High Fault Tolerance Research Articles

AI and Machine Learning Solutions for Real-Time Fault-Tolerant System Design in Critical Applications

This study presents an innovative framework for implementing real-time fault-tolerant systems using artificial intelligence (AI) and machine learning (ML) to enhance reliability and resilience in critical applications. Addressing the needs of sectors such as aerospace, healthcare, automotive, and industrial automation, the proposed system integrates fault detection, isolation, and recovery mechanisms into a multi-layered architecture. Through the use of deep learning for accurate anomaly detection and reinforcement learning for rapid fault isolation, the system achieves high fault tolerance with minimal latency. The framework leverages edge computing for real-time data processing, ensuring timely responses to faults without excessive computational demands. Results from multiple case studies demonstrate significant improvements in fault detection accuracy, isolation speed, and recovery rates, affirming the framework’s adaptability and effectiveness in high-stakes environments. These findings highlight the potential of AI-driven fault-tolerant systems to elevate operational safety and reliability standards across diverse critical industries.

Rewritable Broadband Lossy Absorber Based on Laser Induction Technology

AbstractThe tunable and reversible fabrication function of stealth metasurfaces has significant application value in complex electromagnetic environments., but the extremely low fault tolerance of the existing fabrication methods limits their further development and utilization. In this manuscript, a design and fabrication method for rewritable broadband stealth metasurfaces with memory function is proposed. The reversible phase transition is achieved by laser induced germanium telluride (GeTe) film, which provides the possibility for metasurface to realize the rewriting function. The process of laser induced GeTe and the simulation model of GeTe are investigated, and the conclusions are verified by rewritable broadband polarization converter (RBPC) and rewritable broadband lossy absorber (RBLA). The experimental results show that the reflectivity of fabricated RBLA is less than −10 dB in the range of 7.8–16.1 GHz, which is in good agreement with the numerical simulation results. Meanwhile, there is a highly consistent performance effect before and after repeated induction. The research has the advantages of high efficiency, region selectivity, non volatility and high fault tolerance, which can provide new manufacturing ideas and good candidates for tunable metamaterials.

Disjoint paths construction algorithm in the data center network DPCell

Abstract With the development of the fourth industrial revolution, the importance of data centers has significantly increased. Data centers are widely used in many fields due to their ability to provide efficient, secure, and reliable data storage and processing services. However, with the increasing amount of data, traditional data center networks (DCNs) are currently facing various challenges, prompting academia and industry to propose new DCN architectures. As a dual-port server-based DCN, DPCell has excellent scalability and bisection width, enabling it to meet the demands of large-scale data storage, processing, and computation in the digital revolution. In order to ensure the secure and reliable data communication in the DPCell, this paper designs a disjoint paths communication scheme based on the actual DCN routing requirements. This scheme constructs the optimal number of disjoint paths in DPCell, with a maximum path length of $2^{k}+3$, where $k$ represents the dimension of the DPCell. Furthermore, experiments have verified that the time complexity of this scheme is sublinear, making it more efficient than the current optimal maximum flow algorithm. To a certain extent, this scheme provides DPCell with the required high bandwidth, fault tolerance, and security for data communication.

High fault-tolerant performance of the divide-and-swap cube network

Two crucial metrics used to evaluate the fault tolerance of interconnection networks are connectivity and diagnosability. By improving the connectivity and diagnosability of the interconnection network, its fault tolerance can be enhanced. In this paper, we focus on determining the g-extra connectivity (0≤g≤10) of the divide-and-swap cube DSCn, as well as its diagnosability based on the pessimistic diagnosis strategy and g-extra precise diagnosis strategy, under the PMC and MM* models. The research analysis suggests that compared with some other connectivity and diagnosability of DSCn, such as classical connectivity, structure connectivity, super connectivity, and classical diagnosability, the extra connectivity/diagnosability and pessimistic diagnosability of DSCn enable it to have a higher fault tolerance. Moreover, we propose two O(Nlog2⁡N) effective diagnosis algorithms of DSCn: the g-extra diagnosis algorithm (EX-DiagnosisDSCn) and the pessimistic diagnosis algorithm (PE-DiagnosisDSCn), where the EX-DiagnosisDSCn algorithm can accurately diagnose the state of all processors in DSCn.

