scholarly journals Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 376 ◽  
Author(s):  
Md. Shahinur Alam ◽  
Ki-Chul Kwon ◽  
Md. Ashraful Alam ◽  
Mohammed Y. Abbass ◽  
Shariar Md Imtiaz ◽  
...  
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Recognition System ◽  
Writing Systems ◽  
Depth Sensor ◽  
Handheld Device ◽  
Proposed Model ◽  
Long Short Term Memory

Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.

Sentence similarity evaluation using Sent2Vec and siamese neural network with parallel structure

2021 ◽  
pp. 1-10
Author(s):  
Hye-Jeong Song ◽  
Tak-Sung Heo ◽  
Jong-Dae Kim ◽  
Chan-Young Park ◽  
Yu-Seop Kim
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Parallel Structure ◽  
Short Term ◽  
Similarity Estimation ◽  
Accurate Judgment ◽  
Proposed Model ◽  
Sentence Similarity ◽  
Long Short Term Memory

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

scholarly journals Bimodal Emotion Recognition Model for Minnan Songs

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 145 ◽  
Author(s):  
Zhenglong Xiang ◽  
Xialei Dong ◽  
Yuanxiang Li ◽  
Fei Yu ◽  
Xing Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Music Appreciation ◽  
Research Papers ◽  
Audio Features ◽  
Analysis Theory ◽  
Proposed Model ◽  
Song Lyrics ◽  
Long Short Term Memory

Most of the existing research papers study the emotion recognition of Minnan songs from the perspectives of music analysis theory and music appreciation. However, these investigations do not explore any possibility of carrying out an automatic emotion recognition of Minnan songs. In this paper, we propose a model that consists of four main modules to classify the emotion of Minnan songs by using the bimodal data—song lyrics and audio. In the proposed model, an attention-based Long Short-Term Memory (LSTM) neural network is applied to extract lyrical features, and a Convolutional Neural Network (CNN) is used to extract the audio features from the spectrum. Then, two kinds of extracted features are concatenated by multimodal compact bilinear pooling, and finally, the concatenated features are input to the classifying module to determine the song emotion. We designed three experiment groups to investigate the classifying performance of combinations of the four main parts, the comparisons of proposed model with the current approaches and the influence of a few key parameters on the performance of emotion recognition. The results show that the proposed model exhibits better performance over all other experimental groups. The accuracy, precision and recall of the proposed model exceed 0.80 in a combination of appropriate parameters.

scholarly journals Audio-Textual Emotion Recognition Based on Improved Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Linqin Cai ◽  
Yaxin Hu ◽  
Jiangong Dong ◽  
Sitong Zhou
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Short Term ◽  
Term Memory ◽  
Emotional Recognition ◽  
Long Short Term Memory

With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was resorted to capture the textual features. Furthermore, we applied a deep neural network to learn and classify the fusion features. The final emotional state was determined by the output of both speech and text emotion analysis. Finally, the multimodal fusion experiments were carried out to validate the proposed model on the IEMOCAP database. In comparison with the single modal, the overall recognition accuracy of text increased 6.70%, and that of speech emotion recognition soared 13.85%. Experimental results show that the recognition accuracy of our multimodal is higher than that of the single modal and outperforms other published multimodal models on the test datasets.

scholarly journals Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries

Batteries ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 66
Author(s):  
Tadele Mamo ◽  
Fu-Kwun Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Capacity Degradation ◽  
Long Short Term Memory

Monitoring cycle life can provide a prediction of the remaining battery life. To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. The hyper-parameters of the proposed model are also optimized by a differential evolution algorithm. Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. In addition, the proposed model can achieve higher prediction accuracy of battery end of life.

scholarly journals COMPARISON OF OPTIMIZATION ALGORITHMS OF CONNECTIONIST TEMPORAL CLASSIFIER FOR SPEECH RECOGNITION SYSTEM

2019 ◽  
Vol 9 (3) ◽  
pp. 54-57
Author(s):  
Yedilkhan Amirgaliyev ◽  
Kuanyshbay Kuanyshbay ◽  
Aisultan Shoiynbek
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Short Term Memory ◽  
Optimization Algorithms ◽  
Recognition System ◽  
Model Data ◽  
Short Term ◽  
Term Memory ◽  
The Neural Network ◽  
Long Short Term Memory

This paper evaluates and compares the performances of three well-known optimization algorithms (Adagrad, Adam, Momentum) for faster training the neural network of CTC algorithm for speech recognition. For CTC algorithms recurrent neural network has been used, specifically Long-Short-Term memory. LSTM is effective and often used model. Data has been downloaded from VCTK corpus of Edinburgh University. The results of optimization algorithms have been evaluated by the Label error rate and CTC loss.

scholarly journals Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

2021 ◽  
Vol 14 (1) ◽  
pp. 166
Author(s):  
Xuan Zhang ◽  
Chun Zhu ◽  
Manchao He ◽  
Menglong Dong ◽  
Guangcheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Discrete Element ◽  
Slope Instability ◽  
Monitoring Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters.

scholarly journals A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3493 ◽  
Author(s):  
Chujie Tian ◽  
Jian Ma ◽  
Chunhong Zhang ◽  
Panpan Zhan
Keyword(s):  
Neural Network ◽  
Real World ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Electrical Load ◽  
Short Term ◽  
Term Memory ◽  
Load Forecast ◽  
Proposed Model ◽  
Long Short Term Memory

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

scholarly journals Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 718 ◽  
Author(s):  
Park ◽  
Kim ◽  
Lee ◽  
Kim ◽  
Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Short Term Memory ◽  
Weather Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

scholarly journals Personalized Predictive Models for Identifying Clinical Deterioration Using LSTM in Emergency Departments

2020 ◽  
Author(s):  
Amin Naemi ◽  
Thomas Schmidt ◽  
Marjan Mansourvar ◽  
Uffe Kock Wiil
Keyword(s):  
Neural Network ◽  
Predictive Models ◽  
Short Term Memory ◽  
Vital Signs ◽  
Illness Severity ◽  
Mortality And Morbidity ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Window Approach ◽  
Short Time

Early detection of deterioration at hospitals could be beneficial in terms of reducing mortality and morbidity rates and costs. In this paper, we present a model based on Long Short-Term Memory (LSTM) neural network used in deep learning to predict the illness severity of patients in advance. Hence, by predicting health severity, this model can be used to identify deteriorating patients. Our proposed model utilizes continuous monitored vital signs, including heart rate, respiratory rate, oxygen saturation, and blood pressure automatically collected from patients during hospitalization. In this study, a short-time prediction using a sliding window approach is applied. The performance of the proposed model was compared with the Multi-Layer Perceptron (MLP) neural network, a feedforward class of neural network, based on R2 score and Root Mean Square Error (RMSE) metrics. The results showed that the LSTM has a better performance and could predict the illness severity of patients more accurately.

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