Results 201 to 210 of about 749 (240)

Innovative BWM-TOPSIS-based approach to determine the optimum delivery method for offshore projects. [PDF]

Sci Rep
Raed L, Mahdi I, Ibrahim HMH, Tolba ER, Ebid AM.   +4 more
europepmc   +1 more source

Wearable Tailored Passive Radiative Cooling Textile for Flexible Electronic Integration

Advanced Science, EarlyView.
A scalable, additive‐free wearable tailored passive radiative cooling textile (WRCT) with hierarchically structured fibers delivers exceptional thermal management, combining >95% solar reflectance, 0.96 mid‐infrared emissivity, and ultralow thermal conductivity (0.041 W/m·K).
Lung Chow, Jianpeng Zhang, Zehua Peng, Denglin Ye, Binbin Zhang, Jian Li, Wooyoung Park, Chentao Du, Xingcan Huang, Chun Ki Yiu, Yuze Qiu, Jingkun Zhou, Zhenlin Chen, Yuyu Gao, Weibin Zhu, Pengcheng Wu, Guangyao Zhao, Qiang Zhang, Yuan Guo, Zuowei Sun, Zhan Shen, Shuaiqi Ren, Sang Min Won, Jiyu Li, Xinge Yu   +24 more
wiley   +1 more source

Dual-Branch Deep Learning with Dynamic Stage Detection for CT Tube Life Prediction. [PDF]

Sensors (Basel)
Chen Z, Liu Y, Qin Z, Li H, Xie S, Fan L, Liu Q, Huang J.   +7 more
europepmc   +1 more source

Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance. [PDF]

Sci Rep
Hakami A.
europepmc   +1 more source

Enhancement of Operational Safety in Marine Cargo Cranes on a Container Ship Through the Application of Authenticated Wi-Fi Based Wireless Data Transmission from Multiple Sensors. [PDF]

Sensors (Basel)
Abotaleb M, Mindykowski J.
europepmc   +1 more source

Preexisting Diabetes and Pregnancy: An Endocrine Society and European Society of Endocrinology Joint Clinical Practice Guideline.

J Clin Endocrinol Metab
Wyckoff JA, Lapolla A, Asias-Dinh BD, Barbour LA, Brown FM, Catalano PM, Corcoy R, Di Renzo GC, Drobycki N, Kautzky-Willer A, Murad MH, Stephenson-Gray M, Tabák AG, Weatherup E, Zera C, Singh-Ospina N.   +15 more
europepmc   +1 more source

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T-PdM: a tripartite predictive maintenance framework using machine learning algorithms

International Journal of Computational Science and Engineering, 2022
The purpose of this paper is to propose new predictive maintenance (PdM) framework that has three aims: 1) estimating the remaining useful life (RUL) of a machine; 2) classifying machine health status (failure/non-failure); 3) discovering the relationship between the errors and component failures of machines by using machine learning (ML) techniques ...
Yurek, Ozlem Ece, KUT, RECEP ALP, BİRANT, DERYA   +2 more
openaire   +2 more sources

Leveraging Advanced Process Control (APC) technology in developing Predictive Maintenance (PdM) systems

2012 SEMI Advanced Semiconductor Manufacturing Conference, 2012
Predictive Maintenance (PdM) systems use process and equipment state information to predict when a tool or a particular component in a tool might need maintenance. PdM systems can be realized cost-effectively by leveraging Advanced Process Control (APC) technologies and infrastructure.
J. Moyne, N. Ward, P. Hawkins
openaire   +1 more source

Utilizing a distributed data collection system to perform predictive maintenance (PdM) and equipment reliability studies

1999 IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings (Cat No.99CH36314), 2003
The Diffusion Engineering Department at White Oak Semiconductor designed, installed, and implemented a computer-based Distributed Data Collection System (DDCS) to monitor and record various process equipment sensor variables that are typically not available via a Semiconductor Standard SECS/GEM interface.
J.H. Seltzer, J. Hertle
openaire   +1 more source