查看更多>>摘要：Whispering gallery mode (WGM) photonic thermometer had achieved ultra-high sensitivity and resolution. However, while pursuing extremely high quality factor and thereafter high precision, power-induced self-heating within microcavity can bring a significant systematic error. We propose a photonic sensor with less than millikelvin self-heating effect utilizing silicon nitride (Si3N4) microring resonator with a loaded quality factor of 4.75 x 10(5). By investigating thermal broadening transmission spectra under various probing powers, effective absorption coefficient and thermal relaxation constant of the device were obtained where thermo-refractive and Kerr effect were accounted. Self-heating temperature rise was predicted to be mitigated to 245 mu K under a proposed measurement condition, and this ultra-low self-heating effect was experimentally proved. The proposed approach can be used to establish metrological standards for photonic thermometry and various sensing applications.
查看更多>>摘要：The on-machine inspection occupies certain manufacturing time, so it is important to improve the inspection accuracy as much as possible on the premise of an acceptable sampling scale. We proposed efficient adaptive sampling methods for NURBS curve and surface based on deviation analysis. The deviation is defined as the difference between the theoretical and reconstructed curves. For curve sampling, the less significant points are removed iteratively from initial dense on-curve points. In addition, we derived a closed solution for curve deviation, thus it is superior to existing methods in terms of reconstruction accuracy and time consumption. The curve sampling algorithm is further extended to surface sampling by simplifying it into curve sampling in two directions. Our methods are compared with classic sampling strategies and the results show that the curve and surface reconstruction errors of our method are reduced by 62% and 71% respectively.
查看更多>>摘要：The extraction of significant features is essential for efficient fault diagnosis and prognosis of rolling element bearing. Data fusion is the predominant technology for extracting significant features by fusing several original features. In this paper, seventy-two original features are extracted from bearing vibration data using various signal processing techniques. The relevant features subset is selected from the extracted features using the Random Forest method. The selected features are fused by fourteen dimensionality reduction techniques to extract 2D fault features and health indicators, and a comparison is made between the techniques to identify the most efficient technique. The Bhattacharyya distance and Support vector machine are used to verify fault diagnosis accuracy. A new index is computed for selecting the suitable prognosis health indicator, and the Long short-term memory technique is used to predict the remaining useful life of bearing. Two real-world bearing datasets are utilized to validate the proposed methodology.
查看更多>>摘要：The electromagnetic flow tomography (EMFT) technique aims to map the flow velocity profile by interrogating the electromotive force (emf) from the boundary of flow that travels in an electromagnetic field. In principle, the induced potential depends on not only the flow velocity but also the conductivity distribution. In the case of an electromagnetic flowmeter (EMF), the conductivity of fluid is assumed uniform or at least axisymmetric. However, in the case of multi-phase flows, the conductivity distribution is non-uniform and will affect the induced potential distributions. Therefore, applying the EMF model in the non-uniform/asymmetric conductivity distributions will inevitably introduce errors in calculating the velocity profile. In this paper, the forward problem of EMFT has been investigated by taking the conductivity distribution into account. Numerical simulations showed that the proposed non-uniform model has the unique capability in dealing with the fluid of asymmetric and non-uniform conductivity distributions.
查看更多>>摘要：In industrial production, it is particularly important to diagnose the bearing fault in time under variable loads. The intelligent diagnosis method has strong robustness without human intervention, but it needs a lot of raw data. However, large amounts of data storage is relatively difficult and slow transmission speed. Meanwhile, under different loads, the same fault feature has no significant difference in the process of bearing degradation. To address these problems, this article proposes a new multiscale inverted residual convolutional neural network (MIRCNN) method for fault diagnosis of variable load bearing. Firstly, a semi tensor product compressed sensing (CS) method based on parallel orthogonal matching pursuit (POMP) is proposed. The vibration signal is reconstructed with the proposed method to solve the problems of difficult data storage and slow transmission speed. Then, the convolutional neural network (CNN) is designed for high-dimensional signals, so that the onedimensional signal is converted to three-dimensional image for further training. Finally, the multiscale algorithm is applied to the CNN architecture, and MIRCNN is established by adding inverted residual learning. It can extract the different features between fault signals of variable load bearings, improving the ability to identify faults. Experimental results on two rolling bearing test beds with different bearing types and operating conditions and compared with existing state-of-the-art methods to prove the effectiveness and accuracy of the proposed method.
