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In open-winding permanent magnet synchronous motors (OW-PMSMs) with a single dc bus, zero sequence current (ZSC) would cause unwanted power loss and negatively affect stability of the system, so it should be addressed. In this article, an improved control scheme is proposed, which can maintain good ZSC performance under varying conditions and reduce the switching action times synchronously. First, an extended state observer that can predict the next instant ZSC and calculate the next instant desired zero sequence voltage (ZSV) is established. In this process, the model-free control principle is applied, which overcomes the problem of dependence on the zero-sequence loop parameters. Second, to reduce the switching frequency in traditional equal distribution modulation strategy, a novel modulation method is illustrated. Zero voltage vector is avoided, and the dwell time of active voltage vectors is redistributed to generate the desired ZSV. As a result, the times of switching actions has reduced from 12 to 8, and this reduction in switching actions is accompanied by effective suppression of ZSC. Finally, to test the effectiveness of the proposed method, several experimental results obtained using existing methods and the proposed method are presented.

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services. Deep learning-based KT (DLKT) achieves superb performance in terms of prediction accuracy, but it lacks of explainability, which makes us hard to trust or understand models. The previous work on explaining DLKT was mainly based on gradients or attention scores, which is susceptible to spurious correlations, reducing the credibility of the explanation. To address this limitation, in this article, we propose a causal explanation method based on the genetic algorithm (GA), named genetic causal explainer (GCE), which constructs a causal framework to estimate the attribution of subsequence to the predictions of DLKT models, and a genetic coding system is designed. Further, A multistrategy initialization method inspired by domain prior knowledge is proposed, and a global empirical matrix is introduced to capture the causal correlation knowledge during the search process across instances, and guiding the mutation operators. The GCE as a post hoc explanation method can generate explanation results without affecting model training, and can be applied to analyze different DLKT models. Experimental results demonstrate the GCE perform better than other explanation methods in terms of accuracy and readability in quantitative assessments. Meanwhile, the GCE also shows good application prospects in mining educational laws and comparing KT models.

With the rapid development of wireless technologies, the simultaneous and flexible control of amplitude and phase characteristics of the signals is highly demanded. However, none of the existing power dividers (PDs) can simultaneously achieve this tunability in a single circuit. In this article, a PD with simultaneously tunable power division ratio (PDR) and phase difference is presented for the first time. The proposed PD consists of two highly flexible arms, a matching network, and an isolation network. The arm is constructed by loading three varactor diodes on a quarter-wavelength 3-dB coupled line. A theoretical analysis is conducted to show that the input impedance and phase delay of this arm can be flexibly controlled by the capacitance of the varactor diodes. Subsequently, the PDR and phase difference between the two output ports can be tuned independently, maintaining good return loss and isolation. For demonstration purposes, a highly flexible PD operating at 2.4 GHz is designed, fabricated, and measured. A wide PDR range from −20 to 20 dB and a wide phase difference range from −180° to 180° are observed, with better than 10 dB return loss and 15 dB isolation.

This article proposes to connect the minimum-order sensorless indirect field-oriented control to an independent flux estimator. The stability of the sensorless speed estimation and control is guaranteed with a Lyapunov stability analysis, given that a pure integrator-generated flux estimate is available in the simulation settings. In the real environment, we propose to use controller frame independent voltage model (VM)-based flux estimator that does not rely on any signal from the ensuing sensorless control, as an effort to preserve the validity of the Lyapunov stability proof. A saturation time difference-based offset voltage update law is proposed for robustness improvement, and its parameter robustness analysis is set forth. The key step of the sensorless design is to use the flux estimates to calculate the q-axis flux, which indicates an error in the d-axis angle of the indirect field orientation. It is found in slow and fast reversal experiments that our design is effective to attain dynamic performance while assuring stable operation near zero frequency.

Downlink (DL) and uplink (UL) joint communication and sensing (JCAS) technologies have been individually studied for realizing sensing using DL and UL communication signals, respectively. Since the spatial environment and JCAS channels in the consecutive DL and UL JCAS time slots are generally unchanged, DL and UL JCAS can be jointly designed to achieve better sensing and communication performance. In this paper, we propose a novel DL and UL cooperative (DUC) JCAS scheme, including a unified multiple signal classification (MUSIC)-based JCAS sensing scheme for both DL and UL JCAS and a DUC JCAS fusion method. The unified MUSIC JCAS sensing scheme can accurately estimate angle-of-arrival (AoA), range, and Doppler based on a unified MUSIC-based sensing module. The DUC JCAS fusion method can distinguish between the sensing results of the communication user and other dumb targets. Moreover, by exploiting the channel reciprocity, it can also improve the sensing and channel state information (CSI) estimation accuracy. Extensive simulation results validate the proposed DUC JCAS scheme. It is shown that the minimum location and velocity estimation mean square errors of the proposed DUC JCAS scheme are about 20 dB lower than those of the state-of-the-art separated DL and UL JCAS schemes.

