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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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Context prior-based with residual learning for face detection: A deep convolutional encoder-decoder network

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 88卷 115948-115948页

作者： Zhou, Zexun He, Zhongshi Jia, Yuanyuan Du, Jinglong Wang, Lulu Chen, Ziyu Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Hanshan Normal Univ Coll Comp & Informat Engn Chaozhou 521041 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature hierarchy encoder-decoder network (FHEDN). It is motivated by two observations from contextual semantic information and the mechanism of multi-scale face detection. The proposed network is a scale-variant style architecture and single stage, which are composed of encoder and decoder subnetworks. Based on the assumption that contextual semantic information around face being auxiliary to detect faces, we introduce a residual mechanism to fuse context prior-based information into face feature and formulate the learning chain to train each encoder-decoder pair. In addition, we discuss some important factors in implement details such as the distribution of training dataset, the scale of feature hierarchy, and anchor box size, etc. They have some impact on the detection performance of the final network. Compared with some state-of-the-art algorithms, our method achieves promising performance on the popular benchmarks including AFW, PASCAL FACE, FDDB, and WIDER FACE. Consequently, the proposed approach can be efficiently implemented and routinely applied to detect faces with severe occlusion and arbitrary pose variations in unconstrained scenes. Our code and results are available on https://***/zzxcoder/EvaluationFHEDN.

关键词： Context prior Residual learning Deep convolutional neural network encoder-decoder Face detection

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encoder-decoder with Multi-scale Information Fusion for Semantic Image Segmentation 11

Encoder-Decoder with Multi-scale Information Fusion for Sema...

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11th International Conference on Graphics and Image Processing (ICGIP)

作者： Ma, Xinxin Liu, Kai Ding, Chongyang Yan, Lin Duan, Meiyu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781510635241

Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.

关键词： Semantic image segmentation multi-scale context module skip-layer encoder-decoder

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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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An Automatic Grammar Error Correction Model Based on encoder-decoder Structure for English Texts

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2022年第1期10卷

作者： Wang, Jiahao Huang, Guimin Wang, Yabing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Guangxi Peoples R China Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China

The role of information transmission in social life is irreplaceable, and language is a very important information carrier. Among all kinds of languages, English always occupies an important position. In the process of English learning, grammar error has become a difficult problem for most learners. In this paper, we propose an automatic grammar error correction model based on encoder-decoder structure. Different from traditional encoders, we design a dual-encoder structure to capture the information of source sentence and context sentence separately. The decoder is designed with a gated structure, it can effectively integrate output information of encoders. At the same time, the self-attention mechanism is combined to better solve the problem of long-distance information extraction. In addition, we propose a dynamic beam search algorithm to improve the accuracy of the word prediction process, and achieve dynamic extraction of the decoder output by combining kernel sampling techniques. We add a penalty factor to reduce the probability of generating repeated words, while suppressing the model's preference for generating shorter sentences. Finally, the proposed method is validated on the official English grammar error correction dataset. Experiments show that the dual encoder model in this paper has a good performance.

关键词： encoder-decoder Grammar Error Correction (GEC) deep neural network attention mechanism beam search

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Band-Independent encoder-decoder Network for Pan-Sharpening of Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第7期58卷 5208-5223页

作者： Liu, Chi Zhang, Yongjun Wang, Shugen Sun, Mingwei Ou, Yangjun Wan, Yi Liu, Xiu Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China

Pan-sharpening is a fundamental task for remote sensing image processing. It aims at creating a high-resolution multispectral (HRMS) image from a multispectral (MS) image and a panchromatic (PAN) image. In this article, a new band-independent encoder-decoder network is proposed for pan-sharpening. The network takes a single band of the MS (BMS) image, the PAN image, and the low-resolution PAN (LRPAN) image as inputs. The output of the network is the corresponding band of high-resolution MS (HRBMS) image. In this way, the network can process MS images with any number of bands. The overall structure of the network consists of two encoder-decoder modules at low-resolution and high-resolution, respectively. An auxiliary LRPAN image is used to speed up the training and improve the performance. The partly shared network and hierarchical structure for low-resolution and high-resolution enable a better fusion of features extracted from different scales. With a fast fine-tuning strategy, the trained model can be applied to images from different sensors. Experiments performed on different data sets demonstrate that the proposed method outperforms several state-of-the-art pan-sharpening methods in both visual appearance and objective indexes, and the single-band evaluation results further verify the superiority of the proposed method.

