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Establishment of CNN and encoder-decoder Models for the Prediction of Characteristics of Flow and Heat Transfer around NACA Sections

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ENERGIES 2022年第23期15卷 9204页

作者： Seo, Janghoon Yoon, Hyun-Sik Kim, Min-Il Pusan Natl Univ Dept Naval Architecture & Ocean Engn 2 Busandaehak Ro 63beon Gil Busan 46241 South Korea

The present study established two different models based on the convolutional neural network (CNN) and the encoder-decoder (ED) to predict the characteristics of the flow and heat transfer around the NACA sections. The established CNN predicts the aerodynamic coefficients and the Nusselt number. The established ED model predicts the velocity, pressure and thermal fields to explain the performances of the aerodynamics and heat transfer. These two models were trained and tested by the dataset extracted from the computational fluid dynamics (CFD) simulations. The predictions mostly matched well with the true data. The contours of the velocity components and the pressure coefficients reasonably explained the variation of the aerodynamic coefficients according to the geometric parameter of the NACA section. In order to physically interpret the heat transfer performance, more quantitative and qualitative information are needed owing to the lack of the correlation and the resolution of the thermal fields. Consequently, the present approaches will be useful to design the NACA section-based shape giving higher aerodynamic and heat transfer performances by quickly predicting the force and heat transfer coefficients. In addition, the predicted flow and thermal fields will provide the physical interpretation of the aerodynamic and heat transfer performances.

关键词： convolutional neural network encoder-decoder aerodynamics heat transfer NACA section computational fluid dynamics

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Improving soil moisture prediction using a novel encoder-decoder model with residual learning

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 195卷 106816-106816页

作者： Li, Qingliang Li, Zhongyan Wei Shangguan Wang, Xuezhi Lu Li Yu, Fanhua Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Guangdong Prov Key Lab Climate Change & Nat Disas Southern Marine Sci & Engn Guangdong Lab Zhuhai Guangzhou Guangdong Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

The skillful prediction of soil moisture can provide much help for many practical applications including ecosystem management and precision agriculture. It presents great challenges because the future variation of soil moisture has much uncertainty. Therefore, a novel encoder-decoder deep learning model with residual learning based on Long Short-Term Memory (EDT-LSTM) is developed in this study as an alternative data-intelligence tool. This model includes two layers: the encoder-decoder LSTM layer and LSTM with a fully connected layer, which is used to enhance the prediction ability by considering the intermediate time-series data between the input time step and the predictive time step. We tested EDT-LSTM for soil moisture prediction at the lead time of 1, 3, 5, 7 and 10 days by using data from FLUXNET sites. The result shows that the improvements brought by EDT-LSTM were about 7.95% (1 day), 10.10% (3 days), 12.68% (5 days), 15.49% (7 days) and 19.71% (10 days) in average according to the R-2 taking LSTM as the baseline. Furthermore, the predictability of soil moisture over various conditions (i.e., different hyper-parameters in EDT-LSTM, different predictive models, different climate regions and different sites) has been widely discussed for the understanding of models' behavior in this paper. The proposed EDT-LSTM offered a new tool to predict soil moisture better. The code of EDT-LSTM is publicly available at https://***/ljz1228/CLM-LSTM-soil-moisture-prediction.

关键词： Soil moisture Machine learning LSTM encoder-decoder Residual learning

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A serial semantic segmentation model based on encoder-decoder architecture

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KNOWLEDGE-BASED SYSTEMS 2024年 295卷

作者： Zhou, Yan Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Minist Nat Resources Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Peoples R China

