Search Results (1,093)

Current medical informatics systems face critical challenges, including limited scalability across distributed institutions, insufficient real-time AI-driven decision support, and lack of standardized interoperability for heterogeneous medical data exchange. To address these challenges, this paper proposes a novel distributed web system architecture for medical informatics, integrating artificial intelligence techniques and cloud-based services. The system ensures interoperability via HL7 FHIR standards and preserves data privacy and fault tolerance across interconnected medical institutions. A hybrid AI pipeline combining principal component analysis (PCA), K-Means clustering, and convolutional neural networks (CNNs) is applied to diffusion tensor imaging (DTI) data for early detection of neurological anomalies. The architecture leverages containerized microservices orchestrated with Docker Swarm, enabling adaptive resource management and high availability. Experimental validation confirms reduced latency, improved system reliability, and enhanced compliance with medical data exchange protocols. Results demonstrate superior performance with an average latency of 94 ms, a diagnostic accuracy of 91.3%, and enhanced clinical workflow efficiency compared to traditional monolithic architectures. The proposed solution successfully addresses scalability limitations while maintaining data security and regulatory compliance across multi-institutional deployments. This work contributes to the advancement of intelligent, interoperable, and scalable e-health infrastructures aligned with the evolution of digital healthcare ecosystems. Full article

Matroid is a mathematical structure that extends the concept of independence. The fuzzy graphic binary matroid serves as a generalization of linear dependence, and its properties are examined. Power grid networks, which manage the generation, transmission, and distribution of electrical energy from power plants to consumers, are inherently a complex system. A key objective in analyzing these networks is to ensure a reliable and uninterrupted supply of electricity. However, several critical issues must be addressed, including uncertainty in communication links, detection of redundant or sensitive circuits, evaluation of network resilience under partial failures, and optimization of reliability in interconnected network systems. To support this goal, the concept of a fuzzy graphic binary matroid is applied in the analysis of power grid communication network, offering a framework that not only incorporates fuzziness and binary conditions but also enables systematic identification of weak circuits, redundancy planning, and reliability enhancement. This approach provides a more realistic representation of operational conditions, ensuring better fault tolerance and improved efficiency of the grid. Full article

LoRa is a long-range, low-power wireless communication technology widely used in Internet of Things (IoT) applications. However, its conventional implementation through Long Range Wide Area Network (LoRaWAN) presents operational constraints due to its centralized topology and reliance on gateways. To overcome these limitations, this work designs and validates a gateway-free mesh communication system that operates directly on commercially available commodity microcontrollers, implementing lightweight self-healing mechanisms suitable for resource-constrained devices. The system, based on ESP32 microcontrollers and LoRa modulation, adopts a mesh topology with custom mechanisms including neighbor-based routing, hop-by-hop acknowledgments (ACKs), and controlled retransmissions. Reliability is achieved through hop-by-hop acknowledgments, listen-before-talk (LBT) channel access, and duplicate suppression using alternate link triggering (ALT). A modular prototype was developed and tested under three scenarios such as ideal conditions, intermediate node failure, and extended urban deployment. Results showed robust performance, achieving a Packet Delivery Ratio (PDR), the percentage of successfully delivered DATA packets over those sent, of up to 95% in controlled environments and 75% under urban conditions. In the failure scenario, an average Packet Recovery Ratio (PRR), the proportion of lost packets successfully recovered through retransmissions, of 88.33% was achieved, validating the system’s self-healing capabilities. Each scenario was executed in five independent runs, with values calculated for both traffic directions and averaged. These findings confirm that a compact and fault-tolerant LoRa mesh network, operating without gateways, can be effectively implemented on commodity ESP32-S3 + SX1262 hardware. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

This paper develops the stability-guaranteed adaptive dynamic programming (ADP)-based fault tolerant control (FTC) for nonlinear systems with an actuator fault. Firstly, a fault observer is designed to identify the unknown actuator fault. Then, a critic neural network (NN) is built to approximate the optimal control of the nominal system. Meanwhile, a stability-aware weight update mechanism is proposed based on the Lyapunov stability theorem to relax the restriction of the initial admissible control on the system stability. By integrating the nominal optimal control and the fault estimation, the stability-guaranteed ADP-based FTC is developed to eliminate the influence of the actuator fault. Furthermore, the observer errors, critic NN weight estimation errors, and the closed-loop system are all shown to exhibit uniform ultimate boundedness by using Lyapunov’s direct method. Finally, simulation examples are given to demonstrate the validity of the proposed method. Full article