The use of legitimate unmanned aerial vehicles (UAVs) to surveil and track misbehaved UAVs can serve a crucial role in public safety and security. This paper proposes a new deep reinforcement learning (DRL)-based online control scheme for visual-based UAV-on-UAV tracking and monitoring, where a solar-powered, fixed-wing UAV tracks a suspicious UAV target by having the target inside its effective visual range. The key idea is a new deep deterministic policy gradient (DDPG)-based model, which can cope with the continuous state and action spaces of the monitor and learn the optimal acceleration control policy adapting to the solar power availability and the target’s movement. The state space is designed to be the relative position of the monitor to the target, thereby preventing model infeasibility. Experiments show that the new algorithm can maintain a desired distance from the target, and outperform control- and optimization-based alternatives in terms of energy efficiency and tracking accuracy. An interesting finding is that our algorithm learns faster and better with a constraint of a minimum allowed battery energy reserve. The reason is that, without the constraint, the monitor is more likely to deplete its battery before the end of a surveillance mission.

The hydrogenolysis or hydrodeoxygenation of a carbonyl group, where the C═O group is converted to a CH2group, is of significant interest in a variety of fields. A challenge in electrochemically achieving hydrogenolysis of a carbonyl group with high selectivity is that electrochemical hydrogenation of a carbonyl group, which converts the C═O group to an alcohol group (CH–OH), is demonstrated not to be the initial step of hydrogenolysis. Instead, hydrogenation and hydrogenolysis occur in parallel, and they are competing reactions. This means that although both hydrogenolysis and hydrogenation require adding H atoms to the carbonyl group, they involve different intermediates formed on the electrode surface. Thus, revealing the difference in intermediates, transition states, and kinetic barriers for hydrogenolysis and hydrogenation pathways is the key to understanding and controlling hydrogenolysis/hydrogenation selectivity of carbonyl compounds. In this study, we aimed to identify features of reactant molecules that can affect their hydrogenolysis/hydrogenation selectivity on a Zn electrode that was previously shown to promote hydrogenolysis over hydrogenation. In particular, we examined the electrochemical reduction of para-substituted benzaldehyde compounds with substituent groups having different electron donating/withdrawing abilities. Our results show a strikingly systematic impact of the substituent group where a stronger electron-donating group promotes hydrogenolysis and a stronger electron-withdrawing group promotes hydrogenation. These experimental results are presented with computational results explaining the substituent effects on the thermodynamics and kinetics of electrochemical hydrogenolysis and hydrogenation pathways, which also provide critically needed information and insights into the transition states involved with these pathways.

The fixed-time cooperative control is investigated for heterogeneous multiagent systems (HMASs) containing multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) under Denial of Service (DoS) attacks and external perturbations. First, for the ease of collaborative tasks, an unified model is obtained in the XY plane by transforming the dynamical models of UAVs and UGVs. Then, a fuzzy switching observer is designed to achieve unavailable state estimation during the attack zone. In particular, in virtue of the character of DoS attacks, a novel fixed-time stability theory is set up. On this foundation, a distributed fixed-time secure tracking control strategy is raised by utilizing the adaptive approach to effectively defend against DoS attacks. Notably, distinct from the existing works on finite-time stability for HMASs, the present method exhibits some brilliant features, such as fast response, high accuracy, and attack resistance. Additionally, rational proofs have substantiated that the designed control scheme can not only ensure the boundedness of each closed-loop signal but also guarantee the synchronization error converges to a predefined small neighborhood near the origin in a fixed time. Ultimately, the validity of the proposed method is demonstrated by a representative simulation.

The article focuses on China's strategic emphasis on developing the silver economy to address its aging population, highlighting the importance of leveraging the potential of elderly consumers and promoting active aging. Topics discussed include the economic implications of population aging, strategies for promoting silver consumption, and the need for a systematic approach to developing the aging industry.

The continuous expansion of renewable energy sources and their ongoing integration into existing power networks, alongside the emergence of new types of loads, has led to significant new challenges emerging for power grid source–load coordination scheduling in recent years. To fully harness the demand response (DR) potential of the load side of such systems, this paper proposes an optimal scheduling method based on DR and the Stackelberg game. A Stackelberg game model is established to formulate day-ahead source–load coordinated scheduling problem, taking account of the stochastic dynamic characteristics of the load response process and user electricity consumption satisfaction with electricity consumption. The model is essentially a strongly nonlinear optimization problem, however; thus, to avoid several of the limitations seen in traditional optimization methods, a data-model fusion-driven method is proposed, with electricity prices formulated by the data-driven method used as input for a model-driven method whose output is then used in turn to guide a data-driven formulation of electricity prices. Case studies on IEEE test systems are used to verify the feasibility and accuracy of the proposed method.

A green efficient photoredox-catalyzed decarboxylative alkynylation of carboxylic acids with alkynyl bromides has been developed. This broadly applicable protocol is presented wherein α-amino, aliphatic and α-oxy acids are converted into useful alkynylation products. The commercially-available organic photocatalyst 4CzIPN is used as the photocatalyst, organic base DBU is utilized as the base, and DMSO serves as solvent. This strategy features mild conditions, is metal-free, and is environmentally friendly. The batch and continuous-flow protocols described were applied to obtain a broader substrate scope of functionalization (more than 50 examples). Furthermore, we demonstrate that the use of microflow technology enhanced and intensified the reaction process, achieving significantly reduced reaction times (i.e., 10 ​min of residence time).

