查看更多>>摘要：? 2022 Elsevier LtdData modelling and analysis have become a recent trend in medical and healthcare applications for their ease of visualization and handling. To keep up with the vast amount of information generated by such medical and healthcare applications, the need for computer-aided modelling and intelligent data handling is expected to increase the quality of assessment and visualization. Moreover, reliable modelling requires structured data handling for achieving better data visualization. The un-ordered and raw disease/medical data require formal structuring and grouping for improving the visualization process. Existing models consume too much time for processing a huge volume of data. This, in turn, causes a high error rate in classification, which directly affects system performance. In this paper, biosensors gather patient health information and examine the infectious with a high prediction rate. A biosensor rapidly collects patient health data changes and reduces the time complexity of data modelling and analysis. Moreover, a supervised Non-Variant Structuring (NVS) approach for grouping infectious disease data is introduced. This approach helps improve the visualization of sensor-based acquired raw data. In this structuring process, the associativity and disparity features of the infectious disease data are identified for grouping and analyzing the disease-related features. The introduced structuring method employs a supervised learning technique for identifying the associativity and disparity in different instances of accumulation based on a Hidden Markov Model (HMM). This learning technique reduces the chances of non-partial organization of infectious disease data for better modelling and analysis. The performance of the suggested approach is verified with a sensitivity ratio of 98.2%, a specificity ratio of 96.7%, and accuracy ratio of 95.5%, a prediction error rate of 7.8% less, and a classification time of 10.1% less compared to other existing methods.
查看更多>>摘要：? 2022 Elsevier LtdAccurate perception and representation of rough terrain is essential for hexapod robots to perform excellent motion. However, most existing methods currently rely on external observation sensors with low robustness and random error in harsh environments, which generally result in poor terrain measure effect. Inspired by the discrete contact characteristics between feet and terrain during hexapod robot movement, a rough terrain perception and representation method is proposed by analysing foot locomotion. Based on the vector method of kinematic analysis, a calculation model is constructed to dynamically acquire the foot positions during movement. Fully integrating these foot positions information, a local terrain representation method is proposed to precisely depict the robot landing terrain by establishing a feature point filter mechanism. According to the body posture transformation relationship between adjacent motion moments, a global terrain representation method is presented to accurately reconstruct the traversed terrain by cutting and splicing local terrains. Experiment results show the proposed method can accurately perceive and reconstruct the rough terrain travelled by the robot without using external observation sensors. The maximum average perception error in local terrains and the global terrain are reduced to 5.04 mm and 3.28 mm respectively.
查看更多>>摘要：? 2022 The AuthorsIn this research study, the comprehensive metrological analysis is investigated for a 4-hole orifice with module m = 0.25 installed in the pipeline with an internal diameter of 50 mm. A detailed numerical simulation was performed for the turbulence models: k-ε-realizable and k-ω-BSL. The novelties of the research include model validation by comparing the results of numerical studies with the experiment conducted in the area of developing turbulent flow in the range of Reynolds numbers from 4,200 to 19,000. Such validated models are sought by the system designers and can be used for further analyses and optimisation of this orifice in this flow type. The multi-hole orifices are less sensitive to flow disturbances than the standardized standard centric orifice. In addition, orifices of this type can be mounted in installations with much shorter sections upstream and downstream of the orifice - which is very often the case in industrial flow installations.
查看更多>>摘要：? 2022 Elsevier LtdEurachem/CITAC published a very useful and important Guide on Uncertainty of Qualitative Results. However, the Guide does not include the evaluation of precision, and does not address the alternative of uncertainty of qualitative results obtained from quantitative measurements described by ISO documents.
查看更多>>摘要：? 2022 Elsevier LtdThe high sensitivity of AE sensors enables engineers to detect tiny fault signals of a bearing in advance of failure. However, this process is also easily corrupted by noise, due to the sensitivity of the sensors. Among possible noise sources, electromagnetic interference (EMI) generated by variable frequency drives (VFD) is one of the most difficult noises to address because of its highly nonstationary characteristics. This disturbs the envelope spectrum, which is the conventional method of bearing diagnosis. Thus, in this paper, a method is proposed to adaptively remove EMI from the AE signal for more accurate bearing diagnosis. The proposed method eliminates EMI peaks in the enveloped frequency spectrum, using a motor current signal. To this end, the proposed method employs empirical mode decomposition (EMD) and probabilistic filtering techniques. The proposed method is verified by examining bearing testbed data and effectively eliminates the unwanted peaks of the EMI for AE data.
