Isolation Scheme Research Articles

Seismic isolation design of high-rise shear wall structures in high-intensity areas

Taking a shear wall structure in a high-intensity area of 20 floors as an example, the seismic reduction scheme of the structure was determined, and the Etabs software was used to conduct time history analysis on the seismic and non-seismic structures. The floor shear force, overturning moment, and short-term surface pressure of the isolation support were obtained. The results showed that under frequent earthquake action, the average maximum floor shear force in the X direction of the isolated upper structure was only about 28 % of that of the non-isolated structure. The maximum inter story shear ratio was 0.34, and the maximum floor shear force in the Y direction was only about 25 % of that of the non-isolated structure. The maximum inter story shear ratio was 0.31. The average maximum overturning moment of the upper structure in the X direction after isolation is about 30 % of that of the non-isolated structure. The maximum overturning moment ratio is 0.36, and the average overturning moment of the maximum floor in the Y direction is about 22 % of that of the non-isolated structure. The maximum overturning moment ratio is 0.26. Under rare earthquake action, the maximum inter story displacement angles in the X and Y directions of each floor of the seismic isolation structure are 1/302 and 1/446, respectively. The maximum surface pressure value of the isolation bearing under rare earthquake is 28.9 MPa, the minimum surface pressure value is 5.58 MPa, and the maximum displacement is 579 mm, which meets the requirements of the specifications. Overall, the seismic performance of the seismic isolation structure is good, and the isolation scheme is feasible.

Protection and Fault Isolation Scheme for DC Distribution Network Based on Active Current-Limiting Control

Protection and Fault Isolation Scheme for DC Distribution Network Based on Active Current-Limiting Control

Synergistic frameworks for sensor fault isolation and accommodation in grid-side converters

The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters (GSCs), utilizing the feedback measurements of voltage and current sensors from both the direct current (DC) and alternating current (AC) sides of the converter. However, the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation. Although sensor fault detection algorithms are usually effective under abrupt faults, the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor. Therefore, this work proposes a robust, model-based fault isolation and accommodation scheme. Specifically, a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors, considering modeling uncertainty and measurement noise. The performance analysis in terms of stability, learning capability, and fault isolability is rigorously examined. An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with real-time learning capabilities is applied to a GSC. Finally, the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.

Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller’s ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes.

Identification of Electron Transfer in the System of Ferredoxins and Ferredoxin Reductases from Mycolicibacterium smegmatis

Steroid-26-monooxygenases belong to the cytochrome P450 superfamily and function as part of three-component systems together with ferredoxins and ferredoxin reductases providing electron transport. The P450-dependent redox partners of the actinobacterial strain Mycolicibacterium smegmatis mc2155 were investigated. The genes encoding mycolibacterial ferredoxins (FdxD and FdxE) and ferredoxin reductases (FdrA and FprA) were overexpressed in E. coli cells. A scheme for isolation and purification of synthesized recombinant proteins using affinity chromatography was developed, resulting in electrophoretically homogeneous preparations. Spectral analysis of ferredoxin reductase showed absorption peaks characteristic of FAD-containing proteins. The reaction of cytochrome c reduction using recombinant proteins was carried out, demonstrating that FdxD, FdxE, FdrA, and FprA can act as components of electron transport from the reducing equivalents of NAD(P)H.

Design of active vibration isolation controller for pointing stabilization mechanism based on feedforward–feedback hybrid control

The use of space technology and small space loads is increasingly common. To address this, a mechanism for isolation vibration in small optical load has been proposed. The mechanism includes a coarse and fine stage parallel pointing platform (CFPP). This paper investigates an active vibration isolation scheme for the novel pointing stabilization mechanism. The kinetic energy minimization principle is derived from the analysis of its working mechanism and dynamic feedforward characteristics. This principle is confirmed by the feedforward of the indeterminate degrees of freedom of the under-constrained mechanism. A hybrid control scheme of feedback and feedforward is developed based on the H∞ algorithm and the optimal feedforward control algorithm. Simulation and experimentation have proven that the vibration isolation efficiency of more than 20 dB can be achieved in all three axis rotation directions. This meets the precision pointing requirements of small optical load effectively.

Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

Compact inertial sensors for measuring external disturbances of physics experiments

Compact, high-precision inertial sensors are needed in the control schemes of many modern physics experiments to isolate them from disturbances caused by seismic motion. We present an inertial sensor whose mechanical oscillator fits on a one-inch diameter optic. The oscillators achieve a mechanical Quality factor of a fundamental oscillation mode of 600,000 and a resonance frequency of 50 Hz, giving them a suspension thermal noise floor lower than all commercially available inertial sensors. The motion of this fundamental mode is suitable to encode inertial motion into the sensor readout. The oscillator is combined with an optical resonator readout scheme that achieves a displacement noise of 100 fm/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{\ ext{Hz}}$$\\end{document} above 0.2 Hz. We validate the sensors’ noise floor using a huddle test. Below 20 Hz, the sensor offers comparable performance to the best inertial sensors available today while being a fraction of the size. Above 20 Hz, the sensor is, to the author’s knowledge, the best demonstrated in the literature to date for such a sensor, with a self-noise floor of 0.1 ng/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{\ ext{Hz}}$$\\end{document}. The excellent performance of the sensors across seismically relevant frequencies, vacuum compatibility, and compact size make it a prime candidate for integration into sophisticated seismic isolation schemes, such as those used by gravitational wave detectors.

Seismic Isolation Layout Optimized of Mid-Rise Reinforced Concrete Building Frame Structure

Seismic isolation technology plays a crucial role in enhancing earthquake resistance and mitigating disasters for building structures. In this study, the ETABS analysis software V21.0.1 is utilized to establish a numerical model of a six-story steel reinforced concrete frame structure. Both the time-history analysis method and response spectrum method are employed to calculate the seismic response of the model under earthquake actions. The placement of an isolation layer on the foundation and from the first to fifth floor is considered, with separate calculations conducted for each scenario. Subsequently, a comprehensive comparison and analysis of the dynamic response characteristics among different design schemes are performed. The results demonstrate that the most favorable isolation effect is achieved when the isolation layer is implemented on the foundation or first floor. Compared to non-isolated structures, the natural period of the structure can be extended by 2.2 times and 2 times under the base isolation and first-floor top isolation schemes, respectively. The damping coefficients can reach 0.35 and 0.36, respectively, while the inter-story drift angles can be reduced by 66% and 67%, respectively.

The distributed fault diagnosis for nonlinear complex dynamical networks based on [formula omitted]-detection criterion

The actuator fault diagnosis problem of nonlinear complex dynamical networks with stochastic noise is investigated. Firstly, a local estimation based on a priori knowledge and measurement output is designed to estimate state for each node. A sufficient condition is derived such that the estimation error is stable and the estimation gain matrix is obtained in fault-free case by applying the stability theory. Secondly, considering additive actuator fault in presence of process and measurement noises, the residual signal based on estimation error is designed to detect faults and locate them. Thirdly, the threshold is acquired by applying the covariance matrix of estimation error. A fault detection and isolation logic is designed via the σ-detection criterion. Finally, two different examples are designed to verify the effectiveness of the designed actuator fault detection and isolation scheme.

Microbial metaproteomics--From sample processing to data acquisition and analysis

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Sensor faults in nuclear power plants (NPPs) have the potential to propagate negative impacts on system stability, leading to false alarms and accident misdiagnosis. Existing methods seldom concurrently consider complex spatial–temporal correlations among multi-type sensors in the primary circuit. This study presents a novel sensor fault detection and isolation scheme named the knowledge-guided spatial–temporal model (KGSTM), using the knowledge-guided recurrent unit (KGRU) and the concurrent detection strategy. To organically express part and whole interdependencies from inherent sensor layout, several graphs are specifically designed with pertinent domain knowledge. KGRU consists of the multi-graph convolutional network (MGCN) for fusing various spatial information and the gate recurrent unit (GRU) for extracting dynamic temporal features, further obtaining precise reconstructed signals and residuals. The concurrent detection strategy can explicitly quantify abnormal behaviors to detect and isolate faulty sensors by characterizing spatial–temporal signal variation. Numerical results on two real-world datasets from a pressurized water reactor (PWR) with simulated faults illustrate that the KGSTM has superior performance over various state-of-the-art methods in terms of signal reconstruction and fault detection.

