Robust Detection of Moving Human Target Behind Wall via Impulse through-Wall Radar

Through-wall human target detection is highly desired in military applications. We have developed an impulse through-wall radar (TWR) to address this problem. In order to obtain a robust detection performance, firstly we adopt the exponential average background subtraction (EABS) method to mitigate clutters and improve the signal-to-clutter ratio (SCR). Then, different from the conventional constant false alarm rate (CFAR) methods that are applied along the fast-time dimension, we propose a new CFAR method along the slow-time dimension to resist the residual clutters in the clutter mitigation output because of timing jitters, based on the presence of a larger relative variation of human target moving in and out in comparison with that of residual clutters in the slow-time dimension. The proposed method effectively solves the false alarm issue caused by residual clutters in the conventional CFAR methods, and obtains robust detection performance. Finally, different through-wall experiments are provided to verify the proposed method.Defence Science Journal, 2013, 63(6), pp.636-642, DOI:http://dx.doi.org/10.14429/dsj.63.576

Is I-Voting I-Llegal?

The Voting Rights Act was passed to prevent racial discrimination in all voting booths. Does the existence of a racial digital divide make Internet elections for public office merely a computer geek\u27s pipe dream? Or can i-voting withstand scrutiny under the current state of the law? This i-Brief will consider the current state of the law, and whether disproportionate benefits will be enough to stop this extension of technology dead in its tracks

Intermediate-band Surface Photometry of the Edge-on Galaxy: NGC 4565

We present a deep, 42.79 hr image of the nearby, edge-on galaxy NGC 4565 in the Beijing-Arizona-Taipei-Connecticut (BATC) 6660A band using the large-format CCD system on the 0.6m Schmidt telescope at the Xinglong Station of the National Astronomical Observatories of China (NAOC). we obtain a final image that is calibrated to an accuracy of 0.02 mag in zero point, and for which we can measure galaxy surface brightness to an accuracy of 0.25 mag at a surface brightness at 27.5 mag arcsec^-2 at 6660A, corresponding to a distance of 22 kpc from the center of the disk. The integrated magnitude of NGC4565 in our filter is m6660=8.99 (R magnitude of 9.1) to a surface brightness of 28 mag arcsec-2. We analyze the faint outer parts of this galaxy using a two-dimensional model comprised of three components: an exponential thin disk, an exponential thick disk, and a power-law halo. A total of 12 parameters are included in our model. We determine the best values of our model parameters via 10,000 random initial values, 3,700 of which converge to final values. The thin disk and thick disk parameters we determine here are consistent with those of previous studies of this galaxy. However, our very deep image permits a better determination of the power law fit to the halo, constraining this power law to be between r^-3.2 and r^-4.0, with a best fit value of r^-3.88. We find the axis ratio of the halo to be 0.44 and its core radius to be 14.4 kpc (for an adopted distance of 14.5 Mpc).Comment: 34 pages, 11 figures, will appear in March 2002 of A

Prediction of Residual Stress of Carburized Steel Based on Machine Learning

In recent years, the number of machine learning applications (especially those involving deep learning) applied to predicting and discovering material properties has been increasing. This paper is based on using microstructure and carbon content to train machine learning models to predict the residual stress of carburized steel. First, a semantic segmentation model of the material organization structure (SegModel-MOS) was constructed based on the AlexNet network and initially trained on the PASCAL VOC2012 dataset. Then, the trained model was fine-tuned on an enhanced homemade dataset consisting of optical microstructures. The experimental results show that SegModel-MOS can distinguish acicular martensite, retained austenite, and lath martensite in microstructures. Finally, we used both support vector machine (SVM) and decision tree (DT) algorithms to establish a mapping relationship between the microstructure, carbon content, and residual stress to predict the residual stress of steel from its microstructure and carbon content. The experiments verified that the prediction model constructed in this study exhibits high accuracy and can directly predict residual stress without requiring any long-term measurements. Thus, the developed model provides a new approach to the study of residual stress in steel

Microstructure Image Segmentation of 23crni3mo Steel Carburized Layer Based on a Deep Neural Network

