Sensor fault tolerant control using sliding mode observers

Previous work has considered the use of sliding mode observers for fault detection and isolation (FDI) in uncertain linear systems whereby the unknown faults are reconstructed by appropriate processing of the so-called equivalent output error injection. The paper builds on this work and considers such a scheme within the broader context of fault tolerant control. Specifically, by correcting the faulty measurement by an estimate of the fault obtained from the sliding mode FDI scheme, good closed-loop performance is still maintained. An example of such a scheme, which has been implemented in real-time on a laboratory dc motor rig, is described.     

On the thermodynamic stability of intermetallic phases in the AA6111 aluminum alloy

An experimental investigation of both as-cast and heat-treated AA6111 alloys pointed to the quaternary phase Al₁₆(Fe,Mn)₄Si₃ as the dominant intermetallic phase. Weight fractions of this phase were determined by extracting it from the alloys via a selective dissolution of the FCC matrix in boiling phenol. The fractions found were significantly greater than those predicted by thermodynamic modeling. In an attempt to resolve the contradiction, it was assumed that the thermodynamic stability of the quaternary phase reported in literature had been underestimated. In order to refine its thermodynamic characteristics, conditions of phase equilibria in which Al₁₆(Fe,Mn)₄Si₃ participated were handled within the framework of the CALPHAD method.     

Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing

The proliferation of Building Information Modeling (BIM) applications, in tandem with the extensive variation of building products, pose new demands on design and engineering firms to efficiently manage and reuse BIM content (i.e., data-rich parametric model objects and assembly details). Tasks such as classifying BIM objects, indexing them with meta-data (e.g., category), and searching digital libraries to load objects into models still plague practice with inefficient manual workflows. This research aims to improve the productivity of BIM content management and retrieval by developing an AI-backed BIM content recommender system. Using data from a case-study firm, this research extracted content from over 30,000 technical BIM views (e.g., plans, sections, details) in historical projects to build an unsupervised machine-learning prototype with association rule mining. This prototype explicated the strength of relationships among co-occurring BIM objects. Using this prototype as the backbone AI-engine in live BIM sessions, this research developed a context-aware recommender system that dynamically provides BIM users with a set of objects associable with their modeling context (e.g., type of view, existing objects in the model) and human–computer interactions (e.g., objects selected by the user). By mining association data from hundreds of historical projects, this development marks a departure from the existing prototypes that rely on explicit coding, recurring user input, or subjective ratings to recommend BIM content to users. The simulation and experimental implementation of this recommender system yielded high efficacy in predicting content needs and achieved significant savings in the time spent on conventional BIM workflows.     

COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis

Aim: COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images.Methods: Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet.Results: On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods.Conclusions: CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.     

Information-measuring diagnostics complex for technical maintenance

Hardware-software complex for configuration, support and diagnostics of control systems in aircraft engines and allied devices is described. Application of certain algorithms based on a bank of Kalman filters to detect parametric faults in sensors and actuators is considered.     

Adaptive B-spline-based fuzzy sliding-mode control for an auto-warehousing crane system

In this paper, an adaptive B-splined-based fuzzy sliding mode control (ABFSM) is presented to the application of auto-warehousing crane motion control. The ABFSM comprises an adaptive fuzzy identification controller (AFIC) and a B-spline-based compensation controller (BCC). The AFIC is designed to approximate the ideal controller of a crane system. To alleviate the load of fuzzy rule base construction, only the information from the sliding surface is used as the input of AFIC such that the conciseness and translucency of the control system can be upgraded. On the other hand, the BCC aims to compensate the approximation error of the AFIC. With the introduction of the B-spline function, the boundary of the approximation error can be represented by means of polynomial mapping. Thus, the design of the compensation controller can be achieved based on the characteristics of the B-spline function. In this paper, the objective of the ABFSM is to track the distance-speed reference trajectory of the crane control system. With the tuning law of the AFIC and the BCC, the stability can also be guaranteed by means of Lyapunov function. To validate the performance of the proposed approach, the ABFSM is applied to auto-warehousing crane motion control under various conditions for x, y, and z directions, respectively. From the simulation the advantages of the proposed ABFSM are demonstrated, where the capability to handle the uncertainty with efficiency is verified.     

