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An automated detection model of threat objects for X-Ray baggage inspection based on modified encoder-decoder model

NONDESTRUCTIVE TESTING AND EVALUATION 2024年第8期39卷 2730-2755页

作者： Sara, Dioline Mandava, Ajay Kumar GITAM Sch Technol Dept Elect Elect & Commun Engn Bangalore Campus Bengaluru India

Modern security faces challenges in detecting unauthorized and potentially harmful items in luggage, despite X-ray baggage scanning frameworks and research on efficiently screening highly disguised items. In response to this gap, a groundbreaking Modified encoder-decoder-based model has been introduced. This innovative model takes X-ray scan images as input and generates distinct feature representations for both suspicious and non-suspicious baggage materials. A key focus of the model is to address the denoising challenge inherent in X-ray images which reduces the models efficiency. This is achieved through the implementation of a Poisson Noise Reduction method during the preprocessing stage. Following preprocessing, the model effectively segments the non-threat image, identifying potential threats from the denoised input. The model showcases superior performance, as evidenced by high Peak Signal-to-Noise Ratio (PSNR) and low Mean Squared Error (MSE) values, outperforming existing filtering techniques. Rigorous testing on publicly available SIXray and GDXray datasets validates the effectiveness of the proposed methodology. Performance metrics for the SIXray dataset, including mAP, IoU, and DC values of 97.32%, 73.14%, and 85.12%, respectively, underscore the model's efficacy. Notably, the framework attains an impressive accuracy of 99.17% on the SIXray dataset, affirming its robustness in addressing contemporary security challenges.

关键词： X-ray images baggage screening encoder-decoder wavelet transform U-Net SVM classifier

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An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls

GAS SCIENCE AND ENGINEERING

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GAS SCIENCE AND ENGINEERING 2024年 125卷

作者： Feng, Zhao Tariq, Zeeshan Shen, Xianda Yan, Bicheng Tang, Xuhai Zhang, Fengshou Tongji Univ Coll Civil Engn Dept Geotech Engn Shanghai 200092 Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China King Abdullah Univ Sci & Technol KAUST Phys Sci & Engn Div Thuwal 23955 Saudi Arabia Wuhan Univ Sch Civil Engn Wuhan 430072 Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen 518108 Peoples R China

In Geological Carbon Sequestration (GCS), effectively managing the project requires predicting state variables such as pressure and saturation. However, numerical simulation of multiphase flow in subsurface porous media involves solving large linear algebra systems, resulting in a substantial computational burden. This can make it impractical for real-time history matching or optimization. Meanwhile, deep learning -based surrogate models are emerging as fast and accurate approximators. This study delves into the application of the encoder -decoder Convolutional Long Short -Term Memory (ED-ConvLSTM) neural network for predicting the complex evolution of state variables under dynamic CO 2 injection schemes. Inception blocks enhanced with light -weighted attention modules are introduced in the encoder to extract high -dimensional input features. ConvLSTM is employed to propagate spatial temporal information in the low -dimensional latent space. Further, progressive upsampling blocks are used to reconstruct the latent features for the desired output. Instead of taking discrete time steps as an input feature, the proposed network captures the dynamic dependencies with the inherent ConvLSTM cell. The network has access to data at only portion of the initial time steps during training stage, while it is used to predict the state variables at unseen time steps during testing stage. Results show that the network can produce excellent predictions for both pressure and saturation, even at unseen future time steps. The remarkable generalizability to different geological permeability fields is also evaluated. ED-ConvLSTM outperforms the standard U -Net by far, especially when predicting beyond the training time period. These numerical experiments demonstrate the advantages of ED-ConvLSTM in terms of prediction accuracy, extrapolability and generalizability. This study highlights the importance of incorporating recurrent connections into the deep neural networks for simulating ti

关键词： Geological carbon sequestration Deep learning Surrogate model encoder-decoder Convolutional LSTM

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LGI-rPPG-Net: A shallow encoder-decoder model for rPPG signal estimation from facial video streams