This paper identifies and analyzes the microstructure of a carburized layer by using a deep convolutional neural network, selecting different carburizing processes to conduct surface treatment on 23CrNi3Mo steel, collecting many metallographic pictures of the carburized layer based on laser confocal microscopy, and building a microstructure dataset (MCLD) database for training and testing. Five algorithms—a full convolutional network (FCN), U-Net, DeepLabv3+, pyramid scene parsing network (PSPNet), and image cascade network (ICNet)—are used to segment the self-built microstructural dataset (MCLD). By comparing the five deep learning algorithms, a neural network model suitable for the MCLD database is identified and optimized. The research results achieve recognition, segmentation, and statistic verification of metallographic microstructure images through a deep convolutional neural network. This approach can replace the high cost and complicated process of experimental testing of retained austenite and martensite. This new method is provided to identify and calculate the content of residual austenite and martensite in the carburized layer of low-carbon steel, which lays a theoretical foundation for optimizing the carburizing process

Modeling and Composition Design of Low-Alloy Steel’s Mechanical Properties Based on Neural Networks and Genetic Algorithms

Accurately improving the mechanical properties of low-alloy steel by changing the alloying elements and heat treatment processes is of interest. There is a mutual relationship between the mechanical properties and process components, and the mechanism for this relationship is complicated. The forward selection-deep neural network and genetic algorithm (FS-DNN&GA) composition design model constructed in this paper is a combination of a neural network and genetic algorithm, where the model trained by the neural network is transferred to the genetic algorithm. The FS-DNN&GA model is trained with the American Society of Metals (ASM) Alloy Center Database to design the composition and heat treatment process of alloy steel. First, with the forward selection (FS) method, influencing factors—C, Si, Mn, Cr, quenching temperature, and tempering temperature—are screened and recombined to be the input of different mechanical performance prediction models. Second, the forward selection-deep neural network (FS-DNN) mechanical prediction model is constructed to analyze the FS-DNN model through experimental data to best predict the mechanical performance. Finally, the FS-DNN trained model is brought into the genetic algorithm to construct the FS-DNN&GA model, and the FS-DNN&GA model outputs the corresponding chemical composition and process when the mechanical performance increases or decreases. The experimental results show that the FS-DNN model has high accuracy in predicting the mechanical properties of 50 furnaces of low-alloy steel. The tensile strength mean absolute error (MAE) is 11.7 MPa, and the yield strength MAE is 13.46 MPa. According to the chemical composition and heat treatment process designed by the FS-DNN&GA model, five furnaces of Alloy1–Alloy5 low-alloy steel were smelted, and tensile tests were performed on these five low-alloy steels. The results show that the mechanical properties of the designed alloy steel are completely within the design range, providing useful guidance for the future development of new alloy steel

Characterization of karst conduit structure based on multisource artificial tracer test: A case study of the Cangpuwa underground river in Guizhou Province

Artificial tracer test is an important research method in the field of karst hydrogeology. In this study, to understand the distribution characteristics of karst conduits more comprehensively, multisource tracer tests were carried out on the Cangpuwa underground river in the base flow period and precipitation conditions. The results showed that tracers from Shuiqing, Huangliancun, and Liaojiapo were detected in the base flow period and precipitation conditions. The tracer recoveries under precipitation conditions were 88.12%, 90.01%, and 84.01%, respectively, indicating that the Cangpuwa underground river was a multisource and single-sink underground river. According to the tracer transport characteristics in both the base flow period and precipitation conditions, a conceptual model of the Cangpuwa underground river karst conduit structure was constructed. The flow path from Shuiqing to Cangpuwa had the largest curvature, the flow path from Huanglian to Cangpuwa was mainly a karst conduit, there were two channels on the flow path from Liaojiapo to Cangpuwa, and there was a dissolved pool near the outlet of the underground river. The research results can provide a basis for the investigation and exploitation of water resources in complex karst underground rivers

Deep learning assisted prediction of retained austenite in the carburized layer for evaluating the wear resistance of mild steel

