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This study aims at designing a robust nonparametric identifier for a class of singular perturbed systems (SPSs) with uncertain mathematical models. The identifier structure uses a novel identifier based on a differential neural network (DNN) with rational form, which can take into account the multirate nature of SPS. The identifier uses a mixed learning law including a rational formulation of neural networks which is useful to solve the identification of the fast dynamics in the SPS dynamics. The rational form of the design is proposed in such a way that no-singularities (denominator part of the rational form never touches the origin) are allowed in the identifier dynamics. A proposed control Lyapunov function and a nonlinear parameter identification methodology yield to design the learning laws for the class of novel rational DNN which appears as the main contribution of this study. A complementary matrix inequality-based optimization method allows to get the smallest attainable convergence invariant region. A detailed implementation methodology is also given in the study with the aim of clarifying how the proposed identifier can be used in diverse SPSs. A numerical example considering the dynamics of the enzymatic-substrate-inhibitor system with uncertain dynamics is showing how to apply the DNN identifier using the multirate nature of the proposed DNN identifier for SPSs. The proposed identifier is compared to a classical identifier which is not taking into account the multirate nature of SPS. The benefits of using the rational form for the identifier are highlighted in the numerical performance comparison based on the mean square error (MSE). This example justifies the ability of the suggested identifier to reconstruct both the fast and slow dynamics of the SPS.

Facing the gaps that the traditional long short-term memory (LSTM) and convolution neural network (CNN) cannot differentially deal with the input data based on the corresponding trend and stage information in remaining useful life (RUL) prediction, a more accurate and robust RUL prediction model is constructed. First, a temporally multi-differential LSTM (TMLSTM) with the multi-trend division unit and multicellular unit is proposed, and a spatially multi-differential CNN (SMCNN) with the multi-stage division unit and differentiated convolutions is designed. Then, by combining TMLSTM and SMCNN, a spatiotemporally multi-differential deep neural network (SMDN) is developed for predicting the equipment RUL, which enhances the ability of feature extraction from the spatiotemporal perspective by using the multi-trend and multi-stage information. Via several evaluation indexes, the Commercial Modular Aero Propulsion System Simulation dataset and the wind turbine gearbox bearing dataset are used to validate the superiority of the proposed method over several existing prediction methods.

The lifetime of NAND flash cells significantly degrades with feature-size reductions and multi-level cell technology. On the other hand, we have more and more approximate data such as images and videos that are more error-tolerant than regular data like text. In this paper, we propose Rebirth-FTL which reuses faulty blocks that contain uncorrectable errors to store approximate data for lifetime optimization. Rebirth-FTL effectively manages two spaces, namely, the approximate space and the normal space, with an efficient address translator, a coordinated garbage collection, and a differential wear-leveler. In addition, we develop an MTR (Migration Times Restriction) policy to restrict the movement of the approximate data in the approximate space. We also develop a scheme to pass approximate information from userland to kernel space in Linux. Finally, a lifetime model is presented for lifetime analysis. Our experimental results show that Rebirth-FTL can extend the lifetime by 41.63% on average.

By integrating the event-based mechanism and the model predictive control (MPC) method, an improved event-based MPC framework is constructed for the nonlinear control problem subject to disturbances. Firstly, a new event-triggering condition is suggested, which is constructed on the basis of the gradients of the differences between the optimal state prediction and the actual one at two consecutive sample times. Then, an event-based MPC algorithm is further proposed, in which the dual-mode control technique is incorporated to handle the nonlinear perturbed system. Furthermore, it is strictly demonstrated that the proposed algorithm will ensure the feasibility of the MPC method and the stability of the considered system, while significantly decreasing the number of solving optimization problems, based on which resources for information transfer can be effectively saved. Finally, simulations and comparisons are shown to verify the efficacy of the proposed framework.

In this paper, we propose to use production executions to improve the quality of testing for certain methods of interest for developers. These methods can be methods that are not covered by the existing test suite, or methods that are poorly tested. We devise an approach called PANKTI which monitors applications as they execute in production, and then automatically generates differential unit tests, as well as derived oracles, from the collected data. PANKTI's monitoring and generation focuses on one single programming language, Java. We evaluate it on three real-world, open-source projects: a videoconferencing system, a PDF manipulation library, and an e-commerce application. We show that PANKTI is able to generate differential unit tests by monitoring target methods in production, and that the generated tests improve the quality of the test suite of the application under consideration.

