Results 181 to 190 of about 322 (214)

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Transferable Deep Reinforcement Learning With Edge‐Contour‐Depth Fusion for Autonomous Wireless Capsule Endoscopy Navigation

Advanced Science, EarlyView.
This study presents an anatomical landmark‐guided DRL framework for autonomous wireless capsule endoscopy navigation. Using a lightweight edge‐contour‐depth fusion module, it achieves over 97% coverage across diverse gastric anatomies. To ensure reliability, a two‐stage sim‐to‐real pipeline with an adaptive dynamic programming controller mitigates ...
Haoxuan Wu, Haitao Gao, Qingyang Liu, Sishen Yuan, Haiyang Fang, Mingwu Su, Baijia Liang, Yongzun Yang, Long Bai, Wenzhen Dong, Dihong Xie, Shijian Su, Jiewen Lai, Shing Shin Cheng, Zhen Li, Xiuli Zuo, Hongliang Ren   +16 more
wiley   +1 more source

Artificial intelligence-produced radiographic enhancements in dental clinical care: provider and patient perspectives. [PDF]

Front Oral Health
Slashcheva LD, Schroeder K, Heaton LJ, Cheung HJ, Prosa B, Ferrian N, Grantz J, Jacobi D, O'Malley JJ, Helgeson M, Tranby EP.   +10 more
europepmc   +1 more source

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Multiobjective Coordinated Energy Dispatch and Voyage Scheduling for a Multienergy Ship Microgrid

IEEE Transactions on Industry Applications, 2020
Given the rapid development of the ship industry and increasing ship operating cost and gas emissions, this article studies the optimal operation of a multienergy ship (MES) microgrid to simultaneously reduce the ship operating cost and gas emissions.
Yan Xu, Zhengmao Li
exaly   +2 more sources

Economic and Environmental Generation and Voyage Scheduling of All-Electric Ships

IEEE Transactions on Power Systems, 2016
An all-electric ship (AES) uses integrated power generators and an energy storage system (ESS) to match its propulsion and service loads, thus forming an isolated microgrid. Existing work has hitherto set the minimisation of the AES operational cost as the single optimisation objective, with the reduction of the greenhouse gas (GHG) emission merely ...
Thomas Reindl, Ce Shang, Dipti Srinivasan   +2 more
exaly   +2 more sources

Coordinated Optimal Energy Management and Voyage Scheduling for All-Electric Ships Based on Predicted Shore-Side Electricity Price

IEEE Transactions on Industry Applications, 2021
Unlike a land-based standalone microgrid, a shipboard microgrid of an all-electric ship (AES) needs to shut down generators during berthing at the port for exanimation and maintenance. Therefore, the cost of onshore power plays an important role in an economic operation for AESs.
Yi Tang, Yan Xu, Zhaohao Ding
exaly   +2 more sources

NSGA-II for joint generation and voyage scheduling of an all-electric ship

2016 IEEE Congress on Evolutionary Computation (CEC), 2016
The non-dominated sorting genetic algorithm II (NSGA-II) is applied to the concurrent generation scheduling and load management of the isolated microgrid: the joint generation — voyage scheduling of an all-electric ship (AES). The AES uses integrated power generators and an energy storage system (ESS) to match its propulsion and service loads, thus ...
Thomas Reindl, Ce Shang, Dipti Srinivasan   +2 more
exaly   +2 more sources

Adaptive Voyage-Generation Scheduling in Shipboard Microgrid under Time-varied Weather Conditions

2020 IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I&CPS), 2020
Fully electrification of ships brings great operating flexibility, which makes the shipboard microgrid can quickly respond to various navigation disturbances. In this paper, an adaptive voyage-generation scheduling in shipboard microgrid is proposed for addressing the time-varied weather conditions along the navigation route.
Sidun Fang, Hongdong Wang, Shenxi Zhang   +2 more
exaly   +2 more sources

Joint voyage scheduling and economic dispatch for all-electric ships with virtual energy storage systems

Energy, 2020
Abstract As a special mobile microgrid, an all-electric ship (AES) utilizes diesel generators and energy storage systems to provide electric propulsion and service loads. Unlike previous studies of the minimization of the AES operation using auxiliary energy storage systems, this paper exploits existing shipboard thermal storage and thermal load as a
Sidun Fang, Hai Lan, Ying-Yi Hong
exaly   +2 more sources