The thriving progress of Convolutional Neural Networks (CNNs) and the outstanding efficacy of Visual Transformers (ViTs) have delivered impressive outcomes in the domain of semantic segmentation. However, each model in isolation entails a trade-off between high computational complexity and compromised computational efficiency. To address this challenge, we effectively combine the CNN and encoder-decoder structures in a Transformer-inspired fashion, presenting the Serial Semantic Segmentation Trans via CNN Former (SSS-Former) model. To augment the feature extraction capability, we utilize the meticulously crafted SSS-CSPNet, resulting in a well-designed architecture for the holistic model. We propose a novel SSS-PN attention network that enhances the spatial topological connections of features, leading to improved overall performance. Additionally, the integration of SASPP bridges the semantic gap between multi-scale features and enhances segmentation ability for overlapping objects. To fulfill the requirement of real-time segmentation, we leverage a novel restructuring technique to devise a more lightweight and faster ResSSS-Former model. Abundant experimental results demonstrate that both SSS-Former and ResSSS-Former outperform existing state-of-the-art methods in terms of computational efficiency, result precision, and speed. Remarkably, SSS-Former achieves a mIoU of 58.63 % at 89.1FPS on the ADE20K dataset. On the validation and testing datasets of CityScapes, it obtains mIoU scores of 85.1 % and 85.2 % respectively, with a speed of 94.1FPS. Our optimized ResSSS-Former achieves impressive realtime segmentation results, with an astonishing 100+FPS while maintaining high segmentation accuracy. The compelling results from the ISPRS datasets further validate the effectiveness of our proposed models in segmenting multi-scale and overlapping objects.

关键词： Semantic segmentation Lawin transformer CNN encoder-decoder Attention mechanism

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Water Quality Prediction Based on the KF-LSTM encoder-decoder Network: A Case Study with Missing Data Collection

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WATER 2023年第14期15卷 2542页

作者： Cai, Hao Zhang, Chen Xu, Jianlong Wang, Fei Xiao, Lianghong Huang, Shanxing Zhang, Yufeng Shantou Univ Dept Comp Sci Shantou Peoples R China Guangdong Prov Shantou Ecol Environm Monitoring Ce Shantou 515057 Peoples R China

This paper focuses on water quality prediction in the presence of a large number of missing values in water quality monitoring data. Current water quality monitoring data mostly come from different monitoring stations in different water bodies. As the duration of water quality monitoring increases, the complexity of water quality data also increases, and missing data is a common and difficult to avoid problem in water quality monitoring. In order to fully exploit the valuable features of the monitored data and improve the accuracy of water quality prediction models, we propose a long short-term memory (LSTM) encoder-decoder model that combines a Kalman filter (KF) with an attention mechanism. The Kalman filter in the model can quickly complete the reconstruction and pre-processing of hydrological data. The attention mechanism is added between the decoder and the encoder to solve the problem that traditional recursive neural network models lose long-range information and fully exploit the interaction information among high-dimensional covariate data. Using original data from the Haimen Bay water quality monitoring station in the Lianjiang River Basin for analysis, we trained and tested our model using detection data from 1 January 2019 to 30 June 2020 to predict future water quality. The results show that compared with traditional LSTM models, KF-LSTM models reduce the average absolute error (MAE) by 10%, the mean square error (MSE) by 21.2%, the root mean square error (RMSE) by 13.2%, while increasing the coefficient of determination (R2) by 4.5%. This model is more suitable for situations where there are many missing values in water quality data, while providing new solutions for real-time management of urban aquatic environments.

关键词： Kalman filter LSTM encoder-decoder time series prediction water quality prediction attention mechanism

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SKT-IDS: Unknown attack detection method based on Sigmoid Kernel Transformation and encoder-decoder architecture

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COMPUTERS & SECURITY 2024年 146卷

作者： Zha, Chao Wang, Zhiyu Fan, Yifei Zhang, Xingming Bai, Bing Zhang, Yinjie Shi, Sainan Zhang, Ruyun Chinese Acad Sci Inst Comp Technol Beijing 100049 Peoples R China Zhejiang Lab Intelligent Network Res Inst Hangzhou 311122 Zhejiang Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Intrusion Detection Systems (IDS) are crucial in cybersecurity for monitoring network traffic and identifying potential attacks. Existing IDS research largely focuses on known attack detection, leaving a significant gap in research regarding unknown attack detection, where achieving a balance between false alarm rate (identifying normal traffic as attack traffic) and recall rate of unknown attack detection remains challenging. To address these gaps, we propose a novel IDS based on Sigmoid Kernel Transformation and encoder-decoder architecture, namely SKT-IDS, where SKT stands for Sigmoid Kernel Transformation. We start with pre-training an attention- based encoder for coarse-grained intrusion detection. Then, we use this encoder to build an encoder-decoder model specifically for 0-day attack detection, training it solely on known traffic using the cosine similarity loss function. To enhance detection, we introduce a Sigmoid Kernel Transformation for feature engineering, improving the discriminative ability between normal traffic and 0-day attacks. Finally, we conducted a series of ablation and comparative experiments on the NSL-KDD and CSE-CIC-IDS2018 datasets, confirming the effectiveness of our proposed method. With a false alarm rate of 1%, we achieved recall rates for unknown attack detection of 65% and 69% on the two datasets, respectively, demonstrating significant performance improvements compared to existing state-of-the-art models.

