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A history of high-power laser research and development in the United Kingdom

High Power Laser Science and Engineering 2021年第2期9卷 41-126页

作者： Colin N.Danson Malcolm White John R.M.Barr Thomas Bett Peter Blyth David Bowley Ceri Brenner Robert J.Collins Neal Croxford A.E.Bucker Dangor Laurence Devereux Peter E.Dyer Anthony Dymoke-Bradshaw Christopher B.Edwards Paul Ewart Allister I.Ferguson John M.Girkin Denis R.Hall David C.Hanna Wayne Harris David I.Hillier Christopher J.Hooker Simon M.Hooker Nicholas Hopps Janet Hull David Hunt Dino A.Jaroszynski Mark Kempenaars Helmut Kessler Sir Peter L.Knight Steve Knight Adrian Knowles Ciaran L.S.Lewis Ken S.Lipton Abby Littlechild John Littlechild Peter Maggs Graeme P.A.Malcolm OBE Stuart P.D.Mangles William Martin Paul McKenna Richard O.Moore Clive Morrison Zulfikar Najmudin David Neely Geoff H.C.New Michael J.Norman Ted Paine Anthony W.Parker Rory R.Penman Geoff J.Pert Chris Pietraszewski Andrew Randewich Nadeem H.Rizvi Nigel Seddon MBE Zheng-Ming Sheng David Slater Roland A.Smith Christopher Spindloe Roy Taylor Gary Thomas John W.G.Tisch Justin S.Wark Colin Webb S.Mark Wiggins Dave Willford Trevor Winstone AWE Aldermaston ReadingUK Oxford Centre for High Energy Density Science Department of PhysicsClarendon LaboratoryUniversity of OxfordOxfordUK Centre for Intertial Fusion Studies Blackett LaboratoryImperial College LondonLondonUK Formerly of Electrox LetchworthUK Formerly of Central Laser Facility Rutherford LaboratoryDidcotUK Formerly of Ferranti Defence Systems LincolnUK Leonardo EdinburghUK Retired AWEReadingUK Formerly of AWE ReadingUK Formerly of Laser Lines ltd BanburyUK Formerly of Optilas ltd Milton KeynesUK Retired Pro-Lite TechnologyCranfieldUK specialised imaging ltd PitstoneUK Central Laser Facility STFC Rutherford Appleton LaboratoryDidcotUK Magna-Power Electronics ltd ReadingUK DeBe Lasers Needham Laser TechnologiesWhitchurchUK Blackett Laboratory Imperial College LondonLondonUK SPIE Europe CardiffUK Professor Emeritus(Physics) University of HullKingston upon HullUK Kentech Instruments ltd WallingfordUK Department of Physics Clarendon LaboratoryUniversity of OxfordOxfordUK Professor Emeritus Department of PhysicsUniversity of StrathclydeGlasgowUK Department of Physics Durham UniversityDurhamUK Professor Emeritus(Photonics) Heriot-Watt UniversityEdinburghUK Professor Emeritus University of SouthamptonSouthamptonUK Optical Surfaces ltd KenleyUK JEH Lasers ltd RugbyUK Scottish Universities Physics Alliance Department of PhysicsUniversity of StrathclydeGlasgowUK ITER Organization Saint-Paul-lez-DuranceFrance Manx Precision Optics ltd BallasallaUK Laser Lines ltd BanburyUK Elliot Scientific ltd MetroTest Scientific GroupHarpendenUK Centre for Plasma Physics Queen’s University BelfastBelfastUK Retired Founderand former MDRofin-Sinar UK LtdHullUK Heraeus Noblelight ltd CambridgeUK Mirli Books ChelmsfordUK M Squared Lasers ltd GlasgowUK University of Hertfordshire HatfieldUK E&EO UK ltd Barton-upon-HumberUK Department of Physics University of YorkYorkUK IC Optical Systems ltd BeckenhamUK Laser Micromachining ltd St AsaphUK MBDA UK ltd Brist

The first demonstration of laser action in ruby was made in 1960 by T.H.Maiman of Hughes Research Laboratories,USA.Many laboratories worldwide began the search for lasers using different materials,operating at different wavelengths.In the UK,academia,industry and the central laboratories took up the challenge from the earliest days to develop these systems for a broad range of applications.This historical review looks at the contribution the UK has made to the advancement of the technology,the development of systems and components and their exploitation over the last 60 years.

关键词： high-power lasers history United Kingdom

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TDD-net: a tiny defect detection network for printed circuit boards

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CAAI Transactions on Intelligence Technology 2019年第2期4卷 110-116页

作者： Runwei Ding Linhui Dai Guangpeng Li Hong Liu Key Laboratory of Machine Perception Peking University Shenzhen Graduate School People’s Republic of China Shenzhen Skyworth-RGB Electronic CO. Ltd. People’s Republic of China

Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available.

