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Wei, Chih-Chiang, and Ju-Yueh Cheng. "Nearshore two-step typhoon wind-wave prediction using deep recurrent neural networks." Journal of Hydroinformatics 22, no. 2 (October 24, 2019): 346–67. http://dx.doi.org/10.2166/hydro.2019.084.
Повний текст джерелаАнотація:
Abstract Because Taiwan is located within the subtropical high and on the primary path of western Pacific typhoons, the interaction of these two factors easily causes extreme climate conditions, with strong wind carrying heavy rain and huge wind waves. To obtain precise wind-wave data for weather forecasting and thus minimize the threat posed by wind waves, this study proposes a two-step wind-wave prediction (TSWP) model to predict wind speed and wave height. The TSWP model is further divided into TSWP1, which uses data attributes at the current moment as input values and TSWP2, which uses observations from a lead time and predicts data attributes from input data. The classical one-step wave height prediction (OSWP) approach, which directly predicts wave height, was used as a benchmark to test TSWP. Deep recurrent neural networks (DRNNs) can be used to construct TSWP and OSWP approach-based models in wave height predictions. To compare with the accuracy achieved using DRNNs, linear regression, multilayer perceptron (MLP) networks, and deep neural networks (DNNs) were tested as benchmarks. The Guishandao Buoy Station located off the northeastern shore of Taiwan was used for a case study. The results were as follows: (1) compared with the shallower MLP network, the DNN and DRNN demonstrated a lower prediction error. (2) Compared with OSWP, TSWP1 and TSWP2 provided more accurate results. Therefore, the TSWP approach using a DRNN algorithm can effectively predict wind-wave heights.
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Sharma, Sameer Dev, Sonal Sharma, Rajesh Singh, Anita Gehlot, Neeraj Priyadarshi, and Bhekisipho Twala. "Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals." Applied Sciences 12, no. 17 (August 30, 2022): 8678. http://dx.doi.org/10.3390/app12178678.
Повний текст джерелаАнотація:
Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals’ stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values.
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Ye, Kai-Qiang, Hong Gao, Ping Xiao, and Pei-Cheng Shi. "DRNN-based shift decision for automatic transmission." Advances in Mechanical Engineering 12, no. 11 (November 2020): 168781402097529. http://dx.doi.org/10.1177/1687814020975291.
Повний текст джерелаАнотація:
In research on intelligent shift for automatic transmission, the neural network selected has no feedback and lacks an associative memory function. Thus, its adaptability needs to be improved. To achieve this, an automatic shift strategy based on a deep recurrent neural network (DRNN) is proposed. First, a neural network framework was designed in combination with an eight-speed gearbox that matches a particular type of vehicle. Then, the working principle of the DRNN was applied to the shifting process of an automatic gearbox, and the implementation model of the shift logic was established in MATLAB/Stateflow. A data sample obtained from the model was used to train the DRNN. Training and evaluation of the DRNN were accomplished in Python. Finally, a simulation comparison of the DRNN with a back-propagation (BP) neural network proved that after the epochs have been increased, the DRNN has higher precision and adaptation than a BP neural network. This research provides a theoretical basis and technical support for intelligent control of automatic transmission.
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Fan, J., Q. Li, J. Hou, X. Feng, H. Karimian, and S. Lin. "A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 15–22. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-15-2017.
Повний текст джерелаАнотація:
Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.
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Sun, Xinyao, Anup Basu, and Irene Cheng. "Multi-Sensor Motion Fusion Using Deep Neural Network Learning." International Journal of Multimedia Data Engineering and Management 8, no. 4 (October 2017): 1–18. http://dx.doi.org/10.4018/ijmdem.2017100101.
Повний текст джерелаАнотація:
Hand pose estimation for a continuous sequence has been an important topic not only in computer vision but also human-computer-interaction. Exploring the feasibility to use hand gestures to replace input devices, e.g., mouse, keyboard, joy-stick and touch screen, has attracted increasing attention from academic and industrial researchers. The fast advancement of hand pose estimation techniques is complemented by the rapid development of smart sensors technology such as Kinect and Leap. We introduce a hand pose estimation multi-sensor system. Two tracking models are proposed based on Deep (Recurrent) Neural Network (DRNN) architecture. Data captured from different sensors are analyzed and fused to produce an optimal hand pose sequence. Experimental results show that our models outperform previous methods with better accuracy, meeting real-time application requirement. Performance comparisons between DNN and DRNN, spatial and spatial-temporal features, and single- and dual- sensors, are also presented.
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Popoola, Segun I., Bamidele Adebisi, Ruth Ande, Mohammad Hammoudeh, Kelvin Anoh, and Aderemi A. Atayero. "SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks." Sensors 21, no. 9 (April 24, 2021): 2985. http://dx.doi.org/10.3390/s21092985.
