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In order to accurately estimate the period of X-ray pulsars to improve navigation performance, this paper proposes a pulsar period estimation method based on photon energy using reverse Hilbert coding and double Convolutional Neural Networks (CNNs) discrimination. This method uses photon energy information to perform epoch folding. It converts one-dimensional contour information into two-dimensional image signals through reverse Hilbert curve and recognizes contour image features through CNN models to determine the optimal period. According to the simulation results, the period estimation accuracy of this method is 0.5350 ns, which is 65.71% higher than the χ 2 epoch folding method. The contour phase accuracy is 0.0004767, which is 41.93% higher than the χ 2 epoch folding method. At the same time, the observation time and noise interference that may affect the accuracy of period estimation are also quantitatively analyzed. In addition, in order to simulate real navigation scenarios, this paper also conducts dynamic period estimation experiments on PSR B0531+21 pulsar in a specific period of time. The method proposed in this paper has the advantages of high precision, fast calculation speed, strong anti-interference ability and accurate dynamic period estimation, which can significantly improve the navigation performance of X-ray pulsars. • The proposed period estimation method uses photon energy information for contour weighted folding. • The proposed method uses reverse Hilbert curve to convert one-dimensional contour information into two-dimensional image information. • Convolutional Neural Network models are used to optimize pulsar period estimation. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasing number of articles available in the digital library will require quite a long time and accuracy in sorting articles according to needs. The use of artificial neural networks in finding the right articles as needed through topic segmentation applications will helps this process in terms of speed and accuracy. Several neural network models used in segmentation topic applications include Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The Transformer model since its introduction in 2017 for Natural Language Processing (NLP) has a better level of accuracy compared to the RNN, CNN and LSTM models. Transformer model research for segmentation topic applications, especially articles in Indonesian language, is still very limited. This paper will discuss the use of the Transformer model in segmentation topic applications for Indonesian-language articles. The experimental results found that the accuracy produced by the Transformer model was higher than previous LSTM model with the WindowDiff value generated by the model proposed using Transformer is 0.249 and the LSTM baseline model is 0.363, while the while the Pk value generated by the proposed model is 0.279 and the LSTM baseline model is 0.394. [ABSTRACT FROM AUTHOR]

In this paper, we tackle the task of similar question retrieval (QR) which is essential for Community Question Answering (cQA) and aims to retrieve historical questions that are semantically equivalent to the new queries. Over time, with the sharp increase of community archives and the accumulation of duplicated questions, the QR problem has become increasingly challenging due to the shortness of the community questions as well as the word mismatch problem as users can formulate the same query using different wording. Although many efforts have been devoted to address this problem, existing methods mostly relied on supervised models which significantly depend on massive training data sets and manual feature engineering. Such methods are chiefly constrained by their specificities that ignore the word order and do not capture enough syntactic and semantic information in questions. In this paper, we rely on Neural Networks (NNs) which use a deep analysis of words and questions to take into consideration the semantics as well as the structure of questions to predict the semantic text similarity. We propose a deep learning approach based on a Siamese architecture with Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism to let the model give different words different attention while modeling questions. We also explore the use of Convolutional Neural Networks (CNN) nested within the Siamese architecture to retrieve relevant questions. Different similarity measures were tested to predict the semantic similarity between the pairs of questions. To evaluate the proposed approach, we conducted experiments on large-scale datasets in English and Arabic. [ABSTRACT FROM AUTHOR]

• A novel hand gesture identification is proposed using CNN, ORB and Gabor filter. • It can be used as a pre-trained structure for any type of the hand gesture dataset. • Remarkable results are obtained on the famous databases. • Less computational intricacy in the hardware implementation and online application. In this paper, a new structure of deep learning neural network is introduced to identify the static hand gesture in the sign language. The proposed structure includes the convolutional neural network (CNN) and the classical non-intelligent feature extraction method. In the proposed structure, the hand gesture image, after preprocessing and removing its background, passes through three different streams of feature extraction, to well extract of effective features and determine the hand gesture class. These three streams, that independently extract their own specific features, consist of three widely used methods in the hand gesture classification named CNN, Gabor filter and ORB feature descriptor. Then these features are merged and formed the final feature vector. By combining these efficient methods, in addition to achieving a very high accuracy in hand gestures classifying, the proposed structure becomes more resistant to uncertainties such as rotation and ambiguity in the hand gestures. Another prominent feature of the proposed structure is its comprehensiveness on different image databases, compared to the similar methods. The transfer learning technique demonstrates that the proposed structure has the ability to be used as a pre-trained structure for any type of image database. Finally, the proposed structure is applied to the three different databases of Massey, ASL Alphabet and ASL, which have 2520, 87,000 and 23,400 of hand gesture images, respectively. The results show the mean accuracy of the proposed structure for the Massey test set of 758 images, ASL with 7020 test images, and ASL Alphabet with 26,100 test images, at 99.92%, 99.8%, and 99.80% respectively. [ABSTRACT FROM AUTHOR]

