In this column we explore the concept of the instrumentation ecosystem, a dynamic interconnected environment where stakeholders collectively advance education, research, design, technology, usage, and regulation of in...
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In this column we explore the concept of the instrumentation ecosystem, a dynamic interconnected environment where stakeholders collectively advance education, research, design, technology, usage, and regulation of instrumentation systems. Using non-destructive testing (NDT), with a specific focus on eddy current testing (ECT) as an illustrative case, we unravel the pivotal roles played by the key stakeholders. We argue that comprehending the instrumentation ecosystem's intricacies and dynamics fully illuminates the significance of the technological advancements and fundamental knowledge in the field of instrumentation and measurement.
Currently, traditional variational modal decomposition (VMD) algorithm requires manual selection of the number of decomposition layers K according to the decomposition results, which increases the complexity of the sy...
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Currently, traditional variational modal decomposition (VMD) algorithm requires manual selection of the number of decomposition layers K according to the decomposition results, which increases the complexity of the system. What's more, the noise reduction effect of traditional VMD algorithm needs to be further improved for low frequency signal. A kind of optimized VMD algorithm was proposed, which could select the value of K adaptively. The simulated signal containing noise of TDLAS was processed using optimized VMD algorithm, and further denoised by the least mean square (LMS) algorithm. The result shown that the proposed algorithm not only improved reconstruction effect for absorption signal, but also enhanced the measurement accuracy of TDLAS compared with regular noise reduction algorithms.
We propose a novel update step of a Gaussian mixture particle filter for nonlinear state estimation. The update procedure works as follows: First, unweighted samples are drawn in an optimal deterministic sense from a ...
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ISBN:
(纸本)9798350371420;9781737749769
We propose a novel update step of a Gaussian mixture particle filter for nonlinear state estimation. The update procedure works as follows: First, unweighted samples are drawn in an optimal deterministic sense from a prior Gaussian mixture. These samples are then assigned weights from the likelihood function, and we compute higher-order moments from this sample-based posterior. These moment approximations converge with L-1 instead of L-1/2 as our samples are optimal deterministic. Finally, the continuous posterior approximation is determined as the Gaussian mixture that has minimal Fisher information under the constraint of having the aforementioned moments. To achieve this, we employ a closed-form solution of the Fisher information that involves Gaussian root mixture densities.
In estimation, control, and machine learning under uncertainties, latent variables are usually described by a probability density function (pdf). The optimal reconstruction of a continuous pdf from given samples or mo...
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ISBN:
(纸本)9798350382662;9798350382655
In estimation, control, and machine learning under uncertainties, latent variables are usually described by a probability density function (pdf). The optimal reconstruction of a continuous pdf from given samples or moments is an important and ubiquitous task. Unfortunately, it typically results in an underdetermined optimization problem, as the pdf is not fully constrained by the given samples or moments. For regularization, we use Fisher Information (FI) that acts as a roughness measure, i.e., selects the smoothest pdf fulfilling the constraints, in an information-theoretic sense. For the important class of mixture densities, FI can only be computed numerically. In this paper, we derive a closed-form solution for FI for mixtures by transforming the problem to the space R of root mixtures (RMs). This results in a tandem processing scheme simultaneously working in the original mixture spaceMand the corresponding RM space: The density parameters are optimized in root mixture space based on the closed-form FI. The desired constraints are evaluated in the original mixture space M.
Taste is an essential factor for evaluating the quality of agricultural products. However, it is usually difficult to compare data acquired at different times or by different people because there is no invariant refer...
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Taste is an essential factor for evaluating the quality of agricultural products. However, it is usually difficult to compare data acquired at different times or by different people because there is no invariant reference and because the evaluation methods are largely subjective. Here, we addressed these problems by developing a method for standardizing strawberry sourness and sweetness intensities using a taste sensor approach with a taste standard solution composed of sour and sweet compounds. This standard solution allows highly efficient sensor measurements because it contains the standard compounds citric acid and sucrose. In addition, we found that polyphenol destabilized the sensor response for strawberry sweetness, and its removal from the sample by appropriate treatment with polyvinylpolypyrrolidone allowed stable evaluation of the sweetness intensity. The taste sensor data obtained using this method were in good agreement with the chemical analysis values related to human sensory evaluation.
The construction of a continuous probability density function (pdf) that fits a set of samples is a frequently occurring task in statistics. This is an inherently underdetermined problem, that can only be solved by ma...
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ISBN:
(纸本)9798350371420;9781737749769
The construction of a continuous probability density function (pdf) that fits a set of samples is a frequently occurring task in statistics. This is an inherently underdetermined problem, that can only be solved by making some assumptions about the samples or the distribution to be estimated. This paper proposes a density estimation method based on the premise that each sample represents the same amount of probability mass of the underlying density. The estimated pdf is parameterized as the square of a polynomial spline, which makes further processing of the estimated density very efficient. This pdf is inherently non-negative, ensuring a monotone cumulative distribution function, which makes it easy to generate samples from it through inverse transform sampling. Furthermore, it is cheap to evaluate and easy to integrate, making moment calculations fast. To find the coefficients of the polynomials that make up the spline, an optimization problem is derived. The Fisher information is used as a regularizer in this problem to select the solution that contains the least amount of information. The method is shown to work on samples from a variety of different one-dimensional probability distributions.
The present study reports the successful fabrication of ZnO/alpha-Fe2O3 heterojunctions by facile and simple hydrothermal and spin-coating methods. The effect of spin-coating different FeCl3 precursor layers on the ph...
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The present study reports the successful fabrication of ZnO/alpha-Fe2O3 heterojunctions by facile and simple hydrothermal and spin-coating methods. The effect of spin-coating different FeCl3 precursor layers on the photo electrochemical performance has been investigated. Additionally, the procedure of spin-coating FeCl3 precursors was simulated by fluent software. It is noteworthy that ZnO/alpha-Fe2O3 heterojunctions increase the absorption of visible light, promote the separation and transfer of photogenerated charge carriers, reduce the interfacial resistance between the photoelectrode and electrolyte, and provide a larger electrochemically active contact area in contrast to pure ZnO nanorods. The highest photocurrent density and the highest IPCE (incident photon-to current efficiency) reach 1.82 mA cm-2 and 40 % respectively when spin-coating three layers. Moreover, the mechanism of enhanced PEC performance was further discussed via EIS (electrochemical impedance spectroscopy), Mott-Schottky curves, Energy band structure diagram, and the electrochemical active surface area (ECSA) test.
Due to the low fusion accuracy of traditional internet of things (IoT) sensing data automatic fusion methods, an automatic fusion method of IoT sensing data based on Kalman filter is proposed. Firstly, we adopt the er...
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Today, the volume of data that needs to be communicated between wireless agents and the cloud has surpassed the ability of the available systems to transfer, manage and process it. Yet nature has told us that such an ...
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The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstra...
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The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.
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