Transmission system restoration with co-optimization of repairs,load pickups,and generation dispatch

This paper studies the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the co-optimization of repairs, load pickups, and generation dispatch to produce a sequencing of the repairs that minimizes the size of the blackout over time. The core of this process is a Restoration Ordering Problem (ROP), a non-convex mixed-integer nonlinear program that is outside the capabilities of existing solver technologies. To address this computational barrier, the paper examines two approximations of the power flow equations: The DC model and the recently proposed LPAC model. Systematic, large-scale testing indicates that the DC model is not sufficiently accurate for solving the ROP. In contrast, the LPAC power flow model, which captures line losses, reactive power, and voltage magnitudes, is sufficiently accurate to obtain restoration plans that can be converted into AC-feasible power flows. An experimental study also suggests that the LPAC model provides a robust and appealing tradeoff between accuracy and computational performance for solving the ROP.     

企业开展矿山修复业务的几点思考

目前,中国矿山生态修复中存在诸多历史遗留问题。国家着手推动矿山治理和生态修复,已经取得了一些成效,但仍存在资金需求缺口大,修复后矿山土地使用确权、土地流转、开发利用等方面的问题。从国家政策支持、治理资金出资模式等方面对矿山修复进行了思考。     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

Application of fuzzy image restoration in criminal investigation

The advancement of science and technology has a positive effect on the development of law disciplines. The development of algorithms and artificial intelligence also has a certain impact on judicial practice. Image restoration is a significant technique in image processing. It aims to objectively restore the content or quality of the original image from the degraded image. Image degradation is always generated in image transmission, such as distortion, blur. In modern video surveillance system, image restoration is significant for criminal investigation. However, image restoration based on conventional filter algorithms cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal investigation. We conduct experiments on both degraded images and videos and experimental results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation.     

水电工程下泄生态基流常用计算方法及软件开发

为了分析及确立生态基流,为水电工程环境影响评价提供依据,针对水电工程生态基流估算及决策过程繁复等问题,以2001—2015年水电工程环境影响评价及相关法律法规为基础,分析我国生态基流管理实践中使用最广泛的4种计算方法,即Tennant法、月基本流量法、最小流量法及7Q10法,并编制生态基流决策支持软件,并应用软件在汉江流域进行实例验证。结果表明,该软件能够提供多种计算方法及对比分析功能,可为水电工程下泄生态基流计算及决策提供计算平台。4种计算方法在汉江流域计算结果差异较大。总体而言,干流适合最小流量法及Tennant法,支流适合Tennant法。研究成果具有较大的实际应用和推广价值。     

新时代背景下铀矿绿色勘查技术体系研究*

实施铀矿绿色勘查是新时代生态文明战略背景下全新的勘查模式，也是保障国家安全和能源安全的必然要求。将绿色理念引入铀矿资源勘查中，对铀矿地质勘查的目的、原则、理念、技术手段和工作流程进行了创新，以最少的环境付出和经济成本获取更好的勘查成果和地质信息。在完善铀矿勘查理论体系的基础上，引入“创新、协调、绿色、开放、共享”的发展理念，参照现行的勘查标准选择合适的工程技术，形成了由成矿理论、勘查技术、勘查标准、环境恢复和勘查评价等5个方面构成的铀矿绿色勘查技术体系。     

输水状态下挖方渠道衬砌干地修复渗控方案

南水北调中线工程运行以来,发挥了巨大社会效益,沿途及京津冀地区对其调水需求日增,渠道停水检修将带来较大风险,因此面临输水状态下渠道衬砌板破坏抢修难题。部分挖方渠道衬砌板下有复杂的渗控措施,输水状态下采用围堰干地抢修渠底和较深部位的渠坡衬砌板时,需解决围堰基坑内外水位差可能带来的基坑内渠坡抗浮和渗透破坏问题。鉴于此,在渠道渗控设计及围堰布置基础上,根据“前堵后排”思想,提出一种围堰基坑“外、中、内”三防线渗控方案,即围堰基坑外围渠基透水管封堵,基坑周边安装水泵抽排。三维渗流场模拟结果表明,该方案可显著降低基坑周边衬砌板下砂垫层内水平渗透比降和基坑涌水量,能保证基坑渗流安全。研究成果可为南水北调中线输水状态下挖方渠道围堰干地抢修提供理论依据和技术支持。     

城市河流系统治理策略在鹅颈水生态修复中的应用

在简要介绍城市河流系统治理策略的基础上，详细论述其在鹅颈水生态修复中的应用。鹅颈水治理以流域为空间单位，准确把握河流主要问题，综合考量水资源、水安全、水环境、水生态、水文化，坚持工程和非工程措施并重，分阶段逐步实现洪水风险管理、外源污染控制、河流水质修复、河流生态系统恢复的目标。结果表明，鹅颈水目前已基本消除黑臭，水质逐渐提升至国家地表水Ⅳ类水标准。提出进一步加强河流生态系统恢复、优化管理制度、逐步建立全过程和全指标生态监测评估体系、实施弹性管理等鹅颈水系统治理措施。     

基于移动窗口法的城市遥感生态质量评价研究

为进一步提高城市生态监测的精确度，在前人开展的城市生态遥感监测研究的基础上引入“区域尺度”的概念，充分考虑小区域范围内不同地物间的交互作用，并利用移动窗口模型(Moving Window-Remote Sensing Ecology Index, MW-RSEI)对沈阳市浑南区的遥感影像进行逐像元分析。实验结果表明，MW-RSEI模型和生态遥感监测模型（Remote Sensing Ecology Index, RSEI)在整体生态评价趋势上表现出一致性。但MW-RSEI模型对城市生态中的细节区域表征更明显。考虑到建筑及裸地周围植被的影响，其较差生态区域的占比仅为6%，生态评价为优的区域占比为11%。研究区生态评价结果表现得较为连续，并未出现明显的断层现象。该结果具有现实意义且与实际生态分布更为符合。MW-RSEI模型在城市生态监测中更为适用，可为相关部门提供一定的技术参考。     

Presentation attack detection based on two-stream vision transformers with self-attention fusion

Aiming at the performance degradation of the existing presentation attack detection methods due to the illumination variation, a two-stream vision transformers framework (TSViT) based on transfer learning in two complementary spaces is proposed in this paper. The face images of RGB color space and multi-scale retinex with color restoration (MSRCR) space are fed to TSViT to learn the distinguishing features of presentation attack detection. To effectively fuse features from two sources (RGB color space images and MSRCR images), a feature fusion method based on self-attention is built, which can effectively capture the complementarity of two features. Experiments and analysis on Oulu-NPU, CASIA-MFSD, and Replay-Attack databases show that it outperforms most existing methods in intra-database testing and achieves good generalization performance in cross-database testing.