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 89卷

作者： Chowdhury, Moajjem Hossain Chowdhury, Muhammad E. H. Reaz, Mamun Bin Ibne Ali, Sawal Hamid Md Rakhtala, Seyed Mehdi Murugappan, M. Mahmud, Sakib Shuzan, Nazmul Islam Bakar, Ahmad Ashrif A. Abd Razak, Mohd Ibrahim Bin Shapiai Khan, Muhammad Salman Khandakar, Amith Univ Kebangsaan Malaysia Ctr Adv Elect & Commun Engn Dept Elect Elect & Syst Engn Bangi 43600 Malaysia Qatar Univ Dept Elect Engn 2713 Doha Qatar Independent Univ Dept Elect & Elect Engn Dhaka Bangladesh Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia Golestan Univ Fac Engn Dept Elect Engn Gorgan Iran Kuwait Coll Sci & Technol Dept Elect & Commun Engn Intelligent Signal Proc ISP Res Lab Block 4 Doha 13133 Kuwait

A method to accurately estimate physiological signals from video streams at a minimal cost is invaluable. The importance of such a technique in pre-clinical health monitoring cannot be understated. Remote photoplethysmography (rPPG) can be used as a substitute for finger photoplethysmography (PPG) when such sensors are not recommended, such as for burn victims, premature babies, and patients with sensitive skin. Good quality rPPG signal that is highly correlated to finger PPG can be used to estimate many vital health signs. In this work, a shallow encoder-decoder architecture, LGI-rPPG-Net is proposed. The proposed model aims to produce highly correlated rPPG signals which can be substituted for finger PPG. In the reconstruction of rPPG, the model achieved a very good Pearson's Correlation Coefficient (PCC), Root Mean Squared Error (RMSE), and dynamic time warping distance of 0.862, 0.148, and 0.699, respectively. This highly correlated rPPG was compared to finger PPG by calculating heart rate from rPPG and finger PPG. The model achieved a PCC of 0.984 and RMSE, and MAE of 2.91, 1.51 beats per minute (BPM), respectively. LGI-rPPG-Net model with video streaming to predict rPPG can thus be used as a replacement for finger PPG where in-contact collection is not feasible.

关键词： Remote Photoplethysmography (rPPG) Heart Rate (HR) Convolutional Neural Network (CNN) encoder-decoder Remote Health Monitoring

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Short-term ship roll motion prediction using the encoder-decoder Bi-LSTM with teacher forcing

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OCEAN ENGINEERING 2024年 295卷

作者： Li, Shiyang Wang, Tongtong Li, Guoyuan Skulstad, Robert Zhang, Houxiang Norwegian Univ Sci & Technol Dept Ocean Operat & Civil Engn Alesund 6009 Norway

The safety of maritime operations has become a paramount concern with the advancement of intelligent ships. Ship stability and safety are directly impacted by roll motion, making the prediction of short-term ship roll motion pivotal for assisting navigators in making timely adjustments and averting hazardous roll conditions. However, predicting ship roll motion poses challenges due to nonlinear dynamics. This study aims to predict short-term ship roll motion by leveraging the encoder-decoder structure of Bidirectional Long Short -Term Memory Networks (Bi-LSTM) with teacher forcing. The model is accomplished by employing an encoder- decoder structure to map input sequences to output sequences of varying lengths, and employing teacher forcing to enhance the network's ability to extract information. To refine and analyze the prediction model, aspects such as the quantity of training data to guarantee model generalization, establishing apposite length relationships between input and output sequences, and assessing the model performance in various sea states are investigated. Additionally, comparative experiments assessing roll motion prediction for intervals of 10s, 30 s, 60 s, and 120 s are conducted to substantiate the necessity and effectiveness of the proposed network. The dataset originates from a commercial professional simulator developed by the Norwegian company Offshore Simulator Center AS (OSC).