Performance assessments heavily rely on human ratings. These ratings are typically subject to various forms of error and bias, threatening the assessment outcomes’ validity and fairness. Differential rater functioning (DRF) is a special kind of threat to fairness manifesting itself in unwanted interactions between raters and performance- or construct-irrelevant factors (e.g., examinee gender, rater experience, or time of rating). Most DRF studies have focused on whether raters show differential severity toward known groups of examinees. This study expands the DRF framework and investigates the more complex case of dual DRF effects, where DRF is simultaneously present in rater severity and centrality. Adopting a facets modeling approach, we propose the dual DRF model (DDRFM) for detecting and measuring these effects. In two simulation studies, we found that dual DRF effects (a) negatively affected measurement quality and (b) can reliably be detected and compensated under the DDRFM. Using sample data from a large-scale writing assessment ( N = 1,323), we demonstrate the practical measurement consequences of the dual DRF effects. Findings have implications for researchers and practitioners assessing the psychometric quality of ratings.

This paper has proposed a novel design of dual PM vernier machine with T-shape consequent pole rotor. The analytical model of T-shape magnet rotor based on differential magnetic network method are firstly derived. Given the additional optimizing parameter, edge arc difference, T-shape consequent pole (TCP) rotor has the potential to produce 6% higher fundamental airgap flux density or 30% higher PM utilization ratio than regular fan-shape consequent pole rotor. The influence of magnet size parameters including magnet thickness, pole pitch ratio and arc difference angle on airgap flux density of TCP rotor is then analyzed. The torque performance of TCP in regular consequent pole and dual PM consequent pole vernier machines are also studied. The comparison shows that T-shape consequent pole can produce higher torque than fan-shape magnet in vernier machines. At last, a prototype of dual-PM vernier machine with TCP rotor is built and tested, which has achieved 28 Nm/L measured torque density.

The flexible robot has more advantages over the traditional machine in the cutting process for complex surface workpiece like the 3D printing part which has less residual material. During the process, the geometric smoothness of moving joints is critically vital for machining efficiency and accuracy. The tool orientation should also be limited in certain region considering the interfering requirements for a complex workpiece. Besides, the industrial robot always has six degrees of freedom (Dof) at least, and there are redundant Dofs for robot surface machining. Therefore, a smooth machining process under the tool orientation requirements can be optimized from these redundant Dofs. In this paper, the tool posture differential vector along the tool-tip path curve is optimized firstly. The whole trajectory is numerically integrated by the optimized differential vector step by step. In every calculating step, the minimum 2-norm of the joints' differential vector along the tool path curve is set as part of the optimization object to ensure the minimal change of joints along the machining process. At the same time, a state-related optimal object for the differential vector is established to keep the tool orientation move away from the boundary of the feasible region. Combining with the above two objects, the tool posture differential vector along the tool-tip path curve in every integral step is optimized and the whole machining process trajectory is obtained by numerical integrating method.

In the current manuscript, we deal with the dynamics of a piecewise covid-19 mathematical model with quarantine class and vaccination using SEIQR epidemic model. For this, we discussed the deterministic, stochastic, and fractional forms of the proposed model for different steps. It has a great impact on the infectious disease models and especially for covid-19 because in start the deterministic model played its role but with time due to uncertainty the stochastic model takes place and with long term expansion the use of fractional derivatives are required. The stability of the model is discussed regarding the reproductive number. Using the non-standard finite difference scheme for the numerical solution of the deterministic model and illustrate the obtained results graphically. Further, environmental noises are added to the model for the description of the stochastic model. Then take out the existence and uniqueness of positive solution with extinction for infection. Finally, we utilize a new technique of piecewise differential and integral operators for approximating Caputo-Fabrizio fractional derivative operator for the purpose of constructing of the fractional-order model. Then study the dynamics of the models such as positivity and boundedness of the solutions and local stability analysis. Solved numerically fractional-order model used Newton Polynomial scheme and present the results graphically.

The searching ability of the population-based search algorithms strongly relies on the coordinate system on which they are implemented. However, the widely used coordinate systems in the existing multifactorial optimization (MFO) algorithms are still fixed and might not be suitable for various function landscapes with differential modalities, rotations, and dimensions; thus, the intertask knowledge transfer might not be efficient. Therefore, this article proposes a novel intertask knowledge transfer strategy for MFOs implemented upon an active coordinate system that is established on a common subspace of two search spaces. The proper coordinate system might identify some common modality in a proper subspace to some extent. In this article, to seek the intermediate subspace, we innovatively introduce the geodesic flow that starts from a subspace, reaching another subspace in unit time. A low-dimension intermediate subspace is drawn from a uniform distribution defined on the geodesic flow, and the corresponding coordinate system is given. The intertask trial generation method is applied to the individuals by first projecting them on the low-dimension subspace, which reveals the important invariant features of the multiple function landscapes. Since intermediate subspace is generated from the major eigenvectors of tasks' spaces, this model turns out to be intrinsically regularized by neglecting the minor and small eigenvalues. Therefore, the transfer strategy can alleviate the influence of noise led by redundant dimensions. The proposed method exhibits promising performance in the experiments.