Longitudinal low-frequency vibration control of a propeller-shafting system using a hydraulic leveraged dynamic anti-resonance vibration isolator: A theoretical and experimental study

The longitudinal fluctuating forces of a propeller are a major cause of sound radiation of an underwater vehicle in the low frequency range. A hydraulic leveraged dynamic anti-resonance vibration isolator (HLVI) is proposed to attenuate the longitudinal vibration of a shaft system transmitted to the hull. The semi-analytical model of the shafting system with and without the isolator is set up by employing the frequency response synthesis method, in which the elasticity of the foundation is taken into consideration. Compared to the traditional isolation scheme and DVA, the proposed control scheme will not change the longitudinal effective stiffness and needs a small mass to attenuate the longitudinal vibration of the shafting system. A parametric study was conducted to investigate the key parameters of the isolator and their impact on its isolation performance. An experimental apparatus is set up to validate the isolation scheme. The acceleration frequency response results of the shafting system under axial excitation with both the frequency sweep method and discrete sinusoidal frequencies are presented and discussed. The results indicate that the proposed method is capable of attenuating the corresponding longitudinal vibration of the shafting system.

Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach.

The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based H∞ controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.

A High-Finesse Suspended Interferometric Sensor for Macroscopic Quantum Mechanics with Femtometre Sensitivity.

We present an interferometric sensor for investigating macroscopic quantum mechanics on a table-top scale. The sensor consists of a pair of suspended optical cavities with finesse over 350,000 comprising 10 g fused silica mirrors. The interferometer is suspended by a four-stage, light, in-vacuum suspension with three common stages, which allows for us to suppress common-mode motion at low frequency. The seismic noise is further suppressed by an active isolation scheme, which reduces the input motion to the suspension point by up to an order of magnitude starting from 0.7 Hz. In the current room-temperature operation, we achieve a peak sensitivity of 0.5 fm/Hz in the acoustic frequency band, limited by a combination of readout noise and suspension thermal noise. Additional improvements of the readout electronics and suspension parameters will enable us to reach the quantum radiation pressure noise. Such a sensor can eventually be utilized for demonstrating macroscopic entanglement and for testing semi-classical and quantum gravity models.

Rapid Isolation and Detection of Breast Cancer Circulating Tumor Cells Using Microfluidic Sequential Trapping Array

AbstractCirculating tumor cells (CTCs) have garnered special attention as promising cancer biomarkers. Phenotypic changes of CTCs reveal invaluable information for oncologists in disease prognosis and adjusting their treatment options. Microfluidic technology has emerged as a promising tool for CTC isolation; however, two major hurdles remain to be solved in employing them in CTC analysis. First, a rapid CTC isolation scheme is needed to allow immediate use of patient samples for point‐of‐care treatment monitoring. Second, multiplexed and streamlined CTC imaging is needed to facilitate CTC detection. Here, a microfluidic CTC sequential trapping array (STA) is proposed which addresses these hurdles enabling pipette‐based CTC isolation and simultaneous profiling of multiple CTC protein expressions. The STA device isolates CTCs based on their size difference from blood cells and increases sample processing throughput through its parallel design configuration. It successfully isolates CTC from a depleted peripheral blood mononuclear cells sample of breast cancer patients with a high recovery rate of 80% and discriminates the number and types of CTCs in breast cancer based on their disease stage. These findings will open a new avenue in clinical translation of CTC profiling technologies. It will be an example for future translational developments in CTC‐based cancer management.