In this article, two novel adaptive fault-tolerant control schemes for a class of nonlinear strict-feedback cyber–physical systems (CPSs) with deception attacks are presented. Deception attacks, such as false data-injection attacks, which destroy sensor networks, make the outputs and states of the CPSs unavailable. It is very difficult and challenging for a designer to achieve the tracking control under the circumstance of cyberattacks. To realize the tracking control for the studied CPSs, we propose a new coordinate transformation technology without precedent, where it takes the attack gains into account and uses the compromised states to design the corresponding controllers. In the backstepping design process, Nussbaum functions are presented to alleviate the influence of the unknown attack gains. Furthermore, we consider the actuator faults problem, which includes the loss of effectiveness and the bias fault. By skillfully designing the adaptive laws, the effect of actuator faults is completely eliminated. It is theoretically proved that the first proposed tracking control scheme can guarantee all signals in the closed-loop system are bounded and the output can track the desired reference signal. In addition, the second adaptive control scheme is also developed for the CPSs under the actuator faults and a more general assumption on the deception attacks is proposed simultaneously. Finally, the feasibility of the new proposed methods is verified by MATLAB simulation analysis.

Background: Hispanic/Latino people with HIV (PWH) experience disparities in health outcomes compared with other racial and ethnic groups. Disaggregated data based on race for Hispanic/Latino PWH in the United States are rarely reported, potentially masking inequities. Methods: The Medical Monitoring Project (MMP) is a complex sample survey of adults with diagnosed HIV. We used weighted interview and medical record data collected from June 2015–May 2021 to examine differences in social determinants of health (SDH) and health outcomes by self-reported race among Hispanic/Latino adults with diagnosed HIV. Results: Compared with White Hispanic/Latino PWH, Black Hispanic/Latino PWH were more likely to be unemployed (PR, 1.4; CI, 1.2–1.8), have a disability (PR, 1.3; CI, 1.2–1.5), have experienced homelessness (PR, 1.8; CI, 1.2–2.6), and have been incarcerated (PR, 2.6; CI, 1.5–4.5). American Indian/Alaska Native (AI/AN) (PR, 1.8; CI, 1.1–2.7) and multiracial (PR, 2.0; CI, 1.4–2.9) Hispanic/Latino PWH were more likely to have experienced homelessness than White Hispanic/Latino PWH. Black (PR, 1.3; CI, 1.2–1.5) and multiracial (PR, 1.2; CI, 1.1–1.5) Hispanic/Latino PWH were more likely to be virally unsuppressed than White Hispanic/Latino PWH. Conclusion: Black, multiracial, and AI/AN Hispanic/Latino PWH experience disparities in SDH and HIV outcomes. Lumping Hispanic/Latino people into one racial and ethnic category obscures health disparities, which might limit our progress towards reaching national HIV goals. Future studies should consider disaggregating by other factors such as Hispanic origin, place of birth, immigration status, and primary language. Doing so recognizes the diversity of the Hispanic/Latino population.

This article is concerned with the optimized tracking control problem of high-order strict-feedback nonlinear systems with full-state constraints via dynamical event-triggered strategy. An adaptive optimized control algorithm is proposed using the backstepping technique and reinforcement learning method. The algorithm incorporates an identifier–critic–actor architecture and features a flexible design of dynamic event-triggered mechanism (ETM) to decrease signal transmission. The ETM is adaptively adjusted by the estimated fuzzy adaptive weights and design parameters, making it more efficient in terms of communication resources. Moreover, to ensure the system states do not exceed their constrained sets while achieving the optimization objective, a novel performance index function based on the barrier Lyapunov function is constructed. Finally, it can be demonstrated that the system output accurately tracks the desired reference signal, while ensuring that all closed-loop signals remain bounded. Simulation example confirms the validity of the developed design methodology.

The efficient evaluation of Sommerfeld integrals (SIs) in planar layered media has been a long-term bottleneck in the accurate electromagnetic analysis of modern radio frequency (RF) circuits, chips, and devices. This work investigates the high-performance computing of SIs using modern graphics processing units (GPUs) to alleviate this difficulty. Based on the numerical integration procedure with controllable accuracy for SIs, the GPU parallel schemes for SI heads and SI tails are first presented. By eliminating the redundant calculations in SI of multiple frequencies, highly efficient parallel computing enhanced by tensor cores of GPU is developed, and it evaluates the multiple frequency SIs simultaneously. In addition, the mixed-precision computing that further accelerates computing is also studied and tested. Extensively numerical experiments are carried out on two commercial gaming GPUs and verify the performance of the proposed parallel scheme. It achieves a dozen to hundreds speedup compared to that using two high-end CPUs with full OpenMP parallelization.