查看更多>>摘要：? 2022In this communication the development and performance of an especially designed parallel impedance evaluation technique for fast heat flux sensors is described. The additional signal depends on the inner resistance of the sensors active layer and is calibrated as temperature measurand. Both parallel measured quantities enable are more precise heat transfer measurement. A response signal of the measurement methodology under a thermal radiative load is provided.
查看更多>>摘要：? 2022In view of the inability of the GM model to account for random errors in the coefficient matrix and the fact that high-dimensional matrices reduce operational efficiency for any model, a novel Sequential Solution with reference to the nonlinear Gauss-Hemmert model, namely SSGH, is proposed, in which the associated efficient procedure is implemented by correlating only previous results and observations of the current period. The results show that the accuracy of parameter estimates as well as time-consumption, compared to the batch method based on the non-linear Gauss–Markov model and its sequential method, are significantly improved. Moreover, the proposed method is at least 60% more computationally efficient while maintaining the same level of accuracy as the Gauss-Helmert batch solution. It is undeniable, however, that the impact such as correlations among periods, gross errors and rank deficient, etc., require further investigation.
查看更多>>摘要：? 2022 Elsevier LtdDimensionality reduction methods based on manifold learning are widely adopted for industrial process monitoring. However, in many situations, these methods fail to preserve manifold intrinsic features in low-dimensional space, resulting in reduced process monitoring efficacy. To overcome this problem, a modified locality preserving projection (MLPP) based on the Riemannian metric is put forward. First, the Riemannian metric, which embodies a manifold's geometric information, is estimated from process data. Then, the low dimensional embedding coordinates obtained from LPP are supplemented with an estimate of the Riemannian metric. Finally, a process monitoring model is developed, and kernel density estimation is utilized to approximate confidence bounds for T2 and SPE statistics. The proposed MLPP method is applied to the feature extraction of Twin-Peaks dataset, fault detection of hot strip mill, steam turbine system and Tennessee Eastman processes. The effectiveness of MLPP method is compared with both the manifold learning and deep learning approaches. In addition, the proposed method is evaluated under various noisy conditions. The average fault detection rate of 98.9%, 99.6% and 84.4% in hot strip mill, steam turbine system and Tennessee Eastman processes, respectively, are higher than the existing methods. Quantitative results indicate the superiority of the proposed MLPP method.
查看更多>>摘要：? 2022 Elsevier LtdTo date, data mining (DM) techniques, i.e. artificial intelligence, machine learning, and statistical methods have been utilized in a remarkable number of structural health monitoring (SHM) applications. Nevertheless, there is no classification of these approaches to know the most used techniques in SHM. For this purpose, an intensive review is carried out to classify the aforementioned techniques. In doing so, a brief background, models, functions, and classification of DM techniques are presented. To this end, wide range of researches are collected in order to demonstrate the development of DM techniques, detect the most popular DM techniques, and compare the applicability of existing DM techniques in SHM. Eventually, it is concluded that the application of artificial intelligence has the highest demand rate in SHM while the most popular algorithms including artificial neural network, genetic algorithm, fuzzy logic, and principal component analysis are utilized for damage detection of civil structures.
查看更多>>摘要：? 2022 Elsevier LtdBy introducting a double Chebyshev polynomial as a general nonparametric model for various nonlinear restorying forces (NRFs) and an updated observation equation, and with the help of an Updated Extended Kalman filter with unknown input (UEKF-UI) algorithm and a data fusion technology, a nonparametric identification approach for both NRFs at multiple locations and excitation for multi-degree-of-freedom (MDOF) lumped mass structures is presented. In order to investigate the generality of the proposed identification method, both numerical and experimental studies on different MDOF structure models equipped with a shape memory alloy (SMA) damper and/or a magnetorheological (MR) damper mimicking different types of nonlinearities that widely exist in practical engineering structures under unknown excitation are carried out. Identification results considering different initial estimation errors and measurement noise show that the NRF provided by different types of dampers at multiple locations, excitation and unmeasured dynamic responses can be identified nonparametrically with acceptable accuracy.
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