Rectification Strategy Research Articles

Bifunctional Ion Rectification Layer Separators Toward Superior Reversible Dendrite‐Free Zinc Metal Anodes

AbstractThe slow transport dynamics and poor dendritic growth significantly hinder the performance of zinc metal batteries. Here, a unique difunctional ion rectification strategy is developed using vacuum evaporation technology to design a coated separator for efficient ion transport. The highly conductive Cu‐coated separator acts as an ion redistributor, ensuring homogenized electric field distribution. The excellent znophilicity of the copper coating acts as an ion accelerator, promoting ion transport and facilitating face‐to‐face zinc deposition on the separator. Consequently, this synergy enables stable battery operation at high current densities (cumulative capacity up to 10 800 mAh cm−2 at 8 mA cm−2) and high depth of discharge (over 200 h at 94% DOD). The Cu‐coated separator exhibits a reversible plating/stripping life of over 2400 h with an average coulombic efficiency of 99.88%. Notably, the Cu‐coated separator significantly enhances the rate performance and cycle capacity of Zn||V6O13 and Zn||MnO2 full cells. This functional separator with a difunctional ion rectification strategy presents a promising solution to the challenge of zinc metal anodes.

Fluid-Dynamics-Rectified Chemical Vapor Deposition (CVD) Preparing Graphene-Skinned Glass Fiber Fabric and Its Application in Natural Energy Harvest.

Graphene chemical vapor deposition (CVD) growth directly on target using substrates presents a significant route toward graphene applications. However, the substrates are usually catalytic-inert and special-shaped; thus, large-scale, high-uniformity, and high-quality graphene growth is challenging. Herein, graphene-skinned glass fiber fabric (GGFF) was developed through graphene CVD growth on glass fiber fabric, a Widely used engineering material. A fluid dynamics rectification strategy was first proposed to synergistically regulate the distribution of carbon species in 3D space and their collisions with hierarchical-structured substrates, through which highly uniform deposition of high-quality graphene on fibers in large-scale 3D-woven fabric was realized. This strategy is universal and applicable to CVD systems using various carbon precursors. GGFF exhibits high electrical conductivity and photothermal conversion capability, based on which a natural energy harvester was first developed. It can harvest both solar and raindrop energy through solar heating and droplet-based electricity generating, presenting promising potentials to alleviate energy burdens.

Regulating Li+ transport behavior by cross-scale synergistic rectification strategy for dendrite-free and high area capacity polymeric all-solid-state lithium batteries

The inability to effectively inhibit the lithium (Li) dendrite growth is identified as the real culprit hindering the practical application of polyethylene oxide (PEO)-based electrolytes. Herein, a novel PEO composite electrolyte with ion rectifier is developed based on the cross-scale synergistic rectification strategy. At the micro-scale, the array structure of the ion rectifier suppresses the growth of PEO crystals and their distribution in the non-ionic conduction direction through space confinement, alleviating ion-migration crosstalk and enabling polymer chain rectification. Furthermore, the matrix contains abundant copper ions and oxygen-containing groups that inhibit anion conduction and accelerate Li+ migration at the nanoscale, respectively, to achieve ion flow rectification. Implementing this strategy results in a uniform, fast, and stable Li+ migration/diffusion behavior from the electrolyte to anode interface. The critical current density of the PEO electrolyte is increased to 2.5 mA cm−2, indicating a significant improvement in dendrite growth inhibition. Impressively, the composite electrolytes exhibit long-term stability (>4000 h at 0.2 mA cm−2) and ultra-high current-density tolerance (>200 h at 1 mA cm−2). Moreover, the composite electrolytes enable stable cycling of high-area-capacity (3.11 mAh cm−2, 20 mg cm−2) LiFePO4/Li pouch cells, highlighting the importance of this strategy for the practical application of PEO electrolytes.