Повний текст джерелаАнотація:
Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.
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Kim, Beom-Hun, and Jae-Young Pyun. "ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks." Sensors 20, no. 11 (May 29, 2020): 3069. http://dx.doi.org/10.3390/s20113069.
Повний текст джерелаАнотація:
Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.
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Sharma, Sameer Dev, Sonal Sharma, Rajesh Singh, Anita Gehlot, Neeraj Priyadarshi, and Bhekisipho Twala. "Stress Detection System for Working Pregnant Women Using an Improved Deep Recurrent Neural Network." Electronics 11, no. 18 (September 9, 2022): 2862. http://dx.doi.org/10.3390/electronics11182862.
Повний текст джерелаАнотація:
Stress is a concerning issue in today’s world. Stress in pregnancy harms both the development of children and the health of pregnant women. As a result, assessing the stress levels of working pregnant women is crucial to aid them in developing and growing professionally and personally. In the past, many machine-learning (ML) and deep-learning (DL) algorithms have been made to predict the stress of women. It does, however, have some problems, such as a more complicated design, a high chance of misclassification, a high chance of making mistakes, and less efficiency. With these considerations in mind, our article will use a deep-learning model known as the deep recurrent neural network (DRNN) to predict the stress levels of working pregnant women. Dataset preparation, feature extraction, optimal feature selection, and classification with DRNNs are all included in this framework. Duplicate attributes are removed, and missing values are filled in during the preprocessing of the dataset.
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Wei, Chih-Chiang. "Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions." Advances in Meteorology 2020 (July 23, 2020): 1–18. http://dx.doi.org/10.1155/2020/5462040.
Повний текст джерелаАнотація:
Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property. For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models. Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM). These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity. A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis. The study area is northeastern Taiwan. Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours). Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity. A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN. Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%. These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy.
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Anezi, Faisal Yousif Al. "Arabic Hate Speech Detection Using Deep Recurrent Neural Networks." Applied Sciences 12, no. 12 (June 13, 2022): 6010. http://dx.doi.org/10.3390/app12126010.
Повний текст джерелаАнотація:
With the vast number of comments posted daily on social media and other platforms, manually monitoring internet activity for possible national security risks or cyberbullying is an impossible task. However, with recent advances in machine learning (ML), the automatic monitoring of such posts for possible national security risks and cyberbullying becomes feasible. There is still the issue of privacy on the internet; however, in this study, only the technical aspects of designing an automated system that could monitor and detect hate speech in the Arabic language were targeted, which many companies, such as Facebook, Twitter, and others, could use to prevent hate speech and cyberbullying. For this task, a unique dataset consisting of 4203 comments classified into seven categories, including content against religion, racist content, content against gender equality, violent content, offensive content, insulting/bullying content, normal positive comments, and normal negative comments, was designed. The dataset was extensively preprocessed and labeled, and its features were extracted. In addition, the use of deep recurrent neural networks (RNNs) was proposed for the classification and detection of hate speech. The proposed RNN architecture, called DRNN-2, consisted of 10 layers with 32 batch sizes and 50 iterations for the classification task. Another model consisting of five hidden layers, called DRNN-1, was used only for binary classification. Using the proposed models, a recognition rate of 99.73% was achieved for binary classification, 95.38% for the three classes of Arabic comments, and 84.14% for the seven classes of Arabic comments. This accuracy was high for the classification of a complex language, such as Arabic, into seven different classes. The achieved accuracy was higher than that of similar methods reported in the recent literature, whether for binary classification, three-class classification, or seven-class classification, as discussed in the literature review section.
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Wang, Jinghua, Jin Cheng, Fang Liu, Lei Yan, and Taijie Tang. "Research on the air quality prediction model of Wuhai mining area based on deep learning." E3S Web of Conferences 300 (2021): 02005. http://dx.doi.org/10.1051/e3sconf/202130002005.
Повний текст джерелаАнотація:
With the large-scale and high-intensity mining of coal resources in the Wuhai mining area, the destruction of soil and erosion of rocks has intensified, causing a large amount of surface soil spalling from the mine body and serious damage to the surface vegetation, which has had a serious impact on the quality of the environment in and around the mine. This paper focuses on the corresponding early warning research on air quality in the mining area of Wuhai, and constructs Deep Recurrent Neural Network (DRNN) and Deep Long Short Time Memory Neural Network (DLSTM) air quality prediction models based on the filtered weather factors. The simulation results are also compared and find that the prediction results of DLSTM are better than those of DRNN, with a prediction accuracy of 92.85%. The model is able to accurately predict the values and trends of various air pollutant concentrations in the mining area of Wuhai.
