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SA-Model:multi-feature fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Computer Modeling in Engineering & Sciences 2023年第10期137卷 631-645页

作者： Lingli Zhang Yadong Wu Qikai Chu Pan Li Guijuan Wang Weihan Zhang Yu Qiu Yi Li School of Computer Science and Engineering Sichuan University of Science and EngineeringZigong643000China School of Automation and Information Engineering Sichuan University of Science and EngineeringZigong643000China School of Computer Science and Technology Southwest University of Science and TechnologyMianyang621000China School of Information Engineering Southwest University of Science and TechnologyMianyang621000China

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

关键词： Sentiment analysis Chinese classical poetry natural language processing BERT-wwm-ext ERNIE multi-feature fusion

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Smoke root detection from video sequences based on multi-feature fusion

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Journal of Forestry Research 2022年第6期33卷 1841-1856页

作者： Liming Lou Feng Chen Pengle Cheng Ying Huang School of Technology Beijing Forestry University100083 BeijingPeople’s Republic of China School of Nature Conservation Beijing Forestry University100083 BeijingPeople’s Republic of China Department of Civil Constructionand Environmental EngineeringNorth Dakota State UniversityFargoND 58102USA

Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection.Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection and extract false smoke roots.This study developed a new smoke roots search algorithm based on a multi-feature fusion dynamic extraction strategy.This determines smoke origin candidate points and region based on a multi-frame discrete confidence level.The results show that the new method provides a more complete smoke contour with no background interference,compared to the results using existing methods.Unlike video-based methods that rely on continuous frames,an adaptive threshold method was developed to build the judgment image set composed of non-consecutive frames.The smoke roots origin search algorithm increased the detection rate and significantly reduced false detection rate compared to existing methods.

关键词： Smoke detection multi-feature fusion Search strategy ViBe Choquet

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multi-feature fusion-Guided multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1539-1555页

作者： Weiwei Cai Yaping Song Huan Duan Zhenwei Xia Zhanguo Wei School of Logistics and Transportation Central South University of Forestry and TechnologyChangsha410004China Graduate School Northern Arizona UniversityFlagstaffAZ 86011USA

In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.

关键词： Logistics pallet segmentation image segmentation multi-feature fusion multiscale network bidirectional attention mechanism HSV neural networks deep learning

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A multi-feature fusion Apple Classification Method Based on Image Processing and Improved SVM

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Agricultural Biotechnology 2022年第5期11卷 84-91页

作者： Haibo LIN Yuandong LU Rongcheng DING Yufeng XIU Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of EducationSchool of Mechanical&Automotive EngineeringQingdao University of TechnologyQingdao 266520China

In order to achieve accurate classification of apple, a multi-feature fusion classification method based on image processing and improved SVM was proposed in this paper. The method was mainly divided into four parts, including image preprocessing, background segmentation, feature extraction and multi-feature fusion classification with improved SVM. Firstly, the homomorphic filtering algorithm was used to improve the quality of apple images. Secondly, the images were converted to HLS space. The background was segmented by the QTSU algorithm. Morphological processing was employed to remove fruit stem and surface defect areas. And apple contours were extracted with the Canny algorithm. Then, apples’ size, shape, color, defect and texture features were extracted. Finally, the cross verification method was used to optimize the penalty factor in SVM. A multi-feature fusion classification model was established. And the weight of each index was calculated by Fisher. In this study, 146 apple samples were selected for training and 61 apple samples were selected for testing. The test results showed that the accuracy of the classification method proposed in this paper was 96.72%, which can provide a reference for apple automatic classification.

关键词： Apple classification Image processing Improved SVM multi-feature fusion

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Research on Facial Fatigue Detection of Drivers with multi-feature fusion

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Instrumentation 2023年第1期10卷 23-31页

作者： YE Yuxuan ZHOU Xianchun WANG Wenyan YANG Chuanbin ZOU Qingyu School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044 School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science and TechnologyNanjing 210044

In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.

关键词： HOG Face Posture Detection Deformable Convolution multi-feature fusion Fatigue Detection

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Recognition of weeds at asparagus fields using multi-feature fusion and backpropagation neural network

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International Journal of Agricultural and Biological Engineering 2021年第4期14卷 190-198页

作者： Yafei Wang Xiaodong Zhang Guoxin Ma Xiaoxue Du Naila Shaheen Hanping Mao School of Agricultural Engineering Jiangsu UniversityZhenjiang 212013China Key Laboratory of Modern Agricultural Equipment and Technology Ministry of EducationJiangsu UniversityZhenjiang 212013JiangsuChina School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina

In order to solve the problem of low recognition rates of weeds by a single feature,a method was proposed in this study to identify weeds in Asparagus(Asparagus officinalis L.)field using multi-feature fusion and backpropagation neural network(BPNN).A total of 382 images of weeds competing with asparagus growth were collected,including 135 of Cirsium arvense(L.)Scop.,138 of Conyza sumatrensis(Retz.)E.Walker,and 109 of Calystegia hederacea Wall.The grayscale images were extracted from the RGB images of weeds using the 2G-R-B factor.Threshold segmentation of the grayscale image of weeds was applied using Otsu method.Then the internal holes of the leaves were filled through the expansion and corrosion morphological operations,and other interference targets were removed to obtain the binary image.The foreground image was obtained by masking the binary image and the RGB image.Then,the color moment algorithm was used to extract weeds color feature,the gray level co-occurrence matrix and the Local Binary Pattern(LBP)algorithm was used to extract weeds texture features,and seven Hu invariant moment features and the roundness and slenderness ratio of weeds were extracted as their shape features.According to the shape,color,texture,and fusion features of the test samples,a weed identification model was built.The test results showed that the recognition rate of Cirsium arvense(L.)Scop.,Calystegia hederacea Wall.and Conyza sumatrensis(Retz.)E.Walker were 82.72%(color feature),72.41%(shape feature),86.73%(texture feature)and 93.51%(fusion feature),respectively.Therefore,this method can provide a reference for the study of weeds identification in the asparagus field.

