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Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting

JOURNAL OF HYDROLOGY 2023年第PartB期622卷

作者： Cui, Zhen Guo, Shenglian Zhou, Yanlai Wang, Jun Wuhan Univ State Key Lab Water Resources & Hydropower Engn Sc Wuhan 430072 Peoples R China

In order to improve the flood forecasting accuracy and reflect the forecast uncertainty information in the Three Gorges Reservoir (TGR) interval-basin in China, this study integrates the feature and temporal dual-attention (DA) mechanism and recursive encoder-decoder (RED) structure into the long short-term memory (LSTM) neural network to develop a DA-LSTM-RED model. The feature attention acts on the input variables of the encoder, and the temporal attention mechanism acts on the hidden layer states extracted by the LSTM neural network during encoding process, prompting the proposed model to extract critical input information among different types and moments of input variables to improve the multi-step-ahead flood forecasting accuracy. Second, the copula-based Hydrological Uncertainty Processor (copula-HUP) is used to quantify the forecast uncertainty of the proposed model meanwhile creating multi-step-ahead flood probabilistic forecasts. Combining the long-term 6 h hydrologic data series of the Xiangjiaba-TGR interval-basin and the forecasted precipitation from the European Centre for Medium-Range Weather Forecasts (ECMWF), the effectiveness of the proposed model, the effect of forecast precipitation on multi-step-ahead flood forecasting, and the effect of different copula functions on the probabilistic forecast of copula-HUP are investigated, respectively. The results show that the DALSTM-RED model can effectively improve the forecasting accuracy for long forecast horizons (3-7d) compared to the LSTM-RED model, and the average absolute error metrics are reduced by 10%-17%. Meanwhile, the proposed model can identify input variables with a high correlation with the target output variables, which improves the interpretability of deep learning to a certain extent. The Student copula-HUP has the lowest RB and CRPS metrics than the Frank and Gaussian copula-HUP, which can better quantify the DA-LSTM-RED model's forecast uncertainty. Therefore, combining the proposed mo

关键词： Dual-attention mechanism encoder-decoder Deep learning Hydrological uncertainty processor Copula function Probabilistic forecasting

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A novel method for cage whirl motion capture of high-precision bearing inspired by U-Net

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期117卷

作者： Niu, Xiaoliang Yang, Zhaohui Zhou, Ningning Li, Chonghe Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Beijing Inst Control Engn Beijing Key Lab Long Life Technol Precise Rotat & Beijing 100094 Peoples R China Aviat Ind Dev Res Ctr China AVICADR Res Inst Management Engn Beijing Peoples R China

To solve the problem of cage whirl motion capture and evaluation, this paper developed an efficient non -contact measurement method based on semantic segmentation technology. An encoder-decoder network whose backbone is U-Net is constructed by introducing residual learning and attention mechanism for cage motion state segmentation. A random move augmentation strategy is used to simulate the random movement of cage mass center. The network is trained with 1368 high-speed cage rotational images using the augmentation strategy. Additionally, 150 images are validation set, and 5000 images under different operating conditions are test set. A trained network is applied to the cage whirl motion capture under different operating conditions by matching the suitable parameters during the training phase. The results show that our method effectively predicts the trend of cage whirl motion, with the predicted cage whirl orbit used for the accurate analysis of cage rotational stability.

关键词： High-precision bearing Semantic segmentation encoder-decoder Random move augmentation Cage whirl motion

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TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models

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DRONES 2023年第3期7卷 205-205页

作者： Du, Meng Sun, Yuxin Sun, Bing Wu, Zilong Luo, Lan Bi, Daping Du, Mingyang Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China Sci & Technol Electroopt Informat Secur Control La Tianjin 300308 Peoples R China China Satellite Maritime Tracking & Control Dept Jiangyin 214430 Peoples R China Lanzhou Univ Coll Commun Engn Lanzhou 730030 Peoples R China

Recently, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any SAR-ATR model information, making adversarial attacks challenging. To tackle this issue, we propose a novel attack method called Transferable Adversarial Network (TAN). It can craft highly transferable adversarial examples in real time and attack SAR-ATR models without any prior knowledge, which is of great significance for real-world black-box attacks. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. Particularly, compared to traditional iterative methods, the encoder-decoder model can one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that our approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, making real-time black-box attacks without any prior knowledge a reality.

