Search Results (368)

The Internet of Things (IoT) has transformed multiple industries, providing significant potential for automation, efficiency, and enhanced decision-making. The incorporation of IoT and data analytics in smart grid represents a groundbreaking opportunity for the energy sector, delivering substantial advantages in efficiency, sustainability, and customer empowerment. This integration enables smart grids to autonomously monitor energy flows and adjust to fluctuations in energy demand and supply in a flexible and real-time fashion. Statistical analytics, as a fundamental component of data analytics, provides the necessary tools and techniques to uncover patterns, trends, and insights within datasets. Nevertheless, it is crucial to address privacy and security issues to fully maximize the potential of data analytics in smart grids. This paper makes several significant contributions to the literature on secure, privacy-aware aggregation schemes in smart grids. First, we introduce a Fog-enabled Secure Data Analytics Operations (FESDAO) scheme which offers a distributed architecture incorporating robust security features such as secure aggregation, authentication, fault tolerance and resilience against insider threats. The scheme achieves privacy during data aggregation through a modified Boneh-Goh-Nissim cryptographic scheme along with other mechanisms. Second, FESDAO also supports statistical analytics on metering data at the cloud control center and fog node levels. FESDAO ensures reliable aggregation and accurate data analytical results, even in scenarios where smart meters fail to report data, thereby preserving both analytical operation computation accuracy and latency. We further provide comprehensive security analyses to demonstrate that the proposed approach effectively supports data privacy, source authentication, fault tolerance, and resilience against false data injection and replay attacks. Lastly, we offer thorough performance evaluations to illustrate the efficiency of the suggested scheme in comparison to current state-of-the-art schemes, considering encryption, computation, aggregation, decryption, and communication costs. Moreover, a detailed security analysis has been conducted to verify the scheme’s resistance against insider collusion attacks, replay attack, and false data injection (FDI) attack. Full article

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

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

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

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

In recent years, blockchain consensus mechanisms have evolved significantly from the original proof-of-work design, transitioning towards more efficient and scalable alternatives. This paper presents a comprehensive review and analysis framework for blockchain consensus mechanisms based on a systematic examination of 200+ publications. We categorize consensus mechanisms into four performance-oriented groups: high throughput, strong security, low energy, and flexible scaling, each addressing specific trade-offs in the blockchain trilemma of decentralization, security, and scalability. Through quantitative metrics including transactions per second, energy consumption, fault tolerance, and communication complexity, we evaluate mainstream mechanisms. Our findings reveal that no single consensus mechanism optimally satisfies all performance requirements, with each design involving explicit trade-offs. This paper provides researchers and practitioners with a structured framework for understanding these trade-offs and selecting appropriate consensus mechanisms for specific application contexts. Finally, we discussed future development trends, as well as regulatory and ethical considerations. Full article

In this paper, an adaptive sliding mode fault-tolerant control (FTC) scheme is proposed for small Quadrotor Unmanned Aerial Vehicles (QUAVs) used in education and teaching formation in the presence of systematic unknown external disturbances with actuator failures. A radial basis function neural network (RBFNN) is employed to handle the nonlinear interaction function, and a fault-tolerant-based NN extended state observer (NNESO) is designed to estimate the unknown external disturbance. Meanwhile, an adaptive fault observer is developed to estimate and compensate for the fault parameters of the system. To achieve satisfactory trajectory tracking performance for the QUAV, an adaptive sliding mode control (SMC) strategy is designed. This strategy mitigates the strong coupling effects among the design parameters within the QUAV formation. The stability of the closed-loop system is rigorously demonstrated by Lyapunov analysis, and the controlled QUAV formation can achieve the desired tracking position. Simulation results verify the effectiveness of the proposed control method. Full article

Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems. Full article

This study develops a new integral sliding mode-based method to address the decentralized adaptive fault-tolerant guaranteed cost control (GCC) problem via a dynamic event-triggered (DET) adaptive dynamic programming (ADP) approach. Firstly, integral sliding mode control technology is applied to eliminate the influence of actuator faults, which can guarantee that the large-scale system states stay on the sliding mode surface. Secondly, the ADP algorithm based on DET mode is employed to improve the control performance for equivalent sliding mode surface and reduce computational and communication overhead. Meanwhile, the GCC method is introduced to ensure that the performance cost function is less than an upper bound while maintaining system stability. Then, through Lyapunov stability analysis, it is proven that the presented DET-GCC method based on ADP algorithm can guarantee that all signals are uniformly ultimately bounded. Finally, the validity of the developed approach is confirmed through the simulation results. Full article

The influence of redundant implementations on success of physical attacks against cryptographic devices is currently under-researched. This is especially an issue in application fields such as wearable health, industrial control systems and the like in which devices are accessible to potential attackers. This paper presents results of an investigation of the TMR application impact on the vulnerability of FPGA-based asymmetric cryptographic accelerators to side-channel analysis attacks. We implemented our cryptographic cores using full- and partial-TMR application approaches and experimentally conducted evaluation of their side-channel resistance. Our results reveal that TMR can significantly impact side-channel leakage, either increasing resistance by introducing noise or amplifying leakage depending on the part of the design where redundancy was applied. Full article