Untreated pain in critically ill patients can lead to immunosuppression and increased metabolic activity, with severe clinical consequences such as tachypnea and delirium. Continuous pain assessment is challenging due to nursing shortages and intensive care unit (ICU) workload. Mechanical ventilation equipment obscures the facial features of many patients in the ICU, making previous facial pain detection methods based on full-face images inapplicable. This paper proposes a facial Action Units (AUs) guided pain assessment network for faces under occlusion. The network consists of an AU-guided (AUG) module, a texture feature extraction (TFE) module, and a pain assessment (PA) module. The AUG module automatically detects AUs in the non-occluded areas of the face. In contrast, the TFE module detects the facial landmarks and crops prior knowledge patches, a random exploration patch, and a global feature patch. Then these patches are fed into two convolutional networks to extract texture features. Afterward, the designed AU guidances and texture features are fused in the PA module to assess the pain state. Extensive validation is conducted on a public dataset and two datasets created in this work. The proposed network architecture achieves superior performance in binary classification, four-class classification, and intensity regression tasks. In addition, we have successfully applied the network to actual data collected in the laboratory environment with excellent results.

Compressive sensing (CS) techniques using a few compressed measurements have drawn considerable interest in reconstructing multispectral imagery (MSI). Nonlocal-based tensor methods have been widely used for MSI-CS reconstruction, which employ the nonlocal self-similarity (NSS) property of MSI to obtain satisfactory results. However, such methods only consider the internal priors of MSI while ignoring important external image information, for example deep-driven priors learned from a corpus of natural image datasets. Meanwhile, they usually suffer from annoying ringing artifacts due to the aggregation of overlapping patches. In this article, we propose a novel approach for highly effective MSI-CS reconstruction using multiple complementary priors (MCPs). The proposed MCP jointly exploits nonlocal low-rank and deep image priors under a hybrid plug-and-play framework, which contains multiple pairs of complementary priors, namely, internal and external, shallow and deep, and NSS and local spatial priors. To make the optimization tractable, a well-known alternating direction method of multiplier (ADMM) algorithm based on the alternating minimization framework is developed to solve the proposed MCP-based MSI-CS reconstruction problem. Extensive experimental results demonstrate that the proposed MCP algorithm outperforms many state-of-the-art CS techniques in MSI reconstruction. The source code of the proposed MCP-based MSI-CS reconstruction algorithm is available at: https://github.com/zhazhiyuan/MCP_MSI_CS_Demo.git.

This article proposes a stabilization scheme for a cascaded parabolic partial differential equation (PDE)-ordinary differential equation (ODE) system with state constraints. To begin, by employing the fuzzy-logic system (FLS) technique for the unknown nonlinear functions in the ODE subsystem, the influence of the nonlinearities is successfully eliminated. Then, the infinite and finite-dimensional backstepping methods are skillfully applied to the design of control schemes. Specifically, the infinite-dimensional backstepping transformation and its inverse are utilized to obtain a new PDE subsystem, which is convenient for control design. Next, based on the new target PDE-ODE cascade system, a novel first-order filter is subtly introduced into each step of the finite-dimensional backstepping-based controller design procedure to avoid the issue of “explosion of complexity.” Furthermore, a novel barrier Lyapunov function (BLF) is constructed to guarantee that the states of the ODE subsystem do not violate the constraints. The controller designed in this article ensures that all signals in the closed-loop system are bounded and the states can converge to zero. Finally, a simulation example verifies that the proposed scheme can achieve satisfactory performance.

Federated learning (FL) can suffer from communication bottlenecks when deployed in mobile networks, limiting participating clients and deterring FL convergence. In this context, the impact of practical air interfaces with discrete modulation schemes on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL ( $\text{F}^{2}\text{L}$ ) over an orthogonal frequency division multiple-access (OFDMA) air interface, termed as “OFDMA- $\text{F}^{2}\text{L}$ ”, allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulation scheme, adapting to channel conditions and computing power. Specifically, we derive an upper bound on the optimality gap of OFDMA- $\text{F}^{2}\text{L}$ capturing the impact of these selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a “winner-takes-all” policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA- $\text{F}^{2}\text{L}$ with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18% and 5%, compared to potential alternatives.

This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We highlight emerging challenges and prospective research directions, aiming to provide valuable resources for integrating privacy, equity, and resilience into machine learning systems and help them “learn to unlearn.”

To realize an intelligent cooperative vehicle infrastructure system and high-level autonomous driving, the introduction of the joint communication and sensing (JCS) technique in vehicular networks is indispensable. With directional beamforming, the vehicles equipped with JCS systems could utilize unified radio-frequency transceivers and frequency band resources to achieve vehicle-to-infrastructure (V2I) communication and sensing functions in different directions, respectively. In this concept, we study the computation offloading problem for JCS-based vehicular networks. Specifically, we formulate a long-term multi-objective problem that jointly optimizes the task execution latency and the sensing performance of multiple vehicles. Owing to the time-varying V2I channel gain, the time-varying impulse response of sensed target, and the stochastic traffic, we reformulate it as a Markov decision process and propose a double-stage deep reinforcement learning-based offloading and power allocation (DDOPA) strategy to determine the task offloading and power allocation for each vehicle. Simulation results demonstrate the efficacy of the proposed strategy compared with different strategies, and show that the proposed DDOPA strategy can achieve a trade-off between execution latency and sensing performance.

