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Abstract An improved model predictive control (MPC) with Luenberger state observer is proposed in this article for LC‐coupling hybrid active power filter (LC‐HAPF). Traditionally, the implementation of MPC for LC‐HAPF requires additional sensors to measure coupling capacitor voltage, thereby increases the system cost and reduces reliability. The existence of one‐step sampling delay in practice will also affect the final compensation performance. To relax these problems, both continuous and discrete models are deduced first. Then, the Luenberger observer is utilized based on the discrete model to mitigate the steady‐state error without additional sensors. In addition, the sampling delay compensation is provided by improving the prediction horizon. Finally, the effectiveness of the improved MPC for LC‐HAPF is verified by experiment results, indicating that the proposed method has fast dynamic response and low steady‐state error.

Metal Oxide Thin Film Transistors (MO TFTs) have garnered considerable interest in emerging Internet of Things (IoT) fields such as wearable electronics, displays, Radio Frequency Identification (RFID), and biomedical monitoring, owing to their flexibility and transparency. However, limitations in channel materials make MO TFT-based circuits unipolar. Unipolar circuits often exhibit elevated short-circuit power consumption, which restricts the development of MO TFTs in the IoT sector. This paper introduces a Capacitively Coupled Near-Threshold Biasing (CCNB) technique that leverages the unique Capacitance-Voltage (C-V) characteristics of MO TFTs to bias devices in the near-threshold region, achieving nearly a 95% reduction in power consumption compared to traditional designs with the device coupling ratio (channel capacitance/overlap capacitance) at 40. Furthermore, considering the significance of clock signals in IoT applications, we have also developed a low-power full-swing Ring Oscillator (RO) based on our CCNB technique, resulting in a 90% reduction in power consumption and a nearly 70% reduction in PDP compared to conventional low-power designs.

Effectively preventing and diagnosing laryngeal cancer is imperative for improving prognosis, particularly in high-risk populations. Early diagnosis in these individuals is crucial, offering timely interventions and higher survival rates. There is a growing demand for computer-assisted early prognosis with transparent and accountable interpretations. Methods and procedures: We present an interpretable classification framework based on radiomics for narrow-band imaging in contact endoscopy for laryngeal cancer. The framework aims to address the challenge of early prognosis with transparency and accountability by offering evidence-supported interpretation. Results: Performance metrics, including accuracy, precision, recall, F1 score, area under the curve (AUC), negative predict value (NPV), balanced accuracy(BA) and diagnostic odds ratio (DOR) were employed to analyze the results. The radiomics-based model demonstrated high recall rate (>94% in 3 out the 5 classifiers employed) and balanced accuracy (>89% in 3 classifiers) in distinguishing between benign and malignant cases. The interpretability analysis shed light on the framework’s behavior evidencing three features as main contributors for classification. Conclusion: Our interpretable classifying framework shows promise as a preliminary clinical diagnostic assistant for laryngeal cancer. Its commitment to transparency and accountability renders it an invaluable resource for conveying insightful information and assessing the framework’s effectiveness. Clinical impact: Our framework addresses the challenge of early diagnosis among high-risk populations, providing a transparent and accountable tool for clinicians and researchers.

Operation and Maintenance (O&M) becoming increasingly important in the asset management of photovoltaic (PV) systems. Compared to large PV power plants, distributed PV systems are widely distributed and often located in complex terrain, which seriously increases the pressure of O&M tasks. However, at present, there is still a lack of effective O&M scheduling method for large-scale distributed PV systems, resulting in the untimeliness of O&M and high costs. To this end, the O&M tasks, staff, and equipment are firstly analyzed. Second, considering both planned and random tasks, an O&M dynamic scheduling model for large-scale distributed PV systems is proposed, which comprehensively considers labor cost, depreciation cost, power loss, and transportation cost to optimize the O&M benefits. Afterward, aiming to solve the problem that traditional optimization algorithms are prone to fall into local optima, a hierarchical hybrid algorithm named HGA-BPSO is proposed, which combines the advantages of genetic algorithm (GA) and binary particle swarm optimization (BPSO). Finally, the validity and rationality of the proposed method are verified by the case study of distributed PV poverty alleviation project in Badong County, Hubei Province. The results show that in the three different cases, the proposed method can reasonably dispatch O&M staff and equipment. The total cost of case 1 is 879.8 yuan, the total cost of case 2 is 2962.6 yuan, and the total cost of case 3 is 1728.3 yuan. At the same time, compared with the traditional meta-heuristic algorithm algorithm, the proposed GA-BPSO algorithm can better search for the global optimal.

Aiming to address the issue of low accuracy in industrial network traffic anomaly detection, we propose an improved DeepFM model for multi-type anomaly detection. The dataset undergoes preprocessing, including encoding and non-string numerical operations. The SMOTE-ENN algorithm is utilized to balance the data through oversampling and undersampling. The improved DeepFM model is employed to extract linear, non-linear, and temporal features from the industrial network traffic data. These features are then fed into an anomaly detector classifier constructed based on Softmax to achieve high-performance detection of traffic attacks. The effectiveness of the model is verified using the UNSW-NB15 dataset, with experimental results demonstrating a detection accuracy of 0.95 for DoS attacks, 0.94 for Fuzzers attacks, and 0.92 for Worms attacks, significantly surpassing other algorithms, which confirms the effective utilization of the proposed model for industrial network traffic anomaly detection.

In order to solve the problems of serious deterioration of braking efficiency of traditional eddy current retarder (ECR) in continuous operation and poor braking characteristics of hydraulic retarder (HR) at low speed, a novel four salient poles liquid-cooled electromagnetic retarder (FSP-LER) is proposed. The downhill dynamics model of medium and heavy vehicles is established to analyze the braking demand, the FSP-LER is designed according to the braking demand, and its braking characteristics are predicted by the equivalent magnetic circuit (EMC) method and the finite element method. Hierarchical variable domain fuzzy control strategy combined with retarder is utilized for braking control, and MATLAB is used to establish the controller and the long downhill braking model of the whole vehicle and perform joint simulation. The actual braking characteristics of the retarder prototype were tested by bench test and vehicle road test, and the results showed that the braking torque of the FSP-LER decreased by only 13.49% for 10 min of continuous operation, and the braking efficiency was still maintained by about 86% at a low speed of 500 r/min. The average error between the actual measured value of braking torque and the analyzed calculated value is less than 5%, and the braking torque can reach 2213 N $\cdot $ m when the rotational speed is 1250 r/min, which meets the braking demand of medium and heavy vehicles.

