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Abstract A new concept of battery doctor is proposed for the next generation battery health assessment, first, the comprehensive assessment framework integrating the multiple health indices is formulated, where the bottom‐up assessment hierarchy is used to provide the holistic health indicator from the battery cell to the large‐format battery. Second, several options for defining a uniform indicator state of X is provided to effectively measure the battery health, which contributes to promoting the health assessment from state of charge and stage of health to state of X. Finally, the future challenges and opportunities of developing the battery doctor are disclosed from three different viewpoints, which is to incentivize the technology breakthrough for the next generation battery health assessment.

A joint resource-optimization scheme is investigated for nonorthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast in unmanned aerial vehicle (UAV)-assisted radio-access networks (RANs). This scheme allows a ground base station and UAVs to simultaneously multicast successive video layers in SVC with successive interference cancellation in NOMA. A video quality-maximization problem is formulated as a mixedinteger nonlinear programming problem to determine the UAV deployment and association, RAN spectrum allocation for multicast groups, and UAV transmit power. The optimization problem is decoupled into the UAV deployment-association, spectrum-partition, and UAV transmit-power-control subproblems. A heuristic strategy is designed to determine the UAV deployment and association patterns. An upgraded knapsack algorithm is developed to solve spectrum partition, followed by fast UAV power fine-tuning to further boost the performance. The simulation results confirm that the proposed scheme improves the average peak signal-to-noise ratio, aggregate videoreception rate, and spectrum utilization over various baselines.

AimThis study evaluated the sex-and age-specific usefulness of the Little Developmental Coordination Disorder Questionnaire-Chinese (LDCDQ-CH) in Chinese preschoolers.MethodA population-based sample of 51,110 children aged 3–5 years was recruited. Internal reliability, construct validity, concurrent validity with the Ages and Stages Questionnaire-third edition (ASQ-3), and discriminant validity with the Movement Assessment Battery for Children-second edition (MABC-2) were assessed. Age and sex effects on LDCDQ-CH scores were analyzed using ANOVA and t-tests.ResultsThe LDCDQ-CH exhibited excellent internal consistency and reliability across ages and genders. Confirmatory factor analysis supported the 15-item model’s satisfactory fit. Positive and significant correlations were observed between LDCDQ-CH and ASQ-3 scores, indicating robust concurrent validity. Significant associations were found between LDCDQ-CH and MABC-2 scores. Higher scores were observed in older children and girls, indicating age- and sex-related differences in motor functional performance.ConclusionThe LDCDQ-CH is a reliable and valid tool to support early identification of motor coordination difficulty in Chinese preschoolers, and guiding interventions. Findings support its use across ages and genders, highlighting its potential in the Chinese context. Age- and sex-specific norms are needed for enhanced clinical applicability.

The J-TEXT capability is enhanced compared to two years ago with several upgrades of its diagnostics and the increase of electron cyclotron resonance heating (ECRH) power to 1 MW. With the application of electron cyclotron wave (ECW), the ECW assisted plasma startup is achieved; the tearing mode is suppressed; the toroidal injection of 300 kW ECW drives around 24 kA current; fast electrons are generated with toroidal injected ECW and the runaway current conversion efficiency increases with ECRH power. The mode coupling between 2/1 and 3/1 modes are extensively studied. The coupled 2/1 and 3/1 modes usually lead to major disruption. Their coupling can be either suppressed or avoided by external resonant magnetic perturbation fields and hence avoids the major disruption. It is also found that the 2/1 threshold of external field is significantly reduced by a pre-excited 3/1 mode, which can be either a locked island or an external kink mode. The disruption control is studied by developing prediction methods capable of cross tokamak application and by new mitigation methods, such as the biased electrode or electromagnetic pellet injector. The high-density operation and related disruptions are studied from various aspects. Approaching the density limit, the collapse of the edge shear layer is observed and such collapse can be prevented by applying edge biasing, leading to an increased density limit. The density limit is also observed to increase, if the plasma is operated in the poloidal divertor configuration or the plasma purity is increased by increasing the pre-filled gas pressure or ECRH power during the start-up phase.