The adaptive fuzzy distributed optimization problem of nonlinear multiagent systems with unknown nonlinearities and uncertain disturbances is explored in this article. The unknown nonlinearities in the system model are determined by incorporating fuzzy logic systems. Rather than using the existing known gradient values of local objective functions, this article further takes into account the measured gradient values that are based on the output information. The bounded estimation method and well-defined smooth functions are used to account for the effects of uncertainties. To ensure that the output can asymptotically converge to the optimal solution, a new adaptive fuzzy distributed optimization controller is proposed using the Lyapunov function method and the adaptive backstepping technique. To demonstrate the effectiveness of the developed distributed optimization technique, two practical examples are used.

Diabetes mellitus (DM) is characterized by prolonged hyperglycemia, impaired vascularization, and serious complications, such as blindness and chronic diabetic wounds. About 25% of patients with DM are estimated to encounter impaired healing of diabetic wounds, often leading to lower limb amputation. Multiple factors are attributed to the non-healing of diabetic wounds, including hyperglycaemia, chronic inflammation, and impaired angiogenesis. It is imperative to develop more efficient treatment strategies to tackle healing difficulties in diabetic wounds. Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) are promising for diabetic wound healing considering their anti-inflammatory, pro-angiogenic and pro-proliferative activities. A histone deacetylase 7 (HDAC7)-derived 7-amino-acid peptide (7A) was shown to be highly effective for angiogenesis. However, it has never been investigated whether MSC-EVs are synergistic with 7A for the healing of diabetic wounds. Herein, we propose that MSC-EVs can be combined with 7A to greatly promote diabetic wound healing. The combination of EVs and 7A significantly improved the migration and proliferation of skin fibroblasts. Moreover, EVs alone significantly suppressed LPS-induced inflammation in macrophages, and notably, the combination treatment showed an even better suppression effect. Importantly, the in vivostudy revealed that the combination therapy consisting of EVs and 7A in an alginate hydrogel was more efficient for the healing of diabetic wounds in rats than monotherapy using either EV or 7A hydrogels. The underlying mechanisms include suppression of inflammation, improvement of skin cell proliferation and migration, and enhanced collagen fiber disposition and angiogenesis in wounds. In summary, the MSC-EV-7A hydrogel potentially constitutes a novel therapy for efficient healing of chronic diabetic wounds.

In this article, an adaptive security control scheme is presented for cyber-physical systems (CPSs) suffering from false data injection (FDI) attacks and time-varying state constraints. Firstly, an adaptive bound estimation mechanism is introduced in the backstepping control design to mitigate the effect of FDI attacks. Secondly, to solve the unknown sign time-varying state-feedback gains aroused by the FDI attacks, a type of Nussbaum function is employed in the adaptive security control. Then, by constructing a barrier Lyapunov function, it can be ensured that all signals of controlled system are bounded and the time-varying state constraints are not transgressed. Finally, the provided simulation examples demonstrate the effectiveness of the proposed controller.

AbstractThis paper focuses on the shear behavior of reinforced concrete (RC) beams strengthened with carbon–glass-fiber-reinforced polymer (C-GFRP) plates under different anchoring schemes. The study consisted of four reinforced specimens: specimen CS-1 with no reinforcement and three groups of reinforced specimens SR-1, SR-2 and SR-3. The studies used a four-point bending test to evaluate the reinforcement effect of mechanically anchoring C-GFRP plates. The results of the analysis of load–deflection, strain and bearing capacity indicated that a better reinforcement effect was observed with fiber-reinforced polymer. The test results suggested that the occurrence time of critical oblique cracks was delayed and the development speed of critical oblique cracks was limited by mechanically anchoring the C-GFRP plates. C-GFRP plates significantly increased the stiffness of the reinforced specimen and reflected the superiority of mechanical anchoring. The ultimate shear capacity of the mechanically anchored C-GFRP plates has been significantly improved. With a reasonable increase in the amount of C-GFRP plate, the shear resistance of the test beam became stronger. Compared with the CS-1 specimen without reinforcement, the yield load values of the reinforced specimens SR-1, SR-2 and SR-3 increased by 5.56%, 18.80% and 26.50%, respectively. The ultimate load of reinforced specimens SR-1, SR- 2 and SR-3 increased by 11.64%, 17.47% and 31.51%, respectively. The researchers deduced their theoretical calculation method from the bearing capacity of RC beams strengthened by anchoring with carbon–glass-fiber-reinforced polymer plates. The test findings were in good accord with the theoretical calculation model of bearing capacity suggested in this research.

Multivector model predictive control (MPC) has gained many attractions in motor drive applications due to its accurate and stable control performance. However, two key challenges have limited the control development. First of all, the switching frequency is not fixed and remains at a high level under the full range of operating conditions. More seriously, the zero vectors applied to adjust the output amplitude will generate high common-mode voltage (CMV), resulting in axis current, electromagnetic interference, and a host of other adverse effects. To address the two main concerns, this article proposes a control strategy that can efficiently respond to different operating conditions for permanent magnet synchronous motors (PMSMs). First, the reference voltage is constructed by the deadbeat principle, and the motor operating condition is distinguished according to the amplitude of the reference voltage. Second, to inherit satisfactory performance in steady-state while exhibiting fast current tracking response simultaneously, two voltage generation approaches that avoid the use of zero vectors are designed. Finally, comparative experimental results are presented, and the effectiveness of the proposed strategy is verified.