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Puchkov, Andrey Yu, Maksim I. Dli, and Ekaterina I. Lobaneva. "NEURO-FUZZY CLASSIFIER OF STATE OF THE TECHNOLOGICAL PROCESS." Bulletin of the Saint Petersburg State Institute of Technology (Technical University) 57 (2021): 105–10. http://dx.doi.org/10.36807/1998-9849-2020-57-83-105-110.
Повний текст джерелаАнотація:
The algorithmic structure of the classifier of the state of the technological process is proposed, which provides processing of data coming through several channels of information support of the cyberphysical system. The structure contains an ensemble of deep recurrent neural networks and an output hybrid neural network. The ensemble solves the regression problems for information channels, and the output network serves for their generalization and classification based on fuzzy logic methods. The proposed hybrid architecture makes it possible to take advantage of two methodologies for constructing neural networks – to perform a retrospective analysis of time series using the DRNN ensemble and to generalize the results of their work by the ANFIS system. The structure of the software developed for simulation experiments is described and their results are presented.
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Lee, Geon Woo, and Hong Kook Kim. "Multi-Task Learning U-Net for Single-Channel Speech Enhancement and Mask-Based Voice Activity Detection." Applied Sciences 10, no. 9 (May 6, 2020): 3230. http://dx.doi.org/10.3390/app10093230.
Повний текст джерелаАнотація:
In this paper, a multi-task learning U-shaped neural network (MTU-Net) is proposed and applied to single-channel speech enhancement (SE). The proposed MTU-based SE method estimates an ideal binary mask (IBM) or an ideal ratio mask (IRM) by extending the decoding network of a conventional U-Net to simultaneously model the speech and noise spectra as the target. The effectiveness of the proposed SE method was evaluated under both matched and mismatched noise conditions between training and testing by measuring the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). Consequently, the proposed SE method with IRM achieved a substantial improvement with higher average PESQ scores by 0.17, 0.52, and 0.40 than other state-of-the-art deep-learning-based methods, such as the deep recurrent neural network (DRNN), SE generative adversarial network (SEGAN), and conventional U-Net, respectively. In addition, the STOI scores of the proposed SE method are 0.07, 0.05, and 0.05 higher than those of the DRNN, SEGAN, and U-Net, respectively. Next, voice activity detection (VAD) is also proposed by using the IRM estimated by the proposed MTU-Net-based SE method, which is fundamentally an unsupervised method without any model training. Then, the performance of the proposed VAD method was compared with the performance of supervised learning-based methods using a deep neural network (DNN), a boosted DNN, and a long short-term memory (LSTM) network. Consequently, the proposed VAD methods show a slightly better performance than the three neural network-based methods under mismatched noise conditions.
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AYAYDIN, Anıl, and M. Ali AKCAYOL. "Derin Öğrenme Tabanlı Havacılık Uçuş Verilerinde Gecikme Durumunun Tahmin Edilmesi." Bilişim Teknolojileri Dergisi 15, no. 3 (July 31, 2022): 239–49. http://dx.doi.org/10.17671/gazibtd.1060646.
Повний текст джерелаАнотація:
In this study, three different methods from machine learning and deep learning have been implemented for preventing financial and moral losses that may occur as a result of delays in flights and to take necessary precautions by predicting the flight delay in advance, which are a serious problem in the aviation industry. Deep recurrent neural network (DRNN), long-short term memory (LSTM), and random forest (RF) have been extensively tested and compared employing a real data set covering 368 airports across the world with relevancy the success rate of forecasting of delay on flights. The experimental results showed that the LSTM model had a higher success rate of 96.50% at the recall level than the others.
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Yaprakdal, Fatma, M. Berkay Yılmaz, Mustafa Baysal, and Amjad Anvari-Moghaddam. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid." Sustainability 12, no. 4 (February 22, 2020): 1653. http://dx.doi.org/10.3390/su12041653.
Повний текст джерелаАнотація:
The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.
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Yang, Bin, Wei Zhang, and Haifeng Wang. "Stock Market Forecasting Using Restricted Gene Expression Programming." Computational Intelligence and Neuroscience 2019 (February 5, 2019): 1–14. http://dx.doi.org/10.1155/2019/7198962.
Повний текст джерелаАнотація:
Stock index prediction is considered as a difficult task in the past decade. In order to predict stock index accurately, this paper proposes a novel prediction method based on S-system model. Restricted gene expression programming (RGEP) is proposed to encode and optimize the structure of the S-system. A hybrid intelligent algorithm based on brain storm optimization (BSO) and particle swarm optimization (PSO) is proposed to optimize the parameters of the S-system model. Five real stock market prices such as Dow Jones Index, Hang Seng Index, NASDAQ Index, Shanghai Stock Exchange Composite Index, and SZSE Component Index are collected to validate the performance of our proposed method. Experiment results reveal that our method could perform better than deep recurrent neural network (DRNN), flexible neural tree (FNT), radial basis function (RBF), backpropagation (BP) neural network, and ARIMA for 1-week-ahead and 1-month-ahead stock prediction problems. And our proposed hybrid intelligent algorithm has faster convergence than PSO and BSO.