关键词： Ship roll motion prediction encoder-decoder Bi-LSTM Teacher forcing

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Enhanced Prediction of Soil Carbon via encoder-decoder Neural Networks for a Boreal Study Area in Northern Ontario

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SENSORS 2025年第8期25卷 2583-2583页

作者： Pittman, Rory Hu, Baoxin York Univ Dept Earth & Space Sci & Engn 4700 Keele St Toronto ON M3J 1P3 Canada

Addressing the impacts of carbon in connection with land cover conversion and climate change is of predominant interest for boreal realms. Consequently, boosting accuracy for the prediction of total carbon (C) with soil mapping is a crucial objective, particularly for a boreal study area under risk of land cover transition in northern Ontario, Canada. To enhance the prediction of soil modeling, integrated approaches combining encoder-decoder (ED) with dense neural network (DNN) and convolutional neural network (CNN) formulations suitable for smaller target data sets were developed. These methods were able to effectively extract dominant features within predictor data and augment modeling accuracy. The obtained results were compared with those attained from structural equation modeling (SEM) and random forest (RF), as well as basic DNN and CNN models. A model ensemble based on all approaches was also considered, from which standard deviations were calculated to gauge the prediction uncertainty. Quantile mappings with respect to deciles were also derived from the model ensemble to provide additional insights with prediction. Validation accuracies for the ED-CNN model attained a coefficient of determination (R2) of 0.59. The greatest deviations with predicting C contents corresponded to the wetlands. However, when quantified by decile mapping, forested localities within river valleys encountered the highest uncertainties with prediction, indicting a need for better modeling of sites with intermediate concentrations of soil C.

关键词： soil carbon digital soil mapping encoder-decoder convolutional neural network quantile mapping uncertainty maps boreal forest

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Quick large-scale spatiotemporal flood inundation computation using integrated encoder-decoder LSTM with time distributed spatial output models

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JOURNAL OF HYDROLOGY 2024年 634卷

作者： Wei, Guozhen Xia, Wei He, Bin Shoemaker, Christine Dalian Maritime Univ Coll Transport Engn Dalian 116026 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore Campus Res Excellence & Technol Enterprise CREATE Energy & Environm Sustainabil Megac E2S2 Phase 2 Singapore 138602 Singapore Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy Dalian Univ Technol Sch Hydraul Engn Dalian 116024 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore

Accurate spatiotemporal flood simulations are essential for making informed decisions regarding flood release in affected regions, such as flood detention areas. Traditional spatiotemporal flood simulation approach uses partial differential equation (PDE) models (or physics-based models), which need high computational time. Although many machine learning (ML) models for inundation are increasingly being used to emulate the PDE models to address this issue, utilizing conventional ML models to achieve large-scale spatiotemporal flood prediction (i.e., simulation output in tens of thousands of grids and time steps over the whole flood event) remains a significant challenge. Therefore, we developed a new inundation model (IM) using encoder-decoder long short-term memory (ED-LSTM) with Time Distributed Spatial output model (ED-LSTM-TDS) that can acquire accurate spatially distributed flood information more rapidly. In the new IM framework, each ED-LSTM-TDS is built to simultaneously generate output for multiple (K) cell grids and multiple ED-LSTM-TDS models are built for prediction at all grids of entire PDE model. This study is the first of its kind to employ the ED-LSTM-TDS method to address spatiotemporal flood inundation simulation problems for flood detention areas. A 1994 km2 flood detention area in northeastern China was used as a case study. ED-LSTM-TDS exhibited better performance in predicting flood characteristics (e.g., water depth, velocity) than alternative methods, including ordinary LSTM, artificial neural network (ANN), and multiple linear regression (MLR). In addition, we investigated the trade-off relationship between the accuracy of flood characteristic prediction and the computation time of the proposed model by considering different numbers of K cell grids in each ED-LSTM-TDS model. The final proposed inundation model could accurately predict the spatiotemporal flood characteristics within 1.5 min to acquire the same information that required approx

关键词： encoder-decoder LSTM Physics-based model Flood inundation prediction Machine learning

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STAGED: A Spatial-Temporal Aware Graph encoder-decoder for Fault Diagnosis in Industrial Processes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第2期20卷 1742-1752页

作者： Li, Shizhong Meng, Wenchao He, Shibo Bi, Jichao Liu, Guanglun Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ Key Lab CS&AUS Zhejiang Prov Hangzhou 310027 Peoples R China Zhejiang Inst Ind & Informat Technol Hangzhou 310000 Peoples R China

