物理科学的历史与限度——第六届《哲学分析》讲堂综述

<正>2013年9月25日,第六届《哲学分析》讲堂在上海社科院分部哲学所会议室举行。本次讲堂邀请到了两位著名科学哲学家主讲——一位是拉卡托斯奖得主、不列颠科学院院士、英国科学哲学学会前主席、牛津大学教授哈维·布朗(Harvey R.Brown),报告题目为《从亚里士多德到爱因斯坦的运动理论》;另一位是德国汉诺威     

对马克思主义哲学的多维透视——评《多维视域下的马克思主义哲学研究：阐释、评价与反思》

2018年8月,河北大学政法学院哲学系冯燕芳副教授的《多维视域下的马克思主义哲学研究:阐释、评价与反思》在科学出版社出版。书中以马克思历史唯物主义立场出发,从西方马克思主义、后马克思主义、后现代历史哲学、中国化马克思主义等几个角度来研究不同理论家对马克思主义相关理论的阐释和理论建构。《多维视域下的马克思主义哲学研究:阐释、评价与反思》内容丰富,线索明晰,具有批判意识,并提出"创造性阅读"是马克思主义哲学的正确阅读方式,是极具推广意义的教科书式的马克思主义哲学著作。     

通向21世纪明清学术、思想研究的新范式——《戴震、乾嘉学术与中国文化》的主要论题及其延伸性讨论

在通向21世纪明清学术、思想研究新范式的道路上,《戴震、乾嘉学术与中国文化》在充分总结和评述前人研究成果的基础上作出了有力探索,特别是在某些重要方面突破前人之见并形成比较系统的观点。该书论明,以戴震为代表的乾嘉学术有其哲学形上学方面的追求,且这种追求以"气化即道"的新道论为基础。与此相应,乾嘉学术内在地包含了一套哲学方法论即"人文实证主义"。"气化即道"以及"人文实证主义"的说法揭示出乾嘉学术、思想的独特性,并展现出中国传统学术的现代性价值和通向现代学术的可能性。在"现代性的宏大叙事"模式和话语的支配下,该书体现了宏阔的思想视野和知识视野,但其主题不够突出,这限制了讨论的力度以及深度。     

中国建筑规矩方圆之道——《规矩方圆,天地之和——中国古代都城、建筑群与单体建筑之构图比例研究》学术研讨会综述

清华大学建筑学院王南博士的著作《规矩方圆,天地之和—中国古代都城、建筑群与单体建筑之构图比例研究》(下称《比例研究》,中国城市出版社、中国建筑工业出版社,2018年12月出版)对中国五千年时间跨度的459个城市与建筑实例作了研究,发现基于规矩方圆作图的一系列构图比例,尤其是2:1与3:2得到了极为普遍的运用,与西方人钟爱的基于自然美、人体美的"黄金分割比"相比,此种构图比例堪称"天地之和比"。这一发现,使继承中国经典构图比例、重塑中国城市与建筑的秩序与和谐成为可能。经本刊主编崔愷院士提议,关于《比例研究》的学术研讨会于2019年4月4日在中国建筑工业出版社召开,故宫研究院建筑与规划研究所所长、研究馆员王军应邀主持了会议。现将著作者关于《比例研究》一书的学术报告及学术研讨纪要刊布于此,以飨读者。     

Rapid 3D object detection and modeling using range data from 3D range imaging camera for heavy equipment operation

Automated detection and modeling of 3D objects located in a construction work environment is critical for autonomous heavy equipment operation. Such automation allows for accurate, efficient, and autonomous operation of heavy equipment in a broad range of construction tasks by providing interactive background information. This paper proposes a 3D object detection and modeling system which utilizes range data obtained by 3D range imaging camera to generate 3D object models with an acceptable level of accuracy in a few seconds. The proposed system consists of four steps: data acquisition, pre-processing, object segmentation, and 3D model generation. The system was tested on the modeling of different classes of construction objects on actual construction sites. The results show that the proposed 3D object detection and modeling system achieves a good balance between speed and accuracy, and hence could be used to enhance efficiency and productivity in the autonomous operation of heavy equipment.     

Monocular 3D object detection for construction scene analysis

Three-dimensional (3D) object detection, that is, localizing and classifying all critical objects in a 3D space, is essential for downstream construction scene analysis tasks. However, accurate instance segmentation, few 2D object segmentation and 3D object detection data sets, high-quality feature representations for depth estimation, and limited 3D cues from a single red-green-blue (RGB) image pose significant challenges to 3D object detection and severely hinder its practical applications. In response to these challenges, an improved cascade-based network with a transformer backbone and a boundary-patch-refinement method is proposed to build hierarchical features and refine object boundaries, resulting in better results in 2D object detection and instance segmentation. Furthermore, a novel self-supervised monocular depth learning method is proposed to extract better feature representations for depth estimation from construction site video data with unknown camera parameters. Additionally, a pseudo-LiDAR point cloud method and a 3D object detection method with a density-based clustering algorithm are proposed to detect 3D objects in a construction scene without help from 3D labels, which will serve as a good foundation for other downstream 3D tasks. Finally, the proposed model is evaluated for object instance segmentation and depth estimation on the moving objects in construction sites (MOCS) and construction scene data sets. It brings a 9.16% gain in terms of mean average precision (mAP) for object detection and a 4.92% gain in mask mAP for object instance segmentation. The average order accuracy and relative mean error for depth estimation are improved by 0.94% and 60.56%, respectively. This study aims to overcome the challenges and limitations of 3D object detection and facilitate practical applications in construction scene analysis.     