Comparative analysis of tubular retractors and hook retractors in oblique lumbar interbody fusion at the initial stage of the learning curve

PurposeOblique lumbar interbody fusion (OLIF) still has a steep learning curve that many spinal surgeons who want to develop are hesitant. The purpose of this study is to provide reference for beginners through the comparative analysis of the application of two kinds of retraction devices in the early stage of learning curve.MethodWe prospectively included the first 60 patients with lumbar degenerative diseases treated with OLIF by a surgeon in our department. According to the application of different retraction devices during the operation, the patients were divided into hook retractor group and tubular retractor group. The clinical effects and complications of the two groups were compared.ResultThe average age of hook retractor group was 62 years old, the average age of tubular retractor group was 65 years old. There was no significant difference in age, sex, operative segment, follow-up time and blood loss between the two groups. The operation time in hook retractor group was less than that in tubular retractor group. The incidence of complications in hook retractor group (11.8%) was significantly lower than that in tubular retractor group (38.5%).ConclusionThe tubular retractor group has a higher risk of neurovascular injury in the initial stage of learning, as well as the risk of vertebral fracture. In contrast, the hook retractor group has the advantages of simple method, high fault tolerance and relatively low incidence of complications. Therefore, we believe that the application of hook retractor in the early stage of OLIF learning curve is easier to increase the operator’s confidence and make OLIF more acceptable.

Dynamic Byzantine Fault-Tolerant Consensus Algorithm with Supervised Feedback Mechanisms

Among the existing consensus algorithms, there are very few low-latency consensus algorithms that can simultaneously take into account node dynamics, fault tolerance, and scalability, and which are applicable to large-scale open scenarios where low latency is required. Therefore, this paper proposes a low-latency scalable dynamic consensus algorithm with high fault tolerance utilizing a combination of layered and threshold signature technologies, known briefly as LTSBFT. Firstly, LTSBFT achieves linear communication complexity through the utilization of threshold signature technology and a two-layer structure. Secondly, the mutual supervision feedback mechanism among nodes enables the attainment of linear complexity for reaching consensus on the faulty upper-layer nodes during the view-change. Lastly, a node dynamic protocol was proposed for the first time to support dynamic changes in the number of nodes during the consensus process. That is to say, consensus can still be reached when the number of nodes dynamically changes without interrupting the client’s request for consensus in the network. The experimental results indicate that LTSBFT exhibits lower communication latency and higher throughput compared to the classic HotStuff and PBFT algorithms. Furthermore, in comparison to double-layer PBFT, the LTSBFT has been demonstrated to have improved fault tolerance.

Fixed-time fault-tolerant attitude tracking control for UAV based on fixed-time extended state observer

PurposeTo improve the robustness of carrier-based unmanned aerial vehicle (UAV) with actuator faults attitude tracking control system, this paper aims to propose a fixed-time backstepping (FXTBSC) fault-tolerant control based on a fixed-time extended state observer.Design/methodology/approachA fixed-time extended state observer (FXTESO) is designed to estimate the total disturbance including nonlinear, coupling, actuator faults and external disturbances. The integration of backstepping control and fixed-time technology ensures fixed-time convergence.FindingsThe simulation results of tracking the desired attitude angle show that the anti-interference, fault tolerance and tracking accuracy of FXTBSC-FXTESO are better than the BSC-ESO control method.Originality/valueDifferent from the traditional BSC-ESO, the convergence speed and convergence accuracy of FXTBSC-FXTESO proposed in this paper are better than conventional extended state observer. And the fixed time controller has the advantages of high tracking accuracy, fault tolerance and anti-interference ability.