Abstract Facing the problem of workforce and time costs caused by the rescheduling of large‐scale power grids with renewable energy penetration, and the lack of parallel control research on rotor angle instability and transient voltage instability, a rescheduling method based on trajectory sensitivity and deep reinforcement learning (DRL) is proposed to meet the requirements of rotor angle stability and transient voltage stability at the same time. By introducing the process of rescheduling to meet the transient stability constraint, the Markov decision‐making process of rescheduling is first constructed. Then, a simple dominant instability type identification method is proposed according to the occurrence time, location, voltage pattern, and position of the oscillation centre. Moreover, a rescheduling strategy is formulated based on the dominant instability mode identification, trajectory sensitivity calculation, actionable device selection, action amount computation, and action of the device. Next, according to the improved distributed distributive deep deterministic policy gradients (D4PG), a DRL model is established to map the action to actionable generator pairs and capacitors/reactors, so as to realize parallel rescheduling of rotor angle instability and transient voltage instability. Finally, the improved New England 39‐bus system and an actual power grid are used to verify the effectiveness and advantages of the method.

The drone-small-cell-assisted air-ground integrated network is a promising architecture for enabling diverse vehicle applications. This paper presents a joint resource slicing and vehicle association framework for drone-assisted vehicular networks, which facilitates spectrum sharing among heterogeneous base stations (BSs) and achieves dynamic resource provisioning in the presence of network load dynamics. We formulate the network utility maximization problem as mixed-integer nonlinear programming, considering traffic statistics, quality-of-service (QoS) constraints, varying vehicle locations, load conditions in each cell, and interdrone interference. The original maximization problem is transformed into a biconcave optimization problem to ensure mathematical tractability. An alternate concave search algorithm is then designed to iteratively solve vehicle association patterns and spectrum partitioning among heterogeneous BSs until convergence. Simulation results show that the proposed scheme achieves a significant performance improvement in throughput and spectrum utilization compared with two other baseline schemes.

A fractional order moments-based detector is proposed for the detection of weak signals in additive impulsive noise environment assumed as generalized Gaussian distribution with properly selected parameter values. The asymptotic detection performance is derived and compared with some traditional detectors optimized for operations in Gaussian noise with Nakagami fading communication channels. The analytical and computer simulation results of the fractional order moment-based detector are shown for signal detection with fading channels in the impulsive noise.

Clustering organizes nodes into groups in order to enhance the connectivity and stability of cognitive radio sensor networks. Mainly depending on the channel availability, many existing spectrum-aware clustering algorithms may not achieve the most satisfactory clustering. Taking into account the various influence factors to establish the optimal clustering is a challenge to enhance the network performance. This paper proposes a novel spectrum-aware clustering algorithm based on weighted clustering metric to obtain the optimal clustering by solving an optimization model. The new weighted clustering metric, simultaneously evaluating temporal-spatial correlation, confidence level and residual energy, is used to elect clusterheads and ally member nodes. After clustering, the clusterheads sensing spectrum instead of all member nodes greatly reduces the energy consumption of spectrum sensing and increases the opportunity of data transmission. The performance comparison between the traditional spectrum-aware clustering algorithms and our proposed algorithm has been highlighted with the experiments.

Robust nonlinear filtering is an important method for tracking maneuvering targets in non-Gaussian noise environments. Although there are many robust filters for nonlinear systems, few of them have ideal performance for mixed Gaussian noise and non-Gaussian noise (such as scattering noise) in practical applications. Therefore, a novel cubature formula and maximum correntropy criterion (MCC)-based robust cubature Kalman filter is proposed. First, the fully symmetric cubature criterion and high-order divided difference are used to construct a new fifth-degree cubature formula using fewer symmetric cubature points. Then, a new cost function is obtained by combining the weighted least-squares method and the MCC loss criterion to deal with the abnormal values of non-Gaussian noise, which enhances the robustness; and statistical linearization methods are used to calculate the approximate result of the measurement process. Thus, the final fifth-degree divided difference–maximum correntropy cubature Kalman filter (DD-MCCKF) framework is constructed. A typical surface-maneuvering target-tracking simulation example is used to verify the tracking accuracy and robustness of the proposed filter. Experimental results indicate that the proposed filter has a higher tracking accuracy and better numerical stability than other common nonlinear filters in non-Gaussian noise environments with fewer cubature points used.

