Non-intrusive Framework Research Articles

A novel non-intrusive framework for real-time disaggregation of behind-the-meter solar generation from smart meter data

A novel non-intrusive framework for real-time disaggregation of behind-the-meter solar generation from smart meter data

Depolarization of opinions on social networks through random nudges.

Polarization of opinions has been empirically noted in many online social network platforms. Traditional models of opinion dynamics, based on statistical physics principles, do not account for the emergence of polarization and echo chambers in online network platforms. A recently introduced opinion dynamics model that incorporates the homophily factor-the tendency of agents to connect with those holding similar opinions as their own-captures polarization and echo chamber effects. In this work, we provide a nonintrusive framework for mildly nudging agents in an online community to form random connections. This is shown to lead to significant depolarization of opinions and decrease the echo chamber effects. Though a mild nudge effectively avoids polarization, overdoing this leads to another undesirable effect, namely, radicalization. Further, we obtain the optimal nudge probability to avoid the extremes of polarization and radicalization outcomes.

Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, which might become prohibitive when approximating steady-state solutions of PDEs for multiple combinations of control parameters and initial conditions. Therefore, constructing efficient reduced order models (ROMs) that enable accurate but fast predictions, while retaining the dynamical characteristics of the physical phenomenon as parameters vary, is of paramount importance. In this work, a data-driven, non-intrusive framework which combines ROM construction with reduced dynamics identification, is presented. Starting from a limited amount of full order solutions, the proposed approach leverages autoencoder neural networks with parametric sparse identification of nonlinear dynamics (SINDy) to construct a low-dimensional dynamical model. This model can be queried to efficiently compute full-time solutions at new parameter instances, as well as directly fed to continuation algorithms. These aim at tracking the evolution of periodic steady-state responses as functions of system parameters, avoiding the computation of the transient phase, and allowing to detect instabilities and bifurcations. Featuring an explicit and parametrized modeling of the reduced dynamics, the proposed data-driven framework presents remarkable capabilities to generalize with respect to both time and parameters. Applications to structural mechanics and fluid dynamics problems illustrate the effectiveness and accuracy of the proposed method.

Model order reduction techniques to identify submarining risk in a simplified human body model

This work investigates linear and non-linear parametric reduced order models (ROM) capable of replacing computationally expensive high-fidelity simulations of human body models (HBM) through a non-intrusive approach. Conventional crash simulation methods pose a computational barrier that restricts profound analyses such as uncertainty quantification, sensitivity analysis, or optimization studies. The non-intrusive framework couples dimensionality reduction techniques with machine learning-based surrogate models that yield a fast responding data-driven black-box model. A comparative study is made between linear and non-linear dimensionality reduction techniques. Both techniques report speed-ups of a few orders of magnitude with an accurate generalization of the design space. These accelerations make ROMs a valuable tool for engineers.

Automated Transformer fault diagnosis using infrared thermography imaging, GIST and machine learning technique

Condition monitoring of electrical systems is vital in reducing maintenance costs and enhancing their reliability. By focusing on the monitoring of electrical transformers, which play a crucial role in electrical systems and are the main equipment for electrical transmission and distribution, drastic damages, undesirable loss of power and expensive curative maintenance could be avoided. In this paper, a novel noncontact and non-intrusive framework experimental method is used for the monitoring and the diagnosis of transformer faults based on an infrared thermography technique (IRT). The basic structure of this work begins with applying (IRT) to obtain a thermograph of the considered machine. Second, GIST features of the reference image and all images in the image database are extracted. At last, various faults patterns in the transformer are automatically identified using a machine learning method called Support Vector Machine (SVM). The proposed method effectiveness and capacity are evaluated based on the experimental infrared thermography (IRT) images and the diagnosis results by identifying nine sorts of electrical transformer states among which one is healthy and the remaining eight are of short circuit faults in common core winding type, and showing that it can be considered as a powerful diagnostic tool with high Classification Accuracy (CA) and stability compared to other previously used methods.

