The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for th...The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
The success of robot-assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature-based registration.This process involves extracting anatomical feature points from pre-operative 3D image...The success of robot-assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature-based registration.This process involves extracting anatomical feature points from pre-operative 3D images which can be challenging because of the complex and variable structure of the pelvis.PointMLP_RegNet,a modified PointMLP,was introduced to address this issue.It retains the feature extraction module of PointMLP but replaces the classification layer with a regression layer to predict the coordinates of feature points instead of conducting regular classification.A flowchart for an automatic feature points extraction method was presented,and a series of experiments was conducted on a clinical pelvic dataset to confirm the accuracy and effectiveness of the method.PointMLP_RegNet extracted feature points more accurately,with 8 out of 10 points showing less than 4 mm errors and the remaining two less than 5 mm.Compared to PointNettt and PointNet,it exhibited higher accuracy,robustness and space efficiency.The proposed method will improve the accuracy of anatomical feature points extraction,enhance intra-operative registration precision and facilitate the widespread clinical application of robot-assisted pelvic fracture reduction.展开更多
Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,...Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.展开更多
Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying ...Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.展开更多
Relation extraction plays a crucial role in numerous downstream tasks.Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue.To tackle the problem of low informatio...Relation extraction plays a crucial role in numerous downstream tasks.Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue.To tackle the problem of low information density in dialogues,methods based on trigger enhancement have been proposed,yielding positive results.However,trigger enhancement faces challenges,which cause suboptimal model performance.First,the proportion of annotated triggers is low in DialogRE.Second,feature representations of triggers and arguments often contain conflicting information.In this paper,we propose a novel Multi-Feature Filtering and Fusion trigger enhancement approach to overcome these limitations.We first obtain representations of arguments,and triggers that contain rich semantic information through attention and gate methods.Then,we design a feature filtering mechanism that eliminates conflicting features in the encoding of trigger prototype representations and their corresponding argument pairs.Additionally,we utilize large language models to create prompts based on Chain-of-Thought and In-context Learning for automated trigger extraction.Experiments show that our model increases the average F1 score by 1.3%in the dialogue relation extraction task.Ablation and case studies confirm the effectiveness of our model.Furthermore,the feature filtering method effectively integrates with other trigger enhancement models,enhancing overall performance and demonstrating its ability to resolve feature conflicts.展开更多
This study proposes a multi-scene smoke detection algorithm based on a multi-feature extraction method to address the problems of varying smoke shapes in different scenes,difficulty in locating and detecting transluce...This study proposes a multi-scene smoke detection algorithm based on a multi-feature extraction method to address the problems of varying smoke shapes in different scenes,difficulty in locating and detecting translucent smoke,and variable smoke scales.First,the convolution module of feature extraction in YOLOv5s backbone network is replaced with asymmetric convolution block re-parameterization convolution to improve the detection of different shapes of smoke.Then,coordinate attention mechanism is introduced in the deeper layer of the backbone network to further improve the localization of translucent smoke.Finally,the detection of smoke at different scales is further improved by using the feature pyramid convolution module instead of the standard convolution module of the feature pyramid in the model.The experimental results demonstrate the feasibility and superiority of the proposed model for multi-scene smoke detection.展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnos...Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.展开更多
A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing ch...A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.展开更多
Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extract...Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.展开更多
Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intellige...Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noti...Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.展开更多
Hilbert-Huang transform method has been widely utilized from its inception because of the superiority in varieties of areas. The Hilbert spectrum thus obtained is able to reflect the distribution of the signal energy ...Hilbert-Huang transform method has been widely utilized from its inception because of the superiority in varieties of areas. The Hilbert spectrum thus obtained is able to reflect the distribution of the signal energy in a number of scales accurately. In this paper, a novel feature called ECC is proposed via feature extraction of the Hilbert energy spectrum which describes the distribution of the instantaneous energy. The experimental results conspicuously demonstrate that ECC outperforms the traditional short-term average energy. Combination of the ECC with mel frequency cepstral coefficients （MFCC） delineates the distribution of energy in the time domain and frequency domain, and the features of this group achieve a better recognition effect compared with the feature combination of the short-term average energy, pitch and MFCC. Afterwards, further improvements of ECC are developed. TECC is gained by combining ECC with the teager energy operator, and EFCC is obtained by introducing the instantaneous frequency to the energy. In the experiments, seven status of emotion are selected to be recognized and the highest recognition rate 83.57% is achieved within the classification accuracy of boredom reaching 100%. The numerical results indicate that the proposed features ECC, TECC and EFCC can improve the performance of speech emotion recognition substantially.展开更多
Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration.Various handcraft-based image processing methods have been proposed to e...Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration.Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively,but they have unavoidable disadvantages when used to analyze skin features accurately.This study proposes a hybrid segmentation scheme consisting of Deeplab v3+with an Inception-ResNet-v2 backbone,LightGBM,and morphological processing(MP)to overcome the shortcomings of handcraft-based approaches.First,we apply Deeplab v3+with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells.Then,LightGBM and MP are used to enhance the pixel segmentation quality.Finally,we determine several skin features based on the results of wrinkle and cell segmentation.Our proposed segmentation scheme achieved a mean accuracy of 0.854,mean of intersection over union of 0.749,and mean boundary F1 score of 0.852,which achieved 1.1%,6.7%,and 14.8%improvement over the panoptic-based semantic segmentation method,respectively.展开更多
The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduce...The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.展开更多
To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an au...To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an automatic feature extraction algorithm is proposed.Firstly,the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method.Then,binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation.Finally,the automatic identification of weld defect area is realized based on the sequential traversal of binary tree.Several characteristic analysis dimensions are established for weld defects of UHV key parts,including defect area,perimeter,slenderness ratio,duty cycle,etc.The experiment using theweld detection image of the actual production site shows that the proposedmethod can effectively extract theweak feature information ofweld defects and further provide reference for decision-making.展开更多
A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perfo...A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perform the features extraction. Despite of all studies about this, some features cannot be found by the algorithm or it can extract some pixels unduly. So the current article aims to show the results with the software development that uses the original and reference image to calculate some statistics about the extraction process. Furthermore, the calculated statistics can be used to evaluate the extraction process.展开更多
Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular...Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.展开更多
基金support by the Science and Technology Project of Guangdong Power Exchange Center Co.,Ltd.(No.GDKJXM20222599)National Natural Science Foundation of China(No.52207104)Natural Science Foundation of Guangdong Province(No.2024A1515010426).
文摘The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFB1313800)the National Science Foundation of China(Grant No.NSFC62373259)+1 种基金the Natural Science Foundation of Top Talent of SZTU(Grant No.GDRC202303)the Education Promotion Foundation of Guangdong Province(Grant No.2022ZDJS115).
文摘The success of robot-assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature-based registration.This process involves extracting anatomical feature points from pre-operative 3D images which can be challenging because of the complex and variable structure of the pelvis.PointMLP_RegNet,a modified PointMLP,was introduced to address this issue.It retains the feature extraction module of PointMLP but replaces the classification layer with a regression layer to predict the coordinates of feature points instead of conducting regular classification.A flowchart for an automatic feature points extraction method was presented,and a series of experiments was conducted on a clinical pelvic dataset to confirm the accuracy and effectiveness of the method.PointMLP_RegNet extracted feature points more accurately,with 8 out of 10 points showing less than 4 mm errors and the remaining two less than 5 mm.Compared to PointNettt and PointNet,it exhibited higher accuracy,robustness and space efficiency.The proposed method will improve the accuracy of anatomical feature points extraction,enhance intra-operative registration precision and facilitate the widespread clinical application of robot-assisted pelvic fracture reduction.
基金supported by the National Natural Science Foundation of China under Grant 62306128the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Leading Innovation Project of Changzhou Science and Technology Bureau under Grant CQ20230072.
文摘Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.
基金Supported by National Science and Technology Support Program of China(Grant No.2015BAF07B04)
文摘Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.
基金supported by the National Key Research and Development Program of China(No.2023YFF0905400)the National Natural Science Foundation of China(No.U2341229).
文摘Relation extraction plays a crucial role in numerous downstream tasks.Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue.To tackle the problem of low information density in dialogues,methods based on trigger enhancement have been proposed,yielding positive results.However,trigger enhancement faces challenges,which cause suboptimal model performance.First,the proportion of annotated triggers is low in DialogRE.Second,feature representations of triggers and arguments often contain conflicting information.In this paper,we propose a novel Multi-Feature Filtering and Fusion trigger enhancement approach to overcome these limitations.We first obtain representations of arguments,and triggers that contain rich semantic information through attention and gate methods.Then,we design a feature filtering mechanism that eliminates conflicting features in the encoding of trigger prototype representations and their corresponding argument pairs.Additionally,we utilize large language models to create prompts based on Chain-of-Thought and In-context Learning for automated trigger extraction.Experiments show that our model increases the average F1 score by 1.3%in the dialogue relation extraction task.Ablation and case studies confirm the effectiveness of our model.Furthermore,the feature filtering method effectively integrates with other trigger enhancement models,enhancing overall performance and demonstrating its ability to resolve feature conflicts.