Enhanced Performance of Ultraviolet AlGaN/GaN Photo‐HEMTs by Optimized Channel Isolation Schemes

AbstractSolar blind ultraviolet (UV) photodetectors utilizing AlGaN/GaN high electron mobility transistor (HEMT) structure offer a very high responsivity, photo‐to‐dark current ratio (PDCR), and detectivity. However, the performance of conventional mesa‐isolated photo‐HEMT is limited due to current leakage paths through the side wall and AlGaN barrier. In this work, apart from conventional mesa‐type isolation, ion implantation and metal‐insulator‐semiconductor (MIS) schemes are adopted to isolate the channels of AlGaN/GaN photo‐HEMTs. A significant reduction in dark current is achieved for MIS photo‐HEMT of around four and three orders of magnitude as compared to those of conventional mesa‐ and ion‐implanted photo‐HEMTs, respectively. Moreover, the responsivity and PDCR of MIS photo‐HEMT are around three and four orders of magnitude higher than those of conventional mesa‐isolated photo‐HEMT. Subsequently, MIS photo‐HEMT on Si substrate has achieved a very high detectivity of 4.63 × 1016 Jones at the UV incidence wavelength of 360 nm. The results presented in this work establish the advantages of MIS photo‐HEMT over other types of photo‐HEMTs. In addition, compatibility with GaN device fabrication technology and significantly enhanced optoelectronic performance suggest AlGaN/GaN MIS photo‐HEMTs as a promising candidate for a variety of advanced UV sensing applications.

Diagnosis of Multicomponent Faults in SRM Drives Based on Auxiliary Current Reconstruction Under Soft-Switching Operation

Switched reluctance motors (SRMs) are considered a potential candidate for utilization in numerous safety-critical applications including electric vehicles (EVs) and more/all electric aircraft (MEA), etc., where the reliability of the drive system is critically important. An SRM drive system suffers from various electrical, mechanical, and sensor faults during operation. The existing fault detection schemes investigated each fragile component separately and proposed different approaches for each fault, which not only make the system complex but also less reliable for real-time implementation in safety-critical applications. Thus, a more sophisticated algorithm to detect and discriminate each of these faults in real time becomes attractive. A simple fault detection and isolation scheme for multiple faults in SRM drive systems is reported in this paper. The investigated faults include open- and short-circuit faults in power switches, open-phase winding, and zero-output current sensors faults. An auxiliary phase current for each phase was reconstructed from the dc-link and the faults are identified and discriminated effectively through the digital analysis of the fundamental current and the auxiliary current in real-time. The feasibility of the proposed scheme is verified by the extensive simulation and experiments conducted on a three-phase 12/8 SRM drive system.

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

Seismic evaluation of isolation performance on single layer cylindrical reticulated shells supported along four sides

This study investigated the dynamic characteristics and seismic isolation performance of a single-layer cylindrical grid shell structure with lower support structures and different seismic isolation schemes, which encompass the installation of layered rubber bearings and new three-dimensional isolation bearings. Seven reduced-scale models of the structure were tested on a shaking table, with acceleration, strain, and displacement sensors positioned in typical locations. The results showed that the seismic response of the upper cylindrical grid shell structure was amplified by the lower support structure, which indicates that separate seismic designs for both the upper structure and lower support structure are unsafe. The use of layered rubber bearings at the foundation effectively reduced horizontal seismic response but amplified vertical seismic response. To address this issue, the new three-dimensional isolation bearing was proposed, which showed better overall seismic isolation performance compared to the rubber bearings. Additionally, a new seismic isolation scheme was proposed, using a combination of three-dimensional isolation bearings at both the foundation and column tops. This scheme showed superior seismic isolation efficiency compared to other schemes and can reduce the volume and installation difficulty of the three-dimensional isolation bearing. Considering the functional requirements, cost, and technical difficulty is crucial when selecting suitable seismic isolation measures since different schemes have their advantages and disadvantages and perform differently under various seismic conditions.