关键词： Intrusion detection Sigmoid Kernel Transformation Pre-trained encoder encoder-decoder Cosine similarity

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Cloud Detection Method Based on Spatial-Spectral Features and encoder-decoder Feature Fusion

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Zhang, Jing Shi, Xinlong Wu, Jun Song, Liangnong Li, Yunsong Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China

Cloud obscuration in remote sensing images affects Earth observation tasks by causing blurred and incomplete surface observation information. Regarding this, cloud detection is crucial in the processing of remote sensing images. However, existing cloud detection methods present some challenges, such as missed detection of thin cloud areas and false detection caused by confusing clouds with highlighted areas such as snow and ice. To address these problems, in this article, we proposed a cloud detection network that incorporates spectral feature enhancement and spatial-spectral feature fusion. Based on the difference in reflectivity of clouds and ground objects in the atmosphere, we proposed a short-wave infrared cloud index (SWIR-Index) and designed a feature-guided module to incorporate the spectral feature into the network and guide the training of the network to enhance the network's ability to learn differential features of snow, ice, and clouds. To fully utilize the spectral band information and spatial features of remote sensing images, we developed a spatial-spectral feature fusion module (SSFFM) that extracts spatial features at different scales and performs inter-spectral information fusion of spectral bands. Furthermore, we proposed a encoder-decoder feature fusion module that automatically calculates pixel weights by using a weight extraction block. The ablation study proves that our method can improve the feature extraction ability, reduce the leakage and misdetection, and improve the detection accuracy. Experimental results on Sentinel-2A images demonstrate the superior performance of the method, reaching 98.65(%) overall accuracy (OA) on the WHUS2-CD dataset, 97.50(%) on the S2-CMC dataset, and 92.36(%) on the CloudSEN12 dataset, which outperforms other algorithms.

关键词： Cloud detection encoder-decoder feature fusion short-wave infrared cloud index (SWIR-Index) spatial-spectral information

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Robust Cultivated Land Extraction Using encoder-decoder

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Journal of New Media 2020年第4期2卷 149-155页

作者： Aziguli Wulamu Jingyue Sang Dezheng Zhang and Zuxian Shi Department of Computer School of Computer and Communication EngineeringUniversity of Science and Technology Beijing(USTB)Beijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China 不详

Cultivated land extraction is essential for sustainable development and *** this paper,the network we propose is based on the encoder-decoder structure,which extracts the semantic segmentation neural network of cultivated land from satellite images and uses it for agricultural automation *** encoder consists of two part:the first is the modified Xception,it can used as the feature extraction network,and the second is the atrous convolution,it can used to expand the receptive field and the context information to extract richer feature *** decoder part uses the conventional upsampling operation to restore the original *** addition,we use the combination of BCE and Loves-hinge as a loss function to optimize the Intersection over Union(IoU).Experimental results show that the proposed network structure can solve the problem of cultivated land extraction in Yinchuan City.

关键词： Semantic segmentation encoder-decoder cultivated land extraction atrous convolution

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Hourly solar irradiance forecasting based on encoder-decoder model using series decomposition and dynamic error compensation

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ENERGY CONVERSION AND MANAGEMENT 2022年 270卷

作者： Tong, Junlong Xie, Liping Fang, Shixiong Yang, Wankou Zhang, Kanjian Southeast Univ Sch Automat Nanjing 210096 Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China

