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Dynamic energy system modeling using hybrid physics-based and machinelearning encoder–decoder models

Energy and AI 2022年第3期9卷 128-138页

作者： Derek Machalek Jake Tuttle Klas Andersson Kody M.Powell Department of Chemical Engineering University of UtahSalt Lake CityUTUnited States of America Taber International LLCUnited States of America Department of Space Earthand EnvironmentUniversity of ChalmersGothenburgSweden Department of Mechanical Engineering University of UtahSalt Lake CityUTUnited States of America

Three model configurations are presented for multi-step time series predictions of the heat absorbed by thewater and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into thefuture, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, anovel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. Thehybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, waterwall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared totarget values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. Thehybrid model with the incomplete dataset highlights the importance of the manipulated variables to the *** hybrid model, compared to the pure machine learning model, is over 10% more accurate on averageover 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a localsensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in theboiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

关键词： Hybrid model encoder-decoder Time series Automatic differentiation Thermal power plant

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Innovative Disease Management: An encoder-decoder Solution for Tomato Black Mold Detection 4

Innovative Disease Management: An Encoder-Decoder Solution f...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Vashisht, Divyam Kukreja, Vinay Sharma, Rishabh Jain, Ayushi Choudhury, Ankur Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350305258

The worldwide spread of tomato black mold disease is a major concern since it reduces crop output and quality. Effective disease control and environmentally responsible farming methods depend on rapid and precise disease identification. Using a dense dilated convolution methodology based on an encoder-decoder pair, the suggested presents a novel method for detecting the black mold illness that can occur in tomatoes in this study. The well-known VGG-16 model is used for feature extraction, and 25, 000 photos of tomatoes that the authors collected themselves are used to train and evaluate the model. Accurate segmentation and localization of infected areas are shown to have been achieved by the model, as evidenced by the high values of the Dice Similarity Coefficient (DSC) and the Intersection over Union (IoU). The model's success in approximating ground truth bounds is further supported by the small Hausdorff Distance. To better ensure the safety of our food supply, our studies aid in the development of precision agriculture by providing farmers with a reliable method of disease detection. Our suggested method offers a viable path for early and precise detection of tomato black mold disease by leveraging the power of modern computer vision techniques and deep learning (DL) models, which will help the agricultural community and promote sustainable crop management practices. © 2023 IEEE.

关键词： Deep Learning Dense Dilated Convolution encoder-decoder Segmentation Tomato black mold disease

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encoder-decoder Neural Network Architecture for solving Job Shop Scheduling Problems using Reinforcement Learning

Encoder-Decoder Neural Network Architecture for solving Job ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Magalhaes, Ricardo Martins, Miguel Vieira, Susana Santos, Filipe Sousa, Joao Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (纸本)9781728190488

This paper proposes an encoder-decoder neural network architecture with Attention Mechanism for solving the DRC-FJSSP using Deep Q-Learning. In the DRC-FJSSP the number of operations to schedule is problem dependent. Current state-of-the-art reinforcement learning methods arbitrarily simplify the input information to a fixed-size feature input vector. This way, they end up losing relevant problem information for a large enough number of operations. Furthermore, on the one hand, human schedulers tend to optimize production schedules by moving operations individually into more adequate positions in the schedule. On the other hand, the aforementioned state-of-the-art methods apply heuristics recurrently as their optimization procedure. These limitations come as the cost of the neural network architecture, which is limited to fixed-size inputs and outputs. The architecture proposed in this paper is a Recurrent Neural Network, which enables it to work with inputs and outputs of variable sizes. This decisive feature makes it possible for the agent to move a specific operation to a more adequate position in the schedule and receive explicit problem information, such as the processing times of all operations. In the end, this approach proved to be competitive with a state-of-the-art metaheuristic method, the KGFOA. This promising results come even with a limitation in the available computational resources, which only allowed the development of scarcely trained agent.

关键词： Job-Shop Scheduling Reinforcement Learning Deep Q-Learning encoder-decoder Attention Mechanism Pointer Networks

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Skip-attention encoder-decoder framework for human motion prediction

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MULTIMEDIA SYSTEMS 2022年第2期28卷 413-422页

作者： Zhang, Ruipeng Shu, Xiangbo Yan, Rui Zhang, Jiachao Song, Yan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Nanjing Inst Technol Artificial Intelligence Inst Ind Technol Nanjing Peoples R China

Human motion prediction aims to automatically predict the future motion sequence based on an observed human motion sequence. In this paper, we propose a novel skip-attention encoder-decoder (SAED) framework to model human motion dependences in spatiotemporal space, by utilizing the encoder and decoder to encode the observed motions, and decode the predicted motions, respectively. Overall, this framework has two main insights. First, we design a new self-renewing ConvGRU as the unit of encoder and decoder to effectively capture temporal and spatial skeleton-motion dependencies. Second, we present a new skip-attention mechanism (SAM) to aggregate the motion information of all layers based on their importance. In experiments, quantitative and qualitative results on the Human3.6M and CMU motion capture datasets show the effectiveness of the proposed SAED compared with the related methods.

