Study on Ignition Probability of Flammable Materials after Leakage Accidents

AbstractIt is a key step of quantitative risk analysis (QRA) to estimate ignition probability of flammable materials after leakage accidents. This paper reviews the available literature and expert opinion on how to evaluate and determine ignition probability value, and it was detailedly discussed on the main influencing factors of ignition probability, including flammable material properties, mass flow rate of flammable materials spillage, ignition resources and ignition controls. Moreover, the operational and practical ignition probability value could be estimated from the all way of classifications of flammable material, mass flow rate, ignition resources, hazardous areas and ignition prevention and control measure. Furthermore, the more practical ignition probability model was put forward that the ignition probability was the maximum value of the probability decided by material properties (PMP), mass flow rate(PQ) and ignition resources(PIS) with the factor of preventing and controlling ignition (KIC). Finally, the further research was proposed to assign some feasible weigh factors of the ignition probability for flammable materials after leakage accidents

Pattern formation of a Schnakenberg-type plant root hair initiation model

This paper concentrates on the diversity of patterns in a quite general Schnakenberg-type model. We discuss existence and nonexistence of nonconstant positive steady state solutions as well as their bounds. By means of investigating Turing, steady state and Hopf bifurcations, pattern formation, including Turing patterns, nonconstant spatial patterns or time periodic orbits, is shown. Also, the global dynamics analysis is carried out

Pattern formation of a Schnakenberg-type plant root hair initiation model

This paper concentrates on the diversity of patterns in a quite general Schnakenberg-type model. We discuss existence and nonexistence of nonconstant positive steady state solutions as well as their bounds. By means of investigating Turing, steady state and Hopf bifurcations, pattern formation, including Turing patterns, nonconstant spatial patterns or time periodic orbits, is shown. Also, the global dynamics analysis is carried out

Risk Assessment of Urban Gas Pipeline Based on Different Unknown Measure Functions

Several risk factors threaten the safety of urban gas pipeline. How to effectively identify various risk factors affecting urban gas pipeline and put forward scientific risk assessment method is the focus in the field of urban safety research. To explore the uncertain factors in the process of gas pipeline risk assessment, and propose a practical assessment method, a three-layer index system for the risk assessment of urban gas pipeline was established using unascertained measure theory, which included 5 first-class evaluation factors and 34 second-class evaluation indexes. Four unascertained measure models (linear, parabolic, exponential and sinusoidal) were constructed, and the unascertained measure values of each evaluation index under four unknown measure function models were calculated. The weight of evaluation factors was determined by Analytic Hierarchy Process (AHP), and the confidence criterion was used for discriminant evaluation. Results demonstrate that the risk assessment models constructed with different measurement functions can effectively reduce the uncertainty of urban gas pipeline risk assessment, but for the same object, the risk level of the linear measurement model in 4# pipeline is lower than other measurement functions, and the risk level of sinusoidal measurement model in 8# pipeline is higher than other measurement functions. Therefore, considering the evaluation results under different measure functions and focusing on monitoring objects with different results is necessary when using unascertained measure theory for risk assessment. The conclusions obtained from this study clarify the application conditions of unascertained measure theory in urban gas pipeline risk assessment, which helps to reduce the uncertainty in the assessment process and improve the accuracy of the assessment results

Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.Comment: Accepted by 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, 9 pages, 6 figure

Research on optimization of approach procedures for airports in an alpine environment

Obstacles in alpine environments pose significant challenges to aircraft safety during terminal operations. Key challenges include constraints from obstacles within the terminal clearance area and the labor-intensive manual calculations of flight procedures. The focal point of concern lies in the design of approach procedures, particularly due to the heightened risk of collisions with obstacles during the descent segment in such terrain. To address these challenges, initially, this paper proposes processing the terrain data and visualizing and extracting the topographic data of the alpine airport by adopting a bi-cubic b-spline interpolation and cellular automatic machine model. Then, the paper proposes improving the A* path algorithm to make sure it can obey the standards of flight procedure design, utilizing the improved A* path algorithm to design approach procedures. As fuel consumption is directly connected with the economy of aviation companies, this research finally suggests employing the fuel consumption evaluation model to select the most efficient approach flight procedures. This research takes a case study of a Yunnan airport and simulates and designs the optimized approach procedures by A* path algorithm and evaluation based on fuel consumption. Results indicate that the parameters of optimized approach procedures align with the regulation of flight procedure design and meet the requirements of real flight operation. Therefore, the core tenant of this research can provide a feasible idea for flight procedures with alpine airports and has the potential to reduce workload and enhance operational efficiency