The end-to-end speech recognition approach exhibits higher robustness compared to conventional methods, enhancing recognition accuracy across diverse contexts. However, due to the absence of an independent language model, it struggles to identify vocabulary beyond the training data, thus impacting the recognition of certain specific terms. Adapting to various scenarios necessitates a pivot towards specific domains. This study, based on the CATSLU dataset, constructed two tasks for Chinese contextual biasing, targeting both proper nouns and mixed-domain sentences. Additionally, it explored four methods of contextual biasing at different stages within the speech recognition process: pre-recognition, within the model, decoding, and post-processing stages. Experimental results indicate that all biasing methods to some extent improved the recognition efficacy of the speech recognition model within specific domains.

This paper presents an improved temporal pulse sequence analysis (PSA) method for diagnosing partial discharge (PD) phenomena. The proposed method enables simultaneous high-resolution PD recognition across electrical sensors and ultra-high frequency antennae, facilitating the differentiation of defect types and the quantification of PD severity, as well as, their unique characteristics. This is achieved by utilizing advanced discrete-time integration techniques within two sensor measurement systems. Each PD event is time-stamped in post-processing, with a particular focus on analyzing charge-to-charge and ultra high frequency (UHF) energy-to-energy sequences analysis. The improved temporal PSA merits the advantages of a wide bandwidth of high-frequency current transformers and antennae to improve accuracy and efficiency. To conduct this method, a dual-sensor PD measurement circuit was set up to examine the protrusion on the ground side, the floating electrode, and the free-moving particle defects, which are potentially present in gas-insulated switchgear. Obtained results present dynamic PD variations in both time differences and magnitudes, establishing correlational PD patterns between sensors in a distinct manner. The application of the proposed method can serve as a reliable diagnostics approach for PD detection in gas-insulated switchgear and offer insights into underscoring PD phenomena itself.

Brain-Computer Interface (BCI) is a revolutionary technique that employs wearable electroencephalography (EEG) sensors and artificial intelligence (AI) to monitor and decode brain activity. EEG-based motor imagery (MI) brain signal is widely utilized in various BCI fields including intelligent healthcare, robot control, and smart homes. Yet, the limited capability of decoding brain signals remains a significant obstacle to BCI techniques expansion. In this study, we describe an architecture known as the dual-branch attention temporal convolutional network (DB-ATCNet) for EEG-based MI classification. DB-ATCNet improves MI classification performance with relatively fewer parameters by utilizing a dual-branch convolutional network and channel attention. The DB-ATCNet model consists of two primary modules: attention dual-branch convolution (ADBC) and attention temporal fusion convolution (ATFC). The ADBC module utilizes a dual-branch convolutional network to extract low-level MI-EEG features and incorporates channel attention to improve spatial feature extraction. ATFC employs sliding windows with self-attention to obtain the high-level temporal features, and utilizes feature fusion strategies to minimize information loss. The DB-ATCNet achieved subject-independent accuracies of 87.33% and 69.58% in two-class and four-class classification tasks, respectively, on the PhysioNet dataset. On the BCI Competition IV-2a dataset, it achieved an accuracy of 71.34% and 87.54% for subject-independent and subject-dependent evaluations, respectively, surpassing existing methods. The code is available at https://github.com/zk-xju/DB-ATCNet.

Scoliosis refers to the abnormal curvature of human spine, which is one of the most common deformities in children and adolescents. The Cobb angle is the gold standard for quantifying the severity of scoliosis and is used to assess the severity of scoliosis. Often the accuracy of the Cobb angle measurement relies on the subjective experience of the doctor and the process is very time consuming. In this study, we propose a new deep neural network, ATT UNet 3+, based on UNet 3+. Our approach incorporates a novel hybrid attention mechanism in the network’s upsampling process. This mechanism allows for the appropriate reweighting of fused multi-scale information and facilitates effective supervision of the final output results. The proposed neural network is trained, tested and validated on 155 X-ray ortho-slices. The deep learning network is compared with the more effective neural networks commonly used today. ATT UNet 3+ achieves the best performance in the segmentation evaluation results. Regarding the final Cobb angle calculations, the absolute mean error between the longest distance ellipsoidal point (LDEP) method and expert measurements amounted to 1.6°. ATT UNet 3+ provides a potential tool for segmenting the spine in X-ray, which can improve the efficiency and accuracy of doctors in processing scoliosis pathological images.

The traditional active suspension system controlled by fuzzy PID fails to consider external road information adaptively, lending to low control precision. To solve this problem, this novel variable universe fuzzy PID control strategy, which combines road recognition and chaotic particle swarm optimization (CPSO), is proposed. Firstly, a dynamic model of four degree of freedom vehicle suspension is established based on the half-vehicle model. Secondly, the Back Propagation (BP) neural network is optimized by Tent Sparrow Search Algorithm (Tent-SSA) to construct a road recognition model. When the road recognition module is constructed, the suspension control system can convert the suspension vibration signal into road information, and dynamically adjust the scaling factors of the variable universe fuzzy controller based on the road information. Thirdly, a modified coefficient is added to adjust the parameters obtained from the road recognition model, and the CPSO algorithm is used to optimize it to enhance control precision. Passive suspension, FPID control, and this novel control are constructed and simulated in MATLAB. The results indicate that this novel control strategy has improved in comprehensive performance by 28.47% compared to fuzzy PID control strategies.

Table structure recognition (TSR) is crucial for document analysis, particularly for medical examination report tables (MERTs), impacting efficiency and decision-making in healthcare. Most models for TSR utilize either Graph Convolution Neural Networks (GCNs) or Transformers with html sequences for structure recognition. These methods, however, face challenges with graph inductive bias and instability in training, respectively. We observe that cells within the same row or column of a table not only are closely aligned in their vertical and horizontal coordinates, respectively, but also exhibit highly similar features. In previous work, the spatial feature of coordinates was often only used for concatenation with image features, text features, etc. We believe that explicitly utilizing the unique spatial properties of tables can better encode table features. In this paper, we introduce a novel structure named Dual-Awareness Feature Aggregator (DAFA) for table, which leverages attention mechanisms to effectively extract table features. Based on it, we design an end-to-end model called DAFA-Net requiring only images as input, without the need for additional information such as texts. In addition, we try to address the prevalent challenge of recognizing cross-row and cross-column cells in TSR — a scenario frequently encountered in medical examination reports — by introducing a modified focal loss known as CRCC loss. We conduct extensive experiments on four popular datasets. This includes a dataset specifically dedicated to medical data and others that mirror the complexity typically encountered in medical tables. Experimental results show the effectiveness and potential of our DAFA-Net for TSR within the healthcare sector.