With the rapid development of the metro, a series of problems caused by stray current are becoming increasingly serious. Evaluation of stray current is the primary but difficult step for dealing with the problems. In fact, due to metros are usually constructed by cut-an-cover tunnel, shield tunnel and viaduct, metro structure is one of the most important factors of stray current. It is of significant to evaluate stray current in the long-line with the variable metro structures. Based on this, a uniform model is built in this paper. The return current system is equivalent to multiple differential units considering the variability of the metro structure, differential units at each location can be set according to the actual metro structure. Further, uniform equations for solving stray current are deuced based on the electrical relationships of differential units. For variable metro structures, uniform boundary conditions are discussed to solve the equations. The model is verified by comparing the test data and simulation data of Shenzhen Metro. Finally, stray current along the long-line is evaluated in this paper. It is shown that stray current is smaller along the long-line which is formed by the viaduct.

This work focuses on the robust leader-follower synchronization navigation for the under-actuated vessels with only independent the surge control force and the yaw moment. The ocean disturbances and thruster saturation are simultaneously addressed. A virtual vessel placed at the relative distance with the moving main vessel alleviates the requirements on main vessel's velocity measurements. The disturbance estimation performance is achieved by utilizing the disturbance observer. The additional controls produce the filtered non-achievable command control signals due to the thrust saturation effects. The filtered signals correct feedback errors for avoiding disturbance estimation and rejection compromise. The dynamic surface control technique is employed to derive the under-actuated synchronization control laws. By employing first-order filters, the differential terms are replaced by the algebraic operations such that the control is convenient to implement. It is analyzed that the supply vessel control system is semi-globally stable and the synchronization navigation along with the main vessel is ensured with the small errors. Simulations in the different disturbance cases validate the synchronization scheme.

In general, a power transformer is equipped with differential protection relay with the second harmonic restraint criterion to improve reliability. However, this criterion may fail in some cases, resulting in incorrect operation of protection relay. Therefore, to further upgrade the performance of differential protection, this paper proposes a duty cycle-based scheme as complement. Firstly, the waveform of the differential current in one cycle and its two derived waveforms, constructed by translating and taking absolute, are transformed into corresponding square waves, respectively. Then, duty cycles of these square waves are calculated, and three criteria are presented to quantify the differences between the duty cycles of disturbances and faults. Finally, the criteria and the second harmonic restraint are combined through three logic gates to form the proposed scheme for protection. A large number of simulation experiments have verified the effectiveness of the proposed scheme, which outperforms the frequently used protection schemes.

Motivated by the challenges arising in the field of multi-agent systems (MAS) control, we consider linear heterogenous MAS subject to local communication and investigate the problem of designing distributed controllers for such systems. We provide a game theoretic framework for systematically designing distributed controllers, taking into account individual objectives of the agents and their possibly incomplete knowledge of the MAS. Linear state-feedback control laws are obtained via the introduction of a distributed differential game, namely the combination of local non-cooperative differential games, which are solved in a decentralised fashion. Conditions for stability of the MAS are provided for the special cases of acyclic and strongly connected communication graph topologies. These results are then exploited to provide stability conditions for general graph topologies. The proposed framework is demonstrated on a tracking synchronisation problem associated with the design of a distributed secondary voltage controller for microgrids and on a numerical example.

The performance of fault diagnosis and prognosis (FDP) relies greatly on the model that describes the fault behavior and dynamics under study. In the past decades, many researches focused on establishing reliable, robust, and accurate models to describe fault growth dynamics. However, uncertainties in modelling process severely affect the accuracy and precision of FDP results. A major challenge in FDP is how to deal with the uncertainties that affect the performance of the system. For commonly used state space model, uncertainties exist in both the state transition equation and the measurement equation. In this paper, the influence of uncertainties on FDP results are studied and compared in terms of accuracy and precision. Furthermore, this paper proposes a differential model decomposition approach, which can automatically acquire appropriate model parameters and uncertainties for state space model. The effectiveness and advantages of the proposed method are verified and demonstrated with three different application scenarios, including comparison studies on public data and online experimental verification.

This paper focus on model predictive control (MPC) design in the context of sampled-data control systems with full-state measurements. It is shown that recent results on this area can be successfully generalized to cope with sampled-data MPC. The open-loop plant is subjected to polytopic parameter uncertainty and at sampling times a controlled output variable satisfies a set of convex constraints. A guaranteed H2 performance index with infinity horizon is minimized such as the feedback control preserves asymptotic stability and feasibility. The design conditions are expressed though differential linear matrix inequalities (DLMIs). Continuous-time systems are treated with no kind of discrete-time modeling approximation. Comparisons with classical methods from the literature dealing with continuous-time systems are presented and discussed. Examples are included for illustration.

