, the system is assumed as a two‐state system. After that, interval analysis theory is employed to deal with uncertainty. We consider the four sets of auxiliary propellers in the auxiliary power group as a 3/8 voting system, and β factor model is used to process CCF in the auxiliary power group. The proposed methods prove the validity of proposing interval analysis theory to solve uncertain problems and it is necessary to consider reducing or avoiding CCFs in system. [ABSTRACT FROM AUTHOR]

This paper presents a 25-GS/s 6-bit time-interleaved (TI) SAR ADC in a 40-nm CMOS low-leakage (LL) process. The prototype utilizes 4 × 12 hierarchical sampling architecture to reduce the complexity of track-and-hold circuits and the timing skew calibration. The single-channel SAR ADC adopts asynchronous processing with two alternate comparators. A partially active reference voltage buffer is designed to reduce the power consumption. The method based on sinusoidal signal approximation is employed to calibrate timing skew errors. To characterize the ultra-high-speed ADC, an on-chip design-for-test memory is designed. At 25 GS/s, the ADC achieves the SNDR of 32.18 dB for low input frequency and 27.28 dB for Nyquist frequency. The chip consumes 800 mW and occupies 1.3 × 2.6 mm2, including the TI ADC core and memory. [ABSTRACT FROM AUTHOR]

Highlights • The Tobit Kalman filtering problem is studied for discrete time-varying systems. • Non-Gaussian Levy and time-correlated additive measurement noises are considered. • The measurement differencing method and Levy-Ito theorem are utilized. • A modified recursive filtering algorithm is designed where the unknown noise covariances are carefully calculated. • Simulation results are provided to illustrate the effectiveness of the proposed filter. Abstract The Tobit Kalman filter (TKF) is a powerful tool in solving the state estimation problem for linear systems with censored measurements. This paper is concerned with the Tobit Kalman filtering problem for discrete time-varying systems subject to non-Gaussian Lévy and time-correlated additive measurement noises. By referencing to the measurement differencing method, the time-correlation of the measurement noises is transformed into the cross-correlation between the equivalent measurement noise and the process noise. Then, by resorting to the Lévy-Ito theorem, the non-Gaussian Lévy measurement noises are transformed into equivalent Gaussian noises with unknown covariances. Based on the transformed Gaussian measurement noises, a modified recursive TKF is designed where the unknown noise covariances are carefully calculated. Simulation results are provided to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

Highlights • A feature extraction algorithm is used to realize automatic defect identification. • It divides the TTRs from in image sequence into several parts by the thresholds. • The algorithm finds the low-correlation TTRs by variable interval search. • The normalized covariance matrix is calculated to classify the acquired TTRs. • The typical TTRs compose one matrix to transform the initial image sequence. Abstract In this paper, an improved feature extraction algorithm in Eddy Current Pulsed Thermography (ECPT) is developed to realize automatic defect identification. The proposed feature extraction algorithm includes a data block segmentation, a variable interval search, a correlation value classification and a between-class distance decision function. The data block segmentation and variable interval search are firstly combined to reduce the repetitive calculation in automatic defect identification. The classification and between-class distance are used to select the typical features of thermographic sequence. The method is not only able to extract the main image information, but also can reduce the time of thermographic sequence processing to improve the detection efficiency. Experiments and comparisons are provided to demonstrate the capabilities and benefits (i.e. reducing the processing time) of the proposed algorithm in automatic defect identification. [ABSTRACT FROM AUTHOR]