A wide area backup protection algorithm based on fault current comparison and evidence theory fusion

Abstract Wide-area backup protection using information in the area to determine the fault location comprehensively requires a high fault tolerance ability. However, the traditional wide area backup protection algorithm based on evidence theory assigns a separate value to each adjacent line for its fault probability, and the performance of the algorithm degrades in the condition of more information loss or errors. To solve these problems, this paper proposed a wide area backup protection algorithm based on fault current comparison and improved evidence theory fusion. The proposed method first selects the suspected fault lines by sequence voltage sorting. Then, the information on the fault current sequence component and traditional protection action is collected and preprocessed to form the evidence set, and its basic probability assignment is calculated. Finally, the improved evidence theory is used for fusion to identify fault lines. Simulation results show that the algorithm can accurately identify fault lines under complex operating conditions, multiple information loss and errors, and has high fault tolerance.

A Secure Protocol Authentication Method Based on the Strand Space Model for Blockchain-Based Industrial Internet of Things

The rapid development of the Industrial Internet of Things (IIoT) and its application across various sectors has led to increased interconnectivity and data sharing between devices and sensors. While this has brought convenience to users, it has also raised concerns about information security, including data security and identity authentication. IIoT devices are particularly vulnerable to attacks due to their lack of robust key management systems, efficient authentication processes, high fault tolerance, and other issues. To address these challenges, technologies such as blockchain and the formal analysis of security protocols can be utilized. And blockchain-based Industrial Internet of Things (BIIoT) is the new direction. These technologies leverage the strengths of cryptography and logical reasoning to provide secure data communication and ensure reliable identity authentication and verification, thereby becoming a crucial support for maintaining the security of the Industrial Internet. In this paper, based on the theory of the strand space attack model, we improved the Fiber Channel Password Authentication Protocol (FACP) security protocol in the network environment based on symmetric cryptography and asymmetric cryptography. Specifically, in view of the problem that the challenge value cannot reach a consensus under the symmetric cryptography system, and the subject identity cannot reach a consensus under the asymmetric cryptography system, an improved protocol is designed and implemented to meet the authentication requirements, and the corresponding attack examples are shown. Finally, the effectiveness and security of the protocol were verified by simulating different networking environments. The improved protocol has shown an increase in efficiency compared with the original protocol across three different network configurations. There was a 6.43% increase in efficiency when centralized devices were connected to centralized devices, a 5.81% increase in efficiency when centralized devices were connected to distributed devices, and a 6.32% increase in efficiency when distributed devices were connected to distributed devices. Experimental results show that this protocol can enhance the security and efficiency of communication between devices and between devices and nodes (servers, disks) in commonly used Ethernet passive optical network (EPON) environments without affecting the identity authentication function.

One-to-one node disjoint paths on divide-and-swap cubes

In massive parallel and distributed systems, high fault-tolerance and reliability are extremely important for interconnection networks. Node-disjoint paths play a key role in improving the performance, reliability and scalability of networks.The hypercube, as well as many hypercube-like variants, are among the most popular and attractive network architectures. The divide-and-swap cube DS C n is a newly proposed hypercube variant which has many desirable properties. In this work, we show that there exist d + 1 node-disjoint paths between any two distinct nodes in divide-and-swap cubes. Since the connectivity of the network is d + 1, d + 1 is the maximum number of paths that can be constructed between any two distinct nodes.

The prediction of sound absorption coefficient of film multi-cavity materials based on generalized regression neural network (GRNN)