Target localization is one of the essential tasks in the applications of wireless sensor networks (WSNs). The traditional target localization based on received signal strength may fail to obtain satisfactory localization performance, especially when the number of the RSS measurements is limited. Compressed sensing (CS) has been shown to be an effective technique for target localization due to the intrinsic sparse nature of target localization in WSNs. The CS-based target localization can be formulated to a sparse recovery problem based on l0-norm or l1-norm minimization. Compared to l0-norm and l1-norm, lp-norm (0 p-norm minimization, however, usually obtain suboptimal sparse solutions when the initial point is not in the convergence domain of the globally optimal sparse solution. In this paper, we propose a novel affine scaling steepest descent (ASSD) algorithm to find a satisfying sparse solution of lp-norm minimization. By setting an optimal stepsize at each iteration, our ASSD algorithm can avoid the iterative solutions concentrating on the attraction basin of the suboptimal sparse solution and move to the attraction basin of a sparser solution, so it has high opportunity to obtain the globally optimal sparse solution, and then accurately determine the locations of targets. The experimental results show that our ASSD algorithm performs much better than the traditional BP, OMP, GMP, ASM, IRL1, and ITM algorithms, especially when the number of measurements is insufficient.

Target localization is one of the essential tasks in almost applications of wireless sensor networks. Some traditional compressed sensing (CS)-based target localization methods may achieve low-precision target localization because of using locally optimal sparse solutions. Solving global optimization for the sparse recovery problem remains a challenge in CS-based target localization. In this paper, we propose a novel energy-level jumping algorithm to address this problem, which achieves high-precision target localization by solving the globally optimal sparse solution of l p -norm ( 0 < p < 1 ) minimization. By repeating the process of energy-level jumping, our proposed algorithm establishes a global convergence path from an initial point to the global minimizer. Compared with existing CS-based target localization methods, the simulation results show that our localization algorithm obtain more accurate locations of targets with the significantly reduced number of measurements.

Compressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service satisfaction. In this paper, a cluster-based distributed compressed sensing scheme working together with a quality-of-service aware routing framework is proposed to deliver visual information in cognitive video sensor networks efficiently. First, the correlation among adjacent video sensors determines the member nodes that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurements are obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold. The goal is to maximize the removal of unnecessary traffic without sacrificing video quality. Lastly, the compressed data is transmitted via a distributed spectrum-aware quality-of-service routing scheme, with an objective of minimizing energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed approach can achieve energy-efficient data delivery and reconstruction accuracy of visual information compared with existing quality-of-service routing schemes.

With the further development of oilfield, liquid-gas separation has become an essential problem. Cylindrical cyclone separators are popular in the industrial process due to the advantage that they are simple, compact, and inexpensive to manufacture. In this paper, a three-dimensional turbulence model including Reynolds stress model was established to describe the mixture flow field in the separator. Through the numerical simulation, the separation efficiency was investigated under different parameter cases such as separator length, gas phase outlet diameter, and inlet shape. It can be indicated from the simulation results that the separation efficiency decreases with the increase of the separator length, and the separation efficiency increases firstly and then decreases with the increase of the gas phase outlet diameter as well as the liquid phase outlet. Furthermore, the rectangular inlet is more suitable than the circular inlet with the separation efficiency changing from 66.45% to 79.04%. In the end, the optimal geometrical structure was presented with separation efficiency of 86.15%.

To solve the problem of manpower and time consumption caused by power flow state adjustment in a large-scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process of adjusting the power flow state that satisfies static stability, the Markov decision-making process of adjusting power flow is constructed. Then, based on the positioning of the adjustment target, the selection of actionable devices and the calculation of the amount of action, a power flow state adjustment strategy is developed. The adjustment process is accelerated through sensitivity, transfer ratio and load margin. Then, a parallel deep reinforcement learning model is established, and it maps actions to power flow adjustment to form a pair of generator actions and realises parallel adjustment of multi-sectional objectives. In addition, the reinforcement learning strategy and the deep learning network are improved to promote learning efficiency. Finally, the New England 39-bus standard system and actual power grid are used to verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

An approach of scaling and shifting constellation for MIMO lattice reduction(LR)-aided precoding is proposed. The proposed scheme is beneficial to improve the performance in terms of both BER and ergodic capacity. The feedback is a Gaussian integer matrix in which the entries are preserved in a region with respect to modulo operation. Then, the amount of feedback information is reduced significantly. Simulation results show that the LR-aided precoding scheme using the improved version of the constellation apparently improves system performance. [ABSTRACT FROM AUTHOR]