For DC line protection in AC/DC hybrid system, concerning its poor immunity to fault resistance in the case of inzone fault and mal-operation problem in the case of out-of-zone fault, a new pilot directional protection scheme based on the voltage difference between positive and negative poles is proposed. First, by analyzing the operating conditions of converter in different time intervals, the differential expressions of feed-in current from AC system to DC system are derived. On this basis, combining AC-side fault component network, the calculation models of voltage difference between positive and negative poles in different operating conditions are built. And then, according to the consistency between the calculated voltage difference between positive and negative poles and the actual voltage difference, the protection criterion to identify in-zone and out-of-zone faults on DC line is constructed. Finally, simulation tests in RT-LAB verify that, the proposed scheme can correctly identify in-zone and out-ofzone faults in different fault cases, highly immune to the fault resistance. Besides, with no demand on the synchronicity of information interaction, the proposed scheme is simple and reliable with good operation speed.

On the basis of the double coupled Duffing oscillator, a differential double coupled Duffing oscillator method, called the proposed method 1, is proposed. The large scale state is judged by the differential timing sequence diagram, and its detection result is more intuitive. To overcome the frequency limitation of differential double coupled Duffing oscillator in detecting unknown frequency periodic signal, according to the proposed method 1, based on the intermittent chaos phenomenon, the differential double coupled Duffing oscillator variable step size detection method, called the proposed method 2, is proposed. This method can be used to detect Gaussian noise background periodic signal and K-distributed noise background periodic signal. Compared with the double Duffing method and the double coupled Duffing method, its average signal-to-noise ratio (SNR) threshold is reduced by 4.94 dB and 1.68 dB respectively, and the detection result is more intuitive to judge through the differential timing sequence diagram. The accuracy and universality of the proposed method 2 are proved by the detection of three measured underwater weak target signals. Experiments show that it has some characteristics such as intuitive detection result, high sensitivity, low SNR threshold, few oscillators, low system complexity and so on.

The problem of adaptive fuzzy observer-based tracking control for nonlinear singular systems is considered in this paper. The nonlinear singular systems are composed of two kinds of subsystems, being called the differential subsystem and algebraic subsystem, which are coupled with each other. The systems can be non-strict feedback structures. Through designing a new state observer and a linear controller, an error system with a tunable parameter is obtained. By constructing new one-sided Lipschitz conditions, the regularization and impulse-free conditions are proposed for the error system. With the help of the tunable parameter in the observer, we design an output feedback controller for the nonlinear singular systems to ensure that all states of the closed-loop system are bounded and the tracking and observer errors remain in a neighborhood of the origin. Two examples are provided to illustrate the effectiveness of the presented method.

A statistical orbit determination approach that minimizes the sum of absolute errors incurred by the vector measurements is developed. An iterative linear program is derived for the differential co...

AEGIS, an authenticated encryption algorithm designed by Wu and Preneel, is one of the six winners of the CAESAR competition. In this paper, we comprehensively investigate the existence of collision in the initialization of AEGIS-128 and evaluate the number of AES round functions involved in initialization, which reflects the resistance to differential attack. As a result, we find that there are 40 AES round functions, which is less than 50 ones claimed in the design document. We also prove that AEGIS-128 is strong enough to resist adversary who has access to partial state. In particular, we present a collision-based distinguisher and exploit it to recover the key of 4-step and 5-step (out of the full 10) AEGIS-128. The time and memory complexities are about \begin{document}${2}^{58}$\end{document} and \begin{document}${2}^{26}$\end{document} respectively. Specifically, we quantize the attack of 4-step AEGIS-128, in which we solve the technical issue of dealing with the function that does not fulfill Simon's promise. It is noted that the nonce is not reused in our work. Although we present some results of AEGIS-128 that exceed the existed analysis, the security margin of AEGIS-128 remains large.

The serviceability limit state (SLS) design of foundations typically proceeds by limiting the total settlement of individual foundations and thereby attempting to restrict the differential settlement between pairs of foundations. Due to the uncertain nature of the supporting ground, the magnitude of settlement and differential settlement are random. As it is often the differential settlement that governs serviceability, it is desirable to provide design requirements that suitably restrict differential settlements. This paper investigates, by Monte Carlo simulation, the distribution of the maximum differential settlement between pairs of foundations as a function of the spacing between foundations and the number of foundations — groups of four, nine, or 16 foundations, arranged on a grid — are considered. The effects of the correlations between the equivalent stiffness of the ground under each foundation, as well as between the loads applied to the foundations, on the distribution of the maximum differential settlements and angular distortions are investigated. Ratios of resistance factor to resistance bias factor are presented that can be used to calibrate design requirements on the total settlement of individual foundations that also simultaneously achieve acceptable performance with respect to angular distortion.

