Study on Emergency Response Rank Mode of Flammable and Explosive Hazardous Materials Road Transportation

AbstractHazardous Materials (Hazmat) road transport is a hot issue of societal public safety, and it is extremely important to quickly response and rescue emergency accidents. In this paper, according to fire risk characteristics of transporting flammable and explosive hazmat by road, it was identified and analyzed on the representative hazmat leakage scenarios and the methods of determination of accident emergency regions, and a comprehensive systematic framework of emergency response rank mode for hazmat accidents by road was proposed. Moreover, some recommended practice about initial accident region of transportation of hazmat from NFPA471 and ERG2008 was described, and it was put forward on a quantitative approach to determine the emergency response rank for hazmat road accidents, based utilizing death toll, individual risk and societal risk as an emergency rank criterion. This research pays a very important role in the emergency response and helps the fire commanders to implement the rescue efficiently in hazmat transport accidents by road

Substantially enhanced plasticity of bulk metallic glasses by densifying local atomic packing

Common wisdom to improve ductility of bulk metallic glasses (BMGs) is to introduce local loose packing regions at the expense of strength. Here the authors enhance structural fluctuations of BMGs by introducing dense local packing regions, resulting in simultaneous increase of ductility and strength

Experimental Study on Tensile Properties of GFRP Bars Embedded in Concrete Beams with Working Cracks

This paper presents the test results of an experimental study carried out to investigate the tensile properties of GFRP bars embedded in concrete beams with working cracks. The specimens were conditioned with sustained loading in 50°C alkaline solution and tap water for 6, 12, 18 months. The tensile test results show that the degradation rate of GFRP bars embedded in the concrete specimens with work cracks is larger than that of non-work cracks, while the effect of working cracks on the elastic modulus is not significant. The microstructure of GFRP bar surface before and after the test was observed by scanning electron microscopy (SEM), combined with Fourier-transform infrared spectroscopy (FTIR) and differential-scanning calorimetry (DSC), the degree of hydrolysis reaction and glass transition temperature is also analyzed. Compared with the hydrolysis of resin matrix, it can be found that the deterioration of glass fiber and the delamination at the interfacial is more pronounced for the GFRP bars embedded in concrete beams. As a result, the mainly reason that caused the degradation of GFRP bars embedded in concrete environments are the deterioration of glass fiber and the delamination at the interfacial

Improved Ship Detection Algorithm Based on YOLOX for SAR Outline Enhancement Image

Synthetic aperture radar (SAR) ship detection based on deep learning has the advantages of high accuracy and end-to-end processing, which has received more and more attention. However, SAR ship detection faces many problems, such as fuzzy ship contour, complex background, large scale difference and dense distribution of small targets. To solve these problems, this paper proposes a SAR ship detection method with ultra lightweight and high detection accuracy based on YOLOX. Aiming at the problem of speckle noise and blurred ship contour caused by the special imaging mechanism of SAR, a SAR ship feature enhancement method based on high frequency sub-band channel fusion which makes full use of contour information is proposed. Aiming at the requirement of light-weight detection algorithms for micro-SAR platforms such as small unmanned aerial vehicle and the defect of spatial pooling pyramid structure damaging ship contour features, an ultra-lightweight and high performance detection backbone based on Ghost Cross Stage Partial (GhostCSP) and lightweight spatial dilation convolution pyramid (LSDP) is designed. Aiming at the characteristics of ship scale diversity and unbalanced distribution of channel feature information after contour enhancement in SAR images, four feature layers are used to fuse contextual semantic information and channel attention mechanism is used for feature enhancement, and finally the improved ship target detection method based on YOLOX (ImYOLOX) is formed. Experimental tests on the SAR Ship Detection Dataset (SSDD) show that the proposed method achieves an average precision of 97.45% with a parameter size of 3.31 MB and a model size of 4.35 MB, and its detection performance is ahead of most current SAR ship detection algorithms

FV-MViT: Mobile Vision Transformer for Finger Vein Recognition

In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer’s self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT

Multi-dimensional database technology based on artificial intelligence

Artificial intelligence and database technology are both important fields of computer science. The research results show that more and more industries have a growing demand for artificial intelligence and database. The combination of these two technologies will certainly bring a broader prospect to computer application. This paper mainly discusses the necessity, importance and development of multi-dimensional database technology based on artificial intelligence, introduces the combination strategy of artificial intelligence and multidimensional database and the current research field, and gives the research direction

A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks

To solve the poor real-time performance of the existing fault diagnosis algorithms on transmission system rotating components, this paper proposes a novel high-dimensional OT-Caps (Optimal Transport–Capsule Network) model. Based on the traditional capsule network algorithm, an auxiliary loss is introduced during the offline training process to improve the network architecture. Simultaneously, an optimal transport theory and a generative adversarial network are introduced into the auxiliary loss, which accurately depicts the error distribution of the fault characteristic. The proposed model solves the low real-time performance of the capsule network algorithm due to complex architecture, long calculation time, and oversized hardware resource consumption. Meanwhile, it ensures the high precision, early prediction, and transfer aptitude of fault diagnosis. Finally, the model’s effectiveness is verified by the public data sets and the actual faults data of the transmission system, which provide technical support for the application