Masked Image Modeling Auxiliary Pseudo-Label Propagation with a Clustering Central Rectification Strategy for Cross-Scene Classification

Cross-scene classification focuses on setting up an effective domain adaptation (DA) way to transfer the learnable knowledge from source to target domain, which can be reasonably achieved through the pseudo-label propagation procedure. However, it is hard to bridge the objective existing severe domain discrepancy between source and target domains, and thus, there are several unreliable pseudo-labels generated in target domain and involved into pseudo-label propagation procedure, which would lead to unreliable error accumulation to deteriorate the performance of cross-scene classification. Therefore, in this paper, a novel Masked Image Modeling Auxiliary Pseudo-Label Propagation called MIM-AP2 with clustering central rectification strategy is proposed to improve the quality of pseudo-label propagation for cross-scene classification. First, in order to gracefully bridge the domain discrepancy and improve DA representation ability in-domain, a supervised class-token contrastive learning is designed to find the more consistent contextual clues to achieve knowledge transfer learning from source to target domain. At the same time, it is also incorporated with a self-supervised MIM mechanism according to a low random masking ratio to capture domain-specific information for improving the discriminability in-domain, which can lay a solid foundation for high-quality pseudo-label generation. Second, aiming to alleviate the impact of unreliable error accumulation, a clustering central rectification strategy is designed to adaptively update robustness clustering central representations to assist in rectifying unreliable pseudo-labels and learning a superior target domain specific classifier for cross-scene classification. Finally, extensive experiments are conducted on six cross-scene classification benchmarks, and the results are superior to other DA methods. The average accuracy reached 95.79%, which represents a 21.87% improvement over the baseline. This demonstrates that the proposed MIM-AP2 can provide significantly improved performance.

Characteristic analysis and mitigation strategy for SSCI in series-compensated DFIG-based wind farm controlled by a virtual synchronous generator

The proliferation of virtual synchronous generator (VSG) technology within series-compensated double-fed induction generator (DFIG)-based wind farms is substantially hampered by the attendant risk of subsynchronous control interaction (SSCI), resulting in a significant research deficiency on systematic control interaction analysis and the development of mitigation strategies. The paper proposes an advanced active disturbance rejection control (ADRC) framework, incorporating real-time compensation mechanisms to mitigate the inadequate suppression efficacy attributable to the VSG's diminished output impedance. Initially, the mathematical expression for the VSG output impedance is rigorously deduced, and the positive damping attributes of the VSG in relation to SSCI are elucidated from the perspective of underlying mechanistic principles. Subsequently, the suppressive mechanism of SSCI by the ADRC is revealed in the context of VSG involvement, and the consequent augmentation of SSCI attributed to PI control is systematically derived. In immediate succession, the quanta of oscillation and inductive cross-coupling are encapsulated as the system's aggregate disturbance, thereby streamlining the ADRC to its primary order configuration, permitting the utilization of an extended state observer (ESO) for the dynamic estimation of said disturbance. Furthermore, a fractional-order filter function is instituted to engineer an augmented ESO, which refines the output voltage of the grid-side converter. Concurrently, a meticulous discourse on the rectification strategy for the proposed ESO parameters and its stability ensues. Ultimately, the efficacy of the mechanism analysis, alongside the robustness of the proffered control strategy for SSCI mitigation under diverse perturbation conditions, is corroborated via impedance evaluation and time-domain simulation.

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

Investigating public attitudes on social media is crucial for opinion mining systems. Stance detection aims to predict the attitude towards a specific target expressed in a text. However, effective neural stance detectors require substantial training data, which are challenging to curate due to the dynamic nature of social media. Moreover, deep neural networks (DNNs) lack explainability, rendering them unsuitable for scenarios requiring explanations. We propose a distantly supervised explainable stance detection framework (DS-ESD), comprising an instruction-based chain-of-thought (CoT) method, a generative network, and a transformer-based stance predictor. The CoT method employs prompt templates to extract stance detection explanations from a very large language model (VLLM). The generative network learns the input-explanation mapping, and a transformer-based stance classifier is trained with VLLM-annotated stance labels, implementing distant supervision. We propose a label rectification strategy to mitigate the impact of erroneous labels. Experiments on three benchmark datasets showed that our model outperformed the compared methods, validating its efficacy in stance detection tasks. This research contributes to the advancement of explainable stance detection frameworks, leveraging distant supervision and label rectification strategies to enhance performance and interpretability.