Global-Local non intrusive analysis with robin parameters: application to plastic hardening behavior and crack propagation in 2D and 3D structures

The global/local analysis allows to embed a specific local zone of interest with a different behaviour in a global coarse model. In this local model, fine meshes are usually used to model some structural details and potentially non-linear behaviours, such as plastic hardening and crack propagation. The standard global/local approach can be observed as a Dirichlet-Neumann iterative algorithm where a Dirichlet problem on the local model and a Neumann problem on the global one are solved successively. This paper proposes a new approach for the global/local framework as Robin parameters are considered on both local and global models to obtain more flexibility and improvement for convergence. Particularly, Robin parameters are obtained using a pre-defined strip of elements and the results are later improved by means of single-objective optimization, minimizing the number of iterations to achieve convergence. This improvement is illustrated for cracked domains and plastic hardening in 2D problems and 3D elements within a non-intrusive framework, allowing the usage of commercial finite element software along with open-source research finite element software.

Non-Intrusive Luminance Mapping via High Dynamic Range Imaging and 3-D Reconstruction

Continuous luminance monitoring is challenging because high-dynamic-range cameras are expensive, they need programming, and are intrusive when placed near the occupants’ field-of-view. A new semi-automated and non-intrusive framework is presented for monitoring occupant-perceived luminance using a low-cost camera sensor and Structure-from- Motion (SfM)-Multiview Stereo (MVS) photogrammetry pipeline. Using a short video and a few photos from the occupant position, the 3D space geometry is automatically reconstructed. Retrieved 3D context enables the back-projection of the camera-captured luminance distribution into 3D spaces that are in turn re-projected to occupant-FOVs. The framework was tested and validated in a testbed office. The re-projected luminance field showed with good agreement with luminance measured at the occupant position. The new method can be used for non-intrusive luminance monitoring integrated with daylighting control applications.

Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques

In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.

A Study of Object Transparency Via Charge-Coupled Device Mathematical Modelling Assessment

This research discusses an application of Couple-Charge Device (CCD) linear sensor and laser diode in an optical tomography (OPT) system. Tomography is a system that could capture a cross-sectional image based on sensor data scattered across the periphery of the examining device in the non-invasive and non-intrusive framework. This project focuses on quantifying the level of object transparency based on CCD and mathematical modelling assessment. The objective of this project is to investigate the light refraction, reflection and absorption effect on object transparency. The study will include a discussion on the light parameters and its mathematical expression. The voltage output measured by CCD defines the degree of intensity of light obtained after penetrating through different object transparency. In conclusion, this research has successfully proved the potential of CCD in OPT configuration to detect different size diameters of a targeted transparent object, which is an air bubble in non-flowing crystal-clear liquid.

Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion

We propose a non-intrusive reduced-order modeling method based on proper orthogonal decomposition (POD) and polynomial chaos expansion (PCE) for stochastic representations in uncertainty quantification (UQ) analysis. Firstly, POD provides an optimally ordered basis from a set of selected full-order snapshots. Truncating this optimal basis, we construct a reduced-order model with undetermined coefficients. Then, PCE is utilized to approximate the coefficients of the truncated basis. In the proposed method, we construct a PCE using a non-intrusive regression-based method. Combined with the model reduction ability of POD, the proposed method efficiently provides stochastic representations in UQ analysis. To investigate the performance of the proposed method, we provide three numerical examples, i.e., a highly nonlinear analytical function with three uncertain parameters, two-dimensional (2D) heat-driven cavity flow with a stochastic boundary temperature, and 2D heat diffusion with stochastic conductivity. The results demonstrate that the proposed method significantly reduces the computational costs and storage requirements that arise due to high-dimensional physical and random spaces, while demonstrating a similar accuracy with that of the classical sparse PCE in predicting statistical quantities. Furthermore, the proposed method reasonably predicts the outputs of the full order model using only a few snapshots.

A Study of Object Transparency Via Charge-Coupled Device Mathematical Modelling Assessment

This research discusses an application of Couple-Charge Device (CCD) linear sensor and laser diode in an optical tomography (OPT) system. Tomography is a system that could capture a cross-sectional image based on sensor data scattered across the periphery of the examining device in the non-invasive and non-intrusive framework. This project focuses on quantifying the level of object transparency based on CCD and mathematical modelling assessment. The objective of this project is to investigate the light refraction, reflection and absorption effect on object transparency. The study will include a discussion on the light parameters and its mathematical expression. The voltage output measured by CCD defines the degree of intensity of light obtained after penetrating through different object transparency. In conclusion, this research has successfully proved the potential of CCD in OPT configuration to detect different size diameters of a targeted transparent object, which is an air bubble in non-flowing crystal-clear liquid.