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Deva Hema, D., J. Tharun, G. Arun Dev, and N. Sateesh. "A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3421–26. http://dx.doi.org/10.1166/jctn.2020.9198.
Повний текст джерелаАнотація:
Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.
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Kim, Hyunsoo. "Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait." Applied Sciences 12, no. 9 (April 26, 2022): 4384. http://dx.doi.org/10.3390/app12094384.
Повний текст джерелаАнотація:
Identifying built environment barriers to walkability is the first step toward monitoring and improving our walking environment. Although conventional approaches (i.e., surveys by experts or pedestrians, walking interviews, etc.) to identify built environment barriers have contributed to improving the walking environment, these approaches may require time and effort. To address the limitations of conventional approaches, wearable sensing technologies and data analysis techniques have recently been adopted in the investigation of the built environment. Among various wearable sensors, an inertial measurement unit (IMU) can continuously capture gait-related data, which can be used to identify built environment barriers to walkability. To propose a more efficient method, the author adopts a cascaded bidirectional and unidirectional long short-term memory (LSTM)-based deep recurrent neural network (DRNN) model for classifying human gait activities (normal and abnormal walking) according to walking environmental conditions (i.e., normal and abnormal conditions). This study uses 101,607 gait data collected from the author’s previous study for training and testing a DRNN model. In addition, 31,142 gait data (20 participants) have been newly collected to validate whether the DRNN model is feasible for newly added gait data. The gait activity classification results show that the proposed method can classify normal gaits and abnormal gaits with an accuracy of about 95%. The results also indicate that the proposed method can be used to monitor environmental barriers and improve the walking environment.
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Kim, Hyunsoo. "Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait." Applied Sciences 12, no. 9 (April 26, 2022): 4384. http://dx.doi.org/10.3390/app12094384.
Повний текст джерелаАнотація:
Identifying built environment barriers to walkability is the first step toward monitoring and improving our walking environment. Although conventional approaches (i.e., surveys by experts or pedestrians, walking interviews, etc.) to identify built environment barriers have contributed to improving the walking environment, these approaches may require time and effort. To address the limitations of conventional approaches, wearable sensing technologies and data analysis techniques have recently been adopted in the investigation of the built environment. Among various wearable sensors, an inertial measurement unit (IMU) can continuously capture gait-related data, which can be used to identify built environment barriers to walkability. To propose a more efficient method, the author adopts a cascaded bidirectional and unidirectional long short-term memory (LSTM)-based deep recurrent neural network (DRNN) model for classifying human gait activities (normal and abnormal walking) according to walking environmental conditions (i.e., normal and abnormal conditions). This study uses 101,607 gait data collected from the author’s previous study for training and testing a DRNN model. In addition, 31,142 gait data (20 participants) have been newly collected to validate whether the DRNN model is feasible for newly added gait data. The gait activity classification results show that the proposed method can classify normal gaits and abnormal gaits with an accuracy of about 95%. The results also indicate that the proposed method can be used to monitor environmental barriers and improve the walking environment.
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Gunasekaran, K., R. Pitchai, Gogineni Krishna Chaitanya, D. Selvaraj, S. Annie Sheryl, Hesham S. Almoallim, Sulaiman Ali Alharbi, S. S. Raghavan, and Belachew Girma Tesemma. "A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs." BioMed Research International 2022 (June 7, 2022): 1–15. http://dx.doi.org/10.1155/2022/3163496.
Повний текст джерелаАнотація:
Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye’s blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe.
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Yu, Danning, Kun Ni, and Yunlong Liu. "Deep Q-Network with Predictive State Models in Partially Observable Domains." Mathematical Problems in Engineering 2020 (July 16, 2020): 1–9. http://dx.doi.org/10.1155/2020/1596385.