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article

This paper presents a modular and distributed supervisory control integration framework for intelligent micro-manufacturing systems (MMSs) under event-level failures. Addressing the increasing demand for scalable and reliable supervisory control in both micro- and smart manufacturing, the proposed approach equips each subsystem with a detector automaton that classifies runtime states into Strictly robust, Recoverably robust, or Non-robust categories. Distributed supervisors then make real-time local decisions to ensure fault-tolerant evolution of system behaviors. Unlike conventional centralized or Petri net-based methods, the proposed automaton-based framework supports modular design and structural scalability. Quantitative comparisons show that the robustness-detection cost scales approximately linearly with the summed sizes of local graphs, indicating good structural scalability. Simulation studies validate the feasibility and scalability of the framework, demonstrating its effectiveness in maintaining production cycle reachability and its integration potential for micro-electro-mechanical systems (MEMS)-based production lines, micro-fabrication platforms, and smart factory environments. These results confirm that the proposed method can serve as a robust and deployable control layer for next-generation intelligent and micro-manufacturing integration architectures. Full article

A distributed weighted fusion fractional order filter is proposed for multi-sensor multi-delay fractional order systems. Firstly, the time-delay system is transformed into a non-time-delay system using the state augmentation method, and the optimal augmented fractional Kalman filter is derived. Secondly, in order to reduce the computational burden, a suboptimal fractional order Kalman filter is presented. Compared with the optimal augmented method, it greatly reduces the computational complexity, which is convenient for real-time applications. Then, in order to derive the weighting coefficient for distributed fusion, the calculation formula of filtering error variance matrix between any two sensor subsystems is derived. Finally, the distributed weighted fusion fractional order filter is presented. It is local optimal and globally suboptimal: compared with each local filter, it has higher accuracy; compared with the centralized fusion filter, it has lower accuracy and more fault tolerance. In summary, it is more suitable for practical application. Simulation results verify the effectiveness of the proposed algorithm. Full article

To improve the reliability and fault tolerance of electric helicopter propulsion systems, this paper presents a master–slave fault-tolerant control method based on an extended state observer (ESO) for dual-redundant electric propulsion systems that addresses dynamic coupling disturbances. First, the control architecture puts the master motor in speed loop mode and puts the slave motor in torque loop mode with an ESO to estimate disturbances and compensate for mechanical coupling torque through feedforward control based on Lyapunov stability theory. Second, a least squares parameter identification method establishes a current-torque mapping model to ensure consistent dual-motor output. Then, fault-tolerant switching is implemented, transitioning from normal torque mode coordination to independent speed mode with adaptive PI adjustment during faults. Experimental validation shows that the total torque stabilizes at 240 N·m, and the synchronization error remains within ±0.5 N·m during normal operation. Under single-motor fault scenarios, the ESO detects disturbances within 15 ms with >95% accuracy. The system speed decreases to a minimum of 2280 rpm (5% deviation) and recovers within 3.5 s. Compared to traditional PI control, this method improves torque synchronization by 65.4%, speed stability by 62.6%, and dynamic response by 51.2%. Finally, the results validate that the method effectively suppresses coupling interference and meets aviation safety standards, providing reliable, fault-tolerant solutions for electric helicopter propulsion. Full article

Random number generators play a critical role in ensuring information security, supporting encrypted communications, and preventing data leakage. However, the random number generators widely used in hardware are faced with potential threats such as environmental disturbances and fault injection attacks. Especially in automotive-grade environments, chips encounter threat scenarios involving multidimensional fault injection, which may lead to functional failures or malicious exploitation, endangering the security of the entire system. This paper focuses on a Counter Mode Deterministic Random Bit Generator (CTR-DRBG) based on the AES-128 algorithm and implements a hardware prototype system compliant with the NIST SP 800-22 standard on an FPGA platform. Centering on typical fault modes such as temperature disturbances, voltage glitches, electromagnetic interference, and bit flips, single-dimensional and multidimensional fault injection and simulated fault injection experiments were designed and conducted. The impact characteristics and sensitivities of electromagnetic faults, voltage faults, and temperature faults regarding the output sequences of random numbers were systematically evaluated. The experimental results show that this type of random number generator exhibits modular-level differential vulnerability under physical disturbances, especially in the data transmission processes of encryption paths and critical registers, which demonstrate higher sensitivity to flip-type faults. This research provides a feasible analysis framework and practical basis for the security assessment and fault-tolerant design of random number generators, possessing certain engineering applicability and theoretical reference value. Full article

