In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted...In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.展开更多
Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate tempor...Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.展开更多
Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neur...Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities.展开更多
Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surroundin...Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surrounding landscapes)can automatically guide drivers to choose appropriate speeds on different road categories.This study proposes an improved lightweight convolutional neural network(LW-CNN)that includes drivers’visual perception characteristics(i.e.,depth perception and dynamic vision)to conduct the self-explaining analysis of the facility environment on 2-lane rural roads.Data for this study are gathered through naturalistic driving experiments on 2-lane rural roads across five Chinese provinces.A total of 3502 visual facility environment images,alongside their corresponding operation speeds and speed limits,are collected.The improved LW-CNN exhibits high accuracy and efficiency in predicting operation speeds with these visual facility environment images,achieving a train loss of 0.05%and a validation loss of 0.15%.The semantics of facility environments affecting operation speeds are further identified by combining this LW-CNN with the gradient-weighted class activation mapping(Grad-CAM)algorithm and the semantic segmentation network.Then,six typical 2-lane rural road categories perceived by drivers with different operation speeds and speeding probability(SP)are sum-marized using k-means clustering.An objective and comprehensive analysis of each category’s semantic composition and depth features is conducted to evaluate their influence on drivers’speeding probability and road category perception.The findings of this study can be directly used to optimize facility environments from drivers’visual perception to decrease speeding-related crashes.展开更多
基金Supported by the National Natural Science Foundation of China(61701029)Basic Research Foundation of Beijing Institute of Technology(20170542008)Industry-University Research Innovation Foundation of the Science and Technology Development Center of the Ministry of Education(2018A02012)。
文摘In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.
基金supported by National Key Research and Development Program of China(Key Techniques of Adaptive Grid Integration and Active Synchronization for Extremely High Penetration Distributed Photovoltaic Power Generation,2022YFB2402900).
文摘Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.
基金supported by Natural Science Foundation of Chongqing in China(No.cstc2020jcyj-jqX0004)the Ministry of education of Humanities and Social Science project(No.20YJA790016)+1 种基金the National Natural Science Foundation of China(Grant No.42171298)We thank the patent supporting the method section of the paper(No.202110750360.1).
文摘Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities.
基金supported by the National Natural Science Foundation of China(No.52102416)the Natural Science Foundation of Shanghai(No.22ZR1466000)the Fundamental Research Funds for the Central Universities of China(No.22120240159).
文摘Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surrounding landscapes)can automatically guide drivers to choose appropriate speeds on different road categories.This study proposes an improved lightweight convolutional neural network(LW-CNN)that includes drivers’visual perception characteristics(i.e.,depth perception and dynamic vision)to conduct the self-explaining analysis of the facility environment on 2-lane rural roads.Data for this study are gathered through naturalistic driving experiments on 2-lane rural roads across five Chinese provinces.A total of 3502 visual facility environment images,alongside their corresponding operation speeds and speed limits,are collected.The improved LW-CNN exhibits high accuracy and efficiency in predicting operation speeds with these visual facility environment images,achieving a train loss of 0.05%and a validation loss of 0.15%.The semantics of facility environments affecting operation speeds are further identified by combining this LW-CNN with the gradient-weighted class activation mapping(Grad-CAM)algorithm and the semantic segmentation network.Then,six typical 2-lane rural road categories perceived by drivers with different operation speeds and speeding probability(SP)are sum-marized using k-means clustering.An objective and comprehensive analysis of each category’s semantic composition and depth features is conducted to evaluate their influence on drivers’speeding probability and road category perception.The findings of this study can be directly used to optimize facility environments from drivers’visual perception to decrease speeding-related crashes.
