高比例风电系统的爬坡备用需求评估

在极端天气情况下,风电功率会在短时间尺度内发生大幅度的变化,出现风电功率高风险爬坡事件,严重威胁电力系统的安全稳定运行。开展爬坡备用的需求评估,有助于减小风电出力波动和预测误差对电网运行带来的不利影响。为保障高比例风电系统的备用充裕度,提出一种基于门控循环单元和非参数核密度估计法的组合区间爬坡备用需求预测方法。首先,将风电功率实际数据和日前预测数据构建成多变量时间序列,基于门控循环单元（gate recurrent unit,GRU）模型提高预测结果的准确度。进而,采用非参数核密度估计方法对风电功率预测误差进行置信区间估计,得出给定置信区间下的风电功率预测区间。最后,根据区间预测结果,预测爬坡事件并提取爬坡特征量,建立爬坡备用需求评估模型,评估得出爬坡备用容量需求。基于西北某省级电网的数据开展了算例测试,验证了所提方法的有效性。     

高工作温度碲镉汞红外探测器研究进展

高工作温度碲镉汞红外探测器作为最近来发展起来的新型探测器,在保证性能不变的基础上,实现了尺寸小、重量轻、功耗低等功能,成为军事侦察、无人机、无人平台的重要探测器件。本文阐述了高工作温度碲镉汞红外探测器的基本原理,重点介绍了碲镉汞P-on-N红外探测器的器件结构设计,并且对美国Raytheon、法国Sofradir、德国AIM、美国Teledyne、美国DRS等公司的研究进展进行了综述性介绍。     

光纤F P腔干涉纳米级位移高分辨率测量

激光抗干扰性好、复用能力强,光纤F P腔干涉仪结构简单、灵敏度高,适于纳米级位移测量。为了进一步提高分辨率,扩展测量范围,本文对光纤F P腔干涉信号的相位和功率进行分析。首先给出了二倍次方功率和分辨率的关系,找出了提高分辨率的途径。然后给出了二倍次方功率和干涉条纹变化的关系,确定了测量条件。最后根据光纤波在F P腔内干涉形成的滞后相位,通过多次希尔伯特变换,实现了待测物体位移重构。数值模拟和实验证明了该方法能够实现高分辨率纳米级位移测量,误差小。     

基于级联Dense-UNet和图割的肝脏肿瘤自动分割

腹部CT图像肝脏肿瘤分割是进行肝脏疾病诊断、手术规划和放射治疗的重要前提。针对肝脏肿瘤灰度异质、纹理丰富、边界模糊等因素引起的分割困难,该文提出基于级联Dense-Unet和图割的自动精确鲁棒分割方法。首先运用级联的Dense-UNet获取肝脏肿瘤初始分割结果及感兴趣区域,然后利用图像像素级和区域级特征,分别构建可有效区分肿瘤与非肿瘤的灰度模型和概率模型,并将其融入图割能量函数,进一步精确分割感兴趣区域中的肿瘤组织。最后分别采用LiTS和3Dircadb公共数据库作为训练集与测试集进行实验,并与现有多种自动分割方法进行了比较。结果表明,提出方法可有效分割CT图像中灰度、形状、大小、位置各异的肝脏肿瘤,能提取更精确的肿瘤边界,尤其对于对比度低、边界模糊的肿瘤具有明显优势。     

Physical Violence Detection Based on Distributed Surveillance Cameras

Mobile Networks and Applications - In recent years, physical violence detection has become a research hotspot in the area of human activity recognition. With the improvement and full coverage of...     

Photonic Crystal Palette of Binary Block Copolymer Blends for Full Visible Structural Color Encryption

Structural color (SC) arising from a periodically ordered self-assembled block copolymer (BCP) photonic crystal (PC) is useful for reflective-mode sensing displays owing to its capability of stimuli-responsive structure alteration. However, a set of PC inks, each providing a precisely addressable SC in the full visible range, has rarely been demonstrated. Here, a strategy for developing BCP PC inks with tunable structures is presented. This involves solution-blending of two lamellar-forming BCPs with different molecular weights. By controlling the mixing ratio of the two BCPs, a thin 1D BCP PC film is developed with alternating in-plane lamellae whose periodicity varies linearly from ≈46 to ≈91 nm. Subsequent preferential swelling of one-type lamellae with either solvent or non-volatile ionic liquid causes the photonic band gap of the films to red-shift, giving rise to full-visible-range SC correlated with the pristine nanostructures of the blended films in both liquid and solid states. The BCP PC palette of solution-blended binary solutions is conveniently employed in various coating processes, allowing facile development of BCP SC on the targeted surface. Furthermore, full-color SC paintings are realized with their transparent PC inks, facilitating low-power pattern encryption.     

Relational attention-based Markov logic network for visual navigation

The Journal of Supercomputing - We argue the agent’s low generalization problem for searching target object in challenging visual navigation could be solved by "how" and...     

Extending $$\tau $$ τ -Lop to model MPI blocking primitives on shared memory

The Journal of Supercomputing - MPI communication optimization is essential for high-performance applications. The communication performance models have made some achievements in improving the...     

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start link prediction is favored as one of the most valuable subproblems of traditional link prediction. However, due to the loss of many links in the observation network, the topological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction. In this paper, we propose a framework for solving the cold-start link prediction problem, a joint-weighted symmetric nonnegative matrix factorization model fusing graph regularization information, based on low-rank approximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designed graph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other. Finally, a unified framework for implementing cold-start link prediction is constructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validation on five real networks with attributes shows that the proposed model has very good predictive performance when predicting missing edges of isolated nodes.     

引发产业变革的颠覆性技术内涵与遴选研究

引发产业变革的颠覆性技术具有技术突破性、产品替代性、市场广泛性与产业变革性特征,会对社会发展和人民生活产生深远的影响。本文首先从产业和技术角度出发,提出引发产业变革的颠覆性技术内涵;其次,建立相应的指标评价体系;最后,通过两轮问卷调查,以定性和定量相结合的方式,遴选出20多项引发产业变革的颠覆性技术,从而为进一步研究颠覆性技术与产业变革间的规律、开展技术预测工作提供支撑。