查看更多>>摘要：Miniaturization of sensors using micromachining technology is full of potential and challenges. A miniature capacitance diaphragm gauge for absolute vacuum measurement is developed in this work. Both theoretical calculation and simulation method are used to analyze the working principle of the pressure sensitive diaphragm in different pressure regions. During the manufacturing processes, a silicon block coated with non-evaporable getter films is used to protect the diaphragm and maintain the vacuum pressure of the reference cavity. The capacitance diaphragm gauge with an overall size of 8 x 10 x 1.4 mm(3) was successfully fabricated, and its vacuum metrologies in rough and medium vacuum regime were evaluated systematically. The lower measure-ment limit of the gauge is 0.1 Pa, its maximum and minimum sensitivities in the range of 0.1 Pa to 84 kPa are 10.96 and 0.16 fF/Pa, respectively. The repeatability and the hysteresis errors of the gauge are less than 1.3% and 5.3%.
查看更多>>摘要：In view of the classification of corrosion defects of well controlled manifold pipelines, an ultrasonic defect recognition method based on the combination of support vector machine(SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed. Firstly, perform wavelet packet decomposition on the ultrasonic defect signal waveform to obtain the characteristic vector of characterizes the defect type; Then establish the support vector machine defect classification model, and use the improved artificial fish swarm algorithm to optimize the support vector machine parameters. Finally, a software and hardware experimental platform for the classification of pipeline corrosion defects of the well control manifold is built to carry out software simulation and experimental analysis. The experimental results show that the recognition rate of the defect classification model based on improved artificial fish swarm optimization support vector machine parameters is 94.67% for ultrasonic defect signals at different depths.
查看更多>>摘要：The absolute stress of steel components is a key parameter in the construction and service of steel structures. Traditional stress testing methods have drawbacks of high cost and low accuracy. A new method based on deep learning and ultrasonic technique is proposed to obtain the absolute stress of steel components with different thicknesses. Firstly, ultrasonic signals of steel components under different stress levels were collected and used to build datasets. Secondly, the optimal architecture of one-dimensional convolutional neural networks (CNNs) for stress identification of steel components was determined. Finally, parameters of the network with the optimal architecture were optimized and used to identify the absolute stress of the unknown test dataset. The results show that the average stress identification error for the unknown test dataset is 3.83%. The proposed method can overcome the drawbacks of conventional techniques and provide good references for stress identification of steel components in practical engineering.
查看更多>>摘要：In the paper GUM-Dw3 graphite-walled cylindrical cavity ionization chamber (of design and volume typical for the chambers for radiation therapy dosimetry) was presented as a realization of an ionometric standard for absorbed dose to water. A state-of-the-art uncertainty budget and results of comparison to GUMs standards has been presented. Good agreement of absorbed dose to water with reference standard, with relative deviation of 0.28%, and standard uncertainty (k=2) of 0.48% indicates good quality of the GUM-Dw3 as a primary standard. This paper describes the step-by-step procedure to calculate a perturbation factor, that is a crucial issue for ionometric measurement of absorbed dose to water. Capabilities of FLUKA code for this factor determination have been also investigated.
查看更多>>摘要：Detection of microalbuminuria is a vital factor to prevent the progression of cardiovascular and renal disease. Several clinical studies on large population has shown the significance of dipstick in detection of micro-albuminuria. Although poor-quality of visual assessment has hindered its clinical utility. Automatic detection of trace and higher proteinuria is critical for detection of asymptomatic individuals. In this work, an automated, accessory free analytical system using smartphone for quantification of albuminuria has been investigated. A customised convolutional neural network (CNN) model along with different color spaces has been used to classify albumin concentration in urine dipstick. To mitigate ambient light conditions, smartphone camera was used in "Flash ON " mode. Performance of CNN model in different lighting conditions and with different smartphone models was studied and an accuracy of 88% was achieved on test data.
