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At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

The identification and recovery of explosive fragments can provide a reference for the evaluation of explosive power and the design of explosion-proof measures. At present, fragment detection usually uses a few bands in the visible light or infrared bands for imaging, without fully utilizing multi-band spectral information. Hyperspectral imaging has high spectral resolution and can provide multidimensional reference information for the fragments to be classified. Therefore, this article proposed a spatial–spectral joint method for explosive fragment classification by combining hyperspectral imaging technology. In a laboratory environment, this article collected hyperspectral images of explosion fragments scattered in simulated scenes. In order to extract effective features from redundant spectral information and improve classification accuracy, this paper adopted a classification framework based on deep learning. This framework used a convolutional neural network–bidirectional long short-term memory network (CNN-BiLSTM) as the spectral information classification model and a U-shaped network (U-Net) as the spatial segmentation model. The experimental results showed that the overall accuracy exceeds 95.2%. The analysis results indicated that the method of spatial–spectral combination can accurately identify explosive fragment targets. It validated the feasibility of using hyperspectral imaging for explosive fragment classification in laboratory environments. Due to the complex environment of the actual explosion site, this study still needs to be validated in outdoor environments. Our next step is to use airborne hyperspectral imaging to identify explosive fragments in outdoor environments.

Regional‐scale holistic wind power prediction (WPP) is pivotal to securing the safety, stability, and economic efficiency of power systems. To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fine‐to‐coarse (FTC) feature mapping, and error compensation‐temporal convolutional network‐bidirectional Long short‐term memory network (EC‐TCN‐BiLSTM) is proposed in this paper. First, the regional input features, encompassing data from numerous wind farms, are decomposed using the CEEMDAN algorithm to extract intrinsic mode functions (IMFs) and residuals at different time scales. Second, the decomposed IMFs and residuals are reconstructed using the adaptive FTC feature mapping technique, forming a high‐dimensional feature set in the time‐frequency domain, which boasts fewer features than the original set, thus diminishing the computational intricacy of the prediction model. Third, by combining the strengths of TCN and BiLSTM neural networks, the temporal and spatial correlations of input features can be captured effectively. Fourth, the integration of the EC module corrects the systematic errors of the prediction results, thereby further improving the prediction accuracy. Finally, case studies elucidate the efficacy of the proposed WPP method, illustrating a 0.41%–2.4% diminution in 24‐h‐ahead root mean square error (RMSE) and a 0.68%–2.63% reduction in 96‐h‐ahead RMSE relative to conventional methodologies.

The instability of solar energy is the biggest challenge to its successful integration with modern power grids, and accurate prediction of long‐term solar radiation can effectively solve this problem. In this study, we proposed a novel long‐term solar radiation prediction model based on time series imaging and bidirectional long short‐term memory network. First, inspired by the computer vision algorithm, the recursive graph algorithm is used to transform the one‐dimensional time series into two‐dimensional images, and then convolutional neural network is used to extract the features from the images, thus, the deeper features in the original solar radiation data can be mined. Second, to solve the problem of low accuracy of long‐term solar radiation prediction, a hybrid model BiLSTM‐Transformer is used to predict long‐term solar radiation. The hybrid prediction model can capture the long‐term dependencies, thereby further improving the accuracy of the prediction model. The experimental results show that the hybrid model proposed in this study is superior to other single models and hybrid models in long‐term solar radiation prediction accuracy. The accuracy and stability of the hybrid model are verified by many tests. A novel solar radiation forecasting model based on time series imaging and bidirectional long short‐term memory network.

