Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely h...Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.展开更多
Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the da...Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.展开更多
The electricity industry has witnessed increasing challenges in power system operation and rapid developments of artificial intelligence technologies in the last decades.In this context,studying the approach of securi...The electricity industry has witnessed increasing challenges in power system operation and rapid developments of artificial intelligence technologies in the last decades.In this context,studying the approach of security-constrained unit commitment(SCUC)deci-sionmaking with high adaptability and precision is of great importance.This paper proposes an improved da-tadriven deep learning(DL)approach,following the sample coding and Sequence to Sequence(Seq2Seq)technique.First,an encoding and decoding strategy is utilized for high-dimensional sample matrix dimension compression.A DL SCUC decision model based on a Seq2Seq network with gated recurrent units as neurons is then constructed,and the mapping between load and unit on/off scheme is established through massive data from historical scheduling.Numerical simulation results based on the IEEE 118-bus test system demonstrate the correctness and effectiveness of the proposed approach.展开更多
Due to the lack of parallel data in current grammatical error correction(GEC)task,models based on sequence to sequence framework cannot be adequately trained to obtain higher performance.We propose two data synthesis ...Due to the lack of parallel data in current grammatical error correction(GEC)task,models based on sequence to sequence framework cannot be adequately trained to obtain higher performance.We propose two data synthesis methods which can control the error rate and the ratio of error types on synthetic data.The first approach is to corrupt each word in the monolingual corpus with a fixed probability,including replacement,insertion and deletion.Another approach is to train error generation models and further filtering the decoding results of the models.The experiments on different synthetic data show that the error rate is 40%and that the ratio of error types is the same can improve the model performance better.Finally,we synthesize about 100 million data and achieve comparable performance as the state of the art,which uses twice as much data as we use.展开更多
Question Generation(QG)is the task of generating questions according to the given contexts.Most of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for...Question Generation(QG)is the task of generating questions according to the given contexts.Most of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for providing more details,which seriously suffer from such problems as gradient vanishing and ineffective information utilization.In fact,reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education scenario.To that end,in this paper,we propose a novel Hierarchical Answer-Aware and Context-Aware Network(HACAN)to construct a high-quality passage representation and judge the balance between the sentences and the whole passage.Specifically,a Hierarchical Passage Encoder(HPE)is proposed to construct an answer-aware and context-aware passage representation,with a strategy of utilizing multi-hop reasoning.Then,we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder(HPD)which determines when to utilize the passage information.We conduct extensive experiments on the SQuAD dataset,where the results verify the effectivenesss of our model in comparison with several baselines.展开更多
文摘Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.
基金supported by the National Natural Science Foundation of China(Grant No.42307218)the Foundation of Key Laboratory of Soft Soils and Geoenvironmental Engineering(Zhejiang University),Ministry of Education(Grant No.2022P08)the Natural Science Foundation of Zhejiang Province(Grant No.LTZ21E080001).
文摘Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.
基金supported by the National Natural Sci-ence Foundation of China(No.62233006).
文摘The electricity industry has witnessed increasing challenges in power system operation and rapid developments of artificial intelligence technologies in the last decades.In this context,studying the approach of security-constrained unit commitment(SCUC)deci-sionmaking with high adaptability and precision is of great importance.This paper proposes an improved da-tadriven deep learning(DL)approach,following the sample coding and Sequence to Sequence(Seq2Seq)technique.First,an encoding and decoding strategy is utilized for high-dimensional sample matrix dimension compression.A DL SCUC decision model based on a Seq2Seq network with gated recurrent units as neurons is then constructed,and the mapping between load and unit on/off scheme is established through massive data from historical scheduling.Numerical simulation results based on the IEEE 118-bus test system demonstrate the correctness and effectiveness of the proposed approach.
基金was supported by the funds of Bejing Advanced Innovation Center for Language Resources.(TYZ19005)Research Program of State Language Commission(ZDI135-105,YB135-89).
文摘Due to the lack of parallel data in current grammatical error correction(GEC)task,models based on sequence to sequence framework cannot be adequately trained to obtain higher performance.We propose two data synthesis methods which can control the error rate and the ratio of error types on synthetic data.The first approach is to corrupt each word in the monolingual corpus with a fixed probability,including replacement,insertion and deletion.Another approach is to train error generation models and further filtering the decoding results of the models.The experiments on different synthetic data show that the error rate is 40%and that the ratio of error types is the same can improve the model performance better.Finally,we synthesize about 100 million data and achieve comparable performance as the state of the art,which uses twice as much data as we use.
基金This research was partially supported by the National Key R&D Program of China(No.2021YFF0901003).
文摘Question Generation(QG)is the task of generating questions according to the given contexts.Most of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for providing more details,which seriously suffer from such problems as gradient vanishing and ineffective information utilization.In fact,reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education scenario.To that end,in this paper,we propose a novel Hierarchical Answer-Aware and Context-Aware Network(HACAN)to construct a high-quality passage representation and judge the balance between the sentences and the whole passage.Specifically,a Hierarchical Passage Encoder(HPE)is proposed to construct an answer-aware and context-aware passage representation,with a strategy of utilizing multi-hop reasoning.Then,we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder(HPD)which determines when to utilize the passage information.We conduct extensive experiments on the SQuAD dataset,where the results verify the effectivenesss of our model in comparison with several baselines.