Due to the low permeability, small pore throat, and poor pore connectivity of ultra-low permeability reservoirs, fracturing was required to improve the flow environment of oil in the reservoir during its development. The combination of fracturing and oil displacement could reduce the complexity of the development of ultra-low permeability reservoirs. It was feasible to synthesize new surfactants to prepare fracturing oil displacement agents, but the research and development of new surfactants were difficult and costly. This paper aimed to experimentally investigate the imbibition displacement mechanism of fracturing flooding by combining the surfactant of chemical flooding with the fracturing fluid. With the conditions of the ultra-low permeability reservoir in block Y of Changqing Oilfield, the mechanism of remaining oil start-up and fractured oil displacement agent enhanced oil recovery in the ultra-low permeability reservoir was studied by microfluidic model experiment and NMR experiment. A mixture of fracture oil displacement agents was obtained by mixing heavy alkyl benzene sulfonate surfactant, a nonionic surfactant, and a betaine-type amphoteric surfactant. HAS-6C, HAS-5E, and HAS-5G were selected for their interfacial tension reaching 10−3 mN/m. Oil–water​ interfacial tension of HAS-5E was the lowest, which can be reduced to 10−4 mN/m. The ability to alter the wettability of HAS-6C was the most significant, which could make the contact angle go down to 64.5°. The multi-round dynamic and static imbibition experiments showed that the combination of selected surfactant and slickwater can greatly improve the imbibition recovery, of which HAS-6C could enhance oil recovery by 17%. The HAS-5E system with the lowest interfacial tension had the best effect of starting clustered residual oil, and the clustered residual oil saturation was reduced by 48.8%. The HAS-6C system with stronger wettability had the best effect of starting throat-like residual oil, and the throat-like residual saturation was reduced by 10.8%.

Leveraging the liquid-phase immiscibility effect and phase diagram calculations, a sequence of alloy powders with varying Fe content was designed and fabricated utilizing the gas atomization method. Microstructural characterizations, employing SEM, EDS, and XRD analyses, revealed the successful formation of an incomplete shell on the surfaces of Al-Bi-Fe powders, obviating the need for Sn doping. This study systematically investigated the microstructure, hydrolysis performance, and hydrolysis process of these alloys in deionized water. Notably, Al-10Bi-7Fe exhibited the highest hydrogen production, reaching 961.0 NmL/g, while Al-10Bi-10Fe demonstrated the peak conversion rate at 92.99%. The hydrolysis activation energy of each Al-Bi-Fe alloy powder was calculated using the Arrhenius equation, indicating that a reduction in activation energy was achieved through Fe doping.

Although the double-double (DD) laminates proposed by Tsai provide a promising option for achieving better structural performance with lower manufacturing and maintenance costs, the buckling performance of perforated DD laminates still remains clear. In this study, optimal ply angles, rotation angles, and the corresponding maximum buckling loads are determined for DD laminates with various cutouts, which are used for comparisons to evaluate the effects of cutout size and shape on the buckling behaviour of perforated DD laminates. Apart from conventional circular and elliptical cutouts, the use of a combined-shape cutout for DD laminates is also investigated. As a large number of optimisations are required to obtain the maximum buckling loads for different cases in this study, an efficient optimisation method for perforated DD laminates is proposed based on an artificial neural network (ANN) and a genetic algorithm (GA). Unlike conventional quadaxial (QUAD) laminates, the repetition of a four-ply sublaminate in DD laminates makes their layup to be represented by only two ply angles; hence, the application of ANN models for predicting the buckling behaviour of various perforated DD laminates is studied in this paper. The superior performance of the ANN models is demonstrated by comparisons with other machine learning models. Instead of using the time-consuming FEA, the developed ANN model is utilised within a GA to obtain the maximum buckling load of perforated DD laminates. Compared to the circular cutout, the use of elliptical and combined-shape cutouts leads to more noticeable changes in the optimal ply angles as the cutout size increases. Based on the obtained results, the use of the combined-shape cutout is recommended for DD laminates.

In order to investigate the influence of the CaO and fly ash (FA) dosage and proportion on the mechanical properties, durability, and microstructure of solidified sludge, freeze–thaw (F-T) cycles and dry–wet (D-W) cycles are conducted to study the change in appearance and the strength attenuation of CaO-FA solidified sludge. Low-field nuclear magnetic resonance (LF-NMR) is used to analyze the microstructure of the solidified sludge with various dosages and ratios of CaO-FA. The results demonstrate that the unconfined compressive strength (UCS) and direct shear strength of solidified sludge increase with the prolongation of the curing age. Furthermore, the mechanical properties of solidified sludge are improved as the ratio of CaO-FA increases. As the curing age increases, the distribution of transverse relaxation time (T2) becomes narrow, the spectral area decreases, and the amplitude of the LF-NMR signal shows a downward and leftward tendency. Additionally, with the increase in the number of F-T cycles and D-W cycles, the UCS of solidified sludge declines and the degree of pore deterioration increased gradually. This study offers a theoretical foundation and empirical data for the dredging and treatment of sludge in cold regions.

The sintering process of Cu nanoparticle (Cu NP)/graphene nanoplatelet (GNP) composite solder paste was thoroughly investigated in this work through molecular dynamics simulations. The tensile properties of the sintered Cu NP/GNP composite solder paste were considered by using the uniaxial quasi-static tensile simulation method. The impact of sintering temperature, strain rate, and GNP addition on the tensile properties of Cu NP/GNP sintered structures was thoroughly investigated. The lattice structure, dislocation evolution, and atomic diffusion of the molecular dynamics results were analyzed using the common neighbor analysis (CNA), dislocation extraction algorithm (DXA), and mean square displacement (MSD) methods. The results of the post-processing analysis showed that the addition of GNP and the sintering temperature have an important influence on the mechanical properties of Cu NP/GNP sintered structures. In addition, the incorporation of GNP can significantly improve the mechanical properties of sintered Cu NP/GNP composite solder paste. More specifically, the tensile strength and fracture strain of the sintered composite solder paste will be increased by increasing the tensile strain rate. The strengthening mechanism of the sintered Cu NP/GNP composite solder paste can be attributed to the dislocation strengthening mechanism. Our study provides valuable insight for the development of high-performance composite solder paste with enhanced mechanical properties.

