planar electromagnetic sensor arrays have advantages such as nice coherence, fast response speed and high sensitivity, which can be used for micro damage inspection of crucial parts in equipment, and the key point in ...
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planar electromagnetic sensor arrays have advantages such as nice coherence, fast response speed and high sensitivity, which can be used for micro damage inspection of crucial parts in equipment, and the key point in improving the inspection performance is to achieve a precise measurement of multi-channel transimpedances (the inductive voltages divided by the exciting current of the sensor). The principle and characteristics of planar electromagnetic sensor arrays are introduced in this paper, and a digital lock-in impedance measurement algorithm was investigated, with which the interference and noise in inductive voltage signals can be restrained effectively and the amplitude and phase of the transimpedance can be obtained with good repeatability. An eight channel impedance measurement system was established based on a field programmable gate array and utilized to inspect the micro damage in metal materials, and the experimental data were analyzed. The experimental results show that the impedance measurement has excellent repeatability when the sensorarray is placed in air, and the maximum measurement error of the complete transimpedance measurement system is lower than 10%. A micro crack with a size of 1 mm (length) x 0.1 mm (width) x 1 mm (depth) can be detected through the measurement of multi-channel transimpedance in the planar electromagnetic sensor array.
Purpose-The purpose of this study is to determine the contamination level in natural water resources because the tremendous development in the agriculture sector has increased the amount of contamination in natural wa...
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Purpose-The purpose of this study is to determine the contamination level in natural water resources because the tremendous development in the agriculture sector has increased the amount of contamination in natural water sources. Hence, the water is polluted and unsafe to drink. Design/methodology/approach-Three types of sensorarrays were suggested: parallel, star and delta. The simulation of all types of sensorarray was carried out to calculate the sensors' impedance value, capacitance and inductance during their operation to determine the best sensorarray. The contamination state was simulated by altering the electrical properties values of the environmental domain of the model to represent water contamination. Findings-The simulation results show that all types of sensorarray are sensitive to conductivity, sigma, and permittivity, epsilon (i.e. contaminated water). Furthermore, a set of experiments was conducted to determine the relationship between the sensor's impedance and the water's nitrate and sulphate contamination. The performance of the system was observed where the sensors were tested, with the addition of distilled water with different concentrations of potassium nitrate and potassium sulphate. The sensitivity of the developed sensors was evaluated and the best sensor was selected. Practical implications-Based on the outcomes of the experiments, the star sensorarray has the highest sensitivity and can be used to measure nitrate and sulphate contaminations in water. Originality/value-The star sensorarray presented in this paper has the potential to be used as a useful low-cost tool for water source monitoring.
This paper looked into the electrical properties of contaminants via planarelectromagnetic sensing array (PESA), which had been coated with various types of coaters, namely, N-Methyl-2-pyrrolidone-based membrane and ...
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This paper looked into the electrical properties of contaminants via planarelectromagnetic sensing array (PESA), which had been coated with various types of coaters, namely, N-Methyl-2-pyrrolidone-based membrane and acrylic. Hence, the performance of the coaters had been evaluated based on the minimal difference of estimated results, which was later compared with an Agilent 85070 dielectric probe instrument. Three samples were selected for this paper, namely, nitrate, phosphate, and nickel solutions. These samples were prepared based on varied concentration levels, including 5, 25, 75, and 100 ppm. Besides, the theory for the sensor equivalent to circuit described the calculations to determine the aspects of permittivity and conductivity of the water samples. The results demonstrated that both coaters successfully estimated the electrical properties of the contaminants. The relative permittivity of all samples using the PESA coated with membrane displayed similar trends for the dielectric probe, but a slight variance was present for the PESA coated with acrylic. In fact, the relative permittivity of the nitrate sample ranged from 65 to 78 when the estimation was made using PESA coated with membrane, whereas the acrylic coater demonstrated that the relative permittivity data varied from 0.198 to 41.100. Hence, the acrylic coater failed in precision, thus inappropriate for relative permittivity estimation. Nonetheless, both coaters shared similar pattern for conductivity even with varied concentrations. However, the conductivity increased as the sample concentration increased. As such, this paper concludes that the PESA coated with membrane exhibited better performance compared with acrylic in estimating electrical properties.
The applications of planarelectromagneticsensor are gaining worldwide attention since it was introduced due to simplicity, fast response, convenience, and low cost. Numerous membranes have been investigated to remov...
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ISBN:
(纸本)9781479978632
The applications of planarelectromagneticsensor are gaining worldwide attention since it was introduced due to simplicity, fast response, convenience, and low cost. Numerous membranes have been investigated to remove nitrates from aqueous solution. This paper aims to produce selective membrane for detecting nitrate ion based on planarelectromagneticsensors and compare it with conventional coating using acrylic lacquer. The preparation method of membrane in this study will reduce the cost of pretreatment as no addition of nutrients are required, thus presenting a promising alternative method. In view of this, a membrane of polymer dope of silica is selected as an alternative coater for the planarelectromagneticsensors array as to increase the selectivity in the detection of unwanted nitrate ions in aqueous solution. Finally, the methods in choosing the best coater in supporting the detection of nitrate contamination have been determined and it showed that the planarelectromagneticsensors array coated with the selective membrane yields absolute average sensitivity, |Z%| value range between the range of around 0.007% to 223%, when tested with nitrate concentrations of 5 ppm, 25 ppm, and 100 ppm, which is the highest absolute average sensitivity, |Z%| value range of the results.
This paper presents the comparisons between two models to classify nitrate and phosphate contamination in water supply based on artificial intelligence with multiple inputs parameters. The planarelectromagnetic senso...
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ISBN:
(纸本)9781479978632
This paper presents the comparisons between two models to classify nitrate and phosphate contamination in water supply based on artificial intelligence with multiple inputs parameters. The planar electromagnetic sensor array has been subjected to different water samples contaminated by nitrate and phosphate where output signals have been extracted. In the first method, the signals from the planar electromagnetic sensor array were derived to decompose by Wavelet Transform (WT). The energy and mean features of decomposed signals were extracted and used as inputs for an Artificial Neural Network (ANN) multilayer perceptron (MLP) and Radial Basis Function (RBF) neural networks models. The analysis models were targeted to classify the amount of nitrate and phosphate contamination in water supply. The result shows that the planar electromagnetic sensor array with the assistance of the MLP neural network method is the best alternative as compared to RBF neural network method.
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