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Identification and classification of promoters using the attention mechanism based on long short-term memory

Frontiers of Computer Science 2022年第4期16卷 103-109页

作者： Qingwen Li Lichao Zhang Lei Xu Quan Zou Jin Wu Qingyuan Li Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing100101China Forestry and Fruit Tree Research Institute Wuhan Academy of Agricultural SciencesWuhan430075China School of Intelligent Manufacturing and Equipment Shenzhen Institute of Information TechnologyShenzhen518172China School of Electronic and Communication Engineering Shenzhen PolytechnicShenzhen518055China School of Management Shenzhen PolytechnicShenzhen518055China

A promoter is a short region of DNA that can bind RNA polymerase and initiate gene transcription.It is usually located directly upstream of the transcription initiation site.DNA promoters have been proven to be the main cause of many human diseases,especially diabetes,cancer or Huntington’s disease.Therefore,the classification of promoters has become an interesting problem and has attracted the attention of many researchers in the field of bioinformatics.Various studies have been conducted in order to solve this problem,but their performance still needs further improvement.In this research,we segmented the DNA sequence in a k-mers manner,then trained the word vector model,inputted it into long short-term memory(LSTM)and used the attention mechanism to predict.Our method can achieve 93.45%and 90.59%cross-validation accuracy in the two layers,respectively.Our results are better than others based on the same data set,and provided some ideas for accurately predicting promoters.In addition,this research suggested that natural language processing can play a significant role in biological sequence prediction.

关键词： promoter bioinformatics natural language processing attention mechanism

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Short-Term Household Load Forecasting Based on attention mechanism and CNN-ICPSO-LSTM

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Energy Engineering 2024年第6期121卷 1473-1493页

作者： Lin Ma Liyong Wang Shuang Zeng Yutong Zhao Chang Liu Heng Zhang Qiong Wu Hongbo Ren New Energy Technology Center State Grid Beijing Electric Power Research InstituteBeijing100075China College of Energy and Mechanical Engineering Shanghai University of Electric PowerShanghai200090China

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

关键词： Short-term household load forecasting long short-term memory network attention mechanism hybrid deep learning framework

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Bilateral U-Net semantic segmentation with spatial attention mechanism

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 297-307页

作者： Guangzhe Zhao Yimeng Zhang Maoning Ge Min Yu Beijing University of Civil Engineering and Architecture College of Electrical and Information EngineeringBeijingChina Graduate School of Informatics Nagoya UniversityNagoyaJapan Department of General Surgery Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina

Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small-scale samples,a bilateral U-Net network model with a spatial attention mechanism is designed.The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid Spatial attention(APSA)module compared to the Attenuated Spatial Pyramid module,which can increase the receptive field and enhance the information,and finally adds the context fusion prediction branch that fuses high-semantic and low-semantic prediction results,and the model effectively improves the segmentation accuracy of small data sets.The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks,the algorithm has a better segmentation effect and segmentation accuracy,and its mIOU reaches 75.85%.Moreover,to verify the generality of the model and the effectiveness of the APSA module,experiments were conducted on the VOC 2012 data set,and the APSA module improved mIOU by about 12.2%.

关键词： attention mechanism receptive field semantic fusion semantic segmentation spatial attention module U-Net

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基于注意力机制的LSTM时变水声信道深度学习预测

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哈尔滨工程大学学报（英文版） 2023年第3期22卷 650-658页

作者：朱政亮童峰周跃海张梓峤张福民 National and Local Joint Engineering Research Center for Navigation and Location Service Technology Xiamen UniversityXiamen 361005China College of Ocean and Earth Sciences Xiamen UniversityXiamen 361005China School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlanta 30314USA Cheng Kar-Shun Robotics Institute Hong Kong University of Science and TechnologyHong Kong 00852China

This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic(UWA)communication systems using the long short-term memory(LSTM)model with the attention *** is a deep learning model that combines an attention mechanism with LSTM-type models to capture temporal information with different scales from historical UWA channels.The attention mechanism is used to capture sparsity in the time-delay scales and coherence in the gep-time scale under the LSTM framework.The soft attention mechanism is introduced before the LSTM to support the model to focus on the features of input sequences and help improve the learning capacity of the proposed model.The performance of the proposed model is validated using different simulation time-varying UWA channels.Compared with the adaptive channel predictors and the plain LSTM model,the proposed model is better in terms of channel prediction accuracy.