The Al-Zn-Mg-Cu alloy plate is a structural material widely used in aerospace, and its rolling process plays a crucial role in determining its performance. This study investigated the effects of different pass combinations of forward and spread rolling on the grain characteristics, strength, and fracture toughness of Al-Zn-Mg-Cu aluminum alloy plates under industrial conditions. The results show that initially using a small pass reduction followed by a larger one can improve the grain width and thickness on the Long Transverse–Short Transverse surface. Additionally, increasing the spread rolling pass enhances the grain width-to-thickness ratio on the TS surface. Performance tests indicate that grain characteristics have minimal influence on room-temperature tensile properties. However, a higher grain width-to-thickness ratio significantly improves the alloy’s fracture toughness.

Honeycomb structures made of carbon-fiber-reinforced plastic (CFRP) are increasingly used in the aerospace field due to their excellent energy absorption capability. Attention has been paid to CFRP structures in order to accurately simulate their progressive failure behavior and discuss their ply designability. In this study, the preparation process of a CFRP corrugated sheet (half of the honeycomb structure) and a CFRP honeycomb structure was illustrated. The developed finite element method was verified by a quasi-static test, which was then used to predict the low-velocity impact (LVI) behavior of the CFRP honeycomb, and ultimately, the influence of the ply angle and number on energy absorption was discussed. The results show that the developed finite element method (including the user-defined material subroutine VUMAT) can reproduce the progressive failure behavior of the CFRP corrugated sheet under quasi-static compression and also estimate the LVI behavior. The angle and number of plies of the honeycomb structure have an obvious influence on their energy absorption under LVI. Among them, energy absorption increases with the ply number, but the specific energy absorption is basically constant. The velocity drop ratios for the five different ply angles are 79.12%, 68.49%, 66.88%, 66.86%, and 60.02%, respectively. Therefore, the honeycomb structure with [0/90]s ply angle had the best energy absorption effect. The model proposed in this paper has the potential to significantly reduce experimental expenses, while the research findings can provide valuable technical support for design optimization in aerospace vehicle structures.

Designing efficient electrode materials is necessary for supercapacitors but remains highly challenging. Herein, cobalt sulfide with crystalline/amorphous heterophase (denoted as Co(Al)S) derived from an Al metal–organic framework was constructed by ion exchange/acid etching and subsequent sulfidation strategy. It was found that rational sulfidation by adjusting the sulfur source concentration to a suitable level was favorable to form a 3D nanosheet-interconnected network architecture with a large specific surface area, which promoted ion/electron transport and charge separation. Benefiting from the features of the unique network structure and heterophase accompanied by aluminum, nitrogen and carbon coordinated in amorphous phase, the optimal Co(Al)S(10) exhibited a high specific capacity (1791.8 C g−1 at 1 A g−1), an outstanding rate capability and an excellent cycling stability. Furthermore, the as-assembled Co(Al)S//AC device afforded an energy density of 72.3 Wh kg−1 at a power density of 750 W kg−1, verifying that the Co(Al)S was a promising material for energy storage devices. The developed scheme is expected to promote the application of MOF-derived electrode materials in electrochemical energy storage and conversion fields.

With the increasing global focus on energy efficiency and environmental sustainability, intelligent building materials such as thermochromic glazing have emerged as a hot topic of research. The intent of this paper is to explore the utilization of gel-type thermochromic glazing within the realm of architectural energy conservation calculations. It conducts an exhaustive examination of the material’s attributes, its capacity for energy savings, and the obstacles encountered in real-world applications. Through simulation studies and case analyses, this paper assesses the energy efficiency of gel-type thermochromic glazing across various climates and suggests strategies for optimization. The study revealed that the incorporation of gel-based thermochromic glazing leads to a marked reduction in energy usage within buildings, an improvement in indoor comfort levels, and significant environmental advantages.

The heterogeneity of natural rocks complicates the study of carbon sequestration within these materials and raises concerns about the reproducibility of experimental results. Consequently, identifying appropriate rock-like materials has become critical. This research examined the impact of various factors—humidity, binder content, curing period, and cold pressure—on the bond strength of magnesium oxychloride cement (MOC) through orthogonal testing. The tests utilized a molar ratio of MgO to MgCl2-6H2O to H2O of 7:1:18. Both X-ray diffraction (XRD) and scanning electron microscopy (SEM) were employed to analyze the hydration reactions of MOC and to explore the correlation between the microstructure of the SiO2-MgO-MgCl2 system and its bonding characteristics. The findings indicated that a 5% relative humidity enhances the 7-day bond strength of MOC. Moreover, increasing the curing humidity to 60% relative humidity supports the ongoing hydration of the strength-contributing phases. A binder content ranging from 15% to 25% proved optimal, yielding samples with superior strength and stiffness. While cold pressing initially enhances the bonding properties of MOC, solution loss during the process adversely affects its long-term bonding characteristics. From a mechanical standpoint, the silica-magnesium oxide-magnesium chloride system demonstrates exceptional early strength and resilience, positioning it as a promising rock-like material system.

The development of electric vehicles has facilitated intelligent transportation, which requires the swift and effective detection and tracking of moving vehicles. To satisfy this demand, this paper presents an enhanced DeepSORT algorithm. By selecting YOLO-SSFS as the front-end detector and incorporating a lightweight and high-precision feature training network called FasterNet, the proposed method effectively extracts vehicle appearance attributes. Besides this, the noise scale adaptive Kalman filter is implemented and the conventional cascade matching process is substituted with global join matching, thereby enhancing overall performance and tracking accuracy. Validation conducted on the VisDrone dataset demonstrates the superiority of this method compared to the original DeepSORT algorithm, exhibiting a 4.76% increase in tracking accuracy and a 3.10% improvement in tracking precision. The findings reveal the advantages of the algorithms in the domain of vehicle detection and tracking, allowing significant technological advancements in intelligent transportation systems.

To improve the ride comfort and driving stability of automobiles, an optimal sliding mode control (OSMC) strategy based on the enhanced African vultures optimization algorithm (EAVOA) is proposed. Firstly, the structure and operating principle of a semi-active suspension system (SASS) with a magnetorheological damper (MRD) is comprehensively introduced. Secondly, the OSMC is designed based on a quarter-vehicle suspension model with two degrees of freedom (DOF) to meet the Hurwitz stability theory. Simultaneously, the EAVOA is employed to optimize the control coefficients of the sliding mode surface and the control law parameters. Finally, the EAVOA-OSMC control strategy is utilized to construct the simulation model in MATLAB/Simulink (R2018b), providing a comprehensive analysis for passive suspension systems (PSSs) and suspensions with SMC control. The simulation results demonstrate that the EAVOA-OSMC control strategy outperforms SMC controllers, offering a better control performance in real application.

