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Lingxuan LIU, Zhongshun SHI, Leyuan SHI
《工程管理前沿(英文)》 2018年 第5卷 第4期 页码 487-498 doi: 10.15302/J-FEM-2018042
This study investigates an energy-aware flow shop scheduling problem with a time-dependent learning effect. The relationship between the traditional and the proposed scheduling problem is shown and objective is to determine a job sequence in which the total energy consumption is minimized. To provide an efficient solution framework, composite lower bounds are proposed to be used in a solution approach with the name of Bounds-based Nested Partition (BBNP). A worst-case analysis on shortest process time heuristic is conducted for theoretical measurement. Computational experiments are performed on randomly generated test instances to evaluate the proposed algorithms. Results show that BBNP has better performance than conventional heuristics and provides considerable computational advantage.
关键词: flow shop energy-aware scheduling learning effect nested partition worst-case error bound
不使用任何信任关系构建信任网络 Article
Xin WANG, Ying WANG, Jian-hua GUO
《信息与电子工程前沿(英文)》 2017年 第18卷 第10期 页码 1591-1600 doi: 10.1631/FITEE.1601341
关键词: 信任网络;稀疏学习;同质效应;交互行为
《环境科学与工程前沿(英文)》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1677-1
● MSWNet was proposed to classify municipal solid waste.
关键词: Municipal solid waste sorting Deep residual network Transfer learning Cyclic learning rate Visualization
Spatial prediction of soil contamination based on machine learning: a review
《环境科学与工程前沿(英文)》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1
● A review of machine learning (ML) for spatial prediction of soil contamination.
关键词: Soil contamination Machine learning Prediction Spatial distribution
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
《环境科学与工程前沿(英文)》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1738-5
● A novel integrated machine learning method to analyze O3 changes is proposed.
Machine learning in building energy management: A critical review and future directions
《工程管理前沿(英文)》 2022年 第9卷 第2期 页码 239-256 doi: 10.1007/s42524-021-0181-1
关键词: building energy management machine learning integrated framework knowledge evolution
《化学科学与工程前沿(英文)》 2022年 第16卷 第2期 页码 183-197 doi: 10.1007/s11705-021-2073-7
关键词: machine learning flowsheet simulations constraints exploration
Machine learning for fault diagnosis of high-speed train traction systems: A review
《工程管理前沿(英文)》 doi: 10.1007/s42524-023-0256-2
关键词: high-speed train traction systems machine learning fault diagnosis
Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework
《结构与土木工程前沿(英文)》 页码 994-1010 doi: 10.1007/s11709-023-0942-5
关键词: dynamic prediction moving trajectory pipe jacking GRU deep learning
Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature
《医学前沿(英文)》 2023年 第17卷 第4期 页码 768-780 doi: 10.1007/s11684-023-0982-1
关键词: machine learning methods hypertrophic cardiomyopathy genetic risk
《机械工程前沿(英文)》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7
关键词: deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis
Automated synthesis of steady-state continuous processes using reinforcement learning
《化学科学与工程前沿(英文)》 2022年 第16卷 第2期 页码 288-302 doi: 10.1007/s11705-021-2055-9
关键词: automated process synthesis flowsheet synthesis artificial intelligence machine learning reinforcement learning
State-of-the-art applications of machine learning in the life cycle of solid waste management
《环境科学与工程前沿(英文)》 2023年 第17卷 第4期 doi: 10.1007/s11783-023-1644-x
● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented.
关键词: Machine learning (ML) Solid waste (SW) Bibliometrics SW management Energy utilization Life cycle
通讯式学习——统一的机器学习模式 Review
袁路遥, 朱松纯
《工程(英文)》 2023年 第25卷 第6期 页码 77-100 doi: 10.1016/j.eng.2022.10.017
In this article, we propose a communicative learning (CL) formalism that unifies existing machine learning paradigms, such as passive learning, active learning, algorithmic teaching, and so forth, and facilitates the development of new learning methods. Arising from human cooperative communication, this formalism poses learning as a communicative process and combines pedagogy with the burgeoning field of machine learning. The pedagogical insight facilitates the adoption of alternative information sources in machine learning besides randomly sampled data, such as intentional messages given by a helpful teacher. More specifically, in CL, a teacher and a student exchange information with each other collaboratively to transmit and acquire certain knowledge. Each agent has a mind, which includes the agent's knowledge, utility, and mental dynamics. To establish effective communication, each agent also needs an estimation of its partner's mind. We define expressive mental representations and learning formulation sufficient for such recursive modeling, which endows CL with human-comparable learning efficiency. We demonstrate the application of CL to several prototypical collaboration tasks and illustrate that this formalism allows learning protocols to go beyond Shannon's communication limit. Finally, we present our contribution to the foundations of learning by putting forth hierarchies in learning and defining the halting problem of learning.
关键词: Artificial intelligencehine Cooperative communication Machine learning Pedagogy Theory of mind
Evaluation and prediction of slope stability using machine learning approaches
《结构与土木工程前沿(英文)》 2021年 第15卷 第4期 页码 821-833 doi: 10.1007/s11709-021-0742-8
关键词: slope stability factor of safety regression machine learning repeated cross-validation
标题 作者 时间 类型 操作
Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent learningeffect
Lingxuan LIU, Zhongshun SHI, Leyuan SHI
期刊论文
MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal
期刊论文
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
期刊论文
Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet
期刊论文
Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature
期刊论文
A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
期刊论文