Safety assessment of the NET Predesign

Work carried out by European Associations on Safety and Environment inside the EC Fusion Technology Programme and by NET has been concentrated on safety-related guidance and on safety assessment of the NET Predesign. Emphasis has been put on analysis of accident sequences of regulatory concern up to worst case accidents, including quantification of consequences to components, systems, and plant, and of doses to the public. Probabilistic safety studies for the most important systems and broadly for the entire plant have been performed to supplement the calculated accidental doses by expectation values for their occurrence rate, and to check the relevance of the reference accident sequences selected initially by judgment. Waste masses per year of operation and for decommissioning have been quantified. For two countries and according to the practices and regulations of these countries, volumes of radioactive waste packaged for final disposal have been determined.Presented at the IAEA Technical Committee Meeting on Developments in Fusion Safety, Toronto, June 7–11, 1993.     

A low wall-loading DEMO reactor design with high priority for early and reliable realization of a tokamak fusion reactor over the cost performance

Based on scientific databases adopted for designing ITER plasmas and on the advancement of fusion nuclear technology from the recent R&D program, a low wall-loading DEMO fusion reactor has been designed, where high priority has been given to the early and reliable realization of a tokamak fusion plasma over the cost performance. Since the major radius of this DEMO reactor is chosen to be 10 m, plasma ignition is achievable with a low fusion power of 0.8 GW and an operation period of 4–5 hours is available only with inductive current drive. The low ignition power makes it possible to adopt a first wall with an austenitic stainless steel, for which significant databases and operating experience exists, due to its use in the presence of neutron irradiation in fission reactors. In step with development of advanced materials, a step-wise increase of the fusion power seems to be feasible and realistic, because this DEMO reactor has the potential to produce a fusion power of 5 GW.     

A rationale for the TEI recommendations for feature-structure markup

In this paper, we concentrate on justifying the decisions we made in developing the TEI recommendations for feature structure markup. The first four sections of this paper present the justification for the recommended treatment of feature structures, of features and their values, and of combinations of features or values and of alternations and negations of features and their values. Section 5 departs briefly from the linguistic focus to argue that the markup scheme developed for feature structures is in fact a general-purpose mechanism that can be used for a wide range of applications. Section 6 describes an auxiliary document called a feature system declaration that is used to document and validate a system of feature-structure markup. The seventh and final section illustrates the use of the recommended markup scheme with two examples, lexical tagging and interlinear text analysis.Terry Langendoen is Professor and Head of the Department of Linguistics at The University of Arizona. He was Chair of the TEI Committee on Analysis and Interpretation. He received his PhD in Linguistics from the Massachusetts Institute of Technology in 1964, and held teaching positions at The Ohio State University and the City University of New York (Brooklyn College and the Graduate Center) before moving to Arizona in 1988. He is author, co-author, or co-editor of six books in linguistics, and of numerous articles.Gary Simons is Director of the Academic Computing Department of the Summer Institute of Linguistics, Dallas, TX. He served on the TEI Committee on Analysis and Interpretation. He received his PhD in Linguistics (with minor emphasis in Computer Science) from Cornell University in 1979. Before taking up his current position in 1984, he spent five years in the Solomon Islands doing field work with SIL. He is author, co-author, or co-editor of eight books in the fields of linguistics and linguistic computing.The initial feature-structure recommendations were formulated by the Analysis and Interpretation Committee at a meeting in Tucson, Arizona in March 1990, following suggestions by Mitch Marcus and Beatrice Santorini. The authors received valuable help in the further revision and refinement of the recommendations from Steven Zepp.     

基于声纹特征和集成学习的变压器缺陷诊断方法

变压器运行过程中产生的振动噪声与其运行状态及内部缺陷情况直接相关,对其声纹信号开展特征分析,有助于进一步了解设备运行工况,保障电力系统安全稳定运行。文中以声纹特征分析为基础,兼顾诊断效率与准确性,提出一种基于卷积神经网络及集成学习模型的变压器缺陷诊断方法。该方法以变压器声纹数据的时域及频域信号为多通道输入混合特征,构建了基于卷积神经网络模型和声纹特征分析法的集成学习模型,可实现变压器声纹特征的有效识别,并通过由多个基学习器组成的集成学习模型提高了变压器缺陷诊断的准确性。基于文中所构建的变压器声纹样本库,可得到该方法对变压器单一缺陷的识别准确率为99.2%,对变压器混合缺陷的识别准确率为99.7%。研究结果表明该方法可有效识别变压器的运行状态,为变压器运维检修提供技术参考。     

基于地震灾害场景的主动配电网多维韧性评估方法

为了分析主动配电网承受破坏性扰动事件以及快速恢复重要负荷的韧性支撑能力,融合相量测量单元（PMU）高精度动态感知能力提出了一种新的以地震灾害场景为背景的主动配电网多维韧性评估方法。阐述了配电网韧性的基本概念及特征,并将地震作为极端事件代表,构建了反映配电线路故障率与地震动峰值加速度加权均值的模型,继而采用非序贯蒙特卡罗抽样和K-means++聚类算法筛选出代表性的地震场景;基于系统配置的PMU的强感知力建立反映韧性电网应变力、防御力、恢复力和协同力的评估指标,形成韧性多维特征空间,进而利用事件集点簇中心与最优韧性点的加权欧氏距离评估系统多维综合韧性;分析增强电力线路强度、提高联合系统中分布式电源可供容量这2种措施对系统韧性提升的影响,挖掘韧性电网对抗震防灾的学习力。最后,通过改进的PG&E69系统验证所提方法的有效性和准确性。     

