Institute of Electrical Engineering, Chinese Academy of Sciences

Traditional on‐load voltage regulating transformer are composed of segmented windings controlled by tap changers and power frequency transformers. The tap changer can only perform ‘step‐wise voltage regulation’ and is slow in response. A topology of flexible on‐load voltage regulating transformer is presented in this paper, which only requires connecting a power electronic converter at the tap changer of the on‐load voltage regulating transformer. PEC can fully combine the existing on‐load tap changer to achieve continuous stepless voltage regulation function. This paper conducts modelling of the switch cycle average and analyses the voltage regulation range for the proposed topology. It also performs stability analysis of the small signal model for the introduced converter. A multilayer integrated control strategy is proposed based on the degree of voltage fluctuation in the power grid and the current state of the on‐load tap changer. In addition, in the closed‐loop control strategy of the converter, the quasi proportional‐resonance controller is introduced to improve the tracking performance of AC signal, and the voltage balance control is introduced to reduce the capacitor voltage difference on the DC side of the converter. Finally, simulation and experimental validation confirm the rationality and effectiveness of the proposed topology.

This paper proposes a three-dimensional spatial rotational actuation strategy to improve the propulsion capability of the magnetically controlled capsule endoscope (MCCE) within the intestine. By establishing an actuation model for the MCCE within three-dimensional space, the critical range of actuation angles for effective propulsion and uniform forward motion of the MCCE has been calculated. Experiments have demonstrated that within the critical range of actuation angles, the magnetically controlled capsule endoscope can be effectively initiated and made to achieve a state of uniform motion. The results show that the displacements at different actuation angles exhibit 99% consistency with an average movement speed error of 4.3%. The experimental and simulation results validate the effectiveness of the proposed actuation strategy in three-dimensional space, providing theoretical and experimental evidence for the application of the MCCE in the intestinal environment.

Via event‐triggered strategy, full‐order and reduced‐order filtering syntheses are addressed for uncertain switched systems in the paper. Norm‐bounded uncertainty is considered in each switched subsystem. An event‐triggered strategy is primarily specified to determine whether the information of an uncertain switched system is transmitted or not. Full‐order and reduced‐order filters are considered on account of the outputs of the proposed event‐triggered strategy. By the merging switching signal technique, it can be established as a switched filtering error system with uncertain switched systems. Some sufficient conditions are subsequently presented to ensure exponential stability (ES) with weighted performance for the filtering error system. Moreover, some devised methods of full‐order and reduced‐order filters are also given, and therefore filters with different orders can be selected according to different needs in practical applications. A lower bound more than zero is found to exclude Zeno behavior on the event‐triggered intervals. Finally, the validity and feasibility of this study are illustrated by a practical example.

In this paper, a family of single‐switch high step‐up DC‐DC converters based on switched‐capacitor (SC) cells and coupled inductor (CL) or built‐in transformer (BIT) is proposed. By replacing one active switch in the 3X‐ladder switched‐capacitor converter (SCC) with the primary side of a CL or BIT whose secondary side is incorporated into a multiplexed current path, the proposed topology integrates the high power‐density feature of a ladder SCC with the voltage‐lift effect of a CL or BIT. In addition, the problems of high current spikes and limited efficiency in the voltage regulation range of the traditional ladder SCC are tackled, making them suitable for distributed generation systems, microgrids, and so forth. For illustration purposes, the performance of the topologies within the family is compared in multiple ways, detailed operation principles and design considerations of one specific converter are given. Finally, experimental results are presented to verify the analytical findings.

The integrated energy microgrid (IEM) has good potential for both active and reactive power regulation, which are gradually becoming critical resources for participating in power system ancillary services. This paper constructs a distributed optimization model for the IEMs participating in voltage regulation ancillary services by considering multiple physical network constraints. To reduce computational complexity, the DistFlow power flow model and the second‐order cone relaxation method are introduced to transform the optimization model into a second‐order cone programming problem. Moreover, an improved accelerated consensus alternating direction method of multipliers algorithm is proposed to accelerate distributed optimization solutions of the model while protecting user privacy. Finally, the proposed optimization model and solution method are tested in the modified IEEE‐33 node test case to verify their effectiveness and feasibility.