关键词： weeds recognition image processing feature extraction multi-feature fusion BP neural network asparagus field

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The detection method of low-rate DoS attack based on multi-feature fusion

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Digital Communications and Networks 2020年第4期6卷 504-513页

作者： Liang Liu Huaiyuan Wang Zhijun Wu Meng Yue College of Electronics Information and Automation Civil Aviation University of ChinaChina

As a new type of Denial of Service(DoS)attacks,the Low-rate Denial of Service(LDoS)attacks make the traditional method of detecting Distributed Denial of Service Attack(DDoS)attacks useless due to the characteristics of a low average rate and concealment.With features extracted from the network traffic,a new detection approach based on multi-feature fusion is proposed to solve the problem in this paper.An attack feature set containing the Acknowledge character(ACK)sequence number,the packet size,and the queue length is used to classify normal and LDoS attack traffics.Each feature is digitalized and preprocessed to fit the input of the K-Nearest Neighbor(KNN)classifier separately,and to obtain the decision contour matrix.Then a posteriori probability in the matrix is fused,and the fusion decision index D is used as the basis of detecting the LDoS attacks.Experiments proved that the detection rate of the multi-feature fusion algorithm is higher than those of the single-based detection method and other algorithms.

关键词： Low-rate denial of service attacks Attack features KNN classifier multi-feature fusion

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A classification method of building structures based on multi-feature fusion of UAV remote sensing images

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Earthquake Research Advances 2021年第4期1卷 38-47页

作者： Haoguo Du Yanbo Cao Fanghao Zhang Jiangli Lv Shurong Deng Yongkun Lu Shifang He Yuanshuo Zhang Qinkun Yu Yunnan Earthquake Agency Kunming650224YunnanChina

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

关键词： Remote sensing image Building structure classification multi-feature fusion Object-oriented classification method Texture feature classification method DSM and DEM elevation classification method RGB threshold classification method

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multi-feature fusion Based Relative Pose Adaptive Estimation for On-Orbit Servicing of Non-Cooperative Spacecraft

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Journal of Harbin Institute of Technology(New Series) 2019年第6期26卷 19-30页

作者： Yunhua Wu Nan Yang Zhiming Chen Bing Hua School of Astronautics Nanjing University of Aeronautics and Astronautics

On-orbit servicing, such as spacecraft maintenance, on-orbit assembly, refueling, and de-orbiting, can reduce the cost of space missions, improve the performance of spacecraft, and extend its life span. The relative state between the servicing and target spacecraft is vital for on-orbit servicing missions, especially the final approaching stage. The major challenge of this stage is that the observed features of the target are incomplete or are constantly changing due to the short distance and limited Field of View (FOV) of camera. Different from cooperative spacecraft, non-cooperative target does not have artificial feature markers. Therefore, contour features, including triangle supports of solar array, docking ring, and corner points of the spacecraft body, are used as the measuring features. To overcome the drawback of FOV limitation and imaging ambiguity of the camera, a "selfie stick" structure and a self-calibration strategy were implemented, ensuring that part of the contour features could be observed precisely when the two spacecraft approached each other. The observed features were constantly changing as the relative distance shortened. It was difficult to build a unified measurement model for different types of features, including points, line segments, and circle. Therefore, dual quaternion was implemented to model the relative dynamics and measuring features. With the consideration of state uncertainty of the target, a fuzzy adaptive strong tracking filter( FASTF) combining fuzzy logic adaptive controller (FLAC) with strong tracking filter(STF) was designed to robustly estimate the relative states between the servicing spacecraft and the target. Finally, the effectiveness of the strategy was verified by mathematical simulation. The achievement of this research provides a theoretical and technical foundation for future on-orbit servicing missions.

关键词： on-orbit servicing non-cooperative spacecraft multi-feature fusion fuzzy adaptive filter dual quaternion

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Early Fire Recognition Based on multi-feature fusion of Video Smoke

Early Fire Recognition Based on Multi-Feature Fusion of Vide...

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第36届中国控制会议

作者： Lin Wang Aiguo Li Academy of Information Science and Technology Dalian Maritime University

ISBN: (纸本)9781538629185

An early fire smoke recognition methods based on multi-feature fusion of video smoke is proposed in this paper.Gaussian mixed model and background subtraction method are used to extract the suspected region in foreground.Color feature of the suspected region are extracted according to the color model of early smoke in RGB and HSI spaces.Two-dimensional discrete wavelet transform is used to extract the feature of background blur.The ratio of number of pixels in the suspected region to the number of pixels in corresponding minimum enclosing rectangle is calculated to extract the feature of contour irregularity.Optical flow method is used to the feature of main motion direction.The feature values of color,background blur,contour irregularity and the main motion direction are used to form the feature vector as the input vector of the support vector machine（SVM） to be classified and recognized.Artificial bee colony algorithm is adopted to optimize the parameters of the SVM.Classification test of SVM is implemented by means of public videos and taken videos by the author.Experimental results show that the proposed method can improve the accuracy of smoke detection and can be used in outdoor environment.

关键词： Fire smoke detection multi-feature fusion SVM Gaussian mixed model

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