• A deep convolutional neural network model based on dynamic time warping was proposed for the fault diagnosis. • Supersaturation control based on temperature and flow was applied. • Dynamic time warping makes the data from semi-batch process steady. • Compared to the traditional model, the new model has an outstanding performance in the fault diagnosis. • An average fault diagnosis rate of 88.6% is achieved. The abnormal conditions of the crystallization process seriously affect the crystal quality and the smooth operation of the process. Compared to the continuous steady process, it is a big challenge to realize the fault detection and diagnosis (FDD) in a batch or semi-batch crystallization process which is unsteady and nonlinear. In this paper, a coupled method combining convolutional neural network (CNN) with dynamic time warping (DTW) is proposed for FDD in semi-batch crystallization process based on temperature and flow supersaturation control (TF-SSC). DTW solves the problem that the data is unsteady in a semi-batch process. Different fault data produced by introducing disturbances are calculated through DTW to obtain the similarity which is steady. Then, the similarity of different operating states is preprocessed and classified by the CNN. Compared to the traditional CNN, Resnet18 and Inception10, DTW-CNN method has an outstanding performance in FDD, especially under a small number of samples. [ABSTRACT FROM AUTHOR]

To combat the recent coronavirus disease 2019 (COVID-19), academician and clinician are in search of new approaches to predict the COVID-19 outbreak dynamic trends that may slow down or stop the pandemic. Epidemiological models like Susceptible–Infected–Recovered (SIR) and its variants are helpful to understand the dynamics trend of pandemic that may be used in decision making to optimize possible controls from the infectious disease. But these epidemiological models based on mathematical assumptions may not predict the real pandemic situation. Recently the new machine learning approaches are being used to understand the dynamic trend of COVID-19 spread. In this paper, we designed the recurrent and convolutional neural network models: vanilla LSTM, stacked LSTM, ED_LSTM, BiLSTM, CNN, and hybrid CNN+LSTM model to capture the complex trend of COVID-19 outbreak and perform the forecasting of COVID-19 daily confirmed cases of 7, 14, 21 days for India and its four most affected states (Maharashtra, Kerala, Karnataka, and Tamil Nadu). The root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metric are computed on the testing data to demonstrate the relative performance of these models. The results show that the stacked LSTM and hybrid CNN+LSTM models perform best relative to other models. • Various variants of LSTM and CNN deep learning architectures have been designed. • Predicted COVID-19 confirmed cases for India and its four most affected states. • RMSE and MAPE are computed to compare the performance of the designed models. • Experimental results show that predicted cases are very close to actual cases. [ABSTRACT FROM AUTHOR]

Biometric Systems are automatic methods of verifying the identity of a person based on some characteristics such as fingerprint, face, speech etc. Speech biometrics involves verifying the identity of a person based on voice. The process is called speaker verification, and such biometric systems are called Automatic Speaker Verification systems. However, these systems are prone to malicious attacks such as replay attacks. Thus, Automatic Speaker Verification systems need to be robust against these attacks. Existing approaches have worked majorly with single-replay attacks. The proposed work in this paper explores three back-end models: Convolutional Neural Network, Long Short-Term Memory and hybrid of these two models, with different input feature formats. It analyses their performance in a diverse multi-replay attack detection scenario, using the Voice Spoofing Detection Corpus. In the frontend of the proposed hybrid system, mel -spectrograms is used as the feature extraction technique for Convolutional Neural Network based model, and Constant-Q Cepstral Coefficient approach is used for extracting features for Long Short-Term Memory based model. A comparison of this hybrid model's performance is done with past approaches in single-replay attack detection as well. The average Equal Error Rate achieved on the test set was 0.036 for single-replayed attack and 0.0296 for two times replayed. The hybrid model consistently outperforms other models with lower Equal Error Rate values, thus showing promising results, paving the way for future research along this line. Further, an analysis on unseen noisy audio files suggests the proposed model's utility in noise-robust systems as well. [ABSTRACT FROM AUTHOR]

