ADP-based fault-tolerant consensus control for multiagent systems with irregular state constraints.

This paper investigates the consensus control issue for nonlinear multiagent systems (MASs) subject to irregular state constraints and actuator faults using an adaptive dynamic programming (ADP) algorithm. Unlike the regular state constraints considered in previous studies, this paper addresses irregular state constraints that may exhibit asymmetry, time variation, and can emerge or disappear during operation. By developing a system transformation method based on one-to-one state mapping, equivalent unconstrained MASs can be obtained. Subsequently, a finite-time distributed observer is designed to estimate the state information of the leader, and the consensus control problem is transformed into the tracking control problem for each agent to ensure that actuator faults of any agent cannot affect its neighboring agents. Then, a critic-only ADP-based fault tolerant control strategy, which consists of the optimal control policy for nominal system and online fault compensation for time-varying addictive faults, is proposed to achieve optimal tracking control. To enhance the learning efficiency of critic neural networks (NNs), an improved weight learning law utilizing stored historical data is employed, ensuring the convergence of critic NN weights towards ideal values under a finite excitation condition. Finally, a practical example of multiple manipulator systems is presented to demonstrate the effectiveness of the developed control method.

Microstructure Evolution of Vacuum Diffusion-Bonded 304 Stainless Steel/20 Carbon Steel Bimetallic Interface after Solution Treatment.

In this study, a 304 stainless steel (304 SS)/20 carbon steel (20 CS) bimetal was prepared by vacuum diffusion bonding, with 20 CS as the substrate and 304 SS as the cladding layer, and the interfacial microstructure and bonding strength before and after solution treatment were studied. The 304 SS and The 20 CS formed a strong metallurgical bond after being held at 1380 °C for 60 min without defects such as unbonded regions. Diffusion of Cr and Ni atoms from the 304 SS to the 20 CS occurred, and the C atoms diffused from the 20 CS to the 304 SS, forming a carburized region. A pearlitic region with an average width of approximately 20 µm was formed on the 20 CS side. After solution treatment, austenitization was formed in the carburized region, which was accompanied by the formation of twin crystals. The interfacial bonding strength of the bimetal was measured to be 485 MPa, which increased to 547 MPa after the solution treatment.

Offshore freshened groundwater reservoirs controlled by submarine faults with a complex dependency on antecedent hydrogeological conditions.

Offshore Freshened Groundwater (OFG) reservoirs are gaining attention, as evidence suggests they are more prevalent worldwide than previously thought. OFG systems are generally classified as either passive, a relic of ancient, lower sea levels, or as active, with an onshore-offshore hydrogeologic connection and associated discharge offshore. Previous studies on the mechanisms of OFG were conducted in various hydrogeologic settings, but the role of faults remains understudied. Based on geologic data, we apply hydrogeologic modeling of a faulted submarine confined aquifer in the Levant basin (eastern Mediterranean), to study the impact of faults on OFG. We find that faults that are close to the coastline and within the brackish zone that would have developed without a fault control the offshore salinities regardless of initial conditions. The influence of distal faults, in contrast, depends on antecedent conditions. When initial salinities are such that the distal fault lies in the fresh part of the aquifer, the saline wedge migrates landward toward the fault with sea-level rise, and the fault dictates the steady-state salinity distribution. If the fault is initially within the saline part of the aquifer, freshwater never reaches the fault, likely due to the density-driven flow barrier that the underlying saline wedge generates. These findings suggest a new mode of OFG in which the same geologic system can be either active or passive depending on the hydrologic history. This should be considered in future studies of OFG systems, the functioning of which has implications for marine ecosystems, seafloor geomorphology, and coastal water resources.

Fault detection and diagnosis of grid-connected photovoltaic systems using energy valley optimizer based lightweight CNN and wavelet transform.

Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative solutions for real-time monitoring, reducing latency, and improving response times. In this work, a lightweight Convolutional Neural Network (CNN) is designed and fine-tuned using Energy Valley Optimizer (EVO) for fault diagnosis. The CNN input consists of two-dimensional scalograms generated using Continuous Wavelet Transform (CWT). The proposed diagnosis technique demonstrated superior performance compared to benchmark architectures, namely MobileNet, NASNetMobile, and InceptionV3, achieving higher test accuracies and lower losses on binary and multi-fault classification tasks on balanced, unbalanced, and noisy datasets. Further, a quantitative comparison is conducted with similar recent studies. The obtained results indicate good performance and high reliability of the proposed fault diagnosis method.