A lightweight seedling detection model with improved YOLOv8s is proposed to address the seedling identification problem in the replenishment process in industrial vegetable seedling production.The CBS module for feature extraction in Backbone and Neck has been replaced with a lightweight depthwise separable convolution (DSC) in order to reduce the number of model parameters and increase the speed of detection. Furthermore, the fifth layer of Backbone has been augmented with efficient multiscale attention (EMA), which can aggregate multi-scale spatial structure information more rapidly through the two branches of the parallel structure, thereby enhancing the extraction of multi-scale features. Ultimately, the computational complexity of the model is further reduced by enhancing the structure of the bottleneck to form the VoVGSCSP module, which replaces the C2f module in Neck. The mAP of the improved model on the test set is 96.2%, its parameters, GFLOPS, and model size are 7.88 M, 20.9, and 16.1 MB, respectively. The detection speed of the algorithm is 116.3 frames per second (FPS), which is higher than that of the original model (107.5 FPS). The results indicate that the improved model can accurately identify empty cell and unqualified seedling in the plug tray in real time and has a smaller number of parameters and GFLOPS, making it suitable for use on embedded or mobile devices for seedling replenishment and contributing to the realization of automated and unmanned seedling replenishment.

Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Rolling technology with rigid arc rollers presents an innovative approach for the rapid manufacturing of three-dimensional surface components. The unique design generates a variable gap between the rollers, applying lateral bending and uneven compression to sheet metal, thus producing components with complex curvatures. Employing finite element simulation and experimental studies, this research investigates the effects of rotational speed variations on the bending deformation and dimensional accuracy of three-dimensional surface parts. The research findings indicate that as the ratio of rotational speeds between the convex and concave rolls increases from 0.75 to 2, the longitudinal curvature of the saddle-shaped parts gradually increases and then stabilizes, while the transverse curvature decreases. In contrast, for spherical parts, the longitudinal curvature shows an initial decrease followed by stabilization, with minimal changes observed in the transverse curvature. The degree to which the roller rotational speed ratio impacts curvature intensifies with increasing sheet metal compression. These findings suggest that the roll rotational speed ratio can be used as a new process parameter to enhance the curvature range and surface precision of formed parts.

Objective To observe whether maintaining the appropriate depth of anesthesia with Bispectral Index (BIS) can improve the prognosis of Spinal Cord stimulation (SCS) implantation in patients with chronic Disorders of consciousness (DoC). Methods 103 patients with DoC undergoing SCS implantation were reviewed, and 83 patients with DoC were included according to the standard of inclusion and exclusion Criteria. Patients were divided into a BIS group (n =45) and a non-BIS group (n =38) according to whether BIS monitoring was used during the operation. The depth of anesthesia in the BIS group was maintained between 40–60. The anesthesiologist adjusted the depth of anesthesia in the non-BIS group according to clinical experience. Relevant information such as disease course, cause, anesthesia time, and operation time were collected. Preoperative CRS-R(preoperative) score, postoperative CRS-R(24h), and postoperative CRS-R(3m) changes were collected. Results The CRS-R(3m) score in the BIS group was higher than that in the non-BIS group (preoperative), and the difference was statistically significant (P < 0.05). In CRS-R (24h), the BIS group was higher than the non-BIS group, and the difference was statistically significant (X²=8.787, P =0.004). The improvement of consciousness was included in the multivariate Logistic regression analysis model, and it was found that the thalamus was an independent factor affecting the improvement of consciousness (P < 0.05). During follow-up, 1 patient in the BIS group had a decrease in consciousness from MCS⁻ to VS/ UWS and 2 patients in the non-BIS group died during follow-up. Conclusion Patients can be benefit in hearing in CRS-R (24h). We recommend the use of BIS to monitor the depth of anesthesia in patients with DoC to improve patient outcomes.

The large scale deployment of modern wind turbines and the yearly increase of installed capacity have drawn attention to their operation and maintenance issues. The development of highly reliable and low‐maintenance wind turbines is an urgent demand in order to achieve the low‐carbon goals, and the arrival of fault diagnosis provides assurance for its satisfactory operation and maintenance. Numerous statistical studies have pointed out that generator failures are a main cause of wind turbine system downtime. The generator, as one of the core components, converts rotating mechanical energy into electrical energy. However, the generators can hardly operate reliably towards the end of the turbine life owing to the variable‐speed conditions and harsh electromagnetic environments. This article first provides a comprehensive and up‐to‐date review of the electrical and mechanical failures of various parts (stator, rotor, air gap and bearings) of the generator. Then the fault characteristics and diagnostic processes of generators are investigated, and the principles and processes of fault diagnosis are discussed. Finally, the application of four categories of model‐based, signal‐based, knowledge‐based and hybrid approaches to wind turbine generator fault diagnosis is summarized. The comprehensive review shows that the hybrid approach is now the leading and most accurate tool for real‐time fault diagnosis for wind turbine generators. A qualitative and quantitative assessment of algorithm performance using false alarm rates is proposed. The methodology can subsequently be applied to the wind industry.

In this work, we first define two special sets of real numbers, and then, we construct a half-discrete kernel function where the variables are defined in the whole plane, and the parameters in the kernel function are limited to the newly constructed special sets. Estimate the kernel function in the whole plane by converting it to the first quadrant, and then, a class of new Hilbert-type inequality is established. Additionally, it is proved that the constant factor of the newly established inequality is the best possible. Furthermore, assigning special values to the parameters and using rational fraction expansion of cosecant function, some special results are presented at the end of this article.