Accurate solar irradiance prediction is crucial for harnessing solar energy resources. However, the pattern of irradiance sequence is intricate due to its nonlinear and non-stationary characteristics. In this paper, a deep hybrid model based on encoder-decoder is proposed to cope with the complex pattern for hourly irradiance forecasting. The hybrid deep model integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), encoder-decoder module, and dynamic error compensation (DEC) architecture. The CEEMDAN is implemented to reduce the nonlinear and non-stationarity of the irradiance sequence. The encoder-decoder integrates temporal convolutional networks (TCN), long short-term memory networks (LSTM), and multi-layer perceptron (MLP) for temporal features extraction and multi-step prediction. The DEC architecture dynamically updates the model based on adjacent error information to mine the predictable components of error information. Furthermore, a new loss function is further proposed for multi-objective optimization to balance the performance of multi-step forecasting. In the hourly irradiance forecasting experiments on the three public datasets, the root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of the proposed model are observed to be in a range of 30.693-34.433 W/m2, 19.398-22.900 W/m2, and 0.9872-0.9902, respectively. Compared to the benchmark models (including MLP, LSTM, and TCN), the RMSE and MAE reduce by 10.76%-22.00% and 5.47%-20.40%, respectively. The experimental results indicate that the proposed model shows accurate and robust forecasting performance and is a reliable alternative to hourly irradiance forecasting.

关键词： Solar irradiance forecasting Deep learning Temporal convolutional network encoder-decoder Long short term memory Error compensation

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Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network

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GEOPHYSICS 2019年第6期84卷 B403-B417页

作者： Wu, Hao Zhang, Bo Lin, Tengfei Cao, Danping Lou, Yihuai Univ Alabama Dept Geol Sci Tuscaloosa AL 35487 USA CNPC Res Inst Petr Explorat & Dev Dept Middle East E&P Beijing Peoples R China China Univ Petr East China Sch Geosci Qingdao Shandong Peoples R China

The seismic horizon is a critical input for the structure and stratigraphy modeling of reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for seismic data in which the lateral continuity of reflections is interrupted by faults and unconformities. The process of seismic horizon interpretation can be viewed as segmenting the seismic traces into different parts and each part is a unique object. Thus, we have considered the horizon interpretation as an object detection problem. We use the encoder-decoder convolutional neural network (CNN) to detect the "objects" contained in the seismic traces. The boundary of the objects is regarded as the horizons. The training data are the seismic traces located on a user-defined coarse grid. We give a unique training label to the time window of seismic traces bounded by two manually picked horizons. To efficiently learn the waveform pattern that is bounded by two adjacent horizons, we use variable sizes for the convolution filters, which is different than current CNN-based image segmentation methods. Two field data examples demonstrate that our method is capable of producing accurate horizons across the fault surface and near the unconformity which is beyond the current capability of horizon picking method.

关键词： seismic horizon interpretation encoder-decoder neural network

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Deep encoder-decoder Network-Based Wildfire Segmentation Using Drone Images in Real-Time

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REMOTE SENSING 2022年第24期14卷 6302-6302页

作者： Muksimova, Shakhnoza Mardieva, Sevara Cho, Young-Im Gachon Univ Dept IT Convergence Engn Sujeong Gu Seongnam 13120 Gyeonggi South Korea Gachon Univ Dept Comp Engn Sujeong Gu Seongnam 13120 Gyeonggi South Korea

Wildfire is a hazardous natural phenomenon that leads to significant human fatalities, catastrophic environmental damages, and economic losses. Over the past few years, the intensity and frequency of fires have increased worldwide. Studies have been conducted to develop distinctive solutions to minimize forest fires. Systems for distant fire detection and monitoring have been established, showing improvements in data collection and fire characterization. However, wildfires cover vast areas, making other proposed ground systems unsuitable for optimal coverage. Unmanned aerial vehicles (UAVs) have become the subject of active research in recent years. Deep learning-based image-processing methods demonstrate improved performance in various tasks, including detection and segmentation, which can be utilized to develop modern forest firefighting techniques. In this study, we established a novel two-pathway encoder-decoder-based model to detect and accurately segment wildfires and smoke from the images captured using UAVs in real-time. Our proposed nested decoder uses pre-activated residual blocks and an attention-gating mechanism, thereby improving segmentation accuracy. Moreover, to facilitate robust and generalized training, we prepared a new dataset comprising actual incidences of forest fires and smoke, varying from small to large areas. In terms of practicality, the experimental results reveal that our method significantly outperforms existing detection and segmentation methods, despite being lightweight. In addition, the proposed model is reliable and robust for detecting and segmenting drone camera images from different viewpoints in the presence of wildfire and smoke.

关键词： drone encoder-decoder forest fire and smoke segmentation deep-learning

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