To further enhance the precision of carbon trading price forecasting, this research proposes a combined forecasting model, CEEMDAN–VMD–IPSO–BiLSTM, considering the unsatisfactory high‐frequency sequence decomposition and the reliance on unidirectional neural networks in current carbon price‐prediction models. First of all, the original sequence of carbon prices is decomposed into multiple independent subsequences through the completely ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique. The sample entropy values of each subsequence are calculated to reconstruct them as high‐frequency, low‐frequency, and trend sequences. Second, we employ the variational mode decomposition (VMD) approach to decompose the high‐frequency series. The obtained subsequences, along with the low‐frequency and trend sequences, are separately input into an improved particle swarm optimization (IPSO) optimized bidirectional long short‐term memory neural network (BiLSTM) model for forecasting. Finally, an IPSO–BiLSTM model is used to integrate the forecasting outcomes from the previous step, yielding the ultimate results for predicting carbon prices. The case studies reveal that compared with the benchmark model, this model exhibits superior predictive precision and universality. It offers theoretical support for optimizing carbon market operations and fostering low‐carbon economic growth, holding practical importance. A carbon price‐prediction model based on a secondary decomposition strategy using completely ensemble empirical mode decomposition with adaptive noise and variational mode decomposition methods, with Predictions Performed by a bidirectional long short‐term memory network optimized via an improved particle swarm optimization algorithm.

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

Background RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence and secondary structure specificities is challenging for both predicting RBP binding sites and accurate sequence and structure motifs detection. Results In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, to enable subsequent convolution operations. To reveal the hidden binding knowledge from the observed sequences, the CNNs are applied to learn the abstract features. Considering the close relationship between sequence and predicted structures, we use the BLSTM to capture possible long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets. The results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage compared to other methods is that iDeepS can automatically extract both binding sequence and structure motifs, which will improve our understanding of the mechanisms of binding specificities of RBPs. Conclusion Our study shows that the iDeepS method identifies the sequence and structure motifs to accurately predict RBP binding sites. iDeepS is available at https://github.com/xypan1232/iDeepS .

[Purpose/Significance] From the perspective of data mining of user comments on a social network platform of a university library, the sentiment polarity of user comments is analyzed in a fine-grained way. It provides scientific basis for a university library to understand the real sentiment tendency of its users and improve its service quality. [Method/Process] This paper takes the Chinese comments data of social network platform users of domestic university libraries as the research object. Through the TensorFlow deep learning framework, we used Keras artificial neural network library, combined convolution neural network and bidirectional long short term memory network, introduced hierarchical attention mechanism, and constructed sentiment analysis model based on CNN-BiLSTM-HAN hybrid neural network. [Results/Conclusions] The experiment is carried on by using the data set of user comments on the real social network platform of a university library. The accuracy of this method is 93.38%, and the results show that the model is effective. The model is more complex, as a result, the training time of the model is longer, the universality of the method model needs to be further tested., Emoticons are not used effectively, and the parameter setting needs further study.

One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural network (CNN)). With values of 88.28%, 88.42%, and 89.20% for Commuter Line, Transjakarta, and Commuter Line+Transjakarta, respectively, BERT-BiLSTM generates the highest accuracy on the data. In general, BiLSTM produces the highest accuracy, followed by LSTM, and finally CNN. When it came to word embedding, BERT embedding outperformed Word2Vec and GloVe. In addition, the BERT-BiLSTM model generates the highest precision, recall, and F1-measure values in each data scenario when compared to other models. According to the results of this study, BERT-BiLSTM can enhance the performance of the classification model when compared to previous studies that only used BERT or BiLSTM for emotion detection in Indonesian texts.

Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories. To address the aforementioned issue, this paper proposes a novel approach for ACSA utilizing pre-trained Bidirectional Long Short-Term Memory (BiLSTM) and syntax-aware graph attention network. To address the issue of insufficient existing annotated datasets, a method of using transfer learning is proposed. Firstly, the BiLSTM model is used to pre-train on the document-level sentiment analysis dataset, and the obtained pre-training parameters are transferred to the aspect-level task model. Then, a syntax-aware graph attention network model is proposed to make full use of the syntactic structure and semantic information in the text, and combine the knowledge learned in pre-training to achieve the ACSA task. The performance evaluation of this method is carried out on five user comment text datasets, and the comprehensive ablation experiments prove that this method performs best compared with baseline models.