Повний текст джерелаАнотація:
While deep reinforcement learning (DRL) has achieved great success in some large domains, most of the related algorithms assume that the state of the underlying system is fully observable. However, many real-world problems are actually partially observable. For systems with continuous observation, most of the related algorithms, e.g., the deep Q-network (DQN) and deep recurrent Q-network (DRQN), use history observations to represent states; however, they often make computation-expensive and ignore the information of actions. Predictive state representations (PSRs) can offer a powerful framework for modelling partially observable dynamical systems with discrete or continuous state space, which represents the latent state using completely observable actions and observations. In this paper, we present a PSR model-based DQN approach which combines the strengths of the PSR model and DQN planning. We use a recurrent network to establish the recurrent PSR model, which can fully learn dynamics of the partially continuous observable environment. Then, the model is used for the state representation and update of DQN, which makes DQN no longer rely on a fixed number of history observations or recurrent neural network (RNN) to represent states in the case of partially observable environments. The strong performance of the proposed approach is demonstrated on a set of robotic control tasks from OpenAI Gym by comparing with the technique with the memory-based DRQN and the state-of-the-art recurrent predictive state policy (RPSP) networks. Source code is available at https://github.com/RPSR-DQN/paper-code.git.
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Liu, FeiPeng, and Wei Zhang. "Basketball Motion Posture Recognition Based on Recurrent Deep Learning Model." Mathematical Problems in Engineering 2022 (May 16, 2022): 1–7. http://dx.doi.org/10.1155/2022/8314777.
Повний текст джерелаАнотація:
In order to improve the training effect of athletes and effectively identify the movement posture of basketball players, we propose a basketball motion posture recognition method based on recurrent deep learning. A one-dimensional convolution layer is added to the neural network structure of the deep recurrent Q network (DRQN) to extract the athlete pose feature data before the long short-term memory (LSTM) layer. The acceleration and angular velocity data of athletes are collected by inertial sensors, and the multi-dimensional motion posture features are extracted from the time domain and frequency domain, respectively, and the posture recognition of basketball is realized by DRQN. Finally, the new reinforcement learning algorithm is trained and tested in a time-series-related environment. The experimental results show that the method can effectively recognize the basketball motion posture, and the average accuracy of posture recognition reaches 99.3%.
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Yan, Jianzhuo, Jiaxue Liu, Yongchuan Yu, and Hongxia Xu. "Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model." Water 13, no. 9 (April 30, 2021): 1273. http://dx.doi.org/10.3390/w13091273.
Повний текст джерелаАнотація:
The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.
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Hu, Huangshui, Tingting Wang, Hongzhi Wang, and Chuhang Wang. "Q-learning optimized diagonal recurrent neural network control strategy for brushless direct current motors." Advances in Mechanical Engineering 12, no. 9 (September 2020): 168781402095822. http://dx.doi.org/10.1177/1687814020958221.
Повний текст джерелаАнотація:
In order to improve the working stability of brushless direct current motors (BLDCM), a diagonal recursive neural network (DRNN) control strategy based on Q-learning algorithm is proposed in this paper which is called as Q-DRNN. In Q-DRNN, DRNN iterates over the output variables through a unique recursive loop in the hidden layer, and its key weight is optimized to speed up the iteration. Moreover, an improved Q-learning algorithm is introduced to modify the weight momentum factor of DRNN, which makes DRNN have the ability of learning and online correction so as to make the BLDCM achieve better control effect. In MATLAB/Simulink environment, Q-DRNN is tested and compared with other popular control methods in terms of speed and torque response under different operating conditions, and the results show that Q-DRNN has better adaptive and anti-interference ability as well as stronger robustness.
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CUI, YUDING, and CAIHUA XIONG. "DYNAMIC RECURRENT NEURAL NETWORK BASED CLASSIFICATION SCHEME FOR MYOELECTRIC CONTROL OF UPPER LIMB REHABILITATION ROBOT." Journal of Mechanics in Medicine and Biology 14, no. 06 (December 2014): 1440017. http://dx.doi.org/10.1142/s021951941440017x.
Повний текст джерелаАнотація:
This paper proposes and evaluates the application of a modular dynamic recurrent neural network (DRNN) to classify upper limb motion using myoelectric signals. The DRNN algorithmic issues, including the structure selection, the segmentation of the data and various feature sets such as time-domain features and frequency features, were evaluated experimentally in order to actualize the optimization and configuration of this classification scheme. This was achieved by using a majority vote technique to post-process the output decision stream. The DRNN-based approach was then been compared with two commonly used classification methods: multilayer perceptron (MLP) neural network and linear discriminant analysis (LDA). The DRNN-based motion classification system demonstrated exceptional accuracy and a low computational load for the classification of robust limb motion. The DRNN may also display utility for online training and controlling rehabilitation robots.
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Lv, Ye, Jing Ma, De Cun He, and Xiang Gao. "Diagonal Recurrent Neural Network-Based Electro-Hydraulic Servo System Control." Applied Mechanics and Materials 336-338 (July 2013): 581–84. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.581.