MEMENTO: A novel approach for class incremental learning of encrypted traffic

In the ever-changing digital environment, ensuring the ongoing effectiveness of traffic analysis and security measures is crucial. Therefore, Class Incremental Learning (CIL) in encrypted Traffic Classification (TC) is essential for adapting to evolving network behaviors and the rapid development of new applications. However, the application of CIL techniques in the TC domain is not straightforward, usually leading to unsatisfactory performance figures. Specifically, the improvement goal is to reduce forgetting on old apps and increase the capacity in learning new ones, in order to improve overall classification performance—reducing the drop from a model “trained-from-scratch”.The contribution of this work is the design of a novel fine-tuning approach called MEMENTO, which is obtained through the careful design of different building blocks: memory management, model training, and rectification strategies. In detail, we propose the application of traffic biflows augmentation strategies to better capitalize on old apps biflows, we introduce improvements in the distillation stage, and we design a general rectification strategy that includes several existing proposals.To assess our proposal, we leverage two publicly-available encrypted network traffic datasets, i.e., MIRAGE19 and CESNET-TLS22. As a result, on both datasets MEMENTO achieves a significant improvement in classifying new apps (w.r.t. the best-performing alternative, i.e., BiC) while maintaining stable performance on old ones. Equally important, MEMENTO achieves satisfactory overall TC performance, filling the gap toward a trained-from-scratch model and offering a considerable gain in terms of time (up to 10× speed-up) to obtain up-to-date and running classifiers. The experimental evaluation relies on a comprehensive performance evaluation workbench for CIL proposals, which is based on a wider set of metrics (as opposed to the existing literature in TC).

Unraveling Batch Normalization for Realistic Test-Time Adaptation

While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to inaccurate target estimation. As previous attempts merely introduce source statistics to mitigate this issue, the fundamental problem of inaccurate target estimation still persists, leaving the intrinsic test-time domain shifts unresolved. This paper delves into the problem of mini-batch degradation. By unraveling batch normalization, we discover that the inexact target statistics largely stem from the substantially reduced class diversity in batch. Drawing upon this insight, we introduce a straightforward tool, Test-time Exponential Moving Average (TEMA), to bridge the class diversity gap between training and testing batches. Importantly, our TEMA adaptively extends the scope of typical methods beyond the current batch to incorporate a diverse set of class information, which in turn boosts an accurate target estimation. Built upon this foundation, we further design a novel layer-wise rectification strategy to consistently promote test-time performance. Our proposed method enjoys a unique advantage as it requires neither training nor tuning parameters, offering a truly hassle-free solution. It significantly enhances model robustness against shifted domains and maintains resilience in diverse real-world scenarios with various batch sizes, achieving state-of-the-art performance on several major benchmarks. Code is available at https://github.com/kiwi12138/RealisticTTA.

Synchronous Rectification Strategy of CLLC Resonant Converter Based on Accurate Time-Domain Model

Synchronous Rectification Strategy of CLLC Resonant Converter Based on Accurate Time-Domain Model

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation.

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which requires a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss in the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.

Using text mining and multilevel association rules to process and analyze incident reports in China

Incident investigation reports provide information on defects related to the system safety and indications for improvements. Currently, the analysis of these reports relies heavily on expert’ experience. The foreseeable work-load and lack of understanding about the importance of near misses have created a situation where severe accidents are rigorously investigated, and minor incidents are often omitted. Consequently, incident reports have not been fully analyzed to provide sufficient solutions.The aim of this research is to propose a framework that uses text mining and multilevel association rules to efficiently structure Chinese incident reports and identify important incident patterns, providing an analysis of trends, rectification strategies, and guidance for safety management.A case study of a construction company in China was conducted using two years of incident data dated 2018–2019, including accidents and near misses. To identify incident elements, a pattern extraction workflow involving TextRank, and domain pertinence was devised based on the linguistic and writing styles of Chinese reports. A concept hierarchy was applied to determine the taxonomic relationships within the risk factors. Multilevel association rule mining was adopted and proven to deliver more comprehensive pattern indications. Comparative and cross-analysis of patterns in different time periods revealed the severity and temporal features of incidents as well as the effectiveness of preventive and precautionary measures. The results also highlight the importance of learning from near miss events. Decision makers can formulate countermeasures and management policies based on these results to improve safety performance.