关键词： unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) automatic target recognition (ATR) deep neural network (DNN) adversarial example transferability encoder-decoder real-time attack

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Wall segmentation in 2D images using convolutional neural networks

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1565页

作者： Bjekic, Mihailo Lazovic, Ana Venkatachalam, K. Bacanin, Nebojsa Zivkovic, Miodrag Kvascev, Goran Nikolic, Bosko Everseen Belgrade Serbia Daon Belgrade Serbia Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove Czech Republic Singidunum Univ Dept Informat & Comp Belgrade Serbia Univ Belgrade Sch Elect Engn Belgrade Serbia

Wall segmentation is a special case of semantic segmentation, and the task is to classify each pixel into one of two classes: wall and no-wall. The segmentation model returns a mask showing where objects like windows and furniture are located, as well as walls. This article proposes the module's structure for semantic segmentation of walls in 2D images, which can effectively address the problem of wall segmentation. The proposed model achieved higher accuracy and faster execution than other solutions. An encoder-decoder architecture of the segmentation module was used. Dilated ResNet50/101 network was used as an encoder, representing ResNet50/101 network in which dilated convolutional layers replaced the last convolutional layers. The ADE20K dataset subset containing only interior images, was used for model training, while only its subset was used for model evaluation. Three different approaches to model training were analyzed in the research. On the validation dataset, the best approach based on the proposed structure with the ResNet101 network resulted in an average accuracy at the pixel level of 92.13% and an intersection over union (IoU) of 72.58%. Moreover, all proposed approaches can be applied to recognize other objects in the image to solve specific tasks.

关键词： Semantic segmentation Wall segmentation encoder-decoder ADE20K PSPNet

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Image captioning using transformer-based double attention network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期125卷

作者： Parvin, Hashem Naghsh-Nilchi, Ahmad Reza Mohammadi, Hossein Mahvash Univ Isfahan Fac Comp Engn Dept Artificial Intelligence Esfahan Iran

Image captioning generates a human-like description for a query image, which has attracted considerable attention recently. The most broadly utilized model for image description is an encoder-decoder structure, where the encoder extracts the visual information of the image, and the decoder generates textual descriptions of the image. Transformers have significantly enhanced the performance of image description models. However, a single attention structure in transformers cannot consider more complex relationships between key and query vectors. Furthermore, attention weights are assigned to entire candidate vectors based on the assumption that entire vectors are related. In this paper, a new double-attention framework is presented, which improves the encoder-decoder structure to consider image captioning problems. Hence, a local generator module and a global generator module are designed to predict textual descriptions collaboratively. The proposed approach improves Self-Attention (SA) from two aspects to enhance the performance of image description. First, a Masked Self-Attention module is presented to attend on the most relevant information. Second, to evade a single shallow attention distribution and make deeper internal relations, a Hybrid Weight Distribution (HWD) module is proposed, that develops SA to use the relations between key and query vectors efficiently. Experiments over the Flickr30k and MS-COCO datasets prove that the proposed approach achieves desirable performance on different evaluation measures compared to the state-of-the-art frameworks.

关键词： Self-attention Transformer Image captioning encoder-decoder

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OSLPNet: A neural network model for street lamp post extraction from street view imagery

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Zhang, Tengda Dai, Jiguang Song, Weidong Zhao, Ruishan Zhang, Bing Liaoning Tech Univ Sch Geomat Yulong St Fuxin 123000 Liaoning Provin Peoples R China Liaoning Tech Univ Inst Spatiotemporal Transportat Data Fuxin Peoples R China

Quickly and accurately obtaining street lamp post information has great application value in smart city construction and automatic vehicle navigation. However, the existing deep learning methods are affected by factors such as the perspective effect, different objects with the same spectrum, and occlusion. There can also be some problems in the semantic segmentation results for street lamp posts, such as under-segmentation, misextraction, and discontinuity. In this paper, we present the OSLPNet model for the extraction of street lamp posts from street view imagery. According to the characteristics of the various scales of street lamp posts in the imagery, a multiscale phased controller (MPC) with multi-level receptive fields is proposed to reduce the under-segmentation problem for street lamp posts. According to the unique "elbow" structure of street lamp posts, deformable convolution is introduced to reduce the problem of misextraction of street lamp posts. According to the topological relationship of street lamp post context, a lightweight spatial context (LSC) module is proposed to solve the problem of discontinuous detection of street lamp posts caused by occlusion. We also proposed two street lamp pole datasets, and experimental results showed that our F1 values can reach 85.2% and 82.4% under both datasets, which is superior to the existing state of art method. The code and datasets are publicly available at htt ps://***/ZzzTD/OSLPNet.

关键词： Convolutional neural network encoder-decoder Street lamp post extraction Engineering application Urban component acquisition

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Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2023年 618卷

作者： Sun, Xiaoyong Chen, Fenghao Wang, Yuchen Lin, Xuefen Ma, Weifeng Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Peoples R China

Accurate traffic flow prediction is critical for enhancing traffic network operational efficiency. With the continuous expansion of traffic networks, providing reliable and efficient multi-step traffic flow prediction for large-scale traffic networks with a large number of sensors deployed has become a challenging issue. In this paper, we propose a multi-step many-to-many traffic prediction model for large-scale traffic networks, called spatio-temporal Shared GRU (STSGRU), which receive inputs from multiple sensors and provides predictions for all sensors simultaneously. First, we model the weekly pattern of traffic flow, using periodicity to explore long-term temporal features and provide smooth traffic flow to reduce the impact of data volatility. Second, different from existing models, we propose a shared weight mechanism to achieve many-to-many prediction without mapping traffic networks to images or graph structures. The proposed model strikes a delicate balance between complexity and accuracy. We validate the effectiveness of the proposed method on the Caltrans Performance Measurement System (PeMS) dataset. The results show that our model achieves similar prediction performance with advanced graph neural networks and has higher flexibility. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Traffic flow prediction Gated recurrent unit encoder-decoder Periodicity Shared weight