Fault-tolerant control for bionic robotic fish presents significant challenges due to the complex dynamics and asymmetric propulsion introduced by joint failures. To address this issue, this paper proposes a fault-tolerant following control framework for multi-joint bionic robotic fish by combining fuzzy control methodologies and dynamic model correction. Firstly, offline fault analysis is conducted based on the dynamic model under multi-variable parameter conditions, quantitatively deriving influence factor functions that characterize the effects of different joint faults on velocity and yaw performance of the robotic fish. Secondly, an adaptive-period yaw filtering algorithm combined with an improved line-of-sight navigation method is employed to accommodate the motion characteristics of bionic robotic fish. Thirdly, a dual-loop following control strategy based on fuzzy algorithms is designed, comprising coordinated velocity and yaw control loops, where velocity and yaw influence factors serve as fuzzy controller inputs with expert experience-based rule construction. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed method. The obtained results indicate that the bionic robotic fish can achieve fault-tolerant following control under multiple fault types, offering a valuable solution for underwater operations in complex marine environments. Full article

Understanding the mechanisms and speed of paleo-aridification in the Qaidam Block—driven by tectonic uplift and shifts in atmospheric circulation—provides critical long-term context for assessing modern climate variability and anthropogenic impacts on water resources and desertification. This knowledge is essential for informing sustainable development strategies. We reconstruct the post-Triassic–Jurassic extinction tectonic-climatic evolution of the Qaidam Block on the northern Qinghai-Tibet Plateau margin through an integrated analysis of sedimentary facies, palynological assemblages, and Chemical Index of Alteration values from Late Triassic to Jurassic strata. The Indo-Eurasian convergence drove the uplift of the East Kunlun Orogen and strike-slip movement along the Altyn Tagh Fault, establishing a basin-range system. During the initial Late Triassic to Early Jurassic period, warm-humid conditions supported gymnosperm/fern-dominated ecosystems and facilitated coal formation. A Middle Jurassic shift from extensional to compressional tectonics coincided with a climatic transition from warm-humid, through cold-arid, to hot-arid states. This aridification, evidenced by a Bathonian-stage surge in drought-tolerant Classopollis pollen and a sharp decline in Chemical Index of Alteration values, intensified in the Late Jurassic due to the Yanshanian orogeny and distal subduction effects. Resultant thrust-strike-slip faulting and southeastward depocenter migration, under persistent aridity and intensified atmospheric circulation, drove widespread development of aeolian dune systems (e.g., Hongshuigou Formation) and arid fluvial-lacustrine environments. The tectonic-climate-ecosystem framework reveals how Jurassic tectonic processes amplified feedback to accelerate aridification. This mechanism provides a critical geological analog for addressing the current sustainability challenges facing the Qaidam Basin. Full article

This paper proposes a double-three-phase permanent magnet fault-tolerant machine (DTP-PMFTM) with low short-circuit current for flywheel energy storage systems (FESS) to balance torque performance and short-circuit current suppression. The key innovation lies in its modular winding configuration that ensures electrical isolation between the two winding sets. First, the structural characteristics of the double three-phase windings are analyzed. Subsequently, the harmonic features of the resultant magnetomotive force (MMF) are systematically investigated. To verify the performance, the proposed machine is compared against a conventional winding structure as a baseline, focusing on key parameters such as output torque and short-circuit current. The experimental results demonstrate that the proposed machine achieves an average torque of approximately 14.7 N·m with a torque ripple of about 3.27%, a phase inductance of approximately 3.7 mH, and a short-circuit current of approximately 50.9 A. Crucially, compared to the conventional winding, the modular structure increases the phase inductance by about 32.1% and reduces the short-circuit current by 29.7%. Finally, an experimental platform is established to validate the performance of the machine. Full article

This paper proposes the expressions for the motor airgap torque harmonics induced by a cascaded H-bridge inverter operating with failed cells. These variable frequency drive systems (VFDs), are widely used in oil and gas applications, where a torsional vibration evaluation is a critical challenge for field engineers. This paper proposes mathematical expressions that are crucial for an accurate torsional analysis during the design stage of VFDs, as required by international standards such as API 617, API 672, etc. By accurately reconstructing the electromagnetic torque from the stator voltages and currents in the (αβ0) reference frame, the obtained expressions enable the precise prediction of the exact locations of torque harmonics induced by the inverter under various real-world operating conditions, without the need for installed torque sensors. The neutral-shifted and peak-reduction fault-tolerant control techniques are commonly adopted under faulty operation of these VFDs. However, their effects on the pulsating torques harmonics in machine air-gap remain uncovered. This paper fulfils this gap by conducting a detailed evaluation of spectral characteristics of these fault-tolerant methods. The theoretical analyses are supported by MATLAB/Simulink 2024 based offline simulation and Typhoon based virtual real-time simulation results performed on a (4.16 kV and 7 MW) vector-controlled induction motor fed by a 7-level cascaded H-bridge inverter. According to the theoretical analyses- and simulation results, the Neutral-shifted and Peak-reduction approaches rebalance the motor input line-to-line voltages in the event of an inverter’s failed cells but, in contrast to the normal mode the carrier, all the triplen harmonics are no longer suppressed in the differential voltage and current spectra due to inequal magnitudes in the phase voltages. These additional current harmonics induce extra airgap torque components that can excite the lowly damped eigenmodes of the mechanical shaft found in the oil and gas applications and shut down the power conversion system due torsional vibrations. Full article