A semi-analytical stochastic buckling quantification of porous functionally graded plates

This paper introduces a semi-analytical approach integrated with Monte Carlo simulation for stochastic buckling analyses of porous functionally graded plates arising due to the inevitable source-uncertainties of geometrical configurations and material properties. Analytical derivations based on the classical plate theory in conjunction with three-variable refined shear deformation theory are carried out first leading to closed-form solutions for buckling loads and thereby, the closed-form equations are exploited to conduct the comprehensive stochastic quantification of buckling loads in a non-intrusive framework based on Monte Carlo simulation. The deterministic framework is validated with a separate finite element analysis before implementing it for stochastic analysis. The first-order and second-order perturbation theory integrated with the Taylor series expansion are utilized to derive closed-form expressions for the mean and variance of stochastic buckling loads which are compared with Monte Carlo simulation results. Sensitivity of stochastic buckling loads on individual and compound uncertainties are investigated to determine the relative importance of different uncertainty sources. Effects of plate thickness, volume fraction index, and degree of stochasticity variations on the probability distributions of the first three stochastic buckling loads are investigated for both uniaxial and biaxial load cases. The complete probabilistic descriptions presented in this paper assert that the overlapping areas in probability distribution plots corresponding to the consecutive buckling modes lead to the existence of non-unique critical buckling modes, which could potentially be crucial for analyses and designs of structural systems under an inevitable stochastic environment. This article convincingly demonstrates the importance of considering source-uncertainties in porous functionally graded structures including the critical loopholes for a buckling analysis in the presence of such uncertainties.

Research on Diabetes by Respiration Patterns Access

Diabetes is a standout amongst the most widely recognized illness that influences numerous people. Diabetes can be alluded to as an interminable malady portrayed by abnormal amounts of sugar (glucose) in the blood. Customarily diabetes is recognized by taking blood tests, however this strategy is difficult. Henceforth by planning an Electronic nose diabetes can be recognized with just breathed out breath tests dependent on biomarkers content. In this paper a minimal effort, non-intrusive framework for distinguishing diabetes is proposed. For this reason, number of breath tests were gathered from typical and diabetic patients to distinguish the biomarker content in breath. E-Nose is utilized to identify the diabetes utilizing unpredictable natural mixes from inhale tests. E-Nose is planned utilizing Arduino MEGA 2560 and gas sensors inserted in Nose cover. In the wake of gathering simple sign from the gas sensor, simple sign is changed over into computerized values for highlight extraction and determination. In highlight extraction fitting qualities were chosen. At that point preparing is finished utilizing ANN and qualities are tried for exactness. The outcomes can be seen in PC[1].

SANComSim: A Scalable, Adaptive and Non-intrusive Framework to Optimize Performance in Computational Science Applications

Parallel processing has become the most common solution for developing and executing scientific computing applications. Actually, the best way to obtain good performance ratios is to exploit parallelism in both processing and communications. Although the study of computational performance has historically involved CPU power, currently the CPU is not the only concern in the overall performance. Due to the underlying design of parallel applications, communication networks play a very important role in the field of computational science. Despite the fact that networks used in multicore clusters are fast and have low latency, the amount of transferred data may cause a bottleneck in the communication system, as communication- intensive, parallel applications spend a significant amount of their total execution time exchanging data between processes. Moreover, in most cases, several users are executing different parallel applications at the same time in the cluster. In this paper we present SANComSim, a Scalable, Adaptive and Non-intrusive framework, based on simulation techniques, for optimizing the performance of the network system to execute complex applications. The main objective of this framework is to apply run-time compression, to reduce the data sent through the network, in order to increase the overall system performance. The main features of SANComSim are: adaptability, to dynamically adapt to the current state of the system; portability, the framework is neither focused on a specific programming language nor a platform; non-intrusive, since this framework is based on simulation techniques, which does not require exclusive access of the entire cluster system; scalability, any parallel application, independently of the number of processed and computing nodes, can use this framework to improve performance in cluster systems.