The content, distribution and size of retained austenite (RA) affect its mechanical stability in carburized layers. The stability of RA plays a decisive role in cold work hardening and strain-induced martensitic transformation during sliding friction; these changes determine wear resistance. In this study, a database was established based on laser confocal metallographic images of carburized layers on 23CrNi3MoA steel after different carburizing treatments. Eight algorithms were used to identify and calculate the amounts of RA in the carburized layers. The tribolayers and wear on the surfaces that underwent three carburizing processes, P13, P15, and P17, were characterized and tested. The results showed that the U-Net algorithm with an attention module and drop block regularization was the most suitable for the database. Predictions of the RA contents of surfaces after P13, P15, and P17 treatments were 19.9%, 28.1%, and 40.1%, respectively. The errors of the predictions compared with experimental results were within 5%. The surface carburized by the P15 process contained moderate amounts of RA and had the best wear resistance because the friction strain induced the formation of nanolamellar structures and the transformation of RA to martensite. The results of this study support the use of deep learning to identify and calculate the amounts of RA in carburized layers and optimize carburizing processes of mild steel

A Hollow-Shaped ZIF-8-N-Doped Porous Carbon Fiber for High-Performance Zn-Ion Hybrid Supercapacitors

The advantages of low cost, high theoretical capacity, and dependable safety of aqueous zinc ion hybrid supercapacitors (ZHSCs) enable their promising use in flexible and wearable energy storage devices. However, achieving extended cycling stability in ZHSCs is still challenged by the limited availability of carbon cathode materials that can effectively pair with zinc anode materials. Here, we report a method for synthesising heteroatom-doped carbon nanofibers using electrostatic spinning and metal-organic frameworks (specifically ZIF-8). Assembled Zn//ZPCNF-1.5 ZHSCs exhibited 193 mA h g−1 specific capacity at 1 A g−1 and 162.6 Wh kg−1 energy density at 841.2 kW kg−1. Additionally, the device showed an ultra-long cycle life, maintaining 98% capacity after 20,000 cycles. Experimental analysis revealed an increase in the number of pores and active sites after adding ZIF-8 to the precursor. Furthermore, N doping effectively enhanced Zn2+ ions chemical adsorption and improved Zn-ion storage performance. This work provides a feasible design strategy to enhance ZHSC energy storage capability for practical applications

Study on Salt Dissolution Law of High Salinity Reservoir and Its Influence on Fracturing

For the high-salt reservoir of the Fengcheng Formation in the Mahu area, the production decreases rapidly due to the conductivity decrease after fracturing. The analysis shows that this has a great relationship with the special salt dissolution characteristics of the High salinity reservoir. In order to study the problem of salt dissolution pattern, the effect of different temperatures, the salt concentration of fracturing fluid, the viscosity of fracturing fluid, and injection rate on the rate of salt dissolution was evaluated by using the dynamic experimental evaluation method of salt dissolution. Through the grey correlation analysis of salt rock dissolution rate, it can be found that the degree of influence is from large to small which the influence of temperature is greater than fracturing fluid velocity, followed by fracturing fluid viscosity and, finally, fracturing fluid salt concentration. The results of compressive strength tests on salt-bearing rocks after dissolution show that the compressive strength is greatly reduced after salt dissolution by more than 60%. At the same time, the test results of proppant-free conductivity showed that the conductivity increased first and then decreased sharply after salt dissolution. This shows that in the early stage of salt dissolution, the flow channel will increase through dissolution. The rock strength decreases greatly with the increase of salt dissolution. As a result, collapse leads to a sharp reduction in the facture conductivity. Therefore, it is necessary to choose saturated brine fracturing fluid. In the proppant conductivity experiments, by optimizing the use of saturated brine fracturing fluid with 30/50 mesh or 20/40 mesh ceramic proppant with a sand concentration of 5 Kg/m2, a high facture conductivity can be achieved under high closure pressure conditions. Based on the above study, directions and countermeasures for improving high saline reservoirs are proposed, which point the way to improve the fracturing conductivity