In this brief, for the first time, we propose commonmode (CM) and differential-mode (DM) reflectionless balanced bandpass filters (BPFs) using half-wavelength ring resonator. By loading absorptive stubs at input/output ports and the ends of the coupled-lines (CLs), the BPFs can realize input-/two-port wideband CM and DM reflectionless performance from dc, as well as wide filtering band and all-stop CM suppression. As for the inputreflectionless type, two extra transmission zeros (TZs) can be obtained by loading λ/2 stubs between the CLs. Besides, the impedances of the CLs can determine the CM suppression level, and the absorption performance can be independently adjusted. The operational foundations and explicit equations are discussed and derived. Good agreements between the simulated and measured results verify the design theory.

In this paper, we classify the instantaneous response of resistors with memory into three types: linear (L), separable nonlinear (SN), and non-separable nonlinear (NSN). A particular model of an NSN-type memristive device is introduced and used to demonstrate the possibility of rich dynamics in the memristor-capacitor circuit subjected to a sinusoidal voltage. In particular, our numerical simulations reveal the regimes of double period oscillations, multiple period oscillations, and chaotic oscillations. The comparison with L-type and SN-type memristive devices described by the same differential state equation indicates the importance of the NSN-type response to achieve such complex dynamics in the memristor-capacitor circuit with a first-order memristive device. Moreover, we demonstrate that a compound NSN-type memristive device can be assembled using one L-type memristive device, two resistors, and two diodes. The complex behavior of such compound devices is verified using SPICE modeling.

Accurate parameters are critical to permanent magnet synchronous machine (PMSM) drive. This paper investigates accurate flux linkages, inductances and PM flux linkage estimation for PMSM with core loss compensation. With conventional model, core loss will induce the flux linkage error especially in the deep saturation region. Hence, this paper firstly proposes a novel differential modeling technique to compensate core loss, in which differential measurement is defined as the incremental value calculated from the actual measurements under two different speed conditions. With multiple differential measurements, the flux linkage error due to core loss can be compensated to improve the accuracy of flux linkage estimation. Then, the polynomial based flux linkage model is employed to derive the PM flux linkage and cross-saturation inductances. Self-inductances are estimated from the flux linkage model using least squares method. The proposed approach can accurately estimate parameters without the need of core loss data and can improve the estimation accuracy especially in deep saturation region, which is validated on a laboratory interior PMSM and compared with existing methods under various operating conditions.

This paper addresses the distributed optimal fault estimation (FE) and fault-tolerant control (FTC) issues under the bidirectional interaction between a possibly faulty interconnected system and its associated diagnostic interconnected observer. The proposed methodology is based on a framework, named Stackelberg differential graphical game, in which the interconnected observer and its corresponding interconnected system are formulated as two graphical games, and they respectively play the role of the follower and leader to make sequential decisions of the FE and the FTC. Such a framework allows to fully reveal the property of the bidirectional interaction, based on which a distributed optimal FE and FTC scheme is developed. Moreover, a new concept named interactive Stackelberg equilibrium is proposed, where to reach this equilibrium, an auxiliary controller is suitably designed for each sub-observer. In this way, this auxiliary controller can cooperate with other auxiliary controllers of sub-observers, and non-cooperate with controllers of subsystems, which helps to achieve the distributed optimal FE and FTC goals.

In this paper, a new kind of cascade connection method of differential mode (DM) active EMI filter (AEF) is put forward. Contrary to the conventional idea, it is found different kinds of cascade AEF-connections show different insertion loss. Based upon mathematical analysis, among the eight potential connection methods, two cascade AEF-connection methods are more effective than the other six methods. The insertion loss is much higher than the others. Therefore, the two effective topologies are further analyzed from several perspectives. And the best topology suiting the practical application is found. Several prototypes are fabricated and tested. Experimental results show good agreement with the theoretical calculation.

A Nystrom method for linear second kind Volterra integral equations on unbounded intervals, with sufficiently smooth kernels, is described. The procedure is based on the use of a truncated Lagrange interpolation process and of a truncated Gaussian quadrature formula. The stability and the convergence of the method in suitable weighted spaces of functions are studied and some numerical examples showing its reliability are presented. In particular, the proposed method has been tested for the numerical resolution of some Volterra integral equations arising from the reformulation of differential models describing metastatic tumor growth whose unknown solutions represent biological observables as the metastatic mass or the number of metastases.