CrSBr is a recently discovered two-dimensional anti-ferromagnet. It has attracted much attention due to its superior properties for potential optoelectronic and spintronic applications. However, its complex refractive index with layer dependence has not been systematically studied yet. In this work, we studied the room-temperature complex refractive indices of thin CrSBr flakes of different thicknesses in the visible light range. Using micro-reflectance spectroscopy, we measured the optical contrast of thin CrSBr flakes with respect to different substrates. The complex refractive index was extracted by modeling the optical contrast with the Fresnel equations. We extracted the band gap values of CrSBr in the few-layer limit. We determined the band gaps for monolayer, bilayer, and trilayer CrSBr to be 1.88 eV, 1.81 eV, and 1.77 eV, respectively. As a comparison, the band gap for multilayer CrSBr is outside our measured range, that is, below 1.55 eV. Our results suggest that the band gap of CrSBr decreases as thickness increases.

The effect of moisture on the fracture resistance of asphalt concrete is a significant concern in pavement engineering. To investigate the effect of the water vapor concentration on the fracture properties of asphalt concrete, this study first designed a humidity conditioning program at the relative humidity (RH) levels of 2%, 50%, 80%, and 100% for the three types of asphalt concrete mixtures (AC-13C, AC-20C, and AC-25C).The finite element model was developed to simulate the water vapor diffusion and determine the duration of the conditioning period. The semi-circular bending (SCB) test was then performed at varying temperatures of 5 °C, 15 °C, and 25 °C to evaluate the fracture energy and tensile strength of the humidity-conditioned specimens. The test results showed that the increasing temperature and the RH levels resulted in a lower peak load but greater displacement of the mixtures. Both the fracture energy and tensile strength tended to diminish with the rising temperature. It was also found that moisture had a significant effect on the tensile strength and fracture energy of asphalt concrete. Specifically, as the RH level increased from 2% to 100% (i.e., the water vapor concentration rose from 0.35 g/m3 to 17.27 g/m3), the tensile strength of the three types of mixtures was reduced by 34.84% on average, which revealed that the water vapor led to the loss of adhesion and cohesion within the mixture. The genetic expression programming (GEP) model was developed to quantify the effect of water vapor concentrations and temperature on the fracture indices.

One of the development trends of inverters is toward higher performance. Periodic and non-periodic disturbances will result in excessive harmonic distortion in the output voltage and reduce the performance of single-phase inverters. The linear active disturbance rejection control (LADRC) method is used in inverter control because of its good anti-interference ability. When compared to a full-order LADRC, the reduced-order LADRC (RLADRC) algorithm efficiently reduces the system’s phase lag, simplifies the control structure by reducing the order and the control parameters, and exhibits comparable disturbance rejection capabilities. In this paper, a composite voltage control scheme based on the combination of RLADRC, and the synchronous reference frame proportional–integral​ (SRFPI) control is explored for single-phase off-grid inverters. The basic idea is that the non-periodic disturbances of system can be efficiently eliminated by RLADRC, while the periodic disturbances can be restrained by SRFPI. The design of RLADRC is given in detail. Moreover, the Thevenin equivalent model of the inverter control system is derived for stability and robustness analysis. Finally, compared with SRFPI-LADRC based method, the comparative experimental results demonstrate that the inverter realizes fast transient response, good load regulation and lower total harmonic distortion (THD) with the proposed SRFPI-RLADRC based composite control strategy.

With the continuous growth of the population, with the expansion of the urban area. Human activities and behavior are depleting limited resources, a large number of these private vehicles occupy our roads, it also causes various environmental problems. With the increasing awareness of environmental protection, the way of green travel has gradually become the way of travel advocated by people who living in the metropolis. Convenience and environmental protection are a major advantage of public transport, but unfortunately, the functions, services and environment of many bus stops are not convenient and friendly; Therefore, the smart bus stop was born, it can not only provide real-time bus information, but also provide a safe and comfortable waiting space. However, with the upgrading of service functions, the problem of energy consumption has become increasingly prominent. Powering through solar panels can naturally provide a sustainable energy source for smart bus stops, but many studies have found that solar panels themselves are not environmentally friendly, regardless of their materials and recyclability. The author’s investigation found that relevant studies have confirmed that spraying an aqueous solution containing carbon nanotubes or graphene on mulberry leaves for feeding silkworms doubles the toughness of the silk spit, and the conductivity of carbonized silk is 10 times higher. In addition, the robotic fiber spinning technology will allow silkworms to independently complete the production of silk objects without killing the silkworms. Through the research on graphene silk materials and robotic fiber spinning technology, this paper proposes a biologically autonomous solution to the production of graphene silk thin-film solar panels, so as to seek to solve the pollution problem of solar panels in the smart stop.

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.

Hydrogen fuel cell tractors are emerging as a new power source for tractors. Currently, there is no mature energy management control method available. Existing methods mostly rely on engineers’ experience to determine the output power of the fuel cell and the power battery, resulting in relatively low energy utilization efficiency of the energy system. To address the aforementioned problems, a power optimization method for the energy system of hydrogen fuel cell wheel-driven electric tractor was proposed. A dynamic model of tractor ploughing conditions was established based on the system dynamics theory. From this, based on the equivalent hydrogen consumption theory, the charging and discharging of the power battery were equivalent to the fuel consumption of the hydrogen fuel cell, forming an equivalent hydrogen consumption model for the tractor. Using the state of charge (SOC) of the power battery as a constraint, and with the minimum equivalent hydrogen consumption as the objective function, an instantaneously optimized power allocation method based on load demand in the energy system is proposed by using a traversal algorithm. The optimization method was simulated and tested based on the MATLAB simulation platform, and the results showed under ploughing conditions, compared with the rule-based control strategy, the proposed energy system power optimization method optimized the power output of hydrogen fuel cells and power batteries, allowing the energy system to work in a high-efficiency range, reducing the equivalent hydrogen consumption of the tractor by 7.79%, and solving the energy system power distribution problem.

