- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Function-driven engineering of 1D carbon nanotubes and 0D carbon dots: mechanism, properties and applications
摘要： Multi-person articulated pose tracking is a newly proposed computer vision task which aims at associating corresponding person articulated joints to establish pose trajectories. In this paper, we propose a region-based deep appearance model combined with an LSTM pose model to measure the similarity between different identities. A novel hierarchical association method is proposed to reduce the time consumption for deep feature extraction. We divide the association procedure into two stages and extract deep feature only when the pairs of identities are difficult to distinguish. Extensive experiments are conducted on the newly released multi-person pose tracking benchmark: PoseTrack. The results show that the tracking accuracy gains an obvious improvement when adopting multiple association cues, and the hierarchical association method could improve the tracking speed obviously.
关键词： Region-based deep network,LSTM pose model,Hierarchical association,Multi-person pose tracking
更新于2025-09-19 17:15:36
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Photovoltaic power forecasting based LSTM-Convolutional Network
摘要： The volatile and intermittent nature of solar energy itself presents a significant challenge in integrating it into existing energy systems. Accurate photovoltaic power prediction plays an important role in solving this problem. With the development of deep learning, more and more scholars have applied the deep learning model to time series prediction and achieved very good results. In this paper, a hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction. In the proposed hybrid prediction model, the temporal features of the data are extracted first by the long-short term memory network, and then the spatial features of the data are extracted by the convolutional neural network model. In order to show the superior performance of the proposed hybrid prediction model, the prediction results of the hybrid model are compared with those of the single model (long-short term memory network, convolutional neural network) and the hybrid network (Convolutional-LSTM Network) model, and the results of eight error evaluation indexes are given. The results show that the hybrid prediction model has better prediction effect than the single prediction model, and the proposed hybrid model (extract the temporal characteristics of data first, and then extract the spatial characteristics of data) is better than Convolutional-LSTM Network (extract the spatial characteristics of data first, and then extract the temporal characteristics of data).
关键词： Convolutional-LSTM network,LSTM-Convolutional network,Photovoltaic power forecasting,Convolutional neural network,Deep learning,Long-short term memory
更新于2025-09-16 10:30:52
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[IEEE TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Kochi, India (2019.10.17-2019.10.20)] TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Speech Enabled Visual Question Answering using LSTM and CNN with Real Time Image Capturing for assisting the Visually Impaired
摘要： The proposed work benefits visually impaired individuals in identifying objects and visualizing scenarios around them independent of any external support. In such a situation, the surrounding and ask an open-ended question, classification question, counting question or yes/no question to the application by speech input. The proposed application uses Visual Question Answering (VQA) to integrate image processing and natural language processing which is also capable of speech to text translation and vice versa that helps to identify, recognize and thus obtain details of any particular image. The work uses a classical CNN-LSTM model where image features and language features are computed separately and combined at a later stage using image features and word embedding obtained from the question and runs a multilayer perceptron on the combined features to obtain the results. The model achieves an accuracy of 57 per cent. The model can also be utilized to develop cognitive interpretation better in kids. As the application is speech enabled it is best suited for the visually impaired with an easy to use GUI.
关键词： VGG16,Visually Impaired,Keras Neural Network Library,ImageNet,gTTS,Feature extraction,Image Recognition,VQA,Word2Vec,Speech Recognition,Glove vector,CNN,Multi Layer Perceptron,LSTM
更新于2025-09-16 10:30:52
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Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets
摘要： Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.
关键词： photovoltaic power forecast,GRA-LSTM model,historical power datasets,multivariate meteorological factors
更新于2025-09-16 10:30:52
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LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization
摘要： Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors. A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer. The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features. Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer. Bayesian optimization is used to select the optimal combined features. The effectiveness of the proposed model is verified on two actual PV power plants in one area of China. The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model.
关键词： residual connection,LSTM-attention-embedding model,Bayesian optimization,deep learning,features extraction
更新于2025-09-11 14:15:04
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LASSO & LSTM Integrated Temporal Model for Short-term Solar Intensity Forecasting
摘要： As a special form of the Internet of Things, Smart Grid is an internet of both power and information, in which energy management is critical for making the best use of the power from renewable energy resources such as solar and wind, while efficient energy management is hinged upon precise forecasting of power generation from renewable energy resources. In this paper, we propose a novel least absolute shrinkage and selection operator (LASSO) and long short term memory (LSTM) integrated forecasting model for precise short-term prediction of solar intensity based on meteorological data. It is a fusion of a basic time series model, data clustering, a statistical model and machine learning. The proposed scheme first clusters data using k-means++. For each cluster, a distinctive forecasting model is then constructed by applying LSTM, which learns the non-linear relationships, and LASSO, which captures the linear relationship within the data. Simulation results with open-source datasets demonstrate the effectiveness and accuracy of the proposed model in short-term forecasting of solar intensity.