Повний текст джерелаАнотація:
The electro-hydraulic servo system gradually processes toward the fast, high-power and high-precision direction. The traditional PID control needs to coordinate the contradiction between rapidity and stability, and cannot meet the system performance requirements in the case of parameter variations and external interference. Based on electro-hydraulic servo system structure and principles, system mathematical model was established, and Diagonal Recurrent Neural Network (DRNN)-based adaptive PID controller was designed and compared with positional PID control. The simulation results show that: DRNN adaptive PID control effect is superior to positional PID control, which can effectively improve the system dynamic and anti-interference performance, and has strong self-learning and adaptive capacity.
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Elkenawy, Ahmed, Ahmad M. El-Nagar, Mohammad El-Bardini, and Nabila M. El-Rabaie. "Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems." Transactions of the Institute of Measurement and Control 42, no. 15 (June 16, 2020): 2833–56. http://dx.doi.org/10.1177/0142331220921259.
Повний текст джерелаАнотація:
This paper proposes an observer-based adaptive control for unknown nonlinear systems using an adaptive dynamic programming (ADP) algorithm. First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.
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Xiao, Nianhao, Yuanchen Zou, Yaguang Yin, Peishun Liu, and Ruichun Tang. "DRNN: Deep Residual Neural Network for Heart Disease Prediction." Journal of Physics: Conference Series 1682 (November 2020): 012065. http://dx.doi.org/10.1088/1742-6596/1682/1/012065.
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Fan, Ying Ping, and Hui Da Duan. "Oil-Filled Power Transformers Fault Diagnosis Based on Fuzzy-DRNN." Applied Mechanics and Materials 448-453 (October 2013): 2520–23. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2520.
Повний текст джерелаАнотація:
In recent years, improved three-ratio is an effective method for transformer fault diagnosis based on Dissolved Gas Analysis (DGA). In this paper, a simple dynamic neural network named as diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. Because of the characteristic of improved three-ratio boundary is lack of matching, fuzzy logic in fault diagnosis is presented also to deal with the data of the neural network inputs. DRNN is used to model the fault diagnosis structure, the fuzzy logic is used to improve the faults diagnose reliability. In addition, some cases are used to show the capability of the suggested method in oil-filled power transformers fault diagnosis.
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AUSSEM, ALEX, FIONN MURTAGH, and MARC SARAZIN. "DYNAMICAL RECURRENT NEURAL NETWORKS — TOWARDS ENVIRONMENTAL TIME SERIES PREDICTION." International Journal of Neural Systems 06, no. 02 (June 1995): 145–70. http://dx.doi.org/10.1142/s0129065795000123.
Повний текст джерелаАнотація:
Dynamical Recurrent Neural Networks (DRNN) (Aussem 1995a) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. In this perspective, the model is first appraised on precipitation measurements with traditional nonlinear AR and ARMA techniques using feedforward networks. Then we tackle a complex problem, namely the prediction of astronomical seeing, known to be a very erratic time series. A fuzzy coding approach is used to reduce the complexity of the underlying laws governing the seeing. Then, a fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Based on a carefully selected set of meteorological variables at the same time-point, a nonlinear multiple regression, termed nowcasting (Murtagh et al. 1993, 1995), is carried out on the fuzzily coded seeing records. The DRNN is shown to outperform the fuzzy k-nearest neighbors method.
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Yon, Jung-Heum, Yong-Taek Kim, Jae-Yong Seo, and Hong-Tae Jeon. "Dynamic Multidimensional Wavelet Neural Network and Its Application." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (September 20, 2000): 336–40. http://dx.doi.org/10.20965/jaciii.2000.p0336.
Повний текст джерелаАнотація:
We propose an efficient neural network called a dynamic multidimensional wavelet neural network (DMWNN). Since the resulting network based on wavelet theory can provide the efficient representation of a nonlinear function and has the capability to keep some previous information for later use, it can perform effective dynamic mapping with lower input signal dimensions. These features of the DMWNN show one way to compensate for the weakness of the diagonal recurrent neural network (DRNN) and feed-forward wavelet neural network (FWNN). Effectiveness in application of the proposed neural network is also demonstrated through simulation results.
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Jafari, Amir Hossein, Rached Dhaouadi, and Ali Jhemi. "Nonlinear Friction Estimation in Elastic Drive Systems Using a Dynamic Neural Network-Based Observer." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (July 20, 2013): 637–46. http://dx.doi.org/10.20965/jaciii.2013.p0637.
Повний текст джерелаАнотація:
This paper presents a neural-network based observer for nonlinear elastic drive systems. The proposed nonlinear observer uses a Diagonal Recurrent Neural Network (DRNN) combined with the dynamics of a linear Two-Mass-Model (2MM) system to identify nonlinear characteristics of the drive system such as Coulomb and nonlinear viscous friction torques. Theoretical analysis of the proposed neural-network based observer, including the neural network structure and the training algorithm convergence, are presented and discussed. Simulation results are confirmed experimentally using a 2MM system setup.