Change detection (CD) is one of the most important topics in the field of remote sensing. In this letter, we propose an effective satellite images CD network named DifUnet++. As the presentation of explicit difference is more conducive to extract change features, we design a differential pyramid of two input images as the input of Unet++. Considering the scale diversity of changed regions in remote sensing images, a multiply side-outs fusion strategy is adopted to predict the detection results of different scales. Furthermore, a learning upsampling method is utilized to refine the details of CD. The proposed architecture is evaluated on two public satellite image CD data sets. The experimental results show that our method performs much better than state-of-the-art methods.

Physical unclonable function (PUF) is an important circuit component for realizing cyber-physical security of data and electronic systems. In this work, we present a low-cost PUF implementation using metal-insulator-metal (MIM)-type PECVD SiO₂-based one-time programmable memory (OTPM) devices. We present a custom Verilog-A compact model of OTPM devices for complete PUF hardware implementation in the 180-nm CMOS technology. The implementation of PUF scheme based on a novel differential bit cell using OTPM devices shows strong performance in terms of uniqueness, reliability, and uniformity without additional overheads in terms of bit error correction and postprocessing.

This paper studies the communication time-delay issues in islanded microgrids (MGs) with the distributed secondary control architecture. Firstly, a time-delayed MG small-signal model is developed. Then, a new weight-average-prediction (WAP) controller is proposed to compensate the delayed system state. By introducing a time-delayed differential term in the proposed control law, the traditional time-delayed small-signal model is transformed into a neutral time-delayed mathematic model. Based on the developed model, the stability analysis is conducted considering both fixed time delay and time-varying delay. For the fixed time delay, a novel graphic analytical method is proposed to evaluate the time delay margin, which eliminates the conservatism compared with existing time-domain methods. For the time-varying delay, stability condition is established by a Lyapunov-Krasovskii function and linear matrix inequalities. In addition, some non-linear WAP control methods are discussed to guide the parameter tuning with a higher resolution. Lastly, the proposed method and analytical result are verified in the OPAL-RT real-time test platform. The results demonstrate the effectiveness and high performance of the proposed controller.

The methods using the linear combination or simultaneous equations of GNSS carrier phase and pseudorange as the observations have become the main technology applied in GNSS dynamic differential positioning. Though the usage of pseudorange measurements can improve the efficiency of the integer ambiguity resolution, it may cause larger positioning errors than only using carrier phase. In response, we propose a new rapid integer ambiguity resolution algorithm that only using carrier phase for GNSS differential positioning. Based on weighted constrained least-squares ambiguity decorrelation adjustment (WC-LAMBDA), this method utilizes the baseline resolution of GNSS pseudorange differential positioning to constrain the integer ambiguity resolution of phase-only differential positioning. This method can make full use of the pseudorange constraints to improve the efficiency of phase-only integer ambiguity resolution. Besides, it can avoid the large error in positioning results caused by errors of pseudorange measurements. The experimental results show that the proposed method can shorten the initialization time of the integer ambiguity resolution and achieve a rapid and high precision phase-only differential positioning.

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

This paper studies the differential privacy-preserving problem for multi-agent systems (MASs) in the presence of antagonistic information over signed digraph. As for the structurally balanced case, an e -differential privacy algorithm is proposed, upon which some sufficient conditions guaranteeing almost sure bipartite consensus are given. Based on the above scheme, the tradeoff between the system performance and the privacy guarantee is elaborated, and the optimal noise is also devised. Moreover, the proposed privacy preserving scheme is further applied to the scenario of structurally unbalanced graph, where a criterion with respect to almost sure stability of the considered system is derived, as well as the privacy preserving condition. This extends the balanced interaction scenario, and consequently the cooperative multi-agent systems. Finally, numerical simulations are presented to demonstrate the effectiveness of our results.

Deep learning (DL) libraries reduce the barriers to the DL model construction. In DL libraries, various building blocks are DL operators with different functionality, responsible for processing high-dimensional tensors during training and inference. Thus, the quality of operators could directly impact the quality of models. However, existing DL testing techniques mainly focus on robustness testing of trained neural network models and cannot locate DL operators’ defects. The insufficient test input and undetermined test output in operator testing have become challenging for DL library developers. In this article, we propose an approach, namely Duo, which combines fuzzing techniques and differential testing techniques to generate input and evaluate corresponding output. It implements mutation-based fuzzing to produce tensor inputs by employing nine mutation operators derived from genetic algorithms and differential testing to evaluate outputs’ correctness from multiple operator instances. Duo is implemented in a tool and used to evaluate seven operators from TensorFlow, PyTorch, MNN, and MXNet in an experiment. The result shows that Duo can expose defects of DL operators and realize multidimension evaluation for DL operators from different DL libraries.

