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Qiang J, Zhang S, Liu H, Zhu X, Zhou J. A construction strategy of Kriging surrogate model based on Rosenblatt transformation of associated random variables and its application in groundwater remediation. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 349:119555. [PMID: 37980793 DOI: 10.1016/j.jenvman.2023.119555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/21/2023] [Accepted: 11/04/2023] [Indexed: 11/21/2023]
Abstract
When using simulation-optimization models for optimizing the design of groundwater pumping-treatment plans for pollution, building a surrogate model for the numerical simulation model has become an effective means of overcoming the computational load of such models. However, previous studies often treated pumping time as a single optimization variable, leading to unnecessary excessive pumping. This paper considers the location, pumping rate, start time, and end time of each candidate pumping well as optimization variables, and proposes a Rosenblatt-transform-based optimal Latin hypercube sampling method for the associated random variables to ensure that the start time is less than or equal to the end time. This method is coupled with an adaptive sampling method based on batch local optimal solutions to construct a dynamic adaptive Kriging surrogate model for the numerical model, ensuring that the true value of the optimal remediation scheme is not lost. The results show that, at the final stage of remediation, the pollutant concentration in the 4 scenarios achieves comprehensive compliance. However, when considering the minimization of remediation costs as the evaluation criterion, the remediation scheme in scenario 1 (the pumping start and end times are independent optimization variables for all candidate pumping wells) is optimal. In the optimization design of groundwater pumping-treatment plans, the pumping wells should be arranged in the midstream and downstream regions of the contaminant plume and parallel to the regional flow direction. This paper provides a method reference for the construction and adaptive updating of surrogate models involving associated random variables, as well as guidance for the dynamic optimization of groundwater pumping and treatment at contaminated sites.
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Affiliation(s)
- Jing Qiang
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China; Jiangsu Center for Applied Mathematics (CUMT), Xuzhou, 221116, China
| | - Shuangsheng Zhang
- College of Environmental Engineering, Xuzhou University of Technology, Xuzhou, 221018, China.
| | - Hanhu Liu
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xueqiang Zhu
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
| | - Junjie Zhou
- College of Environmental Engineering, Xuzhou University of Technology, Xuzhou, 221018, China
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Zhu R, Lilak S, Loeffler A, Lizier J, Stieg A, Gimzewski J, Kuncic Z. Online dynamical learning and sequence memory with neuromorphic nanowire networks. Nat Commun 2023; 14:6697. [PMID: 37914696 PMCID: PMC10620219 DOI: 10.1038/s41467-023-42470-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 10/11/2023] [Indexed: 11/03/2023] Open
Abstract
Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.
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Affiliation(s)
- Ruomin Zhu
- School of Physics, The University of Sydney, Sydney, NSW, Australia.
| | - Sam Lilak
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, US
| | - Alon Loeffler
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Joseph Lizier
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Adam Stieg
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, US.
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.
| | - James Gimzewski
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, US.
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, US.
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.
- Research Center for Neuromorphic AI Hardware, Kyutech, Kitakyushu, Japan.
| | - Zdenka Kuncic
- School of Physics, The University of Sydney, Sydney, NSW, Australia.
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
- The University of Sydney Nano Institute, Sydney, NSW, Australia.
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Cheng Q, Fu Y, Huang J, Cheng G, Du H. Event detection based on the label attention mechanism. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01655-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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4
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A bearing prognosis framework based on deep wavelet extreme learning machine and particle filtering. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Raghuwanshi BS, Mangal A, Shukla S. Universum based kernelized weighted extreme learning machine for imbalanced datasets. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01601-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Wan X, Cen L, Chen X, Xie Y. A novel multiple temporal-spatial convolution network for anode current signals classification. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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7
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Constrained class-wise feature selection (CCFS). INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01589-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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8
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A Combined Model Based on the Social Cognitive Optimization Algorithm for Wind Speed Forecasting. Processes (Basel) 2022. [DOI: 10.3390/pr10040689] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
The use of wind power generation can reduce the pollution in the environment and solve the problem of power shortages on offshore islands, grasslands, pastoral areas, mountain areas, and highlands. Wind speed forecasting plays a significant role in wind farms. It can improve economic and social benefits and make an operation schedule for wind turbines on large wind farms. This paper proposes a combined model based on the existing artificial neural network algorithms for wind speed forecasting at different heights. We first use the wavelet threshold method with the original wind speed dataset for noise reduction. After that, the three artificial neural networks, extreme learning machine (ELM), Elman neural network, and Long Short-term Memory (LSTM) neural network, are applied for wind speed forecasting. In addition, the variance reciprocal method and social cognitive optimization (SCO) algorithm are used to optimize the weight coefficients of the combined model. In order to evaluate the forecasting performance of the combined model, we select wind speed data at three heights (20 m, 50 m, and 80 m) at the National Wind Technology Center M2 Tower. The experimental results show that the forecasting performance of the combined model is better than the single model, and it has a good forecasting performance for the wind speed at different heights.
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Fu A, Liu J, Zhang TL. Self-stacking random weight neural network with multi-layer features fusion. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-021-01498-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Alavi J, Ewees AA, Ansari S, Shahid S, Yaseen ZM. A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:20496-20516. [PMID: 34741267 DOI: 10.1007/s11356-021-17190-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 10/21/2021] [Indexed: 06/13/2023]
Abstract
Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.
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Affiliation(s)
- Javad Alavi
- Department of Environmental Sciences and Engineering, Kheradgarayan Motahar Institute of Higher Education, Mashhad, Iran
| | - Ahmed A Ewees
- Computer Department, Damietta University, Damietta, Egypt
| | - Sepideh Ansari
- Department of Environmental Sciences and Engineering, Kheradgarayan Motahar Institute of Higher Education, Mashhad, Iran
| | - Shamsuddin Shahid
- School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia
| | - Zaher Mundher Yaseen
- Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080, Chelyabinsk, Russia.
- New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
- College of Creative Design, Asia University, Taichung City, Taiwan.
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Zhai J, Qi J, Shen C. Binary imbalanced data classification based on diversity oversampling by generative models. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.11.058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Peng P, Zhang W, Zhang Y, Wang H, Zhang H. Non-revisiting genetic cost-sensitive sparse autoencoder for imbalanced fault diagnosis. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108138] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Abstract
AbstractRandom-based learning paradigms exhibit efficient training algorithms and remarkable generalization performances. However, the computational cost of the training procedure scales with the cube of the number of hidden neurons. The paper presents a novel training procedure for random-based neural networks, which combines ensemble techniques and dropout regularization. This limits the computational complexity of the training phase without affecting classification performance significantly; the method best fits Internet of Things (IoT) applications. In the training algorithm, one first generates a pool of random neurons; then, an ensemble of independent sub-networks (each including a fraction of the original pool) is trained; finally, the sub-networks are integrated into one classifier. The experimental validation compared the proposed approach with state-of-the-art solutions, by taking into account both generalization performance and computational complexity. To verify the effectiveness in IoT applications, the training procedures were deployed on a pair of commercially available embedded devices. The results showed that the proposed approach overall improved accuracy, with a minor degradation in performance in a few cases. When considering embedded implementations as compared with conventional architectures, the speedup of the proposed method scored up to 20× in IoT devices.
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Wu D, Wang X, Wu S. A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction. ENTROPY 2021; 23:e23040440. [PMID: 33918679 PMCID: PMC8070264 DOI: 10.3390/e23040440] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 11/16/2022]
Abstract
The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).
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Abstract
Abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
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Li R, Wang X, Song Y, Lei L. Hierarchical extreme learning machine with L21-norm loss and regularization. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01234-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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