基金the Natural Science Foundation of Zhejiang Province(Nos.LY20F020015 and LY21F020015)the National Natural Science Foundation of China(Nos.61972121 and 61902099)。
文摘This study proposes a multi-scene smoke detection algorithm based on a multi-feature extraction method to address the problems of varying smoke shapes in different scenes,difficulty in locating and detecting translucent smoke,and variable smoke scales.First,the convolution module of feature extraction in YOLOv5s backbone network is replaced with asymmetric convolution block re-parameterization convolution to improve the detection of different shapes of smoke.Then,coordinate attention mechanism is introduced in the deeper layer of the backbone network to further improve the localization of translucent smoke.Finally,the detection of smoke at different scales is further improved by using the feature pyramid convolution module instead of the standard convolution module of the feature pyramid in the model.The experimental results demonstrate the feasibility and superiority of the proposed model for multi-scene smoke detection.
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
基金supported by the National Natural Science Foundation of China(Nos.51805376 and U1709208)the Zhejiang Provincial Natural Science Foundation of China(Nos.LY20E050028 and LD21E050001)。
文摘Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.
基金supported by the National Natural Science Foundation of China (60872108)the Postdoctoral Science Foundation of China(200902411+3 种基金20080430903)Heilongjiang Postdoctoral Financial Assistance (LBH-Z08129)the Scientific and Technological Creative Talents Special Research Foundation of Harbin Municipality (2008RFQXG030)Central University Basic Research Professional Expenses Special Fund Project
文摘A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.
基金supported by the National Natural Science Foundation of China [Nos. 61772452, 61379116]the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [No.2019L0847]the Natural Science Foundation of Hebei Province, China [No. F2015203046]
文摘Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.
基金support from the National Natural Science Foundation of China(Grant Nos.52379103,52279103)the Natural Science Foundation of Shandong Province(Grant No.ZR2023YQ049).
文摘Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金The paper is supported by the Research Foundation for OutstandingYoung Teachers , China University of Geosciences ( Wuhan) ( No .CUGQNL0616) Research Foundationfor State Key Laboratory of Geo-logical Processes and Mineral Resources ( No . MGMR2002-02)Hubei Provincial Depart ment of Education (B) .
文摘Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.
基金Project supported by the State Key Laboratory of Robotics and System(Grant No.SKLS-2009-MS-10)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘Hilbert-Huang transform method has been widely utilized from its inception because of the superiority in varieties of areas. The Hilbert spectrum thus obtained is able to reflect the distribution of the signal energy in a number of scales accurately. In this paper, a novel feature called ECC is proposed via feature extraction of the Hilbert energy spectrum which describes the distribution of the instantaneous energy. The experimental results conspicuously demonstrate that ECC outperforms the traditional short-term average energy. Combination of the ECC with mel frequency cepstral coefficients （MFCC） delineates the distribution of energy in the time domain and frequency domain, and the features of this group achieve a better recognition effect compared with the feature combination of the short-term average energy, pitch and MFCC. Afterwards, further improvements of ECC are developed. TECC is gained by combining ECC with the teager energy operator, and EFCC is obtained by introducing the instantaneous frequency to the energy. In the experiments, seven status of emotion are selected to be recognized and the highest recognition rate 83.57% is achieved within the classification accuracy of boredom reaching 100%. The numerical results indicate that the proposed features ECC, TECC and EFCC can improve the performance of speech emotion recognition substantially.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2020R1F1A1074885)was supported by the Brain Korea 21 Project in 2021(No.4199990114242).
文摘Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration.Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively,but they have unavoidable disadvantages when used to analyze skin features accurately.This study proposes a hybrid segmentation scheme consisting of Deeplab v3+with an Inception-ResNet-v2 backbone,LightGBM,and morphological processing(MP)to overcome the shortcomings of handcraft-based approaches.First,we apply Deeplab v3+with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells.Then,LightGBM and MP are used to enhance the pixel segmentation quality.Finally,we determine several skin features based on the results of wrinkle and cell segmentation.Our proposed segmentation scheme achieved a mean accuracy of 0.854,mean of intersection over union of 0.749,and mean boundary F1 score of 0.852,which achieved 1.1%,6.7%,and 14.8%improvement over the panoptic-based semantic segmentation method,respectively.
文摘The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.
文摘To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an automatic feature extraction algorithm is proposed.Firstly,the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method.Then,binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation.Finally,the automatic identification of weld defect area is realized based on the sequential traversal of binary tree.Several characteristic analysis dimensions are established for weld defects of UHV key parts,including defect area,perimeter,slenderness ratio,duty cycle,etc.The experiment using theweld detection image of the actual production site shows that the proposedmethod can effectively extract theweak feature information ofweld defects and further provide reference for decision-making.
文摘A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perform the features extraction. Despite of all studies about this, some features cannot be found by the algorithm or it can extract some pixels unduly. So the current article aims to show the results with the software development that uses the original and reference image to calculate some statistics about the extraction process. Furthermore, the calculated statistics can be used to evaluate the extraction process.
文摘Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.