一种塔式起重机吊钩视频中重物轮廓实时监测方法

本文提供了一种识别塔机吊物目标的方法,增加吊钩视频的智能性,尤其当背景与前景相似时更能辅助司机正确判断吊钩下方工作面的情况,提高塔机工作安全性。     

Flame and smoke detection method for early real-time detection of a tunnel fire

This paper presents an image-processing technique for automatic real-time flame and smoke detection in a tunnel environment. To avoid the large-scale damage caused by fire occurring in different environments, there are many studies about discovering and minimizing an incident as fast as possible. However, we need an original algorithm specialized for a tunnel fire, because this environment is quite different and it is difficult to apply existing fire detection algorithms to a tunnel environment. Therefore, this paper proposes an original algorithm that applies to a tunnel environment. Color and motion information are used to minimize false detections in tunnels, and this information enables us to detect the exact position of an event at an early stage, by detection, test, and verification procedures. The experimental results show the validity and efficiency of the proposed algorithms by a comparison of the characteristics of each algorithm.     

基于目标检测卷积神经网络的图像型火灾探测算法

摘 要：针对传统图像型火灾探测算法误差率高、延迟探测、计算量大等问题,提出了基于目标检测卷积神经网络（Faster-RCNN、R-FCN、SSD和YOLO v3）的图像型火灾探测算法。通过对比实验表明,基于目标检测卷积神经网络的探测算法准确性较高。其中,YOLO v3探测算法的平均精度为84.5%,探测速度为28帧/s,具有更高的稳定性,更适用于图像型火灾探测系统的开发。     

基于OpenCV的动态背景下的运动目标检测

研究在动态背景下对视频图像序列进行运动目标的检测。主要包括三个步骤,分别是运动估计,运动补偿和目标检测。在运动估计中采用的主要是基于特征点匹配算法。这种算法与传统的块匹配算法最大的好处在于它的数据量少,计算简单迅速而且图像的匹配可靠性更高。最后用计算机视觉库OpenCV进行实现。     

面向对象建模技术在房屋质量鉴定中的应用

探讨了用面向对象建模技术构建建筑工程检测中房屋质量鉴定评级管理信息系统的可行性及方法。并以具体房屋为例，采用面向对象的建模技术进行了分析设计，在此基础上简要介绍了所构建系统的特点。     

Surface plasmon resonance-based inhibition assay for real-time detection of Cryptosporidium parvum oocyst

A surface plasmon resonance (SPR)-based inhibition assay method using a polyclonal anti-mouse IgM arrayed Cryptosporidium sensor chip was developed for the real-time detection of Cryptosporidium parvum oocysts. The Cryptosporidium sensor chip was fabricated by subsequent immobilization of streptavidin and polyclonal anti-mouse IgM (secondary antibody) onto heterogeneous self-assembled monolayers (SAMs). The assay consisted of the immunoreaction step between monoclonal anti-C. parvum oocyst (primary antibody) and oocysts, followed by the binding step of the unbound primary antibody onto the secondary antibody surface. It enhanced not only the immunoreaction yield of the oocysts by batch reaction but also the accessibility of analytes to the chip surface by antibody-antibody interaction. Furthermore, the use of optimum concentration of the primary antibody maximized its binding response on the chip. An inversely linear calibration curve for the oocyst concentration versus SPR signal was obtained in the range of 1x10(6)-1x10(2)oocystsml(-1). The oocyst detection was also successfully achieved in natural water systems. These results indicate that the SPR-based inhibition assay using the Cryptosporidium sensor chip has high application potential for the real-time analysis of C. parvum oocyst in laboratory and field water monitoring.     

压实度实时检测技术发展与实践

压实质量问题是造成沥青路面早期损坏的主要原因之一。压实不足将导致车辙、坑槽、松散及其它水损害,过压将导致压密、松散、级配改变、泛油,并可能将集料破碎使得粘结料不能裹覆集料新断裂面,同时压实过程中还存在其它质量问题(如平整度、压实度一致性、构造深度不满足要求,出现轮迹、重叠区域压实不足或过压、裂纹等),部分早期损坏现象及压实质量问题,见图1。尽管压实技术和压路机产品都得到了较大发展,但压实质量检测依然采用传统的灌沙法、取芯法等破坏性试验或核子