In recent years, noise problems have attracted more and more widespread attention with the rapid development of economy, social life and transportation construction. Sound can cause the membrane to vibrate, thereby absorbing sound waves of specific frequencies. In this study, the film was transformed into a bubble structure to form a film multi-cavity structure material. The theoretical calculation model of the sound absorption performance of sound-absorbing materials has always been the focus of research. Generalized regression neural network (GRNN) is a radial basis neural network (RBF) with a flexible network structure, high fault tolerance and strong nonlinear mapping capabilities. This paper uses the generalized regression neural network (GRNN) method to predict the sound absorption coefficient of film multi-cavity materials. Twenty-four sets of film multi-cavity material samples are prepared, and two groups of GRNN model is established. Each group of GRNN model uses 12 sets of data, of which 10 sets are used as training samples for GRNN and 2 sets are used as test samples. The thickness, bubble diameter and porosity of the material are used as inputs of the GRNN, and the sound absorption coefficients at 1/3 octave center frequencies are used as the output of the network. The optimal spread factors obtained by training the two groups of GRNN models are 0.5 and 0.35 respectively. The results show that the minimum error between the model prediction value and the experimental measurement value is 0.001. The established GRNN-1 and GRNN-2 models have high accuracy and high similarity in sound absorption curves. This method is simple, effective and accurate. When there are fewer data samples, GRNN also has outstanding performance in terms of approximation ability and learning speed.

A New Hybrid COA-OOA Based Task Scheduling and Fuzzy Logic Approach to Increase Fault Tolerance in Cloud Computing

INTRODUCTION: Technology is made available to customers worldwide through a distributed computing architecture called cloud computing. In the cloud paradigm, there is a risk of single-point failures, in order to prevent errors and gain confidence from consumers in their cloud services, one problem facing cloud providers is efficiently scheduling tasks.OBJECTIVES: High availability and fault tolerance must be offered to clients by these services. Fuzzy logic and hybrid COA-OOA are used in this study proposed fault-tolerant work scheduling algorithm. Jobs given by users and virtual machines are considered as input for this proposed approach. METHODS: The given tasks are initially scheduled utilizing the FIFO order. Then, it is rescheduled utilizing the Hybrid Coati Optimization Algorithm (COA) - Osprey Optimization Algorithm (OOA) for scheduling the task based on priority.RESULTS: This scheduled job is assigned to the VM for further execution. If the jobs are not executed successfully, then fault tolerant mechanism is carried out. Faults are recognized by employing fuzzy logic in this proposed approach. CONCLUSION: This proposed approach attains 62 sec response time, 61 sec of makespan and 98% success rate. Thus, this proposed approach is the best choice for efficient task scheduling with fault tolerant mechanism.

Mechanical Fault Diagnosis of High-Voltage Circuit Breakers with Dynamic Multi-Attention Graph Convolutional Networks Based on Adaptive Graph Construction

With the rapid development of deep learning, its powerful capabilities make it possible to perform mechanical fault diagnosis of high-voltage circuit breakers (HVCBs). Among deep learning approaches, the convolutional neural network is widely used. However, while it can extract features effectively, it also has some limitations. Specifically, it depends on a large number of training data and only takes data information into account without considering structural information. These shortcomings lead to unused information and unsatisfactory model results. To address these shortcomings, this paper proposes AKNN-DMGCN, a novel dynamic multi-attention graph convolutional network based on an adaptively constructed graph, which can achieve high accuracy and robust mechanical fault diagnosis of HVCBs. First, a novel adaptive k-nearest neighbor (AKNN) graph construction method is proposed to construct informative graphs. The AKNN method can mine the relationship between the original data samples and utilize the data and label information. Thus, it has high fault tolerance to noise signals and can construct a structure graph with rich and accurate information, which can improve the overall model performance. Then, a dynamic multi-attention graph convolutional network (DMGCN) is applied for mechanical fault diagnosis of HVCBs. DMGCN fully utilizes structural and numerical information representing HVCB signals to perform classification. DMGCN has a dynamic multi-attention mechanism with strong expressive ability, which allows it to achieve high diagnostic accuracy. The experimental results indicate that the accuracy of AKNN-DMGCN reaches 97.22% on a balanced dataset and 95.01% on an imbalanced dataset, which demonstrates that the proposed method is effective for both balanced and imbalanced samples.

Tribological Behavior of Sulfonated Polyether Ether Ketone with Three Different Chemical Structures under Water Lubrication.