Data-driven fault diagnosis for critical industrial processes has exhibited promising potential with massive operating data from the supervisory control and data acquisition system. However, automatically extracting the complicated interactions between measurements and subtly integrating it with temporal evolutions have not been fully considered. Besides, with the increasing complexity of industrial processes, accurately locating fault roots is of tremendous significance. In this article, we propose an unsupervised spatial-temporal aware graph encoder-decoder (STAGED) model for industrial fault diagnosis. Firstly, the high-dimensional measurements are constructed as a weighted graph to depict the complicated interactions. Then, the graph convolutional network, long short-term memory network and attention mechanism are applied to learn a comprehensive representation for multi-series. To enforce the model to better capture the temporal evolution, the dual decoder that performs reconstruction and prediction tasks simultaneously is adopted with a well-designed comprehensive loss function. By learning the spatial-temporal evolutions of datasets, faults can be diagnosed and located at a fine-grained level based on reconstruction deviations. To verify the performance of STAGED, experiments on Cranfield three-phase flow facility and secure water treatment datasets are implemented and the results indicate that it can provide insight into fault evolution and accurately diagnose faults.

关键词： Graph convolutional networks fault diagnosis encoder-decoder unsupervised learning time series complex industrial processes.

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Destination intention estimation-based convolutional encoder-decoder for pedestrian trajectory multimodality forecast

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MEASUREMENT 2025年 239卷

作者： Wang, Ruiping Lam, Siew-Kei Wu, Meiqing Hu, Zhijian Wang, Changshuo Wang, Jing Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore North China Univ Technol Sch Elect & Control Engn Beijing Peoples R China

Forecasting pedestrian trajectory is a vital area of research in smart urban mobility, which can be applied to intelligent transportation and intelligent surveillance. Current approaches employ conditional variational autoencoders to model future trajectory multimodality. However, these methods generate multi-modal trajectories for one single destination, ignoring the trajectory multimodality caused by the uncertainty of the pedestrians' destination intention. Besides, they can lead to mode collapse and training instability. To address this issue, we propose a novel destination intention estimation-based convolutional encoder-decoder framework for multimodal trajectory forecast. Specially, we design a destination intention estimator to forecast pedestrian future destination intentions at the last time step. Then, we devise a trajectory decoder module to forecast pedestrian trajectories at each time step with the assistance of the destination intentions. To evaluate our method, we perform experiments on publicly available benchmark datasets and demonstrate that our proposed method achieves the superior results compared with state-of-the-art approaches.

关键词： Trajectory multimodality Smart urban mobility encoder-decoder Social interactions Graph convolution

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A Novel encoder-decoder Model for Short-Term Multistep Wind Power Prediction

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IEEE ACCESS 2025年 13卷 78647-78663页

作者： Wang, Qiushi Wang, Dekuan Li, Yifan China Inst Water Resources & Hydropower Res Beijing 100038 Peoples R China Beijing IWHR Technol Co Ltd Beijing 100038 Peoples R China

Wind power has emerged as a vital renewable energy source. However, its inherent temporal variability and non-stationarity pose significant challenges for accurate forecasting. To solve this problem, this study proposes a novel multi-step wind power forecasting model based on an encoder-decoder architecture. It incorporates a multi-frequency attention mechanism into a multi-layer long short-term memory (LSTM) network. The former increases the ability of the model to capture long-term dependencies and global features, while the later focuses on short-term internal dynamics. Together, they allow the model to accurately extract critical frequency information across various time scales, and adaptively emphasize key features within the temporal domain. The experimental results show that the proposed model outperforms different benchmark methods in terms of many performance indicators, including the mean absolute error, mean squared error, and root mean squared error. This demonstrates its high potential to provide technical support for the precise wind power forecasting, and to contribute to the efficient integration of wind energy into power grids.

关键词： Wind power generation Feature extraction Forecasting Accuracy Predictive models Transformers Time series analysis Attention mechanisms Data mining Market research encoder-decoder multi-frequency attention multi-layer LSTM wind power forecast

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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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