Feasibility of Optical Flow Field Measurements of the Coolant in a Grinding Machine

For industrial grinding processes, the workpiece cooling by metalworking fluids, which strongly influences the workpiece surface layer quality, is not yet fully understood. This leads to high efforts for the empirical determination of suitable cooling parameters, increasing the part manufacturing costs. To close the knowledge gap, a measurement method for the metalworking fluid flow field near the grinding wheel is desired. However, the varying curved surfaces of the liquid phase result in unpredictable light deflections and reflections, which impede optical flow measurements. In order to investigate the yet unknown optical measurement capabilities achievable under these conditions, shadowgraphy in combination with a pattern correlation technique and particle image velocimetry (PIV) are applied in a grinding machine. The results show that particle image velocimetry enables flow field measurements inside the laminar metalworking fluid jet, whereby the shadowgraph imaging velocimetry complements these measurements since it is in particular suitable for regions with spray-like flow regimes. As a conclusion, optical flow field measurements of the metalworking fluid flow in a running grinding machine are shown to be feasible.

Optical Flow Field Measurements of The Coolant In A Grinding Machine

Since the cooling mechanism of industrial grinding is not yet fully understood, a measurement of the coolant liquid flow field near the grinding wheel is desired. However, the curved surfaces of the liquid phase result in unpredictable light deflections and reflections, which impedes optical flow measurements. In order to obtain qualitative and quantitative information regarding the flow velocity field of the coolant liquid, shadowgraphy in combination with a pattern correlation technique as well as particle image velocimetry are applied in a grinding machine and studied with respect to their measurement capabilities. Particle image velocimetry enables flow measurements inside the laminar coolant jet, whereby the shadowgraph imaging velocimetry complements these measurements and is in particular suitable for spray-like flow regimes. As a result, optical flow field measurements of the coolant flow in a grinding machine are shown to be feasible, which is required to understand the flow mechanisms that affect the grinding cooling process.

Design of an Intelligent Symptom Differentiation and Electrical Stimulation Rehabilitation System

In traditional Chinese medicine (TCM), symptoms are mostly differentiated subjectively by doctors. This approach of symptom differentiation lacks objective basis. Moreover, it is difficult to differentiate between symptoms and treat them through electrical stimulation rehabilitation (ESR) in the absence of TCM doctors. To solve these problems, this paper designs an intelligent symptom differentiation (ISD)-ESR system, which includes a software part for dialectical analysis, and a hardware part for electric stimulation of acupoints. The system was designed with the aid of the following technologies: fuzzy analytic hierarchy process (AHP), chromatographic decomposition, spatiotemporal slicing, optical flow field method, collaborative filtering based on deep neural network (DNN), and software-hardware fusion techniques (e.g. electrical stimulation signal control and Bluetooth multi-pass control). The proposed system was applied to treat 30 patients with primary insomnia in the sleep center of a tertiary hospital. The results show that the proposed system achieved an accuracy of 93.3% in symptom differentiation, and significantly improved the effect of electroacupuncture on insomnia (P<0.05). Overall, the proposed system makes up for the defects of existing devices, and improves the effect of rehabilitation treatment.

Object Shape Measurement Based on Brox Optical Flow Estimation and Its Correction Method

In this work, a new method of measuring surface shape based on Brox optical flow estimation is presented. The measuring system consists of a projector, a measured object and a charge coupled device (CCD) camera. The grating fringes are projected onto the reference plane at a small angle. Two fringe images—before and after placing the measured object on the reference plane—are captured, respectively. Then, the optical flow field between two images is evaluated by using Brox optical flow algorithm. The theoretical relationship between the optical flow field and the height of the measured surface is established. According to the relationship, the height distribution of the measured object can be retrieved quickly without phase-to-height transformation. However, the calculated height distribution has been found to be deviated from its true value. To solve the problem, a correction scheme suitable for the optical flow method is proposed. By using the correction scheme, the accuracy of the calculated result is greatly improved. Simulations and experiments are completed to verify the feasibility of the proposed method and the accuracy of the correction method. The results show that the proposed method is more accurate than that of the Fourier transform method. Compared with traditional surface shape measurement, the optical flow method has some obvious advantages: (1) Only two frame images are required to recover the height distribution. (2) Relatively simple in measurement process and calculation so less time consuming. (3) Because the optical flow method contains time factor itself, it is more suitable for dynamic measurement. (4) No restrictions on projection pattern.

A Convection Nowcasting Method Based on Machine Learning

In this study, a convection nowcasting method based on machine learning was proposed. First, the historical data were back-calculated using the pyramid optical flow method. Next, the generated optical flow field information of each pixel and the Red-Green-Blue (RGB) image information were input into the Convolutional Long Short-Term Memory (ConvLSTM) algorithm for training purposes. During the extrapolation process, dynamic characteristics such as the rotation, convergence, and divergence in the optical flow field were also used as predictors to form an optimal nowcasting model. The test analysis demonstrated that the algorithm combined the image feature extraction ability of the convolutional neural network (CNN) and the sequential learning ability of the long short-term memory network (LSTM) model to establish an end-to-end deep learning network, which could deeply extract high-order features of radar echoes such as structural texture, spatial correlation, and temporal evolution compared with the traditional algorithm. Based on learning through the above features, this algorithm can forecast the generation and dissipation trends of convective cells to some extent. The addition of the optical flow information can more accurately simulate nonlinear trends such as the rotation, or merging, or separation of radar echoes. The trajectories of radar echoes obtained through nowcasting are closer to their actual movements, which prolongs the valid forecasting period and improves forecast accuracy.

A Dense Optical Flow Field Estimation with Variational Refinement

Optical flow has long been a focus of research study in computer vision community. Researchers have established extensive work to solve the optical flow estimation. Among the published works, a notable work using variational energy minimization has beena baseline of optical flow estimation for a long time. Variational optical flow optimization solves an approximate global minimum in a well-defined non-linear Markov Energy formulation. It works by first linearizing the energy model and uses a numerical method specifically successive over-relaxation (SOR) method to solve the resulting linear model. An initialization scheme is required for optical flow field in this iterative optimization method. In the original work, a zero initialization is proposed and it works well on the various environments with photometric and geometric distortion. In this work, we have experimented with different flow field initialization scheme under various environment setting. We found out that variational refinement with a good initial flow estimate using state-of-art optical flow algorithms can further improve its accuracy performance.