In bridge maintenance, assessing structural performance requires adherence to rules outlined in safety and regulatory standards which can be effectively and formally represented in both human and machine-readable formats using ontologies. However, ontology-based semantic inference alone falls short when faced with the complicated mathematical operations required for structural analysis. The increasing digitization of bridge engineering has opened doors to data-driven prediction methods. Machine learning (ML)-based models, in particular, have the capacity to learn from historical data and forecast future structural performance with remarkable accuracy. This paper introduces an innovative approach that integrates ML models with an ontological knowledge base for evaluating bridge corrosion. Web Ontology Language and Semantic Web Rule Language are combined to develop the knowledge base. Random forest algorithm is used to train the ML model with a good agreement (coefficient of determination of 0.989 and root mean square error of 1.200). A Python-based module is designed to seamlessly integrate ML predictions with ontology-based semantic inference. The proposed approach not only infers the corrosion ratings based on the rules defined in the Network Rail standard, but also infers the structural safety performance based on predicted structural response under the action of corrosion. To demonstrate the effectiveness of the developed method in enabling accurate and rational evaluations, a real bridge in the UK is showcased as a practical application.

The traditional on-board charging system can only realize the one-way transmission of electric energy from the grid to the power battery. To address this issue, this paper proposes a bidirectional on-board charging system based on a three-phase wye-wye connected CLLLC resonant converter. It adopts a two-stage structure, the front-stage is a bidirectional totem pole converter and the rear-stage is a three-phase wye-wye connected CLLLC resonant converter. Based on the grid characteristics, the working principle of the front-stage converter is analyzed, the equivalent circuit model of the rear-stage converter is derived using fundamental wave analysis, and the voltage gain, impedance characteristics and zero voltage turn-on conditions are analyzed. The parameters of input inductor, DC bus capacitance and resonant network are calculated according to the design specifications of the bidirectional on-board charging system. The control strategy of bidirectional vehicle charging system is studied. The input current and input voltage are kept in the same phase by establishing the small signal circuit model of the front-stage bidirectional totem pole converter. The rear-stage three-phase wye-wye connected CLLLC resonant converter adopts pulse frequency modulation to realize constant voltage and constant current output of the charging system. Simulation software was used to simulate the bidirectional on-board charging system, and a 3.3 kW prototype was trial-produced according to the main circuit parameters. Simulation and experimental results verify the correctness of the design of the bidirectional on-board charging system.

High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.

The YOLOv5 network architecture prioritizes speed and efficiency, but this may limit its ability to capture intricate details of complex objects. To solve the problems of insufficient feature extraction ability and incomplete feature fusion in the YOLOv5 single-stage detection network, we propose a YOLOv5 algorithm based on an improved bidirectional feature pyramid network (BiFPN). The FPN is modified into BiFPN with recursive feature fusion, bidirectional connections, and alignment of multi-scale features. It can utilize different levels of feature information to improve the ability of feature expression, thereby improving model detection accuracy and efficiency. This method incorporates CBAM attention mechanism, which can adaptively adjust the feature map while considering both channel and spatial features, thus more comprehensively expressing information in the feature map. For small targets, due to their small size, they often have high similarity with background and unclear features that make it difficult for traditional loss functions to optimize accurately. Using SIoU as a loss function can improve the recognition rate for small object. The experimental results show that our strategy significantly improves the performance of YOLOv5 on the NEU-DET dataset, reaching 77.5% mAP at 92 Frame Per Second(FPS), which is 3.1% higher than unimproved YOLOv5 network and 19.6% higher than SSD algorithm. Moreover, F1 score has also been improved while showing strong generalization ability on GC-DET dataset as well. Its detection speed is also superior to other algorithms indicating that this improved algorithm can quickly and efficiently detect surface defects in steel materials while significantly improving detection performance.

In Class-Incremental Learning (CIL), the memoryless replay CIL algorithm trains the network model by using the closed training set of the current task, while using knowledge distillation or finetuning constraints to keep the old knowledge. Due to the closed set recognition method used in the training process, the training set of each task are independent of each other, and the knowledge learned by the network model cannot effectively deal with the classes that do not appear in the corresponding task training set, that is, the class samples of other tasks cannot be effectively rejected during testing, resulting in the misrecognition of the same test sample in multiple tasks. This is the main factor that the performance of the memoryless replay CIL is limited. In this paper, from the perspective of open set recognition, a CIL algorithm ADCIL based on anomaly detection mechanism is proposed to enhance the discrimination ability of the network model between tasks, so that each task can learn the knowledge that is effective in the open space only through its own training set, and the robustness of the algorithm is greatly improved. It effectively prevents the catastrophic forgetting phenomenon in CIL.

This paper focuses on the power grid oscillation of grid-side converter (GSC) in doubly-fed induction generator (DFIG) caused by grid sub-synchronous oscillation (SSO), and designs a measure to improve the output power quality of GSC. Firstly, the influence mechanism of multipath disturbance of GSC under SSO is sorted out, and the disturbance factors and action modes are clarified. Secondly, the influence of SSO on the output estimation of phase-locked loop (PLL) in the control strategy and power calculation process is analyzed. Furthermore, the mathematical model of GSC output power considering the influence of PLL is established. At the same time, the key factors of SSO for GSC power oscillation suppression strategy are determined by analyzing the oscillation suppression effect of quasi-resonant controller when SSO amplitude, frequency and phase change. Based on the above theoretical analysis and research, the resonant controller is used to eliminate the estimation error of the PLL; at the same time, an adaptive algorithm is designed according to the mechanism analysis of SSO characteristic changes to improve the fixed resonant frequency of the quasi-resonant controller, and a DFIG-GSC sub-synchronous power oscillation suppression strategy based on the adaptive quasi-resonant controller is proposed, thus eliminating the influence of SSO on the multipath disturbance of GSC and improving the power quality of its output. Finally, the effectiveness of the proposed suppression strategy is verified by simulation and experimental results.

This paper presents a new method of extracting electric energy in the process of realizing thrust vector by magnetron gas plasma. Based on the model scramjet, the combustion plasma scheme is adopted, during the experiment, Cs2CO3 was put into a high-temperature environment to excite its own properties, so as to obtain the product Under the action of external magnetic field, it is verified that electric energy can be extracted in the process of plasma jet deflection, and the results are analyzed from the mechanism. Finally, the specific numerical results of magnetic field intensity and outgoing gas temperature are obtained, the average extraction voltage is 600 mV, indicating that ionized seeds enhance the ionization degree of gas. In the process of gas plasma jet of magnetic control scramjet, the scheme of extracting energy is feasible.