• Construct deep learning models for multi energy on multi simulated building type. • Long short-term memory performs the best in terms of absolute and relative errors. • Long short-term memory model predicts 50.7% of the task with CVRMSE lower than 20% • Convolutional neural network model is sensitive to the network layer number. • Two-layer structure with Huber loss is suitable for long short-term memory model. Accurate prediction of building energy consumption plays a critical role in energy sustainability. Most research focuses on single task prediction on one type of building, which makes model less robust when applied to multi energy vectors prediction on multiple building types. Meanwhile, multi vectors prediction is beneficial for detailed and precise management of energy supply in different buildings. This paper leveraged deep learning models to predict hourly-based multi energy vectors on multiple building types. The deep learning model is trained and tested on a large scale of datasets simulated by EnergyPlus over 16 DOE building archetypes in 936 cities of the US. Convolutional neural network and long short-term memory models were constructed and compared in terms of accuracy and computation efficiency under different hyperparameter designs, including layers, input steps, and loss functions. Artificial neural network is the best none-deep learning model and chosen as the baseline for deep learning models. The results also showed that long short-term memory performs the best on this multi vectors prediction for both absolute and relative errors. It predicts 50.7% of the tasks from different building types with CVRMSE lower than 20%. A two-layer structure is good enough for the long short-term memory model, and it is more stable under conditions of different input steps and layer structures. The features reduction through principal component analysis shows a more comprehensive and ample embedding method should be explored. Further research should be carried out to deal with the inconsistent performance of gas-related energy prediction. [ABSTRACT FROM AUTHOR]

Unplanned outage due to faults in a high-fidelity turbomachine such as steam turbine and centrifugal compressor often results in the reduced reliability and productivity of a factory while increasing its maintenance costs. Shaft orbit images generated from turbomachine vibration signals have been used to diagnose component faults. However, the existing methods were developed mostly by either using features extracted from orbits or utilizing simulation data which may produce inaccurate results in practical applications due to system complexity and data uncertainties. This paper presents a novel deep learning convolution neural network methodology for accurately automatic diagnostics of multiple faults in general rotating machines by adeptly integrating advanced signal processing with orbit images augmentation, considering the high non-linearity and uncertainty of sensed vibration signals. Environmental noise in vibration signals are filtered through the integration of multiresolution discrete wavelet packet transform and Bayesian hypothesis testing-based automatic thresholding. Shaft orbit images generated from the cleansed vibration data are augmented to increase their representativity and generalization. A novel multi-layer convolutional neural network model, OrbitNet, is specially designed to improve its generality and robustness while avoid possible overfitting in fault identification of various turbomachines. The proposed model retains the pattern information in the axis trajectory to the greatest extent, with the ability of accurately capturing features of various faults in different turbomachines. A generic implementation procedure is proposed for automatic fault diagnosis of rotating machinery based on the presented methodology. A comparison study is conducted to demonstrate the effectiveness and feasibility of the proposed methodology by using the sensed vibration signals collected from three real-world centrifugal compressors, two steam turbines and one generator with four different fault modes including imbalance, friction, misalignment and oil whirl. [Display omitted] • Orbit image-based CNN model for automatic fault diagnostics of turbomachines. • Bayesian wavelets signal processing with sample entropy and image augmentation. • Generalized procedure to implement new methodology for multiple fault diagnostics. • A comparison study on sensor data and different faults from various turbomachines. [ABSTRACT FROM AUTHOR]

• Using a CNN model to classify investor sentiments from a major Chinese stock forum. • Using a LTSM model to build a stock price prediction model. • We showed that the hybrid model with sentiments outperforms the baseline model. • The results support that investor sentiment is a driver of stock prices. Whether stock prices are predictable has been the center of debate in academia. In this paper, we propose a hybrid model that combines a deep learning approach with a sentiment analysis model for stock price prediction. We employ a Convolutional Neural Network model for classifying the investors' hidden sentiments, which are extracted from a major stock forum. We then propose a hybrid research model by applying the Long Short-Term Memory (LSTM) Neural Network approach for analyzing the technical indicators from the stock market and the sentiment analysis results from the first step. Furthermore, this work has conducted real-life experiments from six key industries of three time intervals on the Shanghai Stock Exchange (SSE) to validate the effectiveness and applicability of the proposed model. The experiment results indicate that the proposed model has achieved better performance in classifying investor sentiments than the baseline classifiers, and this hybrid approach performs better in predicting stock prices compared to the single model and the models without sentiment analysis. [ABSTRACT FROM AUTHOR]