关键词： Decoding Image resolution Task analysis Remote sensing Feature extraction Sensors Transforms Band-independent deep learning encoder-decoder pan-sharpening

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CapNet: An encoder-decoder based Neural Network Model for Automatic Bangla Image Caption Generation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 752-759页

作者： Rahman, Rashik Saha, Aloke Kumar Murad, Hasan Al Masud, Shah Murtaza Rashid Rahman, Nakiba Nuren Momtaz, A. S. Zaforullah Univ Asia Pacific Comp Sci & Engn Dhaka Bangladesh Chittagong Univ Engn & Technol Comp Sci & Engn Chattogram Bangladesh

Automatic caption generation from images has become an active research topic in the field of Computer Vision (CV) and Natural Language Processing (NLP). Machine generated image caption plays a vital role for the visually impaired people by converting the caption to speech to have a better understanding of their surrounding. Though significant amount of research has been conducted for automatic caption generation in other languages, far too little effort has been devoted to Bangla image caption generation. In this paper, we propose an encoder-decoder based model which takes an image as input and generates the corresponding Bangla caption as output. The encoder network consists of a pretrained image feature extractor called ResNet-50, while the decoder network consists of Bidirectional LSTMs for caption generation. The model has been trained and evaluated using a Bangla image captioning dataset named BanglaLekhaImageCaptions. The proposed model achieved a training accuracy of 91% and BLEU-1, BLEU-2, BLEU-3, BLEU-4 scores of 0.81, 0.67, 0.57, and 0.51 respectively. Moreover, a comparative study for different pretrained feature extractors such as VGG-16 and Xception is presented. Finally, the proposed model has been deployed on an embedded device for analysing the inference time and power consumption.

关键词： -Bangla image caption generation encoder-decoder bidirectional long short term memory (LSTM) bangla natural language processing (NLP)

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Software Reliability Prediction through encoder-decoder Recurrent Neural Networks

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGE...

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INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2022年第3期7卷 325-340页

作者： Li, Chen Zheng, Junjun Okamura, Hiroyuki Dohi, Tadashi Kyushu Inst Technol Fac Comp Sci & Syst Engn Dept Biosci & Bioinformat Iizuka Fukuoka 8208502 Japan Ritsumeikan Univ Dept Informat Sci & Engn Kusatsu 5258577 Japan Hiroshima Univ Grad Sch Adv Sci Engn Higashihiroshima 7398527 Japan

With the growing demand for high reliability and safety software, software reliability prediction has attracted more and more attention to identifying potential faults in software. Software reliability growth models (SRGMs) are the most commonly used prediction models in practical software reliability engineering. However, their unrealistic assumptions and environment-dependent applicability restrict their development. Recurrent neural networks (RNNs), such as the long short-term memory (LSTM), provide an end-to-end learning method, have shown a remarkable ability in time-series forecasting and can be used to solve the above problem for software reliability prediction. In this paper, we present an attention-based encoder-decoder RNN called EDRNN to predict the number of failures in the software. More specifically, the encoder-decoder RNN estimates the cumulative faults with the fault detection time as input. The attention mechanism improves the prediction accuracy in the encoder-decoder architecture. Experimental results demonstrate that our proposed model outperforms other traditional SRGMs and neural network-based models in terms of accuracy.