关键词： encoder-decoder Human motion prediction Skip attention

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IDM-Net: A Multi-Task Supported encoder-decoder Framework for Magnetic Field Inverse Design

IDM-Net: A Multi-Task Supported Encoder-Decoder Framework fo...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Wang, Jiaqi Zhang, Qiankun Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350301571

This study presents a novel end-to-end trainable network named IDM-Net (Inverse Design Network for Magnetic Fields) that facilitates multi-task supported inverse design of magnetic fields. Employing the encoder-decoder idea, IDM-Net accomplishes the inverse design of magnet parameters by leveraging magnetic field data for backpropagation. The encoder is harnessed to extract magnetic field features and classify magnet shapes, while the decoder predicts other properties, including magnet size and position. This innovative approach breaks the constraints of single magnet types in existing research, enabling the inverse design of properties for magnets with diverse shapes. Our experimental results demonstrate remarkable performance, with a 95.2% accuracy in magnet shape classification and a mere 0.28% error in magnet property prediction. By introducing the encoder-decoder idea in the field of inverse design for magnetic fields, we showcase significantly enhanced accuracy and pave the way for broader applications of this technology. © 2023 IEEE.

关键词： deep learning design optimization encoder-decoder inverse design magnetic field

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Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM encoder-decoder With STA Mechanism

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IEEE ACCESS 2024年 12卷 179628-179647页

作者： Lakshmi, S. Krishnamoorthy, A. Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 India

Effective prediction of PM2.5 and PM10 levels is essential for preserving public health and informing governmental actions. Nevertheless, the unpredictable behavior of air fluxes makes it difficult to forecast these concentrations accurately. The objective is to create advanced technology for accurately evaluating the air quality in Delhi, which is essential for implementing effective measures to reduce pollution. Meteorological and air quality data were gathered hourly from 6 monitoring sites in Delhi, as part of the Central Pollution Control Board (CPCB) initiative. The data collection period spanned from June 1, 2018, to October 1, 2019, with measurements conducted at hourly intervals. Address air quality prediction as an issue of predicting spatiotemporal sequences, since individual models have difficulties in successfully capturing both spatial and temporal relationships effectively. This study proposed the hybrid of an innovative encoder-decoder model based on a Bidirectional Convolutional Long-Short term network (BiConvLSTM) with a Spatial-Temporal Attention mechanism that could achieve this by developing spatial-temporal characteristics and, a prediction system that accurately forecasts PM2.5 and PM10 concentrations across several time steps. This model performed extremely well in forecasting PM2.5 and PM10 levels, achieving impressive error metrics. For PM2.5 MSE of 0.0298, MAE of 0.1511, RMSE of 0.1728, and the coefficient of determination (R2) of 0.999. Similarly, for PM10 MSE of 0.0582, MAE of 0.1833, RMSE of 0.3134, and the coefficient of determination (R2) was 0.999. Analyze the model's efficacy over prediction periods of 6,12,24,36,48 hours concerning the distribution of errors and the level of accuracy. This study emphasizes the effectiveness of the proposed method using an extensive comparison with state-of-the-art models such as Transformer, GNN+LSTM, and Seq2Seq with attention.

关键词： Air quality prediction BiConvLSTM deep learning deep learning encoder-decoder encoder-decoder spatial-temporal attention mechanism spatial-temporal attention mechanism prediction of PM2.5 and PM10 prediction of PM2.5 and PM10 prediction of PM2.5 and PM10

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An encoder-decoder and modified U-Net network for microwave imaging of stroke

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第2期34卷 e22995-e22995页

作者： Liu, Jinzhen Chen, Liming Xiong, Hui Tiangong Univ Sch Control Sci & Engn Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Intelligent Control Elect Equipmen Tianjin Peoples R China

Microwave imaging has been widely used in stroke diagnosis as a non-invasive, ionizing radiation-free imaging method. However, the electromagnetic inverse scattering problems of microwave imaging are nonlinear and ill-posed. To further improve the accuracy of microwave imaging algorithms to identify and reconstruct stroke regions. A novel encoder-decoder and Modified U-Net (ED-MUNET) network for microwave imaging of stroke is proposed. The newly proposed ED-MUNET method accomplishes the initial imaging from scattered data to stroke images through an encoder-decoder in the first step. In the second step, the feature information of the reconstructed image is extracted through the modified U-Net network, and the reconstruction from the rough stroke image to the high-resolution image is realized. The second step avoids black-box operations and improves image accuracy. ED-MUNET has fewer artifacts, the relative reconstruction error is less than 0.05, and the reconstructed images are clearer, according to the comparative experiments with other networks. The experimental results showed the superiority of the proposed method for reconstructing stroke images.