计及多能流运行时序性的电-气互联系统协同状态估计

电-气互联综合能源系统可灵活实现多区域能源交互,但其多能流子系统存在的信息交互壁垒、量测延迟等时序不统一因素会给系统精准监测与调度带来不利影响。针对多能流子系统不同的运行时间尺度,构建计及统一时间尺度的顺序协同估计框架,并考虑多源量测设备采样周期的延迟问题,将量测时延融合思想引入估计框架中以矫正多源量测数据的时序不同步。提出基于改进卡尔曼滤波的综合能源系统实时估计算法,通过引入基于改进交替投影的对称正定理论,避免实时估计过程中因误差传播出现非正定导致的估计失败问题。仿真结果与对比分析验证了所提协同状态估计策略能够有效实现对电-气互联综合能源系统的在线精准监控,且估计结果具备更强的数值稳定性。     

基于多模型融合的航空电子产品故障预测方法

针对复杂机载环境应力条件下航空电子产品故障预测所面临的退化趋势差异大、训练数据样本量小等问题,提出了一种改进长短期记忆(Long Short-Term Memory, LSTM)神经网络模型与集成学习框架相结合的故障预测方法,以满足现代综合航空电子系统智能调度管理与自主维护保障的需求。该方法在LSTM模型中引入Dropout机制,构建基于不同历史数据集的差异性LSTM模型组,以解决故障预测时序信息记忆问题与小样本条件下数据驱动模型训练过拟合问题;采用Adaboosting算法计算模型权重,并基于实时数据动态调整,以滤除复杂机载环境应力引入的预测误差,解决多模型融合的性能差异问题。最后,采用NASA公开的锂电池退化数据集进行仿真验证,实验结果表明,相较于传统BP神经网络、经典LSTM和LSTM基模型,该方法具有更高的趋势拟合度和预测精度。     

复杂交通环境下多层交叉融合多目标检测

针对目前复杂交通环境下还存在多目标检测精度和速度不高等问题,以特征金字塔网络(Feature Pyramid Network, FPN)为基础,提出了一种多层融合多目标检测与识别算法,以提高目标检测精度和网络泛化能力。首先,采用ResNet101的五层架构将空间分辨率上采样2倍构建自上而下的特征图,按照元素相加的方式将上采样图和自下而上的特征图合并,并构建一个融合高层语义信息与低层几何信息的特征层;然后,根据BBox回归存在训练样本不平衡问题,选择Efficient IOU Loss损失函数并结合Focal Loss提出一种改进Focal EIOU Loss;最后,充分考虑复杂交通环境下的实际情况,进行人工标注混合数据集进行训练。该模型在KITTI测试集上的平均检测精度和速度比FPN分别提升了2.4%和5 frame/s,在Cityscale测试集上平均检测精度和速度比FPN提升了1.9%和4 frame/s。     

基于GNSS多通道跟踪接收机的阵列反欺骗方法

近年来,基于阵列天线的空域处理方法被认为是最有效的欺骗检测和抑制手段之一。提出了一种基于卫星导航接收机多通道跟踪能力的阵列反欺骗方法。多通道跟踪接收机对每一个捕获到的导航信号在所有阵元通道后均进行跟踪处理并提取信号幅度和载波相位观测量,首先计算载波相位双差平方和进行欺骗检测,然后利用载波相位单差和信号幅度比估计信号导引矢量,从而进行基于子空间投影的欺骗信号抑制或基于波束形成的真实卫星信噪比提升。理论分析和仿真试验证明,该方法可有效检测并消除同一来向的欺骗信号且不影响真实卫星信号质量。由于前端非理想因素带来的幅度和相位偏移都已包含在接收机跟踪通道提取的观测量内,实际应用时对阵列天线校准和射频通道均衡的要求明显降低。     

基于注意力机制的毫米波雷达和视觉融合目标检测算法

传统目标检测大多基于摄像头采集图像进行,虽然近些年出现了许多优秀的检测网络,但在复杂场景下,仍存在大量漏检、误检等问题。针对这些问题,提出了一种基于注意力机制的毫米波雷达和视觉融合目标检测算法。首先将毫米波雷达数据进行扇形点云柱编码（Fan-shaped Cloud Pillar Code,FCPC）,将其转换为前景伪图像;然后,再将其通过坐标关系映射到像素平面,使用卷积注意力模块（Convolutional Block Attention Module,CBAM）对两者特征数据进行融合;采用Yolov4-tiny对融合特征进行检测,并引入Focal Loss对原损失函数进行改进以解决正负样本不均的问题。在Nuscenes数据集上进行模型验证与对比,结果表明,该算法在复杂场景下相比其他单传感器检测算法如Yolov3、Efficientent以及Faster-RCNN等,无论平均检测精度(mean Average Precision,mAP) 还是每秒检测帧数(Frames Per Second,FPS)都有明显的提升。