Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on...Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample training.Based on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this study.The algorithm can effectively depict the load state without the need for prior training.First,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models.The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm.The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal.With low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing cost.Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.展开更多
Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories.Existing approaches commonly employ an encoder-decoder neural network structure to e...Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories.Existing approaches commonly employ an encoder-decoder neural network structure to enhance information extraction during the encoding phase.However,these methods often neglect the inclusion of road rule constraints during trajectory formulation in the decoding phase.This paper proposes a novel method that combines neural networks and rule-based constraints in the decoder stage to improve trajectory prediction accuracy while ensuring compliance with vehicle kinematics and road rules.The approach separates vehicle trajectories into lateral and longitudinal routes and utilizes conditional variational autoencoder(CVAE)to capture trajectory uncertainty.The evaluation results demonstrate a reduction of 32.4%and 27.6%in the average displacement error(ADE)for predicting the top five and top ten trajectories,respectively,compared to the baseline method.展开更多
Developing artificial intelligence-based methods for automated site layout generation can significantly reduce the time and manual effort required in urban planning and design processes.To this end,generative adversar...Developing artificial intelligence-based methods for automated site layout generation can significantly reduce the time and manual effort required in urban planning and design processes.To this end,generative adversarial networks(GANs)have been used in many applications.However,conventional GAN-based models have rarely considered the geometric relations between parcels and buildings,and the generated layouts often fail to meet the design requirements due to the poor integration of essential building attributes.To address these issues,this study proposes a model based on a graph constrained GAN(GCGAN),which consists of a graph variational autoencoder(GraphVAE)and a GAN framework.In this model,parcels are represented as tuples,while site layouts within each parcel are encoded as graphs with ring topology to capture spatial and relational structures.GraphVAE is then trained to generate site layout graphs considering parcel attributes and building design parameters(e.g.,number of buildings).Furthermore,GAN is trained to generate the layouts of building objects according to the graphs produced by GraphVAE.The GCGAN model is evaluated with a dataset that comprises parcels and their corresponding site layouts in the Guangdong-Hong Kong-Macao Greater Bay Area in southern China.Comparative experiments reveal that GCGAN model outperforms other models such as GANmapper,Pix2Pix,and ESGAN in terms of more realistic building patterns and attributes.With its satisfactory performance,the proposed model has the potential to support the planning and design of urban(re)development by providing reliable simulations of site layouts.展开更多
基金supported by National Natural Science Foundation of China(No.52107117)。
文摘Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet.Despite several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample training.Based on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this study.The algorithm can effectively depict the load state without the need for prior training.First,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models.The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm.The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal.With low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing cost.Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.
基金supported in part by the National Natural Science Foundation of China under Grant 52372393,62003238in part by the DongfengTechnology Center(Research and Application of Next-Generation Low-Carbonntelligent Architecture Technology).
文摘Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories.Existing approaches commonly employ an encoder-decoder neural network structure to enhance information extraction during the encoding phase.However,these methods often neglect the inclusion of road rule constraints during trajectory formulation in the decoding phase.This paper proposes a novel method that combines neural networks and rule-based constraints in the decoder stage to improve trajectory prediction accuracy while ensuring compliance with vehicle kinematics and road rules.The approach separates vehicle trajectories into lateral and longitudinal routes and utilizes conditional variational autoencoder(CVAE)to capture trajectory uncertainty.The evaluation results demonstrate a reduction of 32.4%and 27.6%in the average displacement error(ADE)for predicting the top five and top ten trajectories,respectively,compared to the baseline method.
基金supported by the National Natural Science Foundation of China(Grant No.42322110 and No.42271415).
文摘Developing artificial intelligence-based methods for automated site layout generation can significantly reduce the time and manual effort required in urban planning and design processes.To this end,generative adversarial networks(GANs)have been used in many applications.However,conventional GAN-based models have rarely considered the geometric relations between parcels and buildings,and the generated layouts often fail to meet the design requirements due to the poor integration of essential building attributes.To address these issues,this study proposes a model based on a graph constrained GAN(GCGAN),which consists of a graph variational autoencoder(GraphVAE)and a GAN framework.In this model,parcels are represented as tuples,while site layouts within each parcel are encoded as graphs with ring topology to capture spatial and relational structures.GraphVAE is then trained to generate site layout graphs considering parcel attributes and building design parameters(e.g.,number of buildings).Furthermore,GAN is trained to generate the layouts of building objects according to the graphs produced by GraphVAE.The GCGAN model is evaluated with a dataset that comprises parcels and their corresponding site layouts in the Guangdong-Hong Kong-Macao Greater Bay Area in southern China.Comparative experiments reveal that GCGAN model outperforms other models such as GANmapper,Pix2Pix,and ESGAN in terms of more realistic building patterns and attributes.With its satisfactory performance,the proposed model has the potential to support the planning and design of urban(re)development by providing reliable simulations of site layouts.