Nuclear renaissance,public perception and design criteria: An exploratory review

There is currently an international drive to build new nuclear power plants, bringing about what is being termed a “nuclear renaissance”. However, the public perception of nuclear energy has historically been, and continues to be, a key issue, particularly in light of the Fukushima nuclear incident. This paper discusses the disparity between perceived and calculated risks based on the last four decades of research into risk perception. The leading psychological and sociological theories, Psychometric Paradigm and Cultural Theory, respectively, are critically reviewed. The authors then argue that a new nuclear-build policy that promotes a broader approach to design incorporating a wider range of stakeholder inputs, including that of the lay public, may provide a means for reducing the perceived risk of a nuclear plant. Further research towards such a new approach to design is proposed, based on integrating expert and lay stakeholder inputs and taking into account broader socio-cultural factors whilst maintaining the necessary emphasis on safety, technological development, economics and environmental sustainability.     

Laminar forced convection heat transfer around two rotating side-by-side circular cylinder

The present study numerically investigates two-dimensional laminar force convection heat transfer past two rotating circular cylinders in a side-by-side arrangement at a various range of absolute rotational speeds (|α| ? 2) for four different gap spacings (g1) of 3, 1.5, 0.7 and 0.2 at Reynolds number of 100, and a fixed Prandtl numbers of 0.7 (air). As |α| increases, the thermal field became stabilized and eventually steady beyond the critical rotational speed depending on the gap spacing. In general, as |α| increases, because the vertical motion of flow in the region of gap is strongly inhibited, the inner isotherms are early merged and shorter than the outer isotherms in the free-stream sides elongating further downstream. As |α| increases, because the rotating fluid near the cylinders surrounded much space where the steady conduction mode is predominant to the heat transfer, the behavior of the time- and surface-averaged Nusselt number has the decaying pattern with increasing |α| for all gap spacings considered in this study.     

A fuzzy c-means classification of elevation derivatives to extract the morphometric classification of landforms in Snowdonia, Wales

The increasing global coverage of high resolution/large-scale digital elevation data has allowed the study of geomorphological form to receive renewed attention by providing accessible datasets for the characterisation and quantification of land surfaces. Digital elevation models (DEMs) provide quantitative elevation data, but it is the characterisation and extraction of geomorphologically significant measures (morphometric indices) from these raw data that form more informative and useful datasets. Common to many geographical measures, morphometric measures derived from DEMs are dependent on the scale of observation. This paper reports results of employing a fuzzy c-means classification for a sample DEM from Snowdonia, Wales, with a number of morphometric measures at different resolutions as input, and morphometric classification of landforms at each resolution as output. The classifications reveal that different landscape components or morphometric classes are important at different resolutions, and that morphometric classes exhibit resolution dependency in their geographical extents. Examination of the scale dependency and behaviour of morphometric classifications of landforms at different resolutions provides a fuller and more holistic view of the classes present than a single-scale analysis.     

An adaptive sliding mode differentiator for actuator oscillatory failure case reconstruction

This paper proposes an adaptive sliding mode super-twisting differentiator which allows the gains to adapt based on the ‘quality’ of the sliding motion. A Lyapunov based analysis for the adaptive super-twisting scheme is presented to demonstrate its properties. As an example, the adaptive differentiator proposed in this paper has been used as part of a nonlinear FDI scheme for an Oscillatory Failure Case (OFC) in an actuator. The FDI scheme requires an estimate of the rod speed which is provided by the adaptive super-twisting differentiator. Due to the conditions in which the actuator operates, normally the differentiator gains are initialised at low values to ensure good rod speed estimation in fault free conditions. However for large amplitude/frequency OFCs, the gains must adapt in order to maintain sliding and provide a good estimation. Simulations on a high fidelity nonlinear aircraft benchmark model have been carried out for both liquid and solid OFCs.