Evolution Process of Fault Silica Aerogel under High Temperatures: A Molecular Dynamics Approach.

Building fire will seriously threaten human safety. Silica aerogel with low thermal conductivity and thermal stability as fire-retardant material has been widely used in building fireproof structures. However, the natural fragility of silica aerogel will limit its application. In this work, the effects of faults on the thermal stability of silica aerogel are studied by molecular dynamics simulation with large simulation time (20 ns). Additionally, the atomic model of silica aerogel with random faults is built by a straining structure (tensile strains are 10%, 20%, 30%, and 40%). It is found that when the tensile strain is less than 20%, the silica backbone can remain stable. The effects of faults on the thermal stability can be neglected. The silica backbone thermally vibrates during the heating process. However, when the tensile strain is over 30%, it is observed that the faults will enhance the silica backbone merging. Silica aerogel can be stable under 800 K. It is believed that the results of this study will pave the way for the development of fireproof materials.

Encryption-decryption-based distributed fault-tolerant consensus tracking control for multi-agent systems.

This study investigates fault-tolerant consensus tracking for discrete-time multi-agent systems (MASs) subject to external eavesdropping threats and additive actuator faults. First, actuator faults are modeled by difference equations, and decentralized observers are constructed to estimate actuator faults as well as system states. To offset fault-induced effects, ensure secure communication, and alleviate communication congestion, neighboring encrypted state information based on the encryption-decryption strategy (EDS) and estimated fault are integrated into a distributed active fault-tolerant consensus tracking control (FCTC) protocol. Through the properties of compatible norms, criteria for the controller, observer, and dynamic encryption key in EDS are derived to achieve leader-following consensus (LFC) of MASs with bias and drift actuator faults. Simulation results confirm the validity of the encryption-decryption-based distributed FCTC strategy.

Unconventional hcp/fcc Nickel Heteronanocrystal with Asymmetric Convex Sites Boosts Hydrogen Oxidation.

Developing non-platinum group metal catalysts for the sluggish hydrogen oxidation reaction (HOR) is critical for alkaline fuel cells. To date, Ni-based materials are the most promising candidates but still suffer from insufficient performance. Herein, we report an unconventional hcp/fcc Ni (u-hcp/fcc Ni) heteronanocrystal with multiple epitaxial hcp/fcc heterointerfaces and coherent twin boundaries, generating rugged surfaces with plenty of asymmetric convex sites. Systematic analyses discover that such convex sites enable the adsorption of *H in unusual bridge positions with weakened binding energy, circumventing the over-strong *H adsorption on traditional hollow positions, and simultaneously stabilizing interfacial *H2O. It thus synergistically optimizes the HOR thermodynamic process as well as reduces the kinetic barrier of the rate-determining Volmer step. Consequently, the developed u-hcp/fcc Ni exhibits the top-rank alkaline HOR activity with a mass activity of 40.6 mA mgNi -1 (6.3 times higher than fcc Ni control) together with superior stability and high CO-tolerance. These results provide a paradigm for designing high-performance catalysts by shifting the adsorption state of intermediates through configuring surface sites.

Deciphering the Atomic-Scale Structural Origin for Photoluminescence Quenching in Tin-Lead Alloyed Perovskite Nanocrystals.

The development of tin-lead alloyed halide perovskite nanocrystals (PNCs) is highly desirable for creating ultrastable, eco-friendly optoelectronic applications. However, the current incorporation of tin into the lead matrix results in severe photoluminescence (PL) quenching. To date, the precise atomic-scale structural origins of this quenching are still unknown, representing a significant barrier to fully realizing the potential of these materials. Here, we uncover the distinctive defect-related microstructures responsible for PL quenching using atomic-resolution scanning transmission electron microscopy and theoretical calculations. Our findings reveal an increase in point defects and Ruddlesden-Popper (RP) planar faults with increasing tin content. Notably, the point defects include a spectrum of vacancies and previously overlooked antisite defects with bromide vacancies and cation antisite defects emerging as the primary contributors to deep-level defects. Furthermore, the RP planar faults exhibit not only the typical rock-salt stacking pattern found in pure Pb-based PNCs but also previously undocumented microstructures rich in bromide vacancies and deep-level cation antisite defects. Direct strain imaging uncovers severe lattice distortion and significant inhomogeneous strain distributions caused by point defect aggregation, potentially breaking the local force balance and driving RP planar fault formation via lattice slippage. Our work illuminates the nature and evolution of defects in tin-lead alloyed halide perovskite nanocrystals and their profound impact on PL quenching, providing insights that support future material strategies in the development of less toxic tin-lead alloyed perovskite nanocrystals.

