Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.     

煤矿井下避难硐室系统的构建与应用分析

为进一步减少煤矿事故发生后的人员伤亡、财产损失，在分析国内外煤矿井下避难系统发展的基础上，以何家塔煤矿井下避难硐室系统的构建设计与应用为例，详细介绍了井下避难硐室系统的分类、结构构成、功能需求和配套设施等，并通过紧急避险疏散时间的校验计算，提出避难硐室距离采掘工作面以不超过1 000 m为宜，且应按照“地面最安全，先逃生后避险”原则进行避险救援，为国内其他矿井避难硐室的构建应用和相关标准的制定提供了参考。     

压水堆堆芯损伤评价系统研究与开发

堆芯损伤评价是反应堆事故后应急评价的重要组成部分。本文在国外文献基础上,结合国内的运行经验,对压水堆堆芯损伤评价进行研究,并开发了相应的软件程序。堆芯损伤评价包括基于堆芯裸露时间、在线监测仪表读数和取样分析数据三种方法。考虑应急实时要求、电厂实际情况与国际经验,本文采用了基于在线监测仪表读数的评价方法,该方法主要是基于堆芯热电偶读数与安全壳辐射监测仪表读数进行评价,其他监测仪表读数进行辅助合理性证实。     

核电厂应急监测计划的关键问题及探讨

应急监测计划是核电厂应急计划的重要支持性文件,是核电厂应急监测准备和响应的重要依据。本文从核电厂事故应急监测方案、监测方法、监测设施设备配置、应急监测响应、监测能力的保持、质量保证等关键问题进行分析研究,提出相关建议,为核电厂应急监测计划的制订提供参考。     

Evaluation of the containment vessel failure time from operator’s notifications during a severe accident

This paper aims at clarifying the potential and the limit of the method to surmise the timing of the containment vessel (CV) failure utilizing the Emergency Action Levels (EALs) issued as a nuclear operator’s notification in an early phase of the severe accident (SA). We analyzed the timings of the EALs issued in all kinds of the SA sequences of several PWR plant models by using the SA analysis code, MAAP. We found high correlations between the timing of SE41 (EAL issued at CV pressure of 0.5 design pressure) and the timing of the CV failure in the typical scenarios, e.g. over-pressure failures. We could therefore establish an evaluating method to estimate the time for a CV failure. This method has the potential to support the decision-making in nuclear emergency preparedness.     

基于深度残差学习的自动驾驶道路场景理解

随着道路场景理解技术的快速发展，自主驾驶领域取得了长足的进步。在相关任务中，包括道路分割、分类和车辆检测的实时性和准确性是安全性的一个关键问题。为此，提出了一个具有编/解码器网络结构的基于深度残差学习的方法。一方面，编码器网络结构使用不同层次的残差网络来提取高维中的抽象特征，这些特征在接下来的三个任务中共享使用；另一方面，解码器网络结构采用一种子任务的并行计算机制，即道路分割、车辆检测和道路分类任务同时执行。此外，全卷积神经网络用于对提取的图像特征进行上采样以解决道路分割问题。最终，实验结果表明在保证高精度的前提下处理帧率可达到15 fps以上。     

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.     

核电应急柴油发电机组滑油低压保护停机控制的设计优化

通过采用转速分段及增加短延时的改进方法，对当前核电应急柴油发电机组滑油低压保护停机控制设计进行了优化。经论证与试验表明:优化后的保护停机控制对机组起动及运行过程均起到了保护作用，同时确保了滑油压力的正常建立，避免了机组因受外部因素影响导致应急保护停机误动作，提高了机组起动的可靠性。该优化方案已经在核电厂应急柴油发电机组控制系统中得到广泛应用。     

特种车辆自动紧急制动控制策略研究

针对特种车辆在战场以及训练过程中易出现的车辆碰撞事故问题,提出了一套自动紧急制动系统(AEBS)核心控制策略,完成了对车辆AEBS性能的道路验证测试.测试结果表明被试车辆符合JT/T 1242-2019对AEBS性能的要求.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.