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Shen, Dong Kai, Jing Jing Wang, and Zheng Hua Liu. "Robust BackStepping Control Based DRNN for Flight Simulator." Advanced Materials Research 139-141 (October 2010): 1708–13. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1708.
Повний текст джерелаАнотація:
Flight motion simulator is one kind of servo system with uncertainties and nonlinearities. To acquire higher frequency response and good robustness for the flight simulator, we present a Backstepping controller based on a Diagonal Recurrent Neural Network (DRNN) to work out this problem. For one thing, the design procedure of the robust Backstepping controller is described. Subsequently, the principle and the design steps of DRNN are analyzed and expatiated respectively. In the end, simulation results on the flight motion simulator show that robust backstepping control based on DRNN can compensate for external disturbances and enhance robustness of the system control performance. Therefore both robustness and high performance of the flight motion simulator are achieved.
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Oyewola, David Opeoluwa, Emmanuel Gbenga Dada, Sanjay Misra, and Robertas Damaševičius. "Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing." PeerJ Computer Science 7 (March 2, 2021): e352. http://dx.doi.org/10.7717/peerj-cs.352.
Повний текст джерелаАнотація:
For people in developing countries, cassava is a major source of calories and carbohydrates. However, Cassava Mosaic Disease (CMD) has become a major cause of concern among farmers in sub-Saharan Africa countries, which rely on cassava for both business and local consumption. The article proposes a novel deep residual convolution neural network (DRNN) for CMD detection in cassava leaf images. With the aid of distinct block processing, we can counterbalance the imbalanced image dataset of the cassava diseases and increase the number of images available for training and testing. Moreover, we adjust low contrast using Gamma correction and decorrelation stretching to enhance the color separation of an image with significant band-to-band correlation. Experimental results demonstrate that using a balanced dataset of images increases the accuracy of classification. The proposed DRNN model outperforms the plain convolutional neural network (PCNN) by a significant margin of 9.25% on the Cassava Disease Dataset from Kaggle.
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Duan, Hui Da, and Qiao Song Li. "Power Transformers Fault Diagnosis Based on DRNN." Advanced Materials Research 960-961 (June 2014): 700–703. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.700.
Повний текст джерелаАнотація:
In recent years, improved three-ratio is an effective method for transformer fault diagnosis based on Dissolved Gas Analysis (DGA). In this paper, diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. To overcome disadvantages of BP algorithm, a new recursive prediction error algorithm (RPE) is used in this paper.In addition, to demonstrate the effectiveness and veracity of the proposed method, some cases are used in the simulation. The simulation results are satisfactory.
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Xu, Zhi Cheng, Bin Zhu, and Qing Bin Jiang. "Application of Neural Network for Nonlinear Predictive Control." Advanced Materials Research 562-564 (August 2012): 1964–67. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1964.
Повний текст джерелаАнотація:
A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear process, which doesn’t need solve nonlinear optimization problem. Consequently, the computational efforts are reduced drastically. The simulation example shows that the proposed method is an effective control strategy with excellent tracing characteristics and strong robustness.
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Chen, Mengwei, and Guichen Zhang. "EKF-DRNN autopilot for VLCC heading hybrid control." Transactions of the Institute of Measurement and Control 43, no. 13 (June 15, 2021): 2983–99. http://dx.doi.org/10.1177/01423312211021750.
Повний текст джерелаАнотація:
An online Extended Kalman Filter (EKF)-Dynamic Recurrent Neural Network (DRNN) autopilot implementation strategy for Very Large Crude Carrier (VLCC) heading hybrid control with uncertain dynamics is designed in this paper. The autopilot scheme is based on a DRNN control model, which learns VLCC dynamic characteristics, while the VLCC heading control is estimated by the EKF to minimize squared course error. The online EKF-DRNN autopilot provides optimal control on the basis of fuel-saving evaluation criteria using the heading deviation and rudder angle. Therefore, the autopilot output is guaranteed to converge to the desired VLCC trajectory asymptotically. The proposed strategy is evaluated by applying it to VLCC Yuan Kun Yang from COSCO Shipping, and works excellently under different loads, speed and weather conditions. The VLCC heading hybrid controller is also assessed by ‘Z’ manoeuvring and turning test, and the superiority of the online EKF-DRNN autopilot is demonstrated. The remote online monitoring of Yuan Kun Yang’s main navigation data shows that it improved fuel-saving properties despite worsening weather conditions causing increased yawing.
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Jiang, Yu Lian, Jian Chang Liu, and Shu Bin Tan. "Application of Q Learning-Based Self-Tuning PID with DRNN in the Strip Flatness and Gauge System." Applied Mechanics and Materials 494-495 (February 2014): 1377–80. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1377.