关键词： TDD PCBs TDD-Net

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Corrosion Rate and Scale of Cast Iron in Water Distribution Systems

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Transactions of Tianjin University 2016年第6期22卷 516-524页

作者：郭浩田一梅陈灏琳傅玉芬刘星飞 School of Environmental Science and Engineering Tianjin University Tianjin 300072 China Huashuang Water Co. ltd. of Tianjin Waterworks Group Tianjin 300040 China

A specialised electrochemical measurement cell was plugged into a pilot water distribution system to simulate the pipe inner-wall corrosion. The linear polarisation resistance(LPR) technique and electrochemical impedance spectroscopy(EIS) were measured in real time to study the variation of the corrosion rate(CR) and scale of cast iron. Three corrosion stages were observed according to the LPR analysis: an initial stage with significantly fluctuating CR, a developmental stage with slowly decreasing CR, and a stable stage with a low CR of approximately 0.157 5 mm·a^(-1). The EIS revealed that the scales with a compact outer layer and a porous inner layer finally formed in the stable stage, and the polarisation resistance was approximately 2 175 ?·cm^2. A physicochemical analysis of the scales showed that ferric oxides, oxyhydroxides and calcite made the outer layer compact and effectively limited the diffusion of oxygen through the scales, which resulted in a low CR.

关键词：生铁腐蚀规模浇分发系统线性 polarisation 抵抗 EIS XRD

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人工智能白内障协同管理的通用平台

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眼科学报 2023年第10期38卷 665-675页

作者： WU Xiaohang HUANG Yelin LIU Zhenzhen LAI Weiyi LONG Erping ZHANG Kai JIANG Jiewei LIN Duoru CHEN Kexin YU Tongyong WU Dongxuan LI Cong CHEN Yanyi ZOU Minjie CHEN Chuan ZHU Yi GUO Chong ZHANG Xiayin WANG Ruixin YANG Yahan XIANG Yifan CHEN Lijian LIU Congxin XIONG Jianhao GE Zongyuan WANG Dingding XU Guihua DU Shaolin XIAO Chi WU Jianghao ZHU Ke NIE Danyao XU Fan LV Jian CHEN Weirong LIU Yizhi 林浩添无王厚硕(审校) 罗明杰(审校) State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangzhouChina Beijing Tulip Partners Technology Co. LtdBeijingChina School of Computer Science and Technology Xidian UniversityXi’anChina Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhouChina Department of Molecular and Cellular Pharmacology University of Miami Miller School of MedicineMiamiFloridaUSA Department of Electrical and Computer Systems Engineering Faculty of EngineeringMonash UniversityMelbourneVictoriaAustralia Huizhou Municipal Central Hospital HuizhouChina Tung Wah Hospital Sun Yat-sen UniversityDongguanChina Dongguan Guangming Ophthalmic Hospital DongguanChina Kaifeng Eye Hospital KaifengChina Shenzhen Eye Hospital Shenzhen Key Laboratory of OphthalmologyShenzhen University School of MedicineShenzhenChina Department of Ophthalmology People’s Hospital of Guangxi Zhuang Autonomous RegionNanningChina 《眼科学报》出版团队西安交通大学第一附属医院中山大学中山眼科中心眼病防治全国重点实验室广东省眼科视觉科学重点实验室

目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集... 详细信息

目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集模式。使用三步策略对数据集进行标记:1)识别采集模式;2)白内障诊断包括正常晶体眼、白内障眼或白内障术后眼;3)从病因和严重程度检测需转诊的白内障患者。此外,将白内障AI系统与真实世界中的居家自我监测、初级医疗保健机构和专科医院等多级转诊模式相结合。结果:通用AI平台和多级协作模式在三步任务中表现出可靠的诊断性能:1)识别采集模式的受试者操作特征(receiver operating characteristic curve,ROC)曲线下面积(area under the curve,AUC)为99.28%~99.71%);2)白内障诊断对正常晶体眼、白内障或术后眼,在散瞳-裂隙灯模式下的AUC分别为99.82%、99.96%和99.93%,其他采集模式的AUC均>99%;3)需转诊白内障的检测(在所有测试中AUC>91%)。在真实世界的三级转诊模式中,该系统建议30.3%的人转诊,与传统模式相比,眼科医生与人群服务比率大幅提高了10.2倍。结论:通用AI平台和多级协作模式显示了准确的白内障诊断性能和有效的白内障转诊服务。建议AI的医疗转诊模式扩展应用到其他常见疾病和资源密集型情景当中。

关键词：人工智能通用平台资源密集型协同管理采集模式专科医院眼科医生自我监测

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