A rumor about an entity can spread rapidly through online social networks (OSNs), which may lead to serious consequences. This article focuses on developing a cost-effective rumor-refuting strategy in the situation where the rumormonger is strategic. Based on a novel individual-based rumor spreading model, we estimate the expected net benefit of the rumormonger as well as the expected total loss of the rumor victim. On this basis, we reduce the original problem to a differential game-theoretic model. Next, we derive a system for solving the model. By solving the system, we get a dynamic strategy pair. Through extensive comparative experiments, we find that this strategy pair is effective in terms of the solution concept of Nash equilibrium. Hence, we conclude that the rumor-refuting strategy contained in this strategy pair is cost-effective. Finally, we examine the effect of the structure of the OSN on the cost effectiveness of the rumor-supporting and rumor-refuting strategies contained in the proposed strategy pair. To our knowledge, this is the first time the rumor-refuting problem is addressed under the assumption that the rumormonger is strategic.

Link adaptation for downlink 5G ultra-reliable low latency communication (URLLC) is quite challenging. While reduced feedback schemes for link adaptation are crucial to achieve high spectral efficiency, accuracy of sub-band channel quality indicator (CQI) reports is more critical in 5G URLLC because of the ultra-high reliability and efficient resource utilization requirements. The existing differential feedback mechanism cannot reconstruct sub-band CQIs at the BS exactly. In this paper, existing differential feedback mechanism for link adaptation is first investigated using analytical and simulation models to verify its limitations in reconstructing the sub-band CQIs and to study its impact on URLLC reliability and resource utilization efficiency. Then a cognitive link adaptation using a controlled differential feedback scheme for sub-band CQIs is proposed based on the investigations. Extensive simulation results are provided to show the superior performance of the proposed cognitive solution and to illustrate the theoretical analysis.

This paper studies the H − index problem for stochastic systems with Wiener processes and Poisson jumps. A necessary and sufficient condition is established for finite horizon Poisson jump stochastic systems to show that H − index lager than a given prescribed level γ > 0 implies the existence of the solution of the corresponding generalized differential Riccati equation (GDRE). Accordingly, the corresponding results are extended to infinite horizon H − index problems for time-invariant square systems. As the direct application of our results, the fault detection filter design is realized in evaluating the fault detection performance.

This article proposes a fault-tolerant control (FTC) strategy for path following of autonomous vehicles considering faults in the braking actuator. Distinguished from other relevant research, a fault model is designed, and a synthetic fault factor is calculated for the electrohydraulic brake (EHB) system in the case of differential braking. By considering the synthetic fault factor as a time-varying parameter, robust gain-scheduling linear parameter-varying ( LPV ) synthesis H $_\infty $ performance fault-tolerant controller (LPV/ $\text{H}_\infty $ -FTC) is designed to ensure autonomous vehicle stability and safety. In order to deal with different types of braking actuator faults, a generalized fault model is built first. Then, in the case of differential braking, a fault model is proposed for the EHB system based on the generalized fault models, which makes for the analysis and design of the controller. After that, an LPV / H $_\infty $ output feedback fault-tolerant controller and a braking allocation scheme are designed to ensure the autonomous vehicle’s stability and safety under the faults in braking actuators. Finally, the experiments in the HIL system are carried out to verify the effectiveness and real-time performance of the proposed control strategy, and the results show that the proposed control strategy can improve the vehicle’s stability and safety under a wide range of types of faults in the EHB system.

In the paper, we convert a single nonlinear equation to a system consisting of two equations. While a quasi-linear term is added on the first equation, the nonlinear term in the second equation is decomposed at two sides through a weight parameter. After performing a linearization, an iterative scheme is derived, which is proven of third-order convergence for certain parameters. An affine quasi-linear transformation in the plane is established, and the condition for the spectral radius being smaller than one for the convergence of the iterative scheme is derived. By using the splitting method, we can further identify a sufficient condition for the convergence of the iterative scheme. Then, we develop a step-wisely quasi-linear transformation technique to solve nonlinear equations. Proper values of the parameters are qualified by the derived inequalities for both iterative schemes, which accelerate the convergence speed. The performances of the proposed iterative schemes are assessed by numerical tests, whose advantages are fast convergence, saving the function evaluation per iteration and without needing the differential of the given function.