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Enhancing image caption generation through context-aware attention mechanism

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HELIYON 2024年第17期10卷 e36272页

作者： Bhuiyan, Ahatesham Hossain, Eftekhar Hoque, Mohammed Moshiul Dewan, M. Ali Akber Chittagong Univ Engn & Technol Dept Elect & Telecommun Engn Chittagong 4349 Bangladesh Chittagong Univ Engn & Technol Dept Comp Sci & Engn Chittagong 4349 Bangladesh Athabasca Univ Fac Sci & Technol Sch Comp & Informat Syst Athabasca AB T9S 3A3 Canada

Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research has focused on caption generation for English, addressing low-resource languages like Bengali presents challenges, particularly in producing coherent captions linking visual objects with corresponding words. This paper proposes a context-aware attention mechanism over semantic attention to accurately diagnose objects for image captioning in Bengali. The proposed architecture consists of an encoder and a decoder block. We chose ResNet-50 over the other pre-trained models for encoding the image features due to its ability to solve the vanishing gradient problem and recognize complex object features. For decoding generated captions, a bidirectional Gated Recurrent Unit (GRU) architecture combined with an attention mechanism captures contextual dependencies in both directions, resulting in more accurate captions. The paper also highlights the challenge of transferring knowledge between domains, especially with culturally specific images. Evaluation of three Bengali benchmark datasets, namely BAN-Cap, , BanglaLekhaImageCaption, , and Bornon, , demonstrates significant performance improvement in METEOR score over existing methods by approximately 30%, 18%, and 45%, respectively. The proposed context-aware, attention-based image captioning system significantly outperforms current state-of-the-art models in Bengali caption generation despite limitations in reference captions on certain datasets.

关键词： Image captioning encoder-decoder Computer vision Cross-domain transfer Natural language processing Attention mechanism

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Face From Voice: PyTorch Adaptation of Speech2Face Framework

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Procedia Computer Science 2024年 246卷 2892-2901页

作者： Kacper Pietkun Bogna Gondek Marta Browarczyk Faculty of Electronics Telecommunications and Informatics Multimedia Systems Department Gdansk University of Technology Narutowicza 11/12 80-233 Gdańsk Poland

This paper focuses on the problem of face images reconstruction from short audio segments. Built in PyTorch, the speech-to-face pipeline retains the core methodology of the others presented in the previous works, but with the introduction of a few key modifications. Leveraging a comprehensive dataset of internet audio recordings, a deep neural network undergoes training to discern correlations between voice and facial features. Through self-supervised learning, various physical attributes such as age, gender and ethnicity are captured without the need for explicit feature analysis. The evaluation process quantifies the fidelity of reconstructions, measuring the resemblance to the actual facial images solely from audio data through numerical metrics.

关键词： Speech-to-Face encoder-decoder Face generation Audio Spectrogram Transformer Convolutional Neural Networks

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SoundLip: EnablingWord and Sentence-level Lip Interaction for Smart Devices

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2021年第1期5卷 1-28页

作者： Zhang, Qian Wang, Dong Zhao, Run Yu, Yinggang Shanghai Jiao Tong Univ Shanghai Peoples R China

As a natural and convenient interaction modality, voice input has now become indispensable to smart devices (e.g. mobile phones and smart appliances). However, voice input is strongly constrained by surroundings and may raise privacy leakage in public areas. In this paper, we present SoundLip, an end-to-end interaction system enabling users to interact with smart devices via silent voice input. The key insight is to use inaudible acoustic signals to capture the lip movements of users when they issue commands. Previous works have considered lip reading as a naive classification task and thus can only recognize individual words. In contrast, our proposed system enables lip reading at both word and sentence levels, which are more suitable for daily-life use. We exploit the built-in speakers and microphones of smart devices to emit acoustic signals and listen to their reflections, respectively. In order to better abstract representations from multi-frequency and multi-modality acoustic signals, we elaborate a hierarchical convolutional neural network (HCNN) to serve as the front-end as well as recognize individual word commands. Then, for the sentence-level recognition, we exploit a multi-task encoder-decoder network to get around temporal segmentation and output sentences in an end-to-end way. We evaluate SoundLip on 20 individual words and 70 sentences from 12 participants. Our system achieves an accuracy of 91.2% at word-level and a word error rate of 7.1% at sentence-level in both user-independent and environment-independent settings. Given its innovative solution and promising performance, we believe that SoundLip has made a significant contribution to the advancement of silent voice input technology.

关键词： silent voice input acoustic sensing Hierarchical CNN encoder-decoder

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