Further numerical investigation on concrete dynamic behaviors with considering stress non-equilibrium in SHPB test based on the waveform features

In this study,with the meso-scale model reliably validated in our previous work(Construction and Building Materials,2018),the waveform features of plain concrete under various loading conditions and especially with considering stress non-equilibrium are reliably reproduced and predicted.Associating with waveform features,the violation indicator of the specimen stress equilibrium in the split Hopkinson pressure bar test is identified for concrete-like damage softening materi-als.The concrete material behaviors for stress non-equilibrium are further analyzed,e.g.the dynamic increase factor(DIF)and damage development,etc.The conception of“damage failure volume”is introduced,and a new method of defining the development of concrete dynamic damage is given in the nimierical study.What’s more,the“compression wave”and“double peak”phenomena observed in the experiment are further interpreted based on the means of numerical simulation.Waveform features how to reflect the concrete material properties is also concluded.The results show that,the disappearance of the“double peak” phenomenon of reflection curve under high strain rate can be regarded as the indicator of the violation of stress equilibrium.After the violation of the stress equilibrium,the relevant DIFs of the concrete specimen will not change significantly.Especially,the concrete specimen will turn into structural response from material response.The conception of“damage failure volume”can well explain the generation of the“double peak”phenomenon of the reflection curve.The “compression wave” phenomenon of reflection curve under lower strain rates is derived from the unloading expansion recovery of the concrete specimen.Furthermore,under the same loading condition,the amplitude of the first peak of the reflection curve can be used as the evaluation standard of the bonding quality between mortar and aggregates.

Waveform feature monitoring scheme for transformer differential protection

We propose a new scheme for transformer differential protection. This scheme uses different characteristics of the differential currents waveforms (DCWs) under internal fault and magnetizing inrush current conditions. The scheme is based on choosing an appropriate feature of the waveform and monitoring it during the post-disturbance instants. For this purpose, the signal feature is quantified by a discrimination function (DF). Discrimination between internal faults and magnetizing inrush currents is carried out by tracking the signs of three decision-making functions (DMFs) computed from the DFs for three phases. We also present a new algorithm related to the general scheme. The algorithm is based on monitoring the second derivative sign of DCW. The results show that all types of internal faults, even those accompanied by the magnetizing inrush, can be correctly identified from the inrush conditions about half a cycle after the occurrence of a disturbance. Another advantage of the proposed method is that the fault detection algorithm does not depend on the selection of thresholds. Furthermore, the proposed algorithm does not require burdensome computations.

Feature analysis of LIDAR waveforms from forest canopies

Airborne light detection and ranging (LIDAR) can detect the three-dimensional structure of forest canopies by transmitting laser pulses and receiving returned waveforms which contain backscatter from branches and leaves at different heights.We established a solid scatterer model to explain the widened durations found in analyzing the relationship between laser pulses and forest canopies,and obtained the corresponding rule between laser pulse duration and scatterer depth.Based on returned waveform characteristics,scatterers were classified into three types:simple,solid and complex.We developed single-peak derivative and multiple-peak derivative analysis methods to retrieve waveform features and discriminate between scatterer types.Solid scatterer simulations showed that the returned waveforms were widened as scatterer depth increased,and as space between sub-scatterers increased the returned waveforms developed two peaks which subsequently developed into two separate sub-waveforms.There were slight differences between the durations of simulated and measured waveforms.LIDAR waveform data are able to describe the backscatter characteristics of forest canopies,and have potential to improve the estimation accuracy of forest parameters.

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety.The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes.The clinical prototype consists of three parts:the laser beam,photodetectors,and the data acquisition system.The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber.When the bacterial microbes pass by the laser beam,the scattering of light occurs due to variation in size,shape,and morphology.Due to this reason,different types of pathogens show their unique light scattering patterns.The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light.The photodetectors convert the scattered light intensity into a voltage waveform.The waveform features were acquired by using the power spectral characteristics,and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion(mRMR).A support vector machine(SVM)classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes.The resulting average identification accuracies of E.faecalis,E.coli and S.aureus were 99%,87%,and 94%,respectively.The ove rall experimental results yield a higher accuracy of 93.6%,indicating that the proposed device has the potential for label-free identification of pathogens with simplicity,rapidity,and cost-effectiveness.

On-chip classification of micro-particles using laser light scattering and machine learning

The rapid detection of microparticles exhibits a broad range of applications in the field of science and technology. The proposed method differentiates and identifies the 2 μm and 5 μm sized particles using a laser light scattering. The detection method is based on measuring forward light scattering from the particles and then classifying the acquired data using support vector machines. The device is composed of a microfluidic chip linked with photosensors and a laser device using optical fiber. Connecting the photosensors and laser device using optical fibers makes the device more diminutive in size and portable. The prepared sample containing microspheres was passed through the channel, and the surrounding photosensors measured the scattered light. The time-domain features were evaluated from the acquired scattered light, and then the SVM classifier was trained to distinguish the particle’s data. The real-time detection of the particles was performed with an overall classification accuracy of 96.06%. The optimum conditions were evaluated to detect the particles with a minimum concentration of 0.2 μg/m L. The developed system is anticipated to be helpful in developing rapid testing devices for detecting pathogens ranging between 2 μm to 10 μm.