查看更多>>摘要：The health monitoring system for equipment is essential in the smooth proceeding of industrial production. However, the fault features to be detected in monitoring systems are generally selected through projects and expertise, which are not capable for complex and ever-changing fault information and may result in incomplete correspondence to the fault types emerged. To dig deeper for the effective features hidden in the data instead of selecting by experience, a feature selection and fusion method based on poll mode and optimized Weighted Kernel Principal Component Analysis (WKPCA) method is then proposed. Specifically, inspired by poll-mode and multi-criteria strategy, a multi-measure hierarchical model is designed to sort the fault features with high sensitivity, acquiring the feature subset with corresponding weight coefficient. Considering the variation in fault information collected by different sensors, the diagnosis rate in Extreme Learning Machine (ELM) is taken as the index for evaluation of each single sensor, then the sensitivity weight matrix of features extracted by multiple sensors is constructed after linear normalization. To integrate the feature information, WKPCA is applied for the weighted fusion of features, and Quantum Genetic Algorithm (QGA) is used to search for the kernel width parameter when the best separability in the samples under the fusion is reached. Finally, such samples are introduced to drive the diagnostic model of the monitoring system in rolling bearing. The experimental results show that, compared with the traditional feature selection and fusion methods, this method is capable for sorting out highly sensitive features with more fault information self-adaptively, and can improve the separability in the subset of fault samples effectively.
查看更多>>摘要：Aiming at the problem that the liquor picking in traditional brewing technology of Chinese liquor is closely dependent on manual operation, and the existing alcohol content measurement and automatic equipment have a high cost, low detection accuracy, and inaccurate classification, an intelligent liquor picking system based on hops image classification is proposed. First, an industrial camera was used to collect hops sequence images. By labeling the alcohol content and preprocessing the images, a data set of 11 categories of liquor hops images was established. Secondly, an end-to-end network model was established by combining the ResNet convolutional neural network and the ConvLSTM recurrent neural network. The algorithm performance was evaluated and verified on the established hops data set, and the model accuracy reached 96.97%. Finally, an intelligent liquor picking system was built to verify the feasibility of segmented liquor picking using hops images.
Cano-Domingo, CarlosCastellano, Nuria NovasFernandez-Ros, ManuelGazquez-Parra, Jose Antonio...
10页
查看更多>>摘要：In this article we propose a novel methodology for obtaining Schumann Resonances' relevant parameters from ELF transient register. Using this methodology, it is possible to extract a large amount of data and characterize individual transient events and their more relevant features. To use this methodology a new narrow band sensor is presented, centered in the 1st Schumann Resonance mode and specialized in capturing with high precision the associated transient events. The new methodology based on Hilbert transform and Heidler function is presented and used to segment and characterize each transient event. This method is validated first with an automatic classifier algorithm and then an extensive statistical analysis is performed. The validation process is shown as one of the possible applications of the methodology. The introduced set of narrow band hardware and software tools represents an important milestone for the study of transient events focused on a high amount of data.
Shalauddin, MdAkhter, ShamimaBasirun, Wan JefreyAnuar, Nadzirah Sofia...
11页
查看更多>>摘要：An innovative electrochemical sensing platform was fabricated for the simultaneous determination of paracetamol (PCT) and naproxen sodium (NPX). The glassy carbon electrode (GCE) was modified with a hybrid porous nanocomposite of carboxylated nanocellulose (CNCS), nitrogen doped graphene (NDG) and an anionic surfactant sodium dodecyl sulfate (SDS) by simple ultrasonication and drop-casting method. The (CNCS-NDG)-SDS nanocomposite showed high electrocatalytic performance towards the redox reaction of PCT and the oxidation of NPX between 0.2 V and 0.9 V at 0.1 V s- 1 with broad linear ranges from 0.01 to 90 mu M and 0.1 to 60 mu M for PCT and NPX, respectively. The limit of detection is 0.0015 mu M and 0.0018 mu M for PCT and NPX, respectively, while the limit of quantification is 0.015 mu M and 0.018 mu M, respectively. The (CNCS-NDG)-SDS/GCE sensor showed good stability, reproducibility and repeatability for the electrochemical determination of PCT and NPX.
查看更多>>摘要：In order to improve the ultra-wideband (UWB) ranging accuracy, a precise UWB ranging method is developed using pre-corrected strategy and particle swarm optimization (PSO) algorithm. In the pre-corrected strategy, the antenna delay function is established to correct the antenna parameters. In addition, the parameters of Kalman filter function are optimized by PSO algorithm to further improve the UWB ranging accuracy. The experimental results show that the maximum and average errors of the developed method are 7.45 and 3.82 cm, which are much smaller than those of the traditional ranging method (18 and 9.75 cm). Therefore, the developed UWB ranging method can be used in the precise ranging of electric devices.
NSTL
Elsevier