Blade-Coating of High Crystallinity Cesium-Formamidinium Perovskite Formulations.

Up-scalable coating processes need to be developed to manufacture efficient and stable perovskite-based solar modules. In this work, we combine two Lewis base additives (N,N'-dimethylpropyleneurea and thiourea) to fabricate high-quality Cs0.15FA0.85PbI3 perovskite films by blade-coating on large areas. Selected-area electron diffraction patterns reveal a minimization of stacking faults in the α-FAPbI3 phase for this specific cesium-formamidinium composition in both spin-coated and blade-coated perovskite films, demonstrating its scaling potential. The underlying mechanism of the crystallization process and the specific role of thiourea are characterized by Fourier transform infrared spectroscopy and in situ optical absorption, showing clear interaction between thiourea and perovskite precursors and halved film-formation activation energy (from 114 to 49 kJ/mol), which contribute to the obtained specific morphology with the formation of large domain sizes on a short time scale. The blade-coated perovskite solar cells demonstrate a maximum efficiency of approximately 16.9% on an aperture area of 1 cm2.

Cathodoluminescence studies of the optical properties of a zincblende InGaN/GaN single quantum well.

Zincblende GaN has the potential to improve the efficiency of green- and amber-emitting nitride light emitting diodes due to the absence of internal polarisation fields. However, high densities of stacking faults are found in current zincblende GaN structures. This study presents a cathodoluminescence spectroscopy investigation into the low-temperature optical behaviour of a zincblende GaN/InGaN single quantum well structure. In panchromatic cathodoluminescence maps, stacking faults are observed as dark stripes, and are associated with non-radiative recombination centres. Furthermore, power dependent studies were performed to address whether the zincblende single quantum well exhibited a reduction in emission efficiency at higher carrier densities-the phenomenon known as efficiency droop. The single quantum well structure was observed to exhibit droop, and regions with high densities of stacking faults were seen to exacerbate this phenomenon. Overall, this study suggests that achieving efficient emission from zinc-blende GaN/InGaN quantum wells will require reduction in the stacking fault density.

Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers.

In recent years, there has been an increasing use of digital vibration sensors that are based on capacitive MEMS accelerometers for machine vibration monitoring and diagnostics. These sensors simplify the design of monitoring and diagnostic systems, thus reducing implementation costs. However, it is important to understand how effective these digital sensors are in detecting rolling bearing faults. This article describes a method for determining the diagnostic sensitivity of diagnostic parameters provided by commercially available vibration sensors based on MEMS accelerometers. Experimental tests were conducted in laboratory conditions, during which vibrations from 11 healthy and faulty rolling bearings were measured using two commercial vibration sensors based on MEMS accelerometers and a piezoelectric accelerometer as a reference sensor. The results showed that the diagnostic sensitivity of the parameters depends on the upper-frequency band limit of the sensors, and the parameters most sensitive to the typical fatigue faults of rolling bearings are the peak and peak-to-peak amplitudes of vibration acceleration. Despite having a lower upper-frequency range compared to the piezoelectric accelerometer, the commercial vibration sensors were found to be sensitive to rolling bearing faults and can be successfully used in continuous monitoring and diagnostics systems for machines.

X-ray diffraction of metastable structures from supercooled liquid hydrogen.

We report time resolved observations of the crystallization from liquid hydrogen, supercooled to temperatures below the melting point, using 11.2 keV X-ray diffraction from the Linac Coherent Light Source (LCLS). Changes to the metastable solid and liquid structure factors have been dynamically measured. This allows for a direct determination of the lowest energy crystal polymorphs, the stacking probabilities, as well as the liquid and solid densities and temperatures. Such measurements provide experimental evidence of an Arrhenius-like growth kinetics along the stacking direction during supercooling.

Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties.

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.

Stacking Faults Enabled Second Harmonic Generation in Centrosymmetric van der Waals RhI3.