With the development of the shipbuilding industry, it is necessary to improve tribological properties of polyether ether ketone (PEEK) as a water-lubricated bearing material. In this study, the sulfonated PEEK (SPEEK) with three distinct chemical structures was synthesized through direct sulfonated polymerization, and high fault tolerance and a controllable sulfonation degree ensured the batch stability. The tribological and mechanical properties of SPEEK with varying side groups (methyl and tert-butyl) and rigid segments (biphenyl) were compared after sintering in a vacuum furnace. Compared to the as-made PEEK, as the highly electronegative sulfonic acid group enhanced the hydration lubrication, the friction coefficient and wear rate of SPEEK were significantly reduced by 30% and 50% at least without affecting the mechanical properties. And lower steric hindrance and entanglement between molecular chains were proposed to be partially responsible for the lowest friction behavior of SPEEK with methyl side groups, making it a promising and competitive option for water-lubricated bearings.

Key technologies for airworthiness compliance verification of the solid-state power controller

Solid-state power distribution technology has advantages such as lightweight, automation, high power quality, and high fault tolerance, which has a very broad prospect. As the core device of solid-state power distribution technology, the application of the solid-state power controller (SSPP) is faced with the challenge that it is difficult to use the traditional compliance verification methods to demonstrate compliance with relevant airworthiness regulations in the application process. Based on the C919 aircraft power distribution system, this article proposed corresponding compliance verification methods, which combine explanation, analysis, and test verification, to confirm and validate the relevant functions, performance, and impact after the failure of SSPC. This may provide a reference for the development of related technologies in the future.

Innovative Approaches to Full-Text Search with Solr and Lucene

Full-text search engines help efficiently process large volumes of textual material and provide appropriate results. Apache Solr and Apache Lucene are popular full-text search tools for indexing and querying huge datasets. This research study examines Solr and Lucene's strengths, weaknesses, and unique methods for improving full-text search efficiency and accuracy. Apache Lucene, the fundamental full-text search framework, has extensive indexing and querying features. Developers may tailor the search process using its flexible and extendable framework. Advanced indexing algorithms like inverted indices and tokenization underpin Lucene's search capabilities. However, complicated query needs and efficient large-scale data management remain problems. Solr, founded on Lucene, adds faceting, distributed searching, and rich text analysis to its search engine. Enterprise applications may use Solr's high availability, fault tolerance, and large-scale deployments. Solr has performance tuning and setup complexity issues despite these benefits. This study explores novel solutions to these issues and improves full-text search. Advanced tokenization and normalization may improve indexing tactics. Machine learning algorithms increase search relevancy, providing more accurate and contextual results. Query processing optimization is another invention. Caching, query rewriting, and parallel processing may minimize query latency and boost throughput. GPUs are also used to improve query execution. The article also discusses integrating Solr and Lucene with big data platforms and cloud services. Distributed computing frameworks and cloud storage may improve scalability and real-time search. How Solr and Lucene may incorporate AI and NLP to improve search accuracy and user experience is also investigated.

Research on Image Mapping Spectrometer Based on Ultra-Thin Glass Layered Mapping.

The imaging quality of the Mapping Imaging Spectrometer (IMS) is crucial for spectral identification and detection performance. In IMS, the image mapper significantly influences the imaging quality. Traditional image mappers utilize a single-point diamond machining process. This process leads to inevitable edge eating phenomena that further results in noticeable deficiencies in imaging, impacting spectral detection performance. Therefore, we propose a manufacturing process for the image mapper based on ultra-thin layered glass. This process involves precision polishing of ultra-thin glass with two-dimensional angles, systematically assembling it into an image mapper. The surface roughness after coating is generally superior to 10 nm, with a maximum angle deviation of less than 3'. This results in high mapping quality. Subsequently, a principle verification experimental system was established to conduct imaging tests on real targets. The reconstructed spectrum demonstrates excellent alignment with the results obtained from the Computed Tomography Imaging Spectrometer (CTIS). We thereby validate that this approach effectively resolves the issues associated with edge eating (caused by traditional single-point diamond machining), and leads to improved imaging quality. Also when compared to other techniques (like two-photon polymerization (2PP)), this process demonstrates notable advantages such as simplicity, efficiency, low processing costs, high fault tolerance, and stability, showcasing its potential for practical applications.