Prospect Forecast and Rectification Strategy of American Takeout Industry

The global pandemic has a serious impact on economic and social development of all countries. The production and demand of many industries have greatly shrunk. However, the takeout industry in the United States is an exception. Under the influence of COVID-19, the supply and demand of American takeout industry have increased significantly compared with that before the pandemic. The market has become increasingly hot. Once the current market environment changes, it is uncertain whether the American takeout industry can continue to be popular. Due to this reality, the research explores the market trend of American takeout industry in recent years, and forecasts the future takeout market. The study also analyzes whether the American takeout market should follow the Chinese market’s approach. The research reveals that there are several disadvantages in the American takeout market in terms of labor cost and the price of buyers. To address these issues, the study develops new sale method through several ways, for example, reducing the buyer's cost of delivery fee, making the delivery fee paid by the seller as well as by the platform. Moreover, the platform can send vouchers to the buyers together with seller.

A Line Cycle Synchronous Rectification Strategy Based on Time-Domain Analysis for Single- Stage AC–DC LLC Converters

Synchronous rectification that is being widely used in high power and current DC-DC <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> resonant converters to reduce conduction losses can be challenging in single-stage AC-DC <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converters with high output voltage levels (i.e., >200 V) where synchronous rectifier (SR) driving ICs cannot be used. In this paper, a simple AC line cycle synchronous rectification strategy with direct control by a cost-effective microcontroller unit (MCU) is proposed for single-stage AC-DC <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converters with high switching frequencies using wide bandgap devices (i.e., GaN or SiC). The SR gate pulse is generated based on the time-domain calculated conduction time, which is then switched <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> over the AC line cycle to avoid reverse power flow in light load conditions. The proposed strategy reduces the complexity of implementation over any adaptive online calculation or model-based methods that require powerful and expensive MCUs. First, the operation is briefly described followed by the time-domain analysis for AC operation. Next, the calculation and methodology behind the proposed AC line cycle SR driving strategy are discussed in detail. A scaled-down wide bandgap-based AC-DC <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converter prototype with a 250--400 V output voltage range is used with digital control implementation to validate the performance of the proposed synchronous rectification strategy. It is found that maximum efficiency of 98.1% can be achieved which is improved by around 0.5% over the conventional fixed conduction time method. Moreover, it is shown that the proposed method obtains the same efficiency levels as more complex adaptive SR driving approaches.

Self-Supervised Arbitrary-Scale Implicit Point Clouds Upsampling.

Point clouds upsampling (PCU), which aims to generate dense and uniform point clouds from the captured sparse input of 3D sensor such as LiDAR, is a practical yet challenging task. It has potential applications in many real-world scenarios, such as autonomous driving, robotics, AR/VR, etc. Deep neural network based methods achieve remarkable success in PCU. However, most existing deep PCU methods either take the end-to-end supervised training, where large amounts of pairs of sparse input and dense ground-truth are required to serve as the supervision; or treat up-scaling of different factors as independent tasks, where multiple networks are required for different scaling factors, leading to significantly increased model complexity and training time. In this article, we propose a novel method that achieves self-supervised and magnification-flexible PCU simultaneously. No longer explicitly learning the mapping between sparse and dense point clouds, we formulate PCU as the task of seeking nearest projection points on the implicit surface for seed points. We then define two implicit neural functions to estimate projection direction and distance respectively, which can be trained by the pretext learning tasks. Moreover, the projection rectification strategy is tailored to remove outliers so as to keep the shape of object clear and sharp. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than state-of-the-art supervised methods.

Novel synchronous rectification strategy for flyback converter in both DCM and CCM applications

AbstractIn this paper, a novel flyback synchronous rectification (SR) strategy for both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is proposed. The hybrid mode is suitable for the wide input voltage range, and large load range, which can widen the application range. The proposed strategy can reduce the body diode conduction time in the turning‐on interval in both the DCM and CCM and eliminate the cross‐conduction between the primary switch and secondary switch that existed in the conventional CCM SR converter by adding several logic units. A 12‐V/3‐A output mixed‐mode flyback converter prototype is designed and fabricated to verify the strategy. Measurement results show that with the proposed SR strategy, the efficiency is improved by approximately 5.5% in CCM and 0.5% in DCM.

Continuous Modulation of Electrocatalytic Oxygen Reduction Activities of Single‐Atom Catalysts throughp‐nJunction Rectification

AbstractFine‐tuning single‐atom catalysts (SACs) to surpass their activity limit remains challenging at their atomic scale. Herein, we exploitp‐type semiconducting character of SACs having a metal center coordinated to nitrogen donors (MeNx) and rectify their local charge density by ann‐type semiconductor support. With iron phthalocyanine (FePc) as a model SAC, introducing ann‐type gallium monosulfide that features a low work function generates a space‐charged region across the junction interface, and causes distortion of the FeN4moiety and spin‐state transition in the FeIIcenter. This catalyst shows an over two‐fold higher specific oxygen‐reduction activity than that of pristine FePc. We further employ three othern‐type metal chalcogenides of varying work function as supports, and discover a linear correlation between the activities of the supported FeN4and the rectification degrees, which clearly indicates that SACs can be continuously tuned by this rectification strategy.