关键词： electromagnetic inverse scattering problems encoder-decoder microwave imaging stroke imaging U-net

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IDM-Net: A Multi-Task Supported encoder-decoder Framework for Magnetic Field Inverse Design

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷 1页

作者： Wang, Jiaqi Zhang, Qiankun Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China

We propose an end-to-end framework for inversely designing permanent magnets named IDM-Net. It utilizes a fundamental encoder-decoder architecture to handle multiple tasks. In more detail, the encoder is responsible for deeply extracting features of the magnetic field and categorizing different types of magnet shapes. The decoder focuses on inferring significant properties of each specific type of magnet shape, including size, position, and magnetization intensity. Such architecture breaks the critical limitation of designing only a single type of magnet in literature. Further, it allows for flexible choices of encoders' networks, such as convolutional neural networks (CNNs) or transformers, which are widely used in various computer vision tasks. Our experimental results demonstrate that the ResNet50-based and ViT-B/16-based IDM-Nets achieve accuracies of 93.8% and 91.4% in magnet shapes classification and errors of 0.31% and 0.33% in predicting magnetic properties, respectively.

关键词： Superconducting magnets Shape Magnetic resonance imaging Decoding Magnetic confinement Magnetic properties Magnetosphere Deep learning design optimization encoder-decoder inverse design magnetic field

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CA-SegNet: A channel-attention encoder-decoder network for histopathological image segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期96卷

作者： He, Feng Wang, Weibo Ren, Lijuan Zhao, Yixuan Liu, Zhengjun Zhu, Yuemin Harbin Inst Technol Inst Ultraprecis Optoelect Instrument Engn Harbin 150001 Peoples R China Harbin Inst Technol Key Lab Ultraprecis Intelligent Instrumentat Harbin 150001 Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Harbin Inst Technol Res Ctr Adv Microscopy & Instrumentat Harbin 150001 Peoples R China Harbin Inst Technol Sch Phys Harbin 150001 Peoples R China Univ Lyon INSA Lyon CREATIS CNRSUMR 5220INSERMU1294 F-69621 Villeurbanne France

Histopathological image segmentation based on encoder-decoder architectures has emerged as a pivotal research area in medical image analysis. However, due to the irrelevant information within multi-channel representations from the encoder, the coarse reuse of shallow features in skip connections may burden the learning and even adversely affect the decoder. While various variants have been developed to cope with this issue, the performance remains unsatisfactory. In this work, we propose a novel encoder-decoder architecture named CA-SegNet to address the above issue more effectively and achieve advanced histopathological image segmentation. Our novelty is twofold: firstly, a bottleneck-structured decoder is developed to improve the integration of multi-channel feature representations, and secondly, a sequence of channel-attention feature fusion modules (CAFFMs) are developed to adaptively guide the reuse of fine-grained shallow features in skip connections while learning the channel-wise dependencies. Experimental results on different publicly available histopathological image datasets demonstrate that our CA-SegNet outperforms existing state-of-the-art methods on both large and small-scale datasets.

关键词： Deep learning Channel attention encoder-decoder Medical image segmentation Histopathological images

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Convolutional LSTM-Attention Based encoder-decoder Neural Network for Prediction of Chaotic Vibrations of Multi-Dimensional Dynamic Systems

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2024年第20期24卷 2450227-2450227页

作者： Wang, Luyao Dai, Liming Zhao, Haixing Fang, Pan Qinghai Normal Univ Coll Comp Xining 810008 Qinghai Peoples R China Univ Regina Ind Syst Engn Regina SK S4S 0A2 Canada Southwest Petr Univ Sch Mech Engn Chengdu 610500 Peoples R China

The present research proposes a convolutional long-short term memory (ConvLSTM) with an attention mechanism (AM) model, termed as ConvLSTM-AM, to conduct prediction of chaotic vibrations of multi-dimensional dynamic systems. The proposed data-driven model is based on an encoder-decoder architecture where the lengths of inputs and outputs are variable. Different from other conventional benchmarks which consider the temporal correlation solely to deal with the chaotic sequences, this research work takes the spatial information into account. In this sense, the ConvLSTM is adopted as an encoder to acquire useful chaotic spatiotemporal patterns and retain long-term successive dependencies. LSTM and AM in this research are taken as the main structures of the procedure in decoding, in which the LSTM is used as the further temporal processor and AM is stacked on the top to exploit more salient information of the historical data. Among that, a residual connection between the outputs of LSTM and the information of attention is considered in AM to prevent gradient vanishment. Two datasets of chaotic vibrations of multi-dimensional systems are employed to adequately illustrate the effectiveness and feasibility of the proposed model. Besides, five conventional benchmarks are built to demonstrate the advantages of the proposed model in terms of both training and generalization performance. As found in the research, the training time is reduced with lower testing loss in comparing with the other five counterparts, as the spatial information introduced expedites the training convergence. The present research provides a useful guidance for predicting and analysing chaotic vibrations of multi-dimensional dynamic systems.

关键词： Convolutional-LSTM multi-dimensional dynamic system chaotic vibrations encoder-decoder

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