Considering the failure risk and cost-effectiveness of photovoltaic (PV) systems with high granularity maximum power point tracking (MPPT), a fault-tolerant strategy without redundant switches is proposed for the PV system based on differential power processing (DPP) converters. This fault-tolerant strategy optimizes the output power of the PV system under different converter fault conditions by adjusting the coordinated operation of the DPP converter and the centralized converter. Compared to previous methods, in the case of hardware cost advantage, the proposed fault-tolerant strategy achieves the stable operation of the PV system after the converter failure and completes the targeted power optimization of all PV modules. The PV system with the proposed fault-tolerant strategy operates in three modes: distributed MPPT (DMPPT) mode, the coexistence of DMPPT and centralized MPPT (CMPPT) mode, and CMPPT mode. The PV system operates in the DMPPT mode when no switch failure occurs in the DPP converter. In the latter two post-fault operating modes, this paper proposes a simple fault-tolerant method to remove the faulty DPP converter and adjust the MPPT coverage of the centralized converter to recover the power loss caused by the fault. The experimental results validate the proposed fault-tolerant strategy.

This brief studies the adaptive finite-time attitude tracking control of rigid spacecraft systems with full-state constraints and external disturbance. Based on the barrier Lyapunov function, a novel finite-time command filtered backstepping control algorithm is proposed, which can not only avoid computing the differential of virtual control signal, but also guarantee the predefined state constraints are not violated. The adaptive control technique is applied to estimate the upper bound of disturbance, and the designed controller with adaptive updating laws can make the tracking error converge to an adjustable neighborhood of the origin in finite time. Simulation results show the effectiveness of the present control method.

In this paper, stability for a class of stochastic memristor Cohen–Grossberg BAM neural networks with time-varying delays and reaction–diffusion are considered. A set of novel sufficient conditions on p th moment exponential stability of the equilibrium point are investigated for the considered system. We constructed Lyapunov functional and utilized stochastic stability theory and It o ˆ ’s differential formula. Finally, two examples are provided to illustrate the effectiveness of the derived main results.

For many permanent magnet synchronous motor (PMSM) drive applications (e.g., traction or automation), precise torque control is desired. Classically, this is based on extensive offline motor identification, e.g., by direct mapping of torque–flux–current look-up tables. In contrast, this article proposes a torque estimation method based on online differential inductances identification in combination with a data-driven finite-control-set (FCS) model predictive current control (MPCC). This scheme does not require offline identification or expert motor design knowledge. The required flux maps are determined by integrating the differential inductances in the left $i_{\mathrm{d}}$ – $i_{\mathrm{q}}$ half-plane. By considering varying differential inductances, the proposed method is ideally suited for highly utilized PMSM with significant (cross-) saturation effects where estimation models with constant inductances fail. For the identification of the differential inductances, the system excitation, based on the FCS-MPCC working principle, is utilized. Consequently, no additional signal injection is required and the estimation scheme is applicable in the entire speed range. With this method, an open-loop torque control can be realized without knowledge of exact motor parameters except the permanent magnet flux linkage as a datasheet parameter. Extensive experimental investigations on a highly utilized PMSM in the entire speed range including standstill prove the performance of the proposed approach.

In this paper, we introduce a class of deterministic finite-horizon two-player nonzero-sum differential games where one player uses ordinary controls while the other player uses impulse controls. We use the word ‘ordinary’ to mean that Player 1 uses control strategies that are piecewise continuous functions of time. We formulate the necessary and sufficient conditions for the existence of an open-loop Nash equilibrium for this class of differential games. We specialize these results to linear-quadratic games, and show that the open-loop Nash equilibrium strategies can be computed by solving a constrained non-linear optimization problem. In particular, for the impulse player, the equilibrium timing and level of impulses can be obtained. Furthermore, for the special case of linear-state differential games, we obtain analytical characterization of equilibrium number, timing, and the level of impulse in terms of the problem data. We illustrate our results using numerical experiments.

High-performance single-transistor current source is crucial to analog design in advanced CMOS processes. Various approaches to enhance the output resistance of single-transistor current source are studied in this brief in the view of adaptive control. Then a novel compact implementation is proposed, and used to build a fully differential OTA with improved dc gain and common-mode rejection in a 55 nm CMOS with a supply of 1.1 V.

We propose a secure image encryption method using the combination of spatial surface chaotic system(SSCS) and the improved AES algorithm structure. In this scheme, the key of cryptosystem is obtained from the SSCS, this system has better encryption characteristics and its model structure fits the image exactly, and it is designed for image cryptosystems contrasted with the existing a lot of low-dimensional chaotic maps and couple map lattices. The plain image is encrypted with the improved AES algorithm and by performing each round encryption, the key is generated by SSCS in each round, an improved permutation algorithm(IPA) and a reverse diffusion have been presented. The proposed scheme not only improves the efficiency because of the same key stream is shared, but also increases the diffusion effect which can resist differential attack. The presented scheme provides huge key space to deal with the brute-force attacks using the round keys obtained by SSCS, and also very sensitive to initial values of SSCS and plain image. The results of simulation analysis and performance evaluation show that the presented cryptosystem provides strong security performance and may be used as a candidate for real-time implementations.