Multi-helical Lamb Wave Imaging for Pipe-like Structures Based on a Probabilistic Reconstruction Approach

The special form of pipe-like structure provides the helical route for ultrasonic guided wave. Considering the pipe as a flattened plate but with periodical replications, the helical wave becomes intuitional and a corresponding imaging algorithm can be constructed. This work proposes the multihelical Lamb wave imaging method by utilizing the multiple arrival wavepackets which are denoted as different orders. The helical wave signal model is presented and the constant group velocity point is illustrated. The probabilistic reconstruction algorithm is combined with the separation and fusion of different helical routes. To verify the proposed scheme, finite element simulations and corresponding experiments are conducted. The cases of single-defect simulation and two-defect simulation indicate the successful and robust implementation of the imaging algorithm. The test on actual pipe damage is also investigated to show its capability in imaging an irregular defect. The comparison with imaging results from only first arrival demonstrates the advantage of multihelical wave imaging, including the better imaging resolution and higher localization accuracy

Multifrequency Identification and Exploitation in Lamb Wave Inspection

Lamb wave inspection provides a promising method to assess the structural health status. However, Lamb wave modes exhibit different characteristics which vary with frequency significantly. The best excitation frequency usually cannot be determined in specific applications. This work proposes a multifrequency exploitation and identification method. Lamb waves of multiple frequencies are excitated simultaneously to utilize diverse attributes of Lamb waves in different frequency ranges. This paper firstly analyzes the detectability and sensitivity of Lamb wave. Then the multifrequency exploitation scheme and corresponding post-processing method are introduced. Relevant simulations by finite element method are conducted to verify its effectiveness. Experiments of single-frequency and multifrequency excitations are implemented. The investigations indicate that the proposed method can avoid the missing of defects compared with single-frequency excitation. In addition, a post-processing method is suggested and the results demonstrate that the multifrequency excitation also provides high accuracy in defect location

Orthogonal Optimal Design Method for Point-Focusing EMAT Considering Focal Area Dimensions

Although ultrasonic focusing methods have been widely used in electromagnetic acoustic transducers (EMATs) to solve the problem of their low energy conversion efficiency, the influence of focusing accuracy on defect identification also warrants consideration. The dimension of the focal area that acts as an important factor and affects the detection accuracy has not been fully investigated recently to our knowledge. In this work, we report a parameter optimization method using an orthogonal test when considering the focusing intensity and focal area together. The results of the range analysis show that the factor lift-off distance hl has the greatest impact on signal intensity M. Considering the dimensions of the focal area, bandwidth factor α has the largest effect on the effective focal length Lfd. For effective focal width Wfd, the concentric line source (CLS) number n has the largest effect. Therefore, a smaller lift-off distance, larger CLS number, and suitable bandwidth factor α are required in a point-focusing SV EMAT (PFSV-EMAT). The optimal combination of parameters can be obtained by considering the influence of different factors on the results. The experiment shows that the signal intensity of the optimized transducer is nearly 400% higher than non-optimized ones and the effective focal length and width are reduced by 15% and 57%, respectively

Frequency response of an underwater acoustic focusing composite lens

Acoustic lenses composed of metamaterials are used as highly anisotropic subwavelength media and have broad applications in a wide range of industrial areas. As reported in recent research, an acoustic lens composed of a cross-shaped structure can achieve high-intensity 3D focusing in an underwater system. However, the operating characteristics of this lens at different frequencies have not been studied in detail until now. In this work, we studied the focusing performance of a particular acoustic lens at different working frequencies, and the band structure, wave intensity distribution, reflection and transmission coefficients, and refractive index of a unit cell were investigated, as well as the characteristics of the acoustic lens through a simulation and experiment. Errors were minimized in the experiments through reasonable design, and we found that although the wave intensity of a single unit cell decreased as the frequency increased, in the acoustic lens, the intensity of the sound field at its focal point increased with the frequency. The present research provides an improved method for designing acoustic lenses with different working frequencies and can guide nondestructive testing (NDT) and biomedical treatment

Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud

Cloud computing has brought a new method of resource utilization and management. Nowadays some researchers are working on cloud-based instrumentation and measurement systems designated as Instrumentation and Measurement Clouds (IMCs). However, until now, no standard definition or detailed architecture with an implemented system for IMC has been presented. This paper adopts the philosophy of cloud computing and brings forward a relatively standard definition and a novel architecture for IMC. The architecture inherits many key features of cloud computing, such as service provision on demand, scalability and so on, for remote Instrumentation and Measurement (IM) resource utilization and management. In the architecture, instruments and sensors are virtualized into abstracted resources, and commonly used IM functions are wrapped into services. Users can use these resources and services on demand remotely. Platforms implemented under such architecture can reduce the investment for building IM systems greatly, enable remote sharing of IM resources, increase utilization efficiency of various resources, and facilitate processing and analyzing of Big Data from instruments and sensors. Practical systems with a typical application are implemented upon the architecture. Results demonstrate that the novel IMC architecture can provide a new effective and efficient framework for establishing IM systems

TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system

Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usually prohibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning systems. In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads. TENSILE tackled the cold-starting and across-iteration scheduling problem existing in previous works. We implement TENSILE on a deep learning framework built by ourselves and evaluated its performance. The experiment results show that TENSILE can save more GPU memory with less extra time overhead than prior works in both single and multiple dynamic workloads scenarios

ES2008-54087 APPLICATION OF ENTROPY METHOD IN FUZZY SYNTHETIC EVALUATION FOR MICRO-TURBINE PERFORMANCE

ABSTRACT Distributed generation (DG) is becoming the indispensable supplement to the centralized generation. Micro-turbine is paid more and more attention in scientific research and commercial application due to its unique advantages and gradually becomes the core of distributed generation. It is essential to make a synthetic and scientific evaluation on the performance of micro-turbine in order to promote the progress of the distributed generation technology using micro-turbine. This article gives the synthetic performance evaluation of microturbine. Some performance characters (rated capacity, generating efficiency, rotation speed, pressure ratio, fuel consumption, multi-fuel, intake temperature, exhaust temperature, NO x emission level, noises and life time) were chosen as evaluating indicators and some common microturbines were taken as evaluating objects in this paper. Considering the difficulty of fuzzy synthetic evaluation method in calculation of the multiple factors and the ignorance of the relationship among evaluating objects, a new weight evaluation process using entropy method was introduced. The entropy method is an objective way for weight determination. The improved method for weight determination of the evaluating indicators was applied in performance assessment of the microturbines. The evaluation result of the example showed that this method was favorable for fuzzy synthetic evaluation when there was more than one evaluating objects and the entropy method for determination of weight was a very effective method for evaluating indicators. The method predigested the fuzzy synthetic evaluation process greatly and the evaluation results are more reasonable

Genetically predicted serum testosterone and risk of gynecological disorders: a Mendelian randomization study

BackgroundTestosterone plays a key role in women, but the associations of serum testosterone level with gynecological disorders risk are inconclusive in observational studies.MethodsWe leveraged public genome-wide association studies to analyze the effects of four testosterone related exposure factors on nine gynecological diseases. Causal estimates were calculated by inverse variance–weighted (IVW), MR–Egger and weighted median methods. The heterogeneity test was performed on the obtained data through Cochrane’s Q value, and the horizontal pleiotropy test was performed on the data through MR–Egger intercept and MR-PRESSO methods. “mRnd” online analysis tool was used to evaluate the statistical power of MR estimates.ResultsThe results showed that total testosterone and bioavailable testosterone were protective factors for ovarian cancer (odds ratio (OR) = 0.885, P = 0.012; OR = 0.871, P = 0.005) and endometriosis (OR = 0.805, P = 0.020; OR = 0.842, P = 0.028) but were risk factors for endometrial cancer (OR = 1.549, P < 0.001; OR = 1.499, P < 0.001) and polycystic ovary syndrome (PCOS) (OR = 1.606, P = 0.019; OR = 1.637, P = 0.017). dehydroepiandrosterone sulfate (DHEAS) is a protective factor against endometriosis (OR = 0.840, P = 0.016) and premature ovarian failure (POF) (OR = 0.461, P = 0.046) and a risk factor for endometrial cancer (OR= 1.788, P < 0.001) and PCOS (OR= 1.970, P = 0.014). sex hormone-binding globulin (SHBG) is a protective factor against endometrial cancer (OR = 0.823, P < 0.001) and PCOS (OR = 0.715, P = 0.031).ConclusionOur analysis suggested causal associations between serum testosterone level and ovarian cancer, endometrial cancer, endometriosis, PCOS, POF