关键词： Long short-term memory(LSTM) attention mechanism Underwater acoustic communication Underwater acoustic channel Channel prediction

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Narrow Pooling Clothing Classification Based on attention mechanism

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Journal of Donghua University(English Edition) 2022年第4期39卷 367-372页

作者：马骁王绍宇叶少萍樊静宜徐安夏小玲 School of Computer Science and Technology Donghua UniversityShanghai 201620China

In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).

关键词： clothing classification convolutional neural network(CNN) residual network(ResNet) attention mechanism narrow pooling

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AF-Net:A Medical Image Segmentation Network Based on attention mechanism and Feature Fusion

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Computers, Materials & Continua 2021年第11期69卷 1877-1891页

作者： Guimin Hou Jiaohua Qin Xuyu Xiang Yun Tan Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-Net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-Net and other state-of-art methods.

关键词： Deep learning medical image segmentation feature fusion attention mechanism

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High Impedance Fault Detection Based on attention mechanism and Object Identification

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 197-207页

作者： Yongjie Zhang Xiaojun Wang Yiping Luo Dahai Zhang Sohrab Mirsaeidi Jinghan He School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China

Detection of high impedance faults(HIFs)has been traditionally a main challenge in the protection of distribution systems,since they do not generate enough current to be reliably detected by conventional over-current relays.Data-based methods are alternative HIF detection methods which avoid threshold settings by training a classification or regression model.However,most of them lack interpretability and are not compatible with various distribution networks.This paper proposes an object detection-based HIF detection method,which has higher visualization and can be easily applied to different scenarios.First,based on the analysis of HIFs,a Butterworth band-pass filter is designed for HIF harmonic feature extraction.Subsequently,based on the synchronized data provided by distribution-level phasor measurement units,global HIF feature gray-scale images are formed through combining the topology information of the distribution network.To further enhance the feature information,a locally excitatory globally inhibitory oscillator region attention mechanism(LEGIO-RAM)is proposed to highlight the critical feature regions and inhibit useless and fake information.Finally,an object detection network based You Only Look Once(YOLO)v2 is established to achieve fast HIF detection and section location.The obtained results from the simulation of the proposed approach on three different distribution networks and one realistic distribution network verify that the proposed method is highly effective in terms of reliability and generalization.

关键词： attention mechanism distribution-level phasor measurement units high impedance faults object detection

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A landslide extraction method of channel attention mechanismU-Net network based on Sentinel-2A remote sensing images

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International Journal of Digital Earth 2023年第1期16卷 552-577页

作者： Hesheng Chen Yi He Lifeng Zhang Sheng Yao Wang Yang Yumin Fang Yaoxiang Liu Binghai Gao Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhouPeople’s Republic of China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring LanzhouPeople’s Republic of China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring LanzhouPeople’s Republic of China

Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.

关键词： Sentinel-2A remote sensing image landslide extraction U-Net attention mechanism deep learning

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An Improved Solov2 Based on attention mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

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Computers, Materials & Continua 2024年第1期78卷 677-694页

作者： Junpeng Wu Zhenpeng Liu Xingfan Jiang Xinguang Tao Ye Zhang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China School of Electrical Engineering Northeast Electric Power UniversityJilin132012China Baishan Power Supply Company State Grid Jilin Electric Power CoLtd.Jilin134300China

The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.

关键词： Deep learning electrical equipment attention mechanism weighted loss function

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An Underwater Target Detection Algorithm Based on attention mechanism and Improved YOLOv7

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Computers, Materials & Continua 2024年第2期78卷 2829-2845页

作者： Liqiu Ren Zhanying Li Xueyu He Lingyan Kong Yinghao Zhang College of Information Science and Engineering Dalian Polytechnic UniversityDalian116034China

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.

关键词： Deep learning underwater object detection improved YOLOv7 attention mechanism

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