The daily experience of mental stress profoundly influences our health and work performance while concurrently triggering alterations in brain electrical activity. Electroencephalogram (EEG) is a widely adopted method for assessing cognitive and affective states. This study delves into the EEG correlates of stress and the potential use of resting EEG in evaluating stress levels. Over 13 weeks, our longitudinal study focuses on the real-life experiences of college students, collecting data from each of the 18 participants across multiple days in classroom settings. To tackle the complexity arising from the multitude of EEG features and the imbalance in data samples across stress levels, we use the sequential backward selection (SBS) method for feature selection and the adaptive synthetic (ADASYN) sampling algorithm for imbalanced data. Our findings unveil that delta and theta features account for approximately 50% of the selected features through the SBS process. In leave-one-out (LOO) cross-validation, the combination of band power and pair-wise coherence (COH) achieves a maximum balanced accuracy of 94.8% in stress-level detection for the above daily stress dataset. Notably, using ADASYN and borderline synthesized minority over-sampling technique (borderline-SMOTE) methods enhances model accuracy compared to the traditional SMOTE approach. These results provide valuable insights into using EEG signals for assessing stress levels in real-life scenarios, shedding light on potential strategies for managing stress more effectively.

Polar transport ships frequently traverse in the brash ice channel opened by icebreakers. Although the substantial ice resistance caused by direct collisions with the level ice is avoided, the hull still encounters collisions with the brash ice, leading to periodic damage and exacerbating the fatigue issues of the hull structure. To address the fatigue challenges faced by ships sailing in the brash ice channels, this paper proposes an ice-induced fatigue damage assessment method based on the CFD-DEM-FEM. Referring to the brash ice model test conducted at the Hamburg Ship Model Basin (HSVA), a discrete element ice model and a numerical brash ice tank are established using the CFD-DEM coupling method. The simulated ship-ice interaction is compared with HSVA’s experimental results to validate the reliability of the numerical brash ice tank and ice load. The ice load time history resulting from the ship-brash ice collision is applied to the hull, and the hot spot stress time history under each fatigue sub-condition is calculated using the FEM. The improved rain-flow counting method is employed to determine the stress level of the hot spot stress time history, and the S-N curve method based on the linear cumulative damage criterion is used to calculate the total fatigue damage of hot spots. Finally, the results of the proposed method are compared with those of the LR method. This study can serve as a valuable reference for the ice-induced fatigue assessment of ships navigating in brash ice channels.

Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.

A lightweight seedling detection model with improved YOLOv8s is proposed to address the seedling identification problem in the replenishment process In industrial vegetable seedling production, First, the CBS module in the network structure is replaced with depthwise separable convolution (DSC) to reduce the number of parameters and GFLOPS of the model. The efficient multiscale attention (EMA) module is added to the structure to improve the feature extraction capability of the network, focusing on the target regions of empty and unqualified seedlings in seedling trays in complex environments. Second, the VoVGSCSP module is utilized to replace the C2f module in Neck to further lighten the model and improve its accuracy. Compared with the original YOLOv8s model, the Precision, Recall, and mAP of the improved model on the test set are 95.9%, 91.6%, and 96.2%, respectively, and its parameters, GFLOPS, and model size are 7.88 M, 20.9, and 16.1 MB, respectively. The detection speed of the algorithm is 116.3 frames per second (FPS), which is higher than that of the original model (107.5 FPS). The results indicate that the improved model can accurately identify empty cell and unqualified seedling in the plug tray in real time and has a smaller number of parameters and GFLOPS, making it suitable for use on embedded or mobile devices for seedling replenishment and contributing to the realization of automated and unmanned seedling replenishment.

Biomass photoreforming is a promising way of producing sustainable hydrogen thanks to the abundant sources of biomass feedstocks. Solar energy provides the heat and driven force to initial biomass oxidation coupled with H2 evolution. Currently, biomass photoreforming is still far from plant‐scale applications due to the lower solar energy utilization efficiencies, the low H2 yield, and the lack of appropriate photoreactors. The production of H2 from photoreforming of native biomass and platform molecules was summarized and discussed with particular attention to the prospects of scaling up the catalysis technology for mass hydrogen production. The types of photoreforming, including photocatalysis and photothermal catalysis, were discussed, consequently considering the different requirements for photoreactors. We also reviewed the photoreactors that support biomass photoreforming. Numerical simulation methods were implemented for the solid‐liquid two‐phase flow and inter‐particle radiative transfer involved in the reaction process. Developing concentrated photothermal catalytic flowed reactors is beneficial to scale‐up catalytic hydrogen production from biomass.