BibSLEIGH — Yan

Travelled to:

1 × Sweden
2 × USA

Collaborated with:

Feifan Wang F.Ju D.Song K.C.Morris S.Frechette D.Wei M.A.Jafari S.Li Z.Song M.Zhou H.Li J.Liu Hyunwoong Ko Paul Witherell Ndeye Y. Ndiaye Z.Yang Ho Yeung Sundar Krishnamurty Y.Xu A.G.A.Perera S.Oh Y.Xu K.Ji Y.Yu W.Liu

Talks about:

manufactur (4) system (4) build (4) base (4) automat (3) exterior (2) product (2) control (2) smart (2) optim (2)

Person: Yan Lu

DBLP: Lu:Yan

Contributed to:

2015

2013

2007

2017

2018

2019

Wrote 10 papers:

CASE-2015-LuMF #standard: Standards landscape and directions for smart manufacturing systems (YL, KCM, SF), pp. 998–1005.
CASE-2013-LiSZL #assessment #automation #difference #quality #simulation: Sensor data quality assessment for building simulation model calibration based on automatic differentiation (SL, ZS, MZ, YL), pp. 752–757.
CASE-2013-LuSLL #automation #bibliography #recognition: Automatic recognition of spurious surface in building exterior survey (YL, DS, HL, JL), pp. 1047–1052.
CASE-2013-LuSXPO #automation #graph #multi #using: Automatic building exterior mapping using multilayer feature graphs (YL, DS, YX, AGAP, SO), pp. 162–167.
CASE-2013-XuJLYL #energy #optimisation #using: Optimal building energy management using intelligent optimization (YX, KJ, YL, YY, WL), pp. 95–99.
CASE-2007-WeiJL #automation #industrial #on the: On Protecting Industrial Automation and Control Systems against Electronic Attacks (DW, MAJ, YL), pp. 176–181.
CASE-2017-WangJL #case study #cyber-physical #evaluation #performance: A study on performance evaluation and status-based decision for cyber-physical production systems (FW, FJ, YL), pp. 1000–1005.
CASE-2018-WangLJ #realtime: Condition-based Real-time Production Control for Smart Manufacturing Systems (FW, YL, FJ), pp. 1052–1057.
CASE-2019-KoWNL #information management #machine learning: Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing (HK, PW, NYN, YL), pp. 648–654.
CASE-2019-YangLYK #classification #learning #realtime: Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing (ZY, YL, HY, SK), pp. 640–647.