Open-Phase-Tolerant Online Current References for Maximum Torque Range and Minimum Loss With Current and Torque-Ripple Limits for n-Phase Nonsalient PMSMs With Nonsinusoidal Back EMF

Multiphase permanent-magnet synchronous machines (PMSMs) with nonsinusoidal back-electromotive force (back-EMF) offer high fault tolerance and torque density for electric vehicles. Most current-reference generation methods either minimize stator copper loss (SCL) or maximize achievable torque. Optimization of both goals is accomplished by full-torque-range minimum-loss (FRML) strategies, but so far just for sinusoidal back-EMF. Thus, FRML for nonsinusoidal back-EMF should be sought. Moreover, many methods are only suitable for healthy conditions or specific machines, harmonics, or open-phase-fault (OPF) scenarios. Additionally, the torque range may be extended by permitting torque ripple or (transiently) greater rms current, but this approach is not general nor FRML yet. This paper proposes online FRML current-reference generation for multiphase PMSMs with nonsinusoidal back-EMF: nonsinusoidal-back-EMF FRML (NSBE-FRML). When the torque reference is feasible, minimum SCL is attained while maximizing the achievable torque (i.e., FRML). For higher torque references, the instantaneous torque deviation is minimized, and the torque reference is saturated in consecutive samples limiting the torque ripple to a pre-specified threshold. Furthermore, the rms current is limited after transient overload by automatically decreasing the torque reference. The NSBE-FRML is suitable for any harmonics, healthy/OPF conditions, and multiphase PMSMs with negligible saliency ratio. Experiments are performed with a six-phase PMSM.

Learning in colloidal polyaniline nanorods

Liquid-based computing media are massively parallel computing devices with high fault-tolerance and self-healing capabilities. They can compute by propagating and interacting phase waves or by changing their internal coordination.Colloidal suspensions of conductive polymer nanorods are expected to be interesting candidates for developing the computing subsystem in such applications, because of their anisometry which makes them particularly susceptible to electrical fields.In this work, we investigateda suspension of polyaniline nanorods (NRs) to explorethe potential of generatinglearning mechanisms in the colloid and applyingthem in the computing system of future cybernetic systems. We demonstrated that learning, as expressed in the formation of programmable conductive pathways leading to distinct states, can be implemented using Alternated Current (AC) electrical stimulation. We achievedrepeatable programming of colloid resistance anisotropy that can be easily mapped into binary logic, demonstrating that this is due to the AC field effects on the hydrogen bonds that stabilise the dispersoids in the solvent as well as the charges' orientation inside the polymeric chains. We also influencedthe conductivity of polyaniline (PANI) NRs by changing their molecular conformation. The findings establish robustprotocols for programming future liquid robots.

Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis

Fuzzy and temporal reasoning can effectively improve the accuracy of fault diagnosis methods. However, there are challenges in practical applications, such as missing alarm messages, temporal reasoning with complex calculations, and complex modeling processes. Therefore, this study proposes an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system for power system fault diagnosis. First, the ITFRSNP system and its reasoning method are proposed to perform association reasoning between confidence degrees and temporal constraints. Second, a general fault diagnosis model and process are developed based on the ITFRSNP system to diagnose various faulty components and simplify the modeling process. In addition, a search method is provided for identifying suspected faulty components, considering the missing alarm message of the circuit breaker. Simulation results of fault cases demonstrate that the proposed method exhibits high accuracy and fault tolerance. It can precisely identify faulty components despite incorrect operations or inaccurate alarm messages of protective relays and circuit breakers. Moreover, the search method effectively narrows down the diagnostic scope without missing suspected faulty components in scenarios where alarms from boundary circuit breakers are missing, thereby enhancing the fault diagnosis efficiency. The fault diagnosis model features a straightforward structure and reasoning process with minimal computational complexity, making it suitable for real-time diagnosis of complex faults within power systems.