Petroleum is widely used as one of the important energy sources. Engineers need to continuously adjust strategies to maximize oil recovery during development. Then strategy adjustment requires a lot of cost, so it is necessary to select the optimal strategy through production forecasting. In this paper, a self-defined Double-channel Heterogeneous Dynamical Graph network (DHDG) is proposed deal with this problem. It can receive different development strategies predict future production indices base on that. Particularly, the concept of graph network is used for the first time to simulate the well pattern and predict production of all wells in the block at same time. At the same time, in order to fully fit the seepage process between wells, we complicate the edge features of the graph, so that it has enough capacity to express the state information of the formation between wells. Finally, we amplify the features of network output through engineering calculations, which further ensured the integrity of the information transmitted in the recursive process. The example results in two benchmark reservoirs show that it is accurate enough to predict oil production rate in future 600 days and more accurate than LSTM networks.

With distributed photovoltaic (DPV) rapidly developing in recent years, the mismatch between residential load and DPV output leads to serious voltage quality problems. A double layer nested model of distributed energy storage (DES) planning is proposed in this paper to solve this problem. The inner optimization model is established for optimizing DES and DPV operation strategy considering the optimization of voltage quality evaluation results. The voltage quality evaluation indicators include three-phase voltage unbalance, voltage deviation and voltage fluctuation. The outer optimization model is established for DES siting and sizing considering the cost performance ratio of voltage quality improvement. The reactive power output of DPV and DES are the main factors to be considered. The modified particle swarm optimization algorithm is adopted to solve this model. Simulation results of the modified IEEE 33-bus distribution network demonstrate that the voltage safety margin is enhanced and the subsequent voltage management cost is reduced.

Rock undergoing deformation and failure exhibit energy conversion to the testing equipment and the environment This study performed conventional uniaxial compression tests on granite specimens with different Length–Diameter ratios to study the relationship between the Length–Diameter ratio and the dissipation energy of rock during the deformation process. The test results show that the failure form of the specimen varies from vertical split to shear failure with increases in the Length–Diameter ratio. The compressive strength decreases with the increase of the Length–Diameter ratio. Moreover, a theoretical model was proposed for the same relation. The theoretical model was found to be in agreement with the experimental data. A new monotonically decreasing function was developed to relate the rock dissipation energy to the Length–Diameter ratio. The results demonstrate that the Length–Diameter ratio has no discernible influence on the deformation features before the peak strength; however, the influence is apparent after the peak strength.

This paper considers a concentric staged triple swirler lean direct injection (LDI) combustor and conducts numerical analysis to investigate the impact of inner and outer swirl flow of the pilot stage on the spray combustion characteristics and NOx emission. To simulate the flow field, spray dispersion, and the combustion process within the combustion chamber Large eddy simulation (LES) is employed. Three geometric dome parameters are studied including the pilot inner swirl angle, pilot outer swirl angle, and pilot flow rotating direction. The results indicate that: (1) The inner swirling flow is the main factor affecting the spray atomization process inside the swirl cup; (2) In downstream of the dump plane, the outer swirling flow determines the evolution of the precessing vortex core (PVC) structures which affect turbulence intensities in the shear layer and efficient spray dispersion, and (3) NOx emissions are related to the spray atomization efficiency and the average flame length in the combustion chamber. It is also shown that NOx emission in the triple swirler LDI combustors is significantly reduced by increasing the swirling intensity and flow rate of the pilot outer swirler.

In this paper, opportunities for carbon capture and storage (CCS) in India’s west coast is investigated. Specifically, CO2 emissions from the power and industry sectors in the states of Maharashtra and Gujarat and subsurface sinks to store them in oil fields, gas fields or saline aquifers in the closest sedimentary basins are quantified on a plant-by-plant and field-by-field basis.Results indicate that CO2 emission from these two states is 221 Mtpa and 115 Mtpa from the power and industry sectors, respectively. The mid CO2 storage capacity in nearby sedimentary basins is 141 Gt, enough to store 420 years of CO2 emission. Of this, 137 Gt (97.4%) come from saline aquifers, 2.23 Gt (1.6%) from gas fields, and only 1.4 Gt (1.0%) from oil fields. Furthermore, CO2 injection has the potential of recovering 2,620 MMbbl from oil fields and 118 MMbbl from gas fields through enhanced oil recovery (EOR) and enhanced gas recovery (EGR), respectively. The most significant CO2-EOR potential (1,453 MMbbl) is in the offshore Mumbai High oil field. Currently, most gas fields are under primary production and, therefore, will only be ready for CO2 storage when they are near depletion.Results of a CO2 source–sink mapping exercise suggest using four CCS hubs to connect regional CO2 sources and sinks. They are Mumbai, Nagpur and Chandrapur in the state of Maharashtra and Hazira in the state of Gujarat. These hubs can be used to temporarily store CO2 before it is transported to nearby oil fields or saline aquifers for permanent storage. Implementation of CCS projects based on these hubs can remove up to 168 Mtpa CO2 or 50% of stationary emission of these two states with a CO2 source–sink distance of 150 km or less, thus making a significant contribution to India’s decarbonization.

Abstract The ground wire is significantly interfered by the current above 1 μA in the industrial field during the traditional polarisation and depolarisation current (PDC) measurement. This paper explains a method to test PDC from high‐voltage wire using a principle of equipotential picoammeter, which is completely isolated from the ground wire and connected in series with the high voltage wire. In this method, a device is designed with a multi‐range automatic switching function. The device can automatically switch the test range current from 1 pA to 10 mA with a resolution of 1 pA. To demonstrate the device and effectiveness of this new method, the traditional and the proposed new device are used to test the PDC of cable samples in the laboratory as well as in an industrial environment. It is worth noting that there is almost no interference on the Earth wire in the laboratory environment but massive interference in the industrial environment using a traditional method of PDC measurement from ground or Earth wire. The new method of high‐voltage testing can efficiently eliminate the interference and accurately measure PDC in a strong industrial interference environment. Furthermore, the wiring of this new method and device is relatively simple, which is convenient for industrial applications.