We consider the reconstruction problem of non-line-of-sight (NLOS) imaging. The proposed diffusion-based method starts with back-projection from NLOS measurement and iteratively estimates the noise using a trained light-weight U-Net and model-guided iterative gradient descent. Unlike previous large networks, our diffusion-based method relies on a series of iterative refinement steps, each of which is trained with a regression loss. This benefits our iterative approach to capture richer distributions. Furthermore, rather than estimating the posterior mean value, our method generates samples from the target posterior and allows only one trained model being adaptive to different settings, such as diverse sampling time resolution, various spatial resolution and multiple channels of color scenes. Simulation and real-data experiments verify that our proposed method achieves better reconstruction results both in quality and quantity than existing methods.

Reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) have the potential to play a significant role in enhancing the security of the Internet-of-Things (IoT). RISs can be deployed as intelligent reflectors to augment wireless coverage passively. UAVs offer flexible and dynamic IoT platforms for communication, sensing, and monitoring. In this article, a particular interest is given to RIS-assisted, anti-jamming, UAV communication and radio surveillance, which are generally nonconvex and difficult to solve using traditional optimization tools. New artificial intelligence (AI) tools, more specifically, deep reinforcement learning (DRL), are developed to tackle the problems of UAV and RIS design. The use of DRL allows a UAV to learn its trajectory and RIS configuration to diffuse jamming signals and maximize its communication rate based on its received data rate. It also allows the UAV to maximize its eavesdropping rate based on the transmit rate of a suspicious transmitter that the UAV observes when conducting radio surveillance. The UAVs no longer rely on explicit knowledge of the channel state information, and can learn through trial and error. Simulations confirm the effectiveness of using UAVs, RISs, and AI to enhance the security of air-to-ground IoT networks, compared to baseline schemes without RIS or with non-AI-based RIS configurations.

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset (CFO), leading to major challenges in uplink sensing. Unlike existing technologies, our scheme does not require knowing the location of the UE in advance, and retains the linearity of the sensing parameter estimation problem. We first estimate the angle-of-arrivals (AoAs) of multipaths and use them to spatially filter the CSI. Then, we propose a KF-based CSI enhancer that exploits the estimation of Doppler with CFO as the prior information to significantly suppress the time-varying noise-like TO terms in spatially filtered CSIs. Subsequently, we can estimate the accurate ranges of UE and the scatterers based on the KF-enhanced CSI. Finally, we identify the UE’s AoA and range estimation and locate UE, then locate the dumb scatterers using the bi-static system. Simulation results validate the proposed scheme. The localization root mean square error of the proposed method is about 20 dB lower than the benchmarking scheme.

This study examined the impact of current solution treatment on the microstructure and mechanical properties of the Co-28Cr-6Mo-0.22C alloy investment castings. The findings reveal that the current solution treatment significantly promotes the dissolution of carbides at a lower temperature. The optimal conditions for solution treatment are determined as a solution temperature of 1,125 °C and a holding time of 5.0 min. Under these parameters, the size and volume fraction of precipitated phases in the investment castings are measured as 6.2 µm and 1.1vol.%. The yield strength, ultimate tensile strength, and total elongation of the Co-28Cr-6Mo-0.22C investment castings are 535 MPa, 760 MPa, and 12.6%, respectively. These values exceed those obtained with the conventional solution treatment at 1,200 °C for 4.0 h. The findings suggest a phase transformation of M23C6→σ+C following the current solution treatment at 1,125 °C for 5.0 min. In comparison, the traditional solution treatment at 1,200 °C for 4.0 h leads to the formation of M23C6and M6C carbides. It is noteworthy that the non-thermal effect of the current during the solution treatment modifies the free energy of both the matrix and precipitation phase. This modification lowers the phase transition temperature of the M23C6→σ+C reaction, thereby facilitating the dissolution of carbides. As a result, the current solution treatment approach achieves carbide dissolution at a lower temperature and within a significantly shorter time when compared to the traditional solution treatment methods.

In this article, considering denial-of-service (DoS) attacks and stochastic disturbances, the adaptive decentralized fuzzy security problem of a family of large-scale nonlinear systems is addressed. To mitigate the adverse effects from DoS attack, a compensator is constructed to model the output signals, and a compensator-based estimator that utilizes the designed compensator signals is designed to acquire the unmeasurable state variables. Then, an estimator-based secure control scheme is proposed by employing the adaptive backstepping technique against DoS attacks, where the unknown nonlinearities are approximated through fuzzy logic systems due to their powerful approximation capability. By combining of Lyapunov stability theory and a modified average dwell time method that incorporates the constraints imposed on DoS attacks, the stability of the closed-loop system is established. Moreover, the controller guarantees the convergence of tracking errors to a vicinity of the origin even under DoS attacks, and all signals remain semiglobally uniformly ultimately bounded in probability. Eventually, the validity of the established solution is examined in a simulation example.

