Research on Facial Fatigue Detection of Drivers with Multi-feature Fusion

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.

SA-Model:Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

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.

Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network

The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.

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

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.

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

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.

Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

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.

Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.

Medical image fusion based on pulse coupled neural networks and multi-feature fuzzy clustering

Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.

Multi-Feature Fusion Based Relative Pose Adaptive Estimation for On-Orbit Servicing of Non-Cooperative Spacecraft

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.

A Multi-feature Fusion Apple Classification Method Based on Image Processing and Improved SVM

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.

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

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.

FMF的火焰显著性检测

为准确定位火源点,实现火灾预警,提出一种基于人眼视觉注意机制的实时监测火灾预警方法。首先,根据图像对抗理论,提取视频序列中每一帧图像的亮度和颜色特征;其次,运用像素级显著性检测算法,构建描述特征信息的多尺度空间高斯金字塔;然后,运用跨尺度特征相加方法,融合中心-邻域对比度金字塔,得到静态显著性图;最后,结合动态帧差法,将多特征融合(FMF)算法得到的显著性图作动态帧差,寻找视频帧中属于火焰的区域,在公开的数据集上就4种评价指标与6种代表性算法作对比。结果表明:FMF算法通过显著性分析方法描述多尺度空间特征信息,其鲁棒性更强;与6种算法相比,FMF算法在准确率和漏检率上有较明显的优势,且能准确识别与定位火焰,防范火灾的发生。

Research on Feature Fusion Technology of Fruit and Vegetable Image Recognition Based on SVM

In order to improve the accuracy and stability of fruit and vegetable image recognition by single feature, this project proposed multi-feature fusion algorithms and SVM classification algorithms. This project not only introduces the Reproducing Kernel Hilbert space to improve the multi-feature compatibility and improve multi-feature fusion algorithm, but also introduces TPS transformation model in SVM classifier to improve the classification accuracy, real-time and robustness of integration feature. By using multi-feature fusion algorithms and SVM classification algorithms, experimental results show that we can recognize the common fruit and vegetable images efficiently and accurately.

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

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.

Multi-features fusion for short-term photovoltaic power prediction

In recent years,in order to achieve the goal of“carbon peaking and carbon neutralization”,many countries have focused on the development of clean energy,and the prediction of photovoltaic power generation has become a hot research topic.However,many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation,and they rarely consider the multi-features fusion methods for power prediction.This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China,and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation.Next,the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets.Finally,traditional time series prediction methods,such as Recurrent Neural Network(RNN),Convolution Neural Network(CNN)combined with eXtreme Gradient Boosting(XGBoost),are applied to study the impact of different feature fusion methods on power prediction.The results show that the prediction accuracy of Long Short-Term Memory(LSTM),stacked Long Short-Term Memory(stacked LSTM),Bi-directional LSTM(Bi-LSTM),Temporal Convolutional Network(TCN),and XGBoost algorithms can be greatly improved by the method of integrating historical generation power and meteorological features.Therefore,the feature fusion based photovoltaic power prediction method proposed in this paper is of great significance to the development of the photovoltaic power generation industry.

变异函数对泛克里金法的细粒子比星-地融合影响研究

泛克里金方法进行星-地融合可有效提高MODIS FMF的精度,然而由于地基站点稀少造成融合前需要利用长时间序列数据获取变异函数的主要参数(块金值、基台值和变程),故不能满足基于卫星瞬时观测遥感PM2.5的PMRS模型的需求。本文对2010年12月至2016年11月中国中东部地区的数据进行了变异函数参数的计算和分析,结果表明不同年份相关距离变化情况相一致,夏季显著高于冬季,基台值呈现与相关距离相反的趋势。通过利用2016年冬季变异函数中的变程(控制实验)和2011-2016年冬季变异函数的变程季均值(对比实验)作为初始值,对2016年冬季中国中东部地区MODIS FMF和地基FMF进行了融合,弃一交叉验证结果显示控制实验下FMF融合结果与地基FMF偏差最大值由0.552降低至0.198左右(对比实验下最大偏差为0.218),平均误差相近(分别为0.070、0.080)。2种实验估算的PM2.5平均值(分别为77.6、78.8μg/m3)仅相差1.2μg/m3,与在位测量的PM2.5观测值相比,误差平均值均为37.4μg/m3。由此可见,融合结果对初始变程值的变化敏感度不高,在季节相同的情况下,变程的多年季均值可有效替代相应季节的变程值。