This work was dedicated to the simulation of fiber composite structures consisting of carbon fibers and mineral impregnation. The aim of this study was to generate a micromodel that predicts the properties of a mineral-impregnated carbon fiber reinforcement. The numerical characterization was based on the discrete microscopic modeling of the individual phases using a representative volume element. In addition, the stochastic nature of the fiber strength, the anisotropic damage mechanisms of the brittle matrix, and the non-linear bonding behavior between the filaments and the matrix were considered in the material models. The material models were adjusted based on the literature sources and our own experimental investigations. This was followed by the validation of the representative volume element, quantifying the evolution of stiffness and damage under longitudinal tensile loading. The numerical results of material stiffness, as well as the tensile strength of the representative volume element, were compared with the results of the experimental investigations. To verify the robustness of the numerical model, significant model parameters were subjected to a sensitivity analysis.

The Qinghai–Tibet Plateau is the main permafrost area in China. Concrete structures constructed on permafrost are affected by the early negative-temperature environment. In particular, the negative-temperature environment seriously affects the strength growth process and the frost resistance of concrete (FRC). Therefore, this study considered the influence of the gas content, water–binder ratio (w/b), age, and other factors on the strength variation law and FRC under −3 °C curing conditions. Nuclear magnetic resonance (NMR) was used to analyze the pore structure of concrete before and after freeze–thaw cycles (FTCs). The results showed that the compressive strength of the concrete (CSC) under −3 °C curing was only 57.8–86.4% of that cured under standard conditions. The CSC under −3 °C curing showed an obvious age-lag phenomenon. The FRC under −3 °C curing was much lower than that under standard curing. The porosity of the concrete under −3 °C curing was greater, with a higher percentage of harmful and multi-harmful pores than that under standard curing. The concrete properties deteriorated primarily because curing at −3 °C hindered the hydration reaction compared with standard methods. This hindrance resulted in diminished hydration development, weakening the concrete’s structural integrity. Under both curing conditions, when the gas content was between 3.2% and 3.8%, the frost resistance was the best. This is because a gas content within this range effectively enhances the internal pore structure, therefore relieving the swelling pressure caused by FTCs. Based on the freeze–thaw damage (FTD) model proposed by previous authors, a new model for the CSC under −3 °C curing reaching that of the concrete under standard curing for 28 d was established in this study. This advanced model was capable of accurately assessing the FTD of concrete structures in permafrost regions. Finally, the life expectancy of concrete in Northwest China was predicted. The life of the concrete reached 46.9 years under standard curing, while the longest life of the concrete under −3 °C curing was only 12.9 years. Therefore, attention should be paid to constructing and curing concrete structures in cold environments.

Abstract Silicone grease (SG) is used for lubrication during cable accessory installation and can penetrate into the silicone rubber (SiR), leading to properties deterioration of the SiR. In this study, the effects of SG on the breakdown characteristics of the cross‐linked polyethylene (XLPE)–SiR interface are investigated. First, the variation of the XLPE–SiR interface breakdown voltage with SG coating time is experimentally explored. The interface breakdown voltage significantly increases after SG is applied, remains stable for coating times of up to approximately 144 h and then rapidly decreases; the minimum interface breakdown voltage is lower than that without the SG coating. Next, the effects of SG on the chemical composition and surface topography of the SiR are examined by infrared spectroscopy and optical profilometry, respectively. The SG penetration does not change the functional groups of the SiR but significantly increases its surface roughness. Finally, the interface electric‐field distribution after coating with SG is analysed by finite‐element simulation, revealing that the residual SG at the interface distorts the interface electric field after a long coating time. The increase in the SiR surface roughness caused by SG diffusion and the interface electric‐field distortion caused by the residual SG together lead to the decrease of the interface breakdown voltage.

In today’s increasingly serious world energy crisis, Renewable energy such as wind energy has gradually penetrated into life. Aiming at the uncertainty of wind power and the need of a mass of sample data in nonparametric kernel density estimation, a wind power interval prediction method based on hybrid semi-cloud model and nonparametric kernel density estimation is proposed. Firstly, aiming at the asymmetrical distribution of prediction error, a qualitative description method of error concept using hybrid semi-cloud model is proposed. And the conceptual data produced by the hybrid semi-cloud model are filtered through the “3En rule”. Secondly, according to the fitting characteristics of nonparametric fitting, the conceptual cloud droplets are combined with nonparametric kernel density estimation to get the shortest prediction interval under different confidence levels. Finally, in order to verify the effectiveness of the method, the simulation of a wind farm data in the Oklahoma State is carried out, and the relative entropy is introduced to evaluate the fitting effect of the nonparametric fitting method. The comprehensive performance of the prediction interval under different methods is compared by three evaluation indexes: interval coverage probability, prediction interval width and comprehensive evaluation index F value. The experimental results show that compared with the prediction results of the other two methods, the method used in this paper has the best fitting effect, and the relative entropy is reduced by 1.1542. The comprehensive evaluation index F value of the prediction interval under each confidence level has been significantly improved, among which the increase at 80% confidence level is the most, and the F value increases by 0.0063.

Abstract The urgent need for power transmission is the reason for leading research on the safer operation of high‐voltage cables. These high‐voltage cables are emerging as an efficient technology for underground power transmission. However, the electrochemical corrosion of aluminium (Al) metal sheaths in these cables is a common and challenging degradation process. Herein, the corrosion mechanisms and electrochemical analysis are investigated by using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), electrochemical impedance spectroscopy (EIS) and laboratory‐designed electrochemical characterisation techniques. In the corrosion experiments, the authors evaluated the corrosion rate by measuring the release rate of hydrogen gas and explored the different roles of sodium polyacrylate (NaPA) during the corrosion process. It is found that the complexation reactions between NaPA and Al inhibited corrosion while increased the resistance of the buffer layer. The proposed mechanisms of corrosion in this study can improve the lifespan and sustainability of high‐voltage power transmission.

Motor driving technology and battery charging technology are the two core technologies of electric vehicles (EVs). They have a profound impact on the performance and mileage of EVs. Based on the topology of a traditional EV motor controller, this study employs the time-sharing multiplexing insulated gate bipolar transistor (IGBT) power module form to expand the functions of the motor controller. This paper proposes an integrated driving–charging controller structure that enables the controller to realize the motor driving and on-board charging simultaneously. A decoupling analysis was performed at the topology level, mathematical models of two bidirectional converters in the integrated topology were established, and a reasonable control strategy for the double-closed-loop control system was designed. The simulation system was built in Simulink, and the simulation results verified the effectiveness of the system topology and control strategy. We made an integrated controller experimental platform based on TMS320F28335. A resistive load simulated the power battery to verify the charging mode, and a 5 kW permanent magnet synchronous motor (PMSM) was used to verify the driving mode. The experimental results demonstrated the driving–charging integrated controller’s feasibility for electric vehicles.