This article revisits the consensus control problem of networked parabolic partial differential equation (PDE) systems perturbed to nonlinear terms and uncertain disturbances. The presence of spatial variables and reaction–diffusion terms in the system model makes designing the adaptive distributed protocol for PDE systems more challenging than for ordinary differential dynamics. A novel adaptive distributed controller is designed by including a novel compensating term in the form of the hyperbolic tangent function and a positive integral function. The asymptotic consensus can be achieved by using the Lyapunov method and PDE theory. It should be emphasized that all three types of cases-those without a leader, those with a leader, and those with multiple leaders-are looked into. In addition, in comparison with the related works, the designed control scheme does not require the global information of the graph, which is a fully distributed paradigm. Finally, three examples are utilized to show how efficient the proposed distributed algorithm is.

Finite control set model predictive current control (FCS–MPCC) has attracted increasing attention in recent years. However, large current ripples, torque ripples, and computational effort limit its widespread application. To address aforementioned problems, an improved MPCC method is proposed for open-winding permanent magnet synchronous motors herein. First, FCS–MPCC method based on deadbeat principle (FCS–MPCC–DB) is presented to reduce the computational effort. During the process, zero-sequence voltage is considered separately, avoiding enumerating all feasible voltage vectors (VVs). Second, an optimization strategy is proposed to reduce current ripples, torque ripples, and suppress zero-sequence current simultaneously. The switching state of one of two voltage-source inverters (VSIs) system is fixed, and the optimal vector duration ratios of the other VSI are obtained from a novel cost function. In other words, only one VSI system applies a fixed switching state, and a zero VV and an active VV are applied in the other VSI, so we name it half-FCS–MPCC–DB. Finally, several simulation and experimental results are presented to prove the effectiveness of proposed method.

Signal capture is at the forefront of perceiving and understanding the environment; thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the “capturing images first and processing afterward” mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.

This article concentrates on the event-triggered optimized leader-follower consensus control for stochastic nonlinear multi-agent systems (MASs) with actuator bias faults. An adaptive state identifier is designed to estimate unknown states in real time. In addition, an adaptive dynamic programming (ADP)-based algorithm with a critic-actor architecture is developed to learn the optimized control policy online. Based on the online estimating information from the designed fault estimator, a fault-tolerant (FT) controller is designed to compensate for the deleterious effects of actuator bias faults. The proposed control scheme employs a dynamic event-triggered mechanism (ETM) to reduce network resources by tuning dynamic parameters. All the signals can be proven to be bounded by utilizing the Lyapunov stability method, and the Zeno behavior can be excluded. Simulation results show the merit and validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive fixed-time tracking control problem for a class of stochastic nonlinear systems with constrained states and input saturation. By utilizing a barrier Lyapunov function (BLF), the issue caused by constrained states is settled. Simultaneously, a smooth function is constructed to approximate input saturation and the unknown nonlinear function is approximated by neural networks (NNs). In view of the fixed-time tracking control problem of stochastic nonlinear systems, a fixed-time stability lemma for stochastic nonlinear systems has been established and proved. Under the designed controller, the closed-loop system is semi-globally fixed-time stable in probability (SGFSP), and all the states are constrained within a compact set. Moreover, the controlled system in this paper achieves equilibrium within a fixed-time internal. Lastly, the presented method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.

Existing approaches to constrained-input optimal control problems mainly focus on systems with input saturation, whereas other constraints, such as combined inequality constraints and state-dependent constraints, are seldom discussed. In this article, a reinforcement learning (RL)-based algorithm is developed for constrained-input optimal control of discrete-time (DT) systems. The deterministic policy gradient (DPG) is introduced to iteratively search the optimal solution to the Hamilton–Jacobi–Bellman (HJB) equation. To deal with input constraints, an action mapping (AM) mechanism is proposed. The objective of this mechanism is to transform the exploration space from the subspace generated by the given inequality constraints to the standard Cartesian product space, which can be searched effectively by existing algorithms. By using the proposed architecture, the learned policy can output control signals satisfying the given constraints, and the original reward function can be kept unchanged. In our study, the convergence analysis is given. It is shown that the iterative algorithm is convergent to the optimal solution of the HJB equation. In addition, the continuity of the iterative estimated $Q$ -function is investigated. Two numerical examples are provided to demonstrate the effectiveness of our approach.

As a spotlighted nonlocal image representation model, group sparse representation (GSR) has demonstrated a great potential in diverse image restoration tasks. Most of the existing GSR-based image restoration approaches exploit the nonlocal self-similarity (NSS) prior by clustering similar patches into groups and imposing sparsity to each group coefficient, which can effectively preserve image texture information. However, these methods have imposed only plain sparsity over each individual patch of the group, while neglecting other beneficial image properties, e.g., low-rankness (LR), leads to degraded image restoration results. In this article, we propose a novel low-rankness guided group sparse representation (LGSR) model for highly effective image restoration applications. The proposed LGSR jointly utilizes the sparsity and LR priors of each group of similar patches under a unified framework. The two priors serve as the complementary priors in LGSR for effectively preserving the texture and structure information of natural images. Moreover, we apply an alternating minimization algorithm with an adaptively adjusted parameter scheme to solve the proposed LGSR-based image restoration problem. Extensive experiments are conducted to demonstrate that the proposed LGSR achieves superior results compared with many popular or state-of-the-art algorithms in various image restoration tasks, including denoising, inpainting, and compressive sensing (CS).