查看更多>>摘要：This work presents a "refractive index" sensor based on "photonic crystal fiber" and "surface plasmon resonance" phenomenon. A combination of Gold (Au) with Titanium Nitride (TiN) is used as a metallic layer in the presented sensor design. Water and transformer oil having RI1.330 and RI1.340 are analyzed through a developed spectroscopy setup which lays the condition for the RI sensor designing. A new relationship between transmission (%), absorbance (AU), and refractive index (RI) is developed and presented in this article. The sensor analysis is performed using the "finite element method" and "perfectly matched layer" conditions. The proposed sensor has obtained a "wavelength sensitivity (WS) " of 10000nm/RIU and 11000nm/RIU correspondings to TM mode and TE mode, respectively. A high "amplitude sensitivity (AS)" of 11280RIU(-1) and 7550RIU(-1) is obtained for TM mode and TE mode, respectively. The "sensor resolution (SR) " of 1.00 x 10(-5)RIU and 9.09 x 10(-6)RIU are reported for TM mode and TE mode, respectively. High linear fitting between resonance wavelength and RI, "full-wave half maximum (FWHM)" and "figure of merit (FOM)" are also reported for the proposed sensor. The peak values of the sensing parameters are obtained for RI1.330 and RI1.340, corresponding to the TM mode and TE mode, respectively.
查看更多>>摘要：Precise measurement of Polyester Filament Yarn (PFY) tension in the spinning process is critical to ensure good uniformity of product quality. A newly proposed real-time method is used to estimate the tension based on the natural frequencies extraction of the PFY. The theoretical relationship is established among the PFY tension, the spinning speed, and the first natural frequency based on the characteristics of the transverse dynamic vibration of the PFY in the spinning process. Then, an improved wavelet threshold denoising model has been applied to denoising the vibration signals, and the gravity center frequency of the denoised amplitude spectrum is used to extract the natural frequency of the PFY, and then the PFY tension can be estimated efficiently by employing the extracted natural frequencies. To monitor the uniformity of product quality, a new real-time evaluation index, namely the PFY tension fluctuation rate, is proposed to evaluate the overall fluctuation of PFY tension, which can be used for quantitative analysis and comparison of PFY tension. Additionally, the measurement data is of great significance to the evaluation and optimization of the performance of high-speed spinning equipment. The research intends to serve as a basis for the design of the spinning technology as well as its reliable operation.
查看更多>>摘要：When the global position system (GPS) signal is unavailable, the performance of the GPS/inertial navigation system (INS) integrated navigation system degrades severely. In this article, the performance of the ultra low cost inertial measurement unit (IMU) is studied and the objective is to enhance its performance during GPS outages. To be specific, a performance compensation method is proposed, which consists of two parts. First, to deal with the large noise and drift of the micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU), a wavelet regional correlation threshold denoising algorithm is proposed. Then, to improve the performance of traditional LSTM network when dealing with navigation data with strong coupling, a convolutional neural network-long short-term memory (CNN-LSTM) model is formulated. It employs CNN to quickly extract the features of the input, and utilizes LSTM network to output pseudo-GPS signals as the compensation object. Finally, simulation experiments and real road tests are implemented to evaluate the proposed method. Comparison experiment results show that the proposed method can effectively improve the performance of the integrated navigation system during GPS outages.
查看更多>>摘要：Dental caries is a worldwide disease, which will cause adverse effects if it is not treated in time. To accurate detect the early caries, a dental caries recognized approach based on infrared thermal diffusion, titled thermalwave radar imaging (TWRI) technique was utilized to detect dental caries at an early stage in this present study. One-dimensional (1D) thermal-wave model under linear frequency modulation (or -chirp) thermal flux was built and analyzed the chirp thermal-wave diffusion behavior. In addition, simulated caries environment in vitro was designed and proposed for the treatment of artificial dental caries. Four intact tooth tissues were demineralized with this etching agent for different time on the dental occlusal surface. For the safety of the laser application in clinical trials, the relationship between laser power intensity and dental surface temperature rise has been investigated. When the laser power intensity was controlled within 23.3 W under the irradiation area is 2 mm x 2 mm, the surface temperature rise of dental specimen can be dominated within 5 degrees C. The comparison experiment between TWRI and lock-in thermography was investigated, and TWRI phase represents a high signal-to noise ratio to the early caries. mu CT was utilized to 3D tomography for the dental specimen to obtain the actual caries inside the specimen. The experimental results illustrated that TWRI approach can be used as an effective method for early caries diagnosis.
查看更多>>摘要：Optical frequency comb spectrometry has become one of the leading fields devising new ways to investigate molecular structure and characterize optical elements due to its advantage in speed and accuracy. In this work we demonstrate the first dual-comb realization of cavity mode-width and mode-dispersion spectroscopies, achieving single-spectrum acquisition time in millisecond range, maintaining high spectral resolution limited by the comb tooth width. Interleaving of a few tens of spectra allows us to measure shapes of 13 kHz-wide enhancement cavity modes and determine their widths and positions with Hz-level precision. Thanks to this we can simultaneously measure the loss and dispersion of both the cavity and the intracavity medium. As a demonstration, we measure spectra of a gas mixture of methane and nitrogen and compare them with a simulation based on the HITRAN database. This novel method has many possible applications, including gas metrology, high-reflectivity mirror characterization or trace gas sensing.
NSTL
Elsevier