Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Głowacz, Adam

doi:10.3390/s21082853

Cited by 148 publications

(39 citation statements)

References 40 publications

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“…As future work, we will investigate and utilize suitable fixed-point arithmetic for our fundamental operators and will analyze the tradeoffs in terms of area, speedup, and accuracy. Additionally, as future work, we will investigate and utilize our proposed embedded architectures for the CO-based SVM for various applications/fields including fault diagnosis [60].…”

Section: Discussionmentioning

confidence: 99%

An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms

Ramadurgam

Perera

2021

Electronics

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Machine learning is becoming the cornerstones of smart and autonomous systems. Machine learning algorithms can be categorized into supervised learning (classification) and unsupervised learning (clustering). Among many classification algorithms, the Support Vector Machine (SVM) classifier is one of the most commonly used machine learning algorithms. By incorporating convex optimization techniques into the SVM classifier, we can further enhance the accuracy and classification process of the SVM by finding the optimal solution. Many machine learning algorithms, including SVM classification, are compute-intensive and data-intensive, requiring significant processing power. Furthermore, many machine learning algorithms have found their way into portable and embedded devices, which have stringent requirements. In this research work, we introduce a novel, unique, and efficient Field Programmable Gate Array (FPGA)-based hardware accelerator for a convex optimization-based SVM classifier for embedded platforms, considering the constraints associated with these platforms and the requirements of the applications running on these devices. We incorporate suitable mathematical kernels and decomposition methods to systematically solve the convex optimization for machine learning applications with a large volume of data. Our proposed architectures are generic, parameterized, and scalable; hence, without changing internal architectures, our designs can be used to process different datasets with varying sizes, can be executed on different platforms, and can be utilized for various machine learning applications. We also introduce system-level architectures and techniques to facilitate real-time processing. Experiments are performed using two different benchmark datasets to evaluate the feasibility and efficiency of our hardware architecture, in terms of timing, speedup, area, and accuracy. Our embedded hardware design achieves up to 79 times speedup compared to its embedded software counterpart, and can also achieve up to 100% classification accuracy.

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Section: Discussionmentioning

confidence: 99%

An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms

Ramadurgam

Perera

2021

Electronics

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“…Thus, some techniques need to be adopted to transfer the binary classifier to a multiple classifier. The SVM has a capability to train and test the feature vectors and works efficiently with unstructured and semi structured data [ 16 ]. As a result, it works fast with linearly separable.…”

Section: Introductionmentioning

confidence: 99%

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Razaque

Frej

Almiani

et al. 2021

Sensors

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Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

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“…The progress of science is often determined by the progress of measurements, so there is intensive research in this area [ 1 , 2 , 3 ]. The studies consider the whole measuring channel [ 4 , 5 , 6 ] and its individual components [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Energy Structure and Luminescence of CeF3 Crystals

et al. 2021

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The results of the calculation of the energy band structure and luminescent research of CeF3 crystals are presented. The existence of two 5d1 and 5d2 subbands of the conduction band genetically derived from 5d states of Ce3+ ions with different effective electron masses of 4.9 me and 0.9 me, respectively, is revealed. The large electron effective mass in the 5d1 subband facilitates the localization of electronic excitations forming the 4f-5d cerium Frenkel self-trapped excitons responsible for the CeF3 luminescence. The structure of the excitation spectra of the exciton luminescence peaked at 290 nm, and the defect luminescence at 340 nm confirms the aforementioned calculated features of the conduction band of CeF3 crystals. The peculiarities of the excitation spectra of the luminescence of CaF2:Ce crystals dependent on the cerium concentration are considered with respect to the phase formation possibility of CeF3.

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Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Cited by 148 publications

References 40 publications

An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms

An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Energy Structure and Luminescence of CeF3 Crystals

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