Risk Identification and Prevention and Control Measures of Urban Rail Transit Construction

Urban rail transit is a key bridge to promote the sustainable development of national economy. The risk problems existing in the actual construction process will have a negative impact on the rail transit to play its own role, and it is closely related to the stable development of the country and society. Based on this, this paper probes into the risk identification of urban rail transit engineering construction in order to provide corresponding reference for future research

The prediction of the quality of results in Logic Synthesis using Transformer and Graph Neural Networks

In the logic synthesis stage, structure transformations in the synthesis tool need to be combined into optimization sequences and act on the circuit to meet the specified circuit area and delay. However, logic synthesis optimization sequences are time-consuming to run, and predicting the quality of the results (QoR) against the synthesis optimization sequence for a circuit can help engineers find a better optimization sequence faster. In this work, we propose a deep learning method to predict the QoR of unseen circuit-optimization sequences pairs. Specifically, the structure transformations are translated into vectors by embedding methods and advanced natural language processing (NLP) technology (Transformer) is used to extract the features of the optimization sequences. In addition, to enable the prediction process of the model to be generalized from circuit to circuit, the graph representation of the circuit is represented as an adjacency matrix and a feature matrix. Graph neural networks(GNN) are used to extract the structural features of the circuits. For this problem, the Transformer and three typical GNNs are used. Furthermore, the Transformer and GNNs are adopted as a joint learning policy for the QoR prediction of the unseen circuit-optimization sequences. The methods resulting from the combination of Transformer and GNNs are benchmarked. The experimental results show that the joint learning of Transformer and GraphSage gives the best results. The Mean Absolute Error (MAE) of the predicted result is 0.412

Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification

МЕТОДИКА ПРИНЯТИЯ РЕШЕНИЙ ПО РЕЗУЛЬТАТАМ АНАЛИЗА ПРОИЗВОДСТВЕННОЙ ДЕЯТЕЛЬНОСТИ АВИАКОМПАНИИ

The article deals with the mathematical solution of the decision-making problem on typical airline management. An original technique based on statistical hypothesis testing and the information field analysis, containing various information which influences decision-making is given. A criterion for evaluation of decisions effectiveness is introduced. It is concluded that the task of choosing the management decision is the task of verifying statistical hypotheses based on available authentic and unauthentic information items, i.e. partially reliable information.В статье рассматривается математическое решение задачи принятия решения по управлению типовой авиакомпанией. Предлагается оригинальная методика, основанная на проверки статистических гипотез и заключающаяся в анализе информационного поля, содержащего разнообразную информацию, влияющую на принятие решения. Вводится критерий оценки эффективности решений. Делается вывод, что задача выбора управленческого решения представляет собой задачу проверки статистических гипотез на основании имеющихся достоверных и недостоверных информационных элементов, т.е. в условиях частично достоверной информации

Evaluation of global fire simulations in CMIP6 Earth system models

Fire is the primary form of terrestrial ecosystem disturbance on a global scale and an important Earth system process. Most Earth system models (ESMs) have incorporated fire modeling, with 19 out of them submitting model outputs of fire-related variables to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study provides the first comprehensive evaluation of CMIP6 historical fire simulations by comparing them with multiple satellite-based products and charcoal-based historical reconstructions. Our results show that most CMIP6 models simulate the present-day global burned area and fire carbon emissions within the range of satellite-based products. They also capture the major features of observed spatial patterns and seasonal cycles, the relationship of fires with precipitation and population density, and the influence of El Niño-Southern Oscillation (ENSO) on the interannual variability of tropical fires. Regional fire carbon emissions simulated by the CMIP6 models from 1850 to 2010 generally align with the charcoal-based reconstructions, although there are regional mismatches, such as in southern South America and eastern temperate North America prior to the 1910s and in temperate North America, eastern boreal North America, Europe, and boreal Asia since the 1980s. The CMIP6 simulations have addressed three critical issues identified in the CMIP5: (1) the simulated global burned area less than half of the observations, (2) the failure to reproduce the high burned area fraction observed in Africa, and (3) the weak fire seasonal variability. Furthermore, the CMIP6 models exhibit improved accuracy in capturing the observed relationship between fires and both climatic and socioeconomic drivers, and better align with the historical long-term trends indicated by charcoal-based reconstructions in most regions worldwide. However, the CMIP6 models still fail to reproduce the decline in global burned area and fire carbon emissions observed over the past two decades, mainly attributed to an underestimation of anthropogenic fire suppression, and the spring peak in fires in the Northern Hemisphere mid-latitudes, mainly due to an underestimation of crop fires. In addition, the model underestimates the fire sensitivity to wet-dry conditions, indicating the need to improve fuel wetness estimation. Based on these findings, we present specific guidance for fire scheme development and suggest the post-processing methodology for using CMIP6 multi-model outputs to generate reliable fire projection products

ПОДХОДЫ К ПОСТРОЕНИЮ СИСТЕМ УПРАВЛЕНИЯ Б ЕЗОПАСНОСТЬЮ ПОЛЕТОВ В АВИАКОМПАНИИ

The article presents three approaches of building a safety management system (SMS) in airlines in the framework of implementation of ICAO SARPs that apply methods of risk assessment based on use of operational activity of airline taking into account existing and implementing "protections" or "safety barriers".В статье приведены три подхода к построению системы управления безопасностью полетов (СУБП) в авиакомпании в рамках выполнения SARPs ИКАО, в которых применяются методы оценки риска, основанные на использовании данных эксплуатационной деятельности авиакомпании с учетом существующих и внедряемых «защит» или «барьеров безопасности»

Fault Detection for Interval Type-2 T-S Fuzzy Networked Systems via Event-Triggered Control

This paper investigates the event-triggered fault diagnosis (FD) problem for interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy networked systems. Firstly, an FD fuzzy filter is proposed by using IT2 T-S fuzzy theory to generate a residual signal. This means that the FD filter premise variable needs to not be identical to the nonlinear networked systems (NNSs). The evaluation functions are referenced to determine the occurrence of system faults. Secondly, under the event-triggered mechanism, a fault residual system (FRS) is established with parameter uncertainty, external disturbance and time delay, which can reduce signal transmission and communication pressure. Thirdly, the progressive stability of the fault residual system is guaranteed by using the Lyapunov theory. For the energy bounded condition of external noise interference, the performance criterion is established using linear matrix inequalities. The matrix parameters of the target FD filter are obtained by the convex optimization method. A less conservative fault diagnosis method can be obtained. Finally, the simulation example is provided to illustrate the effectiveness and the practicalities of the proposed theoretical method