关键词： Internet of Things (IoT),Least absolute shrinkage and selection operator (LASSO),Short-term solar power forecasting,Long short term memory (LSTM),K-means++
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Exploring Convolutional Lstm for Polsar Image Classification
摘要： Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important applications in PolSAR image processing. More and more deep learning methods are applied to PolSAR image classification. As we know, the polarimetric response of a target is related to the orientation of the target, but the features in rotation domain are not fully used in deep learning. We use a convolutional LSTM (ConvLSTM) along with a sequence of polarization coherent matrices in rotation domain for PolSAR image classification. First, nine different polarization orientation angles (POA) are used to generate nine polarization coherent matrices in rotation domain. Second, a deep learning model that stacked with multiple ConvLSTM layers and fully connected layers is proposed for classification. Finally, the sequence of polarization coherent matrices is fed into the ConvLSTM to classify PolSAR images. Experiments show that the classification results of ConvLSTM are better than the LeNet-5.
关键词： convolutional LSTM,PolSAR,rotation domain,classification
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Guiyang, China (2018.8.22-2018.8.24)] 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Image Caption via Visual Attention Switch on DenseNet
摘要： We introduce a novel approach that is used to convert language descriptions. This method follows the most popular encoder-decoder architecture. The encoder uses the recently proposed densely convolutional neural network (DenseNet) to extract the feature maps. Meanwhile, the decoder uses the long short time memory (LSTM) to parse the feature maps to descriptions. We predict the next word of descriptions by taking the effective combination of feature maps with word embedding of current input word by (cid:179)visual attention switch(cid:180). Finally, we compare the performance of the proposed model with other baseline models and achieve good results.
关键词： Visual attention switch,DenseNet,LSTM,Image caption,Encoder-decoder architecture
更新于2025-09-10 09:29:36
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Time-Dependent Pre-attention Model for Image Captioning
摘要： The task of automatically generating image captions draws a lot of attention in the past few years because it shows great potential in a wide range of application scenarios. The encoder-decoder structure with attention mechanism has been extensively applied to solve this task. However, most researches apply attention mechanism only to pay attention to image features but neglect the relations between image features which we think play an important role in scene understanding. To tackle this problem, we propose a novel attention mechanism named “attention to Time-Dependent Pre-Attention” (TDPA-attention) and the TDPA-attention is combined with a hierarchical LSTM decoder to compose our captioning model (TDPA-model). Within our TDPA-attention, at every time step, every image feature pays attention to all image features according to a semantic context and the attended feature is treated as an aggregated feature that contains relations between this image feature and all image features. All these aggregated features form a new feature set that the hierarchical LSTM decoder attends to. We evaluate our model on public image caption dataset Microsoft COCO and achieve state-of-the-art performance on most evaluation metrics.
关键词： Microsoft COCO,hierarchical LSTM,attention mechanism,TDPA-attention,image captioning
更新于2025-09-09 09:28:46
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Deep-Learning-Assisted Network Orchestration for On-Demand and Cost-Effective vNF Service Chaining in Inter-DC Elastic Optical Networks
摘要： This work addresses the relatively long setup latency and complicated network control and management caused by on-demand virtual network function service chain (vNF-SC) provisioning in inter-datacenter elastic optical networks. We first design a provisioning framework with resource pre-deployment to resolve the aforementioned challenge. Specifically, the framework is designed as a discrete-time system, in which the operations are performed periodically in fixed time slots (TS). Each TS includes a pre-deployment phase followed by a provisioning phase. In the pre-deployment phase, a deep-learning (DL) model is designed to predict future vNF-SC requests, then lightpath establishment and vNF deployment are performed accordingly to pre-deploy resources for the predicted requests. Then, the system proceeds to the provisioning phase, which collects dynamic vNF-SC requests from clients and serves them in real-time by steering their traffic through the required vNFs in sequence. In order to forecast the high-dimensional data of future vNF-SC requests accurately, we design our DL model based on the long/short-term memory-based neural network and develop an effective training scheme for it. Then, the provisioning framework and DL model are optimized from several perspectives. We evaluate our proposed framework with simulations that leverage real traffic traces. The results indicate that our DL model achieves higher request prediction accuracy and lower blocking probability than two benchmarks that also predict vNF-SC requests and follow the principle of the proposed provisioning framework.
关键词： Long/short-term memory (LSTM),Elastic optical networks (EONs),Datacenter (DC),Service chaining,Network function virtualization (NFV),Deep learning
更新于2025-09-09 09:28:46