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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Herein we used seven descriptors based on understanding dissolution behavior to establish two solubilityprediction models by machine learning algorithms.

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1397-1414 doi: 10.1007/s11709-022-0860-y

Abstract: indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learningBy comparing the proposed method and the conventional sensitivity index-based methods, it is demonstratedthat the computational cost is considerably reduced because the reinforcement learning model is trainedBesides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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Machine learning-based seismic assessment of framed structures with soil-structure interaction

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 205-223 doi: 10.1007/s11709-022-0909-y

Abstract: The objective of the current study is to propose an expert system framework based on a supervised machinelearning technique (MLT) to predict the seismic performance of low- to mid-rise frame structures consideringThe methodology of the framework is based on examining different MLTs to obtain the highest possibleMoreover, the framework provides recommendations for building component design based on the newly introduced

Keywords： seismic hazard     artificial neural network     soil-structure interaction     seismic analysis    

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Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Engineering doi: 10.1016/j.eng.2023.08.011

Abstract: and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-based

Keywords： prediction     Physics information informed     Real-time prediction     Earthquake engineering     Data-driven machine learning    

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Reliability Topology Optimization of Collaborative Design for Complex Products Under Uncertainties Based

Zhaoxi Hong,Xiangyu Jiang,Yixiong Feng,Qinyu Tian,Jianrong Tan

Engineering 2023, Volume 22, Issue 3,   Pages 71-81 doi: 10.1016/j.eng.2021.06.027

Abstract: We propose a new reliability topology optimization method based on the reliability-and-optimization decoupledmodel and teaching-learning-based optimization (TLBO) algorithm.The TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial

Keywords： Plates structure     Reliability Collaborative topology optimization     Teaching–learning-based optimization    

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Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery Article

Asif Khan, Nadeem Javaid

Engineering 2020, Volume 6, Issue 7,   Pages 812-826 doi: 10.1016/j.eng.2020.06.004

Abstract: This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO

Keywords： 单位尺寸     独立系统     可再生能源     储能系统     优化     负荷缺电率    

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Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm

Ziang Li,Zhengtao Ding,Meihong Wang

Engineering 2017, Volume 3, Issue 2,   Pages 257-265 doi: 10.1016/J.ENG.2017.02.014

Abstract:

In this paper, a reinforcement learning (RL)-based Sarsa temporal-difference (TD) algorithm is appliedfor a unified bidding and operation strategy for a coal-fired power plant with monoethanolamine (MEA)-baseddesigned operation and bidding strategies discussed in most of the relevant literature, the Sarsa TD-based

Keywords： carbon capture     Chemical absorption     CO2 allowance market     Optimal decision-making     Reinforcement learning    

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Learning-based parameter prediction for quality control in three-dimensional medical image compression Research Articles

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1169-1178 doi: 10.1631/FITEE.2000234

Abstract: Optimal compression parameters need to be determined based on the specific quality requirement.The direct application of a video-based scheme in predicting the ideal parameters for 3D cannot guaranteeIts kernel is a support vector regression (SVR) based learning model that is capable of predicting theoptimal QP from both video-based and structural image features extracted directly from raw data, avoidingExperimental results on several datasets verify that our approach outperforms current video-based methods

Keywords： 医学图像压缩；高效视频编码（HEVC）；质量控制；基于学习方法    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingmethods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-basedACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predictextracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-basedThe RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46%decision support for moving trajectory control and serve as a foundation for the application of deep learning

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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What can be taught in architectural design? —

Xing RUAN

Frontiers of Structural and Civil Engineering 2010, Volume 4, Issue 4,   Pages 450-455 doi: 10.1007/s11709-010-0098-y

Abstract: This essay begins with a reflection on what has been taught in architectural design since the turn of the twentieth century. I shall trace back to the two disciplinary foundations of the French école des Beaux-Arts – and – in the education of an architect in the eighteenth and nineteenth centuries. I shall then attempt to superimpose and on a modern disciplinary framework, say that of mathematics, which leads to musings on a series of architectural problems that include pattern versus type, stability versus mobility, orthogonal versus oblique, confinement versus transparency, and aging versus metallic sheen. These paradoxes, I suggest, demand the education of an architect to address both the instrumental pattern of a building configuration and the ambient felt qualities of a room, rather than vision alone.

Keywords： architectural design     teaching     parti and poché    

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learningdesigned to fuse both the feature extraction and classification blocks into an intelligent and compact learning

Keywords： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Title Author Date Type Operation

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Journal Article

Machine learning-based seismic assessment of framed structures with soil-structure interaction

Journal Article

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Journal Article

Reliability Topology Optimization of Collaborative Design for Complex Products Under Uncertainties Based

Zhaoxi Hong,Xiangyu Jiang,Yixiong Feng,Qinyu Tian,Jianrong Tan

Journal Article

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery

Asif Khan, Nadeem Javaid

Journal Article

Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm

Ziang Li,Zhengtao Ding,Meihong Wang

Journal Article

Learning-based parameter prediction for quality control in three-dimensional medical image compression

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

What can be taught in architectural design? —

Xing RUAN

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article