The present paper addresses the design of a fault-tolerant control (FTC) system based on model predictive control (MPC) that aims to deal with sensor additive faults. The main contribution of this work lies in an FTC design for systems with measurement noise composed of a fault estimator, observers for fault reconfiguration, a reconfiguration unit, and an MPC. The proposed FTC uses a linearized plant model with measurement noise and sensor faults in the design procedure. Simulation results with constant and time-varying faults are presented to validate the performance of the FTC, considering a non-linear heat exchanger model. Full article

This paper investigates the attitude control problems of spacecraft subject to external disturbances and compound actuator faults, including both additive and multiplicative components. To address these problems, an improved learning observer (ILO) is proposed. Compared to traditional learning observers (TLOs), the improved learning observer incorporates the previous-step state estimation error as an iterative term. Based on the observer’s outputs, a robust adaptive fault-tolerant attitude control scheme is developed using the backstepping method, under a prescribed performance bound (PPB). This control framework guarantees that the attitude tracking error adheres to prescribed transient performance specifications, such as bounded overshoot and accelerated convergence. Unlike conventional control schemes, the proposed approach ensures that system trajectories remain strictly within the desired bound throughout the transient process. A comprehensive Lyapunov-based analysis rigorously demonstrates the global uniform ultimate boundedness of all closed-loop signals. Numerical simulations substantiate the efficacy of the proposed approach, highlighting the enhanced disturbance estimation capability of the ILO in comparison to the TLO, as well as the superior transient tracking performance of the PPB-based control strategy relative to existing methods. Full article

This research proposes a novel adaptive robust fault-tolerant controller for symmetrical robotic manipulators subject to model uncertainties and actuator failures. The key innovation lies in the design of a new sliding manifold that effectively integrates the advantages of a hyperbolic tangent function-based practical sliding manifold and a fast terminal sliding manifold. This structure not only eliminates the reaching phase and accelerates error convergence but also significantly enhances system robustness while mitigating chattering. Moreover, the proposed manifold ensures the global non-singularity of the equivalent control law, thereby improving overall stability. Another major contribution is an adjustable adaptive strategy that dynamically estimates the unknown bounds of fault information and external disturbances, reducing the reliance on prior knowledge. The stability and convergence of the robotic system under the proposed scheme are theoretically analyzed and guaranteed. Finally, simulation experiments demonstrate the superior performance of the proposed scheme. Full article

With the increasing application of smart-material-based actuators for vibration suppression in flexible spacecraft, there is a growing need for advanced control strategies suited to distributed-parameter systems. This paper proposes a distributed cooperative control (DCC) scheme to address phase inconsistencies in actuator outputs within a decentralized control framework. The piezoelectric actuators embedded in flexible appendages are modeled as a multi-agent system that utilizes local information to improve coordination. A consensus-based cooperative controller is designed to synchronize actuator actions, with closed-loop stability rigorously established via Lyapunov’s direct method. The robustness of the controller is evaluated through Monte Carlo simulations under varying initial conditions. Comparative numerical results demonstrate that the proposed DCC achieves superior performance and energy efficiency over conventional decentralized control, along with inherent fault tolerance due to its distributed topology. Furthermore, the practical implementability of the approach is supported by discrete-time controller validation and automatic code generation, confirming its readiness for real-time embedded deployment. The study highlights the potential of DCC for enhancing vibration suppression in next-generation flexible spacecraft. Full article

Locally Repairable Codes (LRCs) have become the dominant design in wide-stripe erasure coding storage systems due to their excellent locality and low repair bandwidth. In such systems, the repair degree—defined as the number of helper nodes contacted during data recovery—is a key performance metric. However, as stripe width increases, the probability of multiple simultaneous node failures grows, which significantly raises the repair degree in traditional LRCs. Addressing this challenge, we propose a new family of codes called TFR-LRCs (Locally Repairable Codes for balancing fault tolerance and repair efficiency). TFR-LRCs introduce flexible design choices that allow trade-offs between fault tolerance and repair degree: they can reduce the repair degree by slightly increasing storage overhead, or enhance fault tolerance by tolerating a slightly higher repair degree. We design a matrix-based construction to generate TFR-LRCs and evaluate their performance through extensive simulations. The results show that, under multiple failure scenarios, TFR-LRC reduces the repair degree by up to 35% compared with conventional LRCs, while preserving the original LRC structure. Moreover, under identical code parameters, TFR-LRC achieves improved fault tolerance, tolerating up to $g+2$ failures versus $g+1$ in conventional LRCs, with minimal additional cost. Notably, in maintenance mode, where entire racks may become temporarily unavailable, TFR-LRC demonstrates substantially better recovery efficiency compared to existing LRC schemes, making it a practical choice for real-world deployments. Full article