This brief addresses the adaptive neural asymptotic tracking issue for uncertain non-strict feedback systems subject to full-state constraints. By introducing the significant nonlinear transformed function (NTF), the command filtered technology, and the boundary estimation method into control design, a novel command filtered backstepping adaptive controller is proposed. The proposed control scheme is able to not only deal with full-state constraints but also avoid the “explosion of complexity” issue. By means of a Lyapunov stability analysis, we prove that: 1) the tracking error asymptotically converges to zero; 2) all the variables in the controlled systems are bounded; and 3) all the states are constrained in the asymmetric predefined sets. Finally, a numerical simulation is used to demonstrate the validity of the proposed algorithm.

The challenging problem of robust formation control pertaining to omnidirectional robots with model uncertainty and actuator saturation is comprehensively investigated in this article. First, an observer-based formation controller is designed to ensure the semiglobal uniform ultimate boundedness of the system's formation tracking error. Then, both global stability and practical finite-time stability are achieved to ascertain the practicality of the observer design. Apart from restricting the control input amplitude, we also investigate the reverse effect caused by saturated and coupled control input. As such, an adaptive compensator is formulated to attenuate the state oscillation caused by the reverse effect. Both numerical simulations and hardware-in-the-loop experiments are carried out to illustrate and evaluate the effectiveness and potentials of the proposed new techniques for real-world applications.

In this paper, a security consistent tracking control scheme with event-triggered strategy and sensor attacks is developed for a class of nonlinear multi-agent systems. For the sensor attacks on the system, a security measurement preselector and a state observer are introduced to combat the impact of the attacks and achieve secure state estimation. In addition, command filtering technology is introduced to overcome the "complexity explosion" caused by the use of the backstepping approach. Subsequently, a new dynamic event-triggered strategy is proposed, in which the triggering conditions are no longer constants but can be adjusted in real time according to the adaptive variables, so that the designed event-triggered mechanism has stronger online update ability. The measurement states are only transmitted through the network based on event-triggered conditions. The proposed adaptive backstepping algorithm not only ensures the security of the system under sensor attacks but also saves network resources and ensures the consistent tracking performance of multi-agent systems. The boundedness of all closed-loop signals is proved by Lyapunov stability analysis. Simulation examples show the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

Dual-beam steering has the advantage of providing extensive and efficient scanning in multitarget environment. However, the existing methods of dual-beam steering antenna arrays are generally large or complex in structure. Therefore, this communication proposes a simplified beamforming network based on the theoretical analysis on a Hadamard matrix. The proposed beamforming network was constructed using a simplified topology, consisting of three 0°/180° couplers and four p-i-n diodes. For verification, a dual-beam steering antenna array at 2.4 GHz was designed, fabricated, and measured. Both simulated and measured results agree well with each other. The size of the fabricated network is $0.70\,\,\lambda \text{g}\,\,\times 0.93\,\,\lambda \text{g}$ , which is only 20.3% of the size occupied by a traditional Hadamard matrix. At the center frequency, the implemented antenna array realizes steering angles of 0°, ±19°, ±31°, and ±51°, demonstrating good dual-beam steering properties.

This article investigates the event-triggered optimized tracking control problem for stochastic nonlinear systems based on reinforcement learning (RL). By using the backstepping strategy, an adaptive RL algorithm is performed under the identifier-critic-actor architecture to achieve event-triggered optimized control (ETOC). Moreover, a novel dynamically adjustable event-triggered mechanism is delicately designed, which adjusts the triggering threshold online to economize communication resources and reduce the computation burden. To overcome the difficulty that the virtual control signals are discontinuous due to the state-triggering, the virtual controllers are designed with the continuous sampling states signals, and the actual optimal controller is redesigned by using the triggered states in the last step. Furthermore, the proposed ETOC in this article has significant advantages in terms of saving network resources because the event-triggered mechanism is employed in the sensor-to-controller channel and the event-sampled states are utilized to directly activate the control actions. Finally, it can be guaranteed that all signals of the stochastic system are bounded under the presented ETOC method. A simulation example is carried out to illustrate the effectiveness of the proposed ETOC algorithm.

Image reconstruction from partial observations has attracted increasing attention. Conventional image reconstruction methods with hand-crafted priors often fail to recover fine image details due to the poor representation capability of the hand-crafted priors. Deep learning methods attack this problem by directly learning mapping functions between the observations and the targeted images can achieve much better results. However, most powerful deep networks lack transparency and are nontrivial to design heuristically. This paper proposes a novel image reconstruction method based on the Maximum a Posterior (MAP) estimation framework using learned Gaussian Scale Mixture (GSM) prior. Unlike existing unfolding methods that only estimate the image means (i.e., the denoising prior) but neglected the variances, we propose characterizing images by the GSM models with learned means and variances through a deep network. Furthermore, to learn the long-range dependencies of images, we develop an enhanced variant based on the Swin Transformer for learning GSM models. All parameters of the MAP estimator and the deep network are jointly optimized through end-to-end training. Extensive simulation and real data experimental results on spectral compressive imaging and image super-resolution demonstrate that the proposed method outperforms existing state-of-the-art methods.