Results 61 to 70 of about 236,656 (279)

Phase Transitions of Neural Networks

, 1997
The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network.
Biehl M., Opper M., Wolfgang Kinzel, Yeomans J.   +3 more
core   +1 more source

Multimodal Actuation and Environment Adaptive Strategies of Bio‐Inspired Micro/Nanorobots in Precision Medicine

Advanced Robotics Research, EarlyView.
An introduction for multidrive and environment‐adaptive micro/nanorobotics: design and fabrication strategies, intelligent actuation, and their applications. Various intelligent actuation approaches—magnetic, acoustic, optical, chemical, and biological—can be synergistically designed to enhance flexibility and adaptive behavior for precision medicine ...
Aiqing Ma, Han Gong, Jiacheng Ouyang, Liqi Chen, Tianfang Kang, Yiran He, Guojun Huang, Sidi Zheng, Ting Yin, Hong Pan, Lintao Cai   +10 more
wiley   +1 more source

Research on Fault Diagnosis of Chillers Based on Improved BP Network

Zhileng xuebao, 2015
The overall detection rate using conventional neural networks to detect and diagnose the chillers’ fault is low, even this method can’t detect the fault completely.
Shi Shubiao, Chen Huanxin, Li Guannan, Hu Yunpeng, Li Haorong, Hu Wenju, Li Jiong   +6 more
doaj  

Bayesian Neural Networks: Essentials

, 2021
Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural ...
openaire   +2 more sources

Hard‐Magnetic Soft Millirobots in Underactuated Systems

Advanced Robotics Research, EarlyView.
This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang, Zixuan Zhang, Bingyan Li, Junkai Wang, Hongcheng Gu   +4 more
wiley   +1 more source

Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers

, 2017
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effective classification are occlusions, variable lighting conditions, and image ...
Cangelosi, Angelo, Patacchiola, Massimiliano, Spataro, William, Surace, Luca, Sönmez, Elena Battini   +4 more
core   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

Modeling Breast Cancer Using Data Mining Methods

مجله انفورماتیک سلامت و زیست پزشکی, 2018
Introduction: Breast cancer is the most common form of cancer in women. Breast cancer detection is considered as one of the most important issues in medical science.
Parvaneh Dehghan, Maedeh Mogharabi, Iman Zabbah, Kamran Layeghi, Ali Maroosi   +4 more
doaj  

Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization

Frontiers in Earth Science
Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling.
Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal   +6 more
doaj   +1 more source

BOCK : Bayesian Optimization with Cylindrical Kernels [PDF]

, 2018
A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space.
Gavves, Efstratios, Oh, ChangYong, Welling, Max   +2 more
core   +1 more source