关键词： Software reliability Recurrent neural networks (RNNs) Long short-term memory (LSTM) encoder-decoder Attention mechanism

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PixISegNet: pixel-level iris segmentation network using convolutional encoder-decoder with stacked hourglass bottleneck

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IET BIOMETRICS 2020年第1期9卷 11-24页

作者： Jha, Ranjeet Ranjan Jaswal, Gaurav Gupta, Divij Saini, Shreshth Nigam, Aditya Indian Inst Technol Sch Comp & Elect Engn Mandi Himachal Prades India Indian Inst Technol Jodhpur Dept Elect Engn Jodhpur Rajasthan India

In this paper, we present a new iris ROI segmentation algorithm using a deep convolutional neural network (NN) to achieve the state-of-the-art segmentation performance on well-known iris image data sets. The authors' model surpasses the performance of state-of-the-art Iris DenseNet framework by applying several strategies, including multi-scale/ multi-orientation training, model training from scratch, and proper hyper-parameterisation of crucial parameters. The proposed PixISegNet consists of an autoencoder which primarily uses long and short skip connections and a stacked hourglass network between encoder and decoder. There is a continuous scale up-down in stacked hourglass networks, which helps in extracting features at multiple scales and robustly segments the iris even in an occluded environment. Furthermore, cross-entropy loss and content loss optimise the proposed model. The content loss considers the high-level features, thus operating at a different scale of abstraction, which compliments the cross-entropy loss, which considers pixel-to-pixel classification loss. Additionally, they have checked the robustness of the proposed network by rotating images to certain degrees with a change in the aspect ratio along with blurring and a change in contrast. Experimental results on the various iris characteristics demonstrate the superiority of the proposed method over state-of-the-art iris segmentation methods considered in this study. In order to demonstrate the network generalisation, they deploy a very stringent TOTA (i.e. train-once-test-all) strategy. Their proposed method achieves $E_1$E1 scores of 0.00672, 0.00916 and 0.00117 on UBIRIS-V2, IIT-D and CASIA V3.0 Interval data sets, respectively. Moreover, such a deep convolutional NN for segmentation when included in an end-to-end iris recognition system with a siamese based matching network will augment the performance of the siamese network.

关键词： image segmentation iris recognition feature extraction image coding decoding convolutional neural nets entropy codes image classification image restoration image matching siamese matching network salient iris features pixel-level iris segmentation network stacked hourglass bottleneck nonregular reflections deep convolutional neural network stacked hourglass network cross-entropy loss pixel-to-pixel classification loss deep convolutional NN encoder-decoder image segmentation performance content loss optimisation hyper-parameterisation Iris-DenseNet framework iris image data sets iris ROI image segmentation algorithm biometric segmentation research multiscale-multiorientation training Pix-SegNet train-once-test-all strategy TOTA strategy CASIA V3 0 interval data sets IIT-D interval data sets UBIRIS-V2 interval data sets

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LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 137卷 93-105页

作者： Lyu, Pingyang Chen, Ning Mao, Shanjun Li, Mei Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China

Gas is one of the most dangerous byproducts of coal in mines. Before gas accidents occur, an abnormally increased gas concentration can be observed. Therefore, a prediction of the gas concentration in coal mines is of great significance to prevent the gas accident and ensure the production safety in the mines. By calculating the Pearson correlation coefficient for the gas concentration of different sensors, the spatial correlation of the gas concentration that is monitored for each mining face is verified. We present multi-step prediction results for gas concentration time series based on the ARMA model, the CHAOS model and the encoder-decoder model (single-sensor and multi-sensor) and compare these results. The encoder-decoder model provides high robustness in a multi-step prediction and can predict the gas concentration for five different time steps. Its prediction error is significantly lower than those of the ARMA and the CHAOS models. The prediction accuracy is further improved through a fusion with information of other sensors. In this way, this study provides a novel concept and method for gas accident prevention. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： LSTM encoder-decoder Gas concentration Multi-Sensor information Multi-Step prediction

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