Second harmonic generation (SHG) in van der Waals (vdW) materials has garnered significant attention due to its potential for integrated nonlinear optical and optoelectronic applications. Stacking faults in vdW materials are a typical kind of planar defect that introduces a degree of freedom to modulate the crystal symmetry and resultant SHG response. However, the physical origin and tunability of stacking-fault-governed SHG in vdW materials remain unclear. Here, taking the intrinsically centrosymmetric vdW RhI3 as an example, we theoretically reveal the origin of stacking-fault-governed SHG response, where the SHG response comes from the energetically favorable AC̅ stacking fault of which the electrical transitions along the high-symmetry paths Γ-M and Γ-K in the Brillion zone play the dominant role at 810 nm. Such a stacking-fault-governed SHG response is further confirmed via structural characterizations and SHG measurements. Furthermore, by applying hydrostatic pressure on RhI3, the correlation between structural evolution and SHG response is revealed with SHG enhancement up to 6.9 times, where the decreased electronic transition energies and higher momentum matrix elements due to the stronger interlayer interactions upon compression magnify the SHG susceptibility. This study develops a promising foundation for nonlinear nano-optics applications through the strategic design of stacking faults.

An effective method for small objects detection based on MDFFAM and LKSPP.

Object detection is one of the research hotspots in computer vision. However, most existing object detectors struggle with the identification of small targets. Therefore, the paper proposes two modules: the MDFFAM (Multi-Directional Feature Fusion Attention Mechanism) and the LKSPP (Large Kernel Spatial Pyramid Pooling), to enhance the detector's effectiveness in identifying subtle faults on the surface of mechanical equipment. LKSPP aims to expand the receptive field to capture high-level semantic features through large kernels. Meanwhile, the MDFFAM allows the network to efficiently utilize spatial location information and adaptively recognize detection priorities. In the detection task, MDFFAM effectively captures feature information in three spatial directions: width, height, and channel, with the location information fully utilized to establish stable long-range dependencies. Moreover, LKSPP boasts a larger receptive field and imposes less computational burden compared to the SPPCSPC by YOLOv7. Finally, experiments demonstrate that the proposed module effectively improves the detection accuracy for small targets, surpassing the state-of-the-art object detector, YOLOv7. Remarkably, MDFFAM incurs almost negligible computational overhead.

Application of artificial intelligence in mine ventilation: a brief review.

In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.

Influence of rock type and geophysical properties on radon flux density.

The most significant source of human exposure to ionizing radiation is the radioactive gas radon (basically 222Rn) and its daughter decay products, creating more than half of the effective dose from all natural sources. Radon enters buildings mainly from dense rocks, which are below building foundations at depths of 1 m and more. In this paper long-term measurements of radon flux density are analyzed, with radon exhalation from the surface of the most common rocks-loams, sandy loams, clays, clay shales, several types of sandy-gravel-pebble deposits, clay and rocky limestone. The influence of geophysical properties of rocks on radon flux density due to exhalation from surfaces of those rocks was studied. Based on the results obtained, a method of local assessment of the hazard from radon and its progeny in buildings is proposed, which is based on the geophysical properties of rocks below the foundations of those buildings.

Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis.

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained.

Resilient exponential tracking for disturbed systems with communication links faults.

Resilience is to appraise the ability of disturbed systems to recover cooperative performance after suffering from failures or disturbances. In this paper, the improvement on the exponential tracking resilience for disturbed Euler-Lagrange systems is explored by settling the unknown time-variant faults imposed on the communication interaction between agents. First, we transform the resilient exponential tracking problem into designing the trajectory and velocity observers for leaders, and showcase that the proposed observers are resilient to communication interaction malfunctions. Second, a disturbance observer is manifested to estimate disturbances precisely, which is needless to know the upper bound of disturbance. The reliable observers and estimator are incorporated into the resilient tracking control frame. Further, the global exponential stabilization of the tracking systems is performed by utilizing the Lyapunov theory. Moreover, benefiting from feasible and reliable observation and estimation results, the proposed control framework enables to realize a satisfactory resilient exponential tracking performance even in the case of communication links faults (CLFs) and disturbances. Comprehensive studies are executed on a group of satellite systems, and the simulations results verify the effectiveness of the proposed resilient approaches in a time-variant tracking case.