Continuous Modulation of Electrocatalytic Oxygen Reduction Activities of Single-Atom Catalysts through p-n Junction Rectification.

Fine-tuning single-atom catalysts (SACs) to surpass their activity limit remains challenging at their atomic scale. Herein, we exploit p-type semiconducting character of SACs having a metal center coordinated to nitrogen donors (MeNx ) and rectify their local charge density by an n-type semiconductor support. With iron phthalocyanine (FePc) as a model SAC, introducing an n-type gallium monosulfide that features a low work function generates a space-charged region across the junction interface, and causes distortion of the FeN4 moiety and spin-state transition in the FeII center. This catalyst shows an over two-fold higher specific oxygen-reduction activity than that of pristine FePc. We further employ three other n-type metal chalcogenides of varying work function as supports, and discover a linear correlation between the activities of the supported FeN4 and the rectification degrees, which clearly indicates that SACs can be continuously tuned by this rectification strategy.

Towards Optimal Robustness of Network Controllability by Nested-Edge Rectification

When a network is attacked, the network controllability decreases and the network is at risk of collapse. A network with good controllability robustness can better maintain its own controllability while under attack to provide time for network recovery. In order to explore how to build a network with optimal controllability robustness, an exhaustive search with adding edges was executed on a given set of small-sized networks. By exhaustive search, we mean: (1) All possible ways of adding edges, except self-loops, were considered and calculated at the time of adding each edge. (2) All possible node removal sequences were taken into account. The nested ring structure (NRS) was obtained from the result of the exhaustive search. NRS has a backbone ring, and the remaining edges of each node point to the nearest nodes along the direction of the backbone ring’s edges. The NRS satisfies an empirically necessary condition (ENC) and has great ability to resist random attacks. Therefore, nested edge rectifcation (NER) was designed to optimize the network for controllability robustness by constructing NRS in networks. NER was compared with the random edge rectification (RER) strategy and the unconstrained rewiring (UCR) strategy on synthetic networks and real-world networks by simulation. The simulation results show that NER can better improve the robustness of network’s controllability, and NER can also quickly improve the initial network controllability for networks with more than one driver node. In addition, as NER is executed, NRS gains more edges in the network, so the network has better controllability robustness. NER will be helpful for network model design or network optimization in future.

The Contribution of Facilitation Techniques to Proximal Head Control as an Influencing Factor of Postural Control in a Peripheral Vestibular Injury: a Case Report

The Vestibular injury associated with the Peripheral Nervous System is a pathology that causes various alterations in individuals by the absence of afferent information. It mainly affects the orientation of the head and its relationship to the trunk of the body, influencing the individual’s stability by a deficit in balance and coordination. In this context a 69-year-old female subject who had undergone surgery to remove a Schwannoma from the right VIII cranial nerve or Vestibular Schwannoma, with changes in head position and postural control in seated and bipedal positions and also in gait pattern, was evaluated. After a 10-week intervention program (1-hour sessions 2 to 3 times a week) which facilitation techniques based on the Bobath Concept were used, there was a considerable improvement in the subject’s motor and functional status. The subject acquired the cervical rectification strategies necessary for better head alignment, better postural control, and a consequently greater autonomy in walking and daily activities.

Analysis of University Student Cadres' Retreat Actions in China Based on K-Core Algorithm and FCM Model

In recent years, there have been constant disputes over college student cadres. Starting from the public opinion crisis of College Students’ autonomy and using the “K-core” algorithm, this paper found that the college student cadres could not realize their own correct roles. The fuzzy cognitive map (FCM) model was used to analyze 49 influencing factors of N University's retreat action. It is found that the five factors of mass support, ability, ideology and morality, work tasks, and performance evaluation are important factors leading to the retreat action. This research proposes role reconstruction and system rectification strategies for the five variables in order to achieve a true feeling of student autonomy and good governance.