Повний текст джерелаАнотація:
In view of the process of automatic flatness control and automatic gauge control that is a nonlinear system with multi-dimensions, multi-variables, strong coupling and time variation, a novel control method called self-tuning PID with diagonal recurrent neural network (DRNN-PID) based on Q learning is proposed. It is able to coordinate the coupling of flatness control and gauge control agents to get the satisfactory control requirements without decoupling directly and amend output control laws by DRNN-PID adaptively. Decomposition-coordination is utilized to establish a novel multi-agent system for coordination control including flatness agent, gauge agent and Q learning agent. Simulation result demonstrates the validity of our proposed method.
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Wei, Zhi Qiang, and Dan Jin. "Position Tracking System of Filling Machine Based on Compound Control Strategy." Applied Mechanics and Materials 380-384 (August 2013): 321–24. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.321.
Повний текст джерелаАнотація:
In view of the complexity and periodic motion of automatic filling machine, a novel compound control strategy based on single neuron PID model reference adaptive control and repetitive control is proposed. Diagonal recurrent neural network (DRNN) is used as on-line identifier of system for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting. The dynamic state performance can be improved by adaptive PID controller based on DRNN on-line Identification and the steady state performance is improved by modified repetitive controller. Simulation results show that the control system has good ability of restraining disturbances and high position tracking precision and good robustness.
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Razaque, Abdul, Bandar Alotaibi, Munif Alotaibi, Shujaat Hussain, Aziz Alotaibi, and Vladimir Jotsov. "Clickbait Detection Using Deep Recurrent Neural Network." Applied Sciences 12, no. 1 (January 5, 2022): 504. http://dx.doi.org/10.3390/app12010504.
Повний текст джерелаАнотація:
People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions.
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Song, Y. M., C. Zhang, and Y. Q. Yu. "Neural Networks Based Active Vibration Control of Flexible Linkage Mechanisms." Journal of Mechanical Design 123, no. 2 (May 1, 2000): 266–71. http://dx.doi.org/10.1115/1.1348269.
Повний текст джерелаАнотація:
An investigation is presented into the neural networks based active vibration control of flexible linkage mechanisms. A smart mechanism featuring piezoceramic actuators and strain gauge sensors is designed. A nonlinear adaptive control strategy named Neural Networks based Direct Self-Tuning Control (NNBDSC) is employed to suppress the elastodynamic responses of the smart mechanism. To improve the initial robustness of the NNBDSC, the Dynamic Recurrent Neural Network (DRNN) controllers are designed off-line to approximate the inverse dynamics of the smart mechanism. Through on-line control, the strain crest of the flexible link is reduced 60 percent or so and the dynamic performance of the smart mechanism is improved significantly.
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Thomas, Merin, and Latha C.A. "Sentimental analysis using recurrent neural network." International Journal of Engineering & Technology 7, no. 2.27 (August 2, 2018): 88. http://dx.doi.org/10.14419/ijet.v7i2.27.12635.
Повний текст джерелаАнотація:
Sentiment analysis has been an important topic of discussion from two decades since Lee published his first paper on the sentimental analysis in 2002. Apart from the sentimental analysis in English, it has spread its wing to other natural languages whose significance is very important in a multi linguistic country like India. The traditional approaches in machine learning have paved better accuracy for the Analysis. Deep Learning approaches have gained its momentum in recent years in sentimental analysis. Deep learning mimics the human learning so expectations are to meet higher levels of accuracy. In this paper we have implemented sentimental analysis of tweets in South Indian language Malayalam. The model used is Recurrent Neural Networks Long Short-Term Memory, a deep learning technique to predict the sentiments analysis. Achieved accuracy was found increasing with quality and depth of the datasets.
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Ahmad, Sk Syeed. "DNA Fragment Assembly using Deep Recurrent Neural Network." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 1142–49. http://dx.doi.org/10.22214/ijraset.2020.5181.
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Shang, Kailin, Ziyi Chen, Zhixin Liu, Lihong Song, Wenfeng Zheng, Bo Yang, Shan Liu, and Lirong Yin. "Haze Prediction Model Using Deep Recurrent Neural Network." Atmosphere 12, no. 12 (December 6, 2021): 1625. http://dx.doi.org/10.3390/atmos12121625.
Повний текст джерелаАнотація:
In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.
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Dube, Lucky, and Ehab H. E. Bayoumi. "DRNN Robust DTC for Induction Motor Drive Systems Using FSTPI." Journal Européen des Systèmes Automatisés 54, no. 4 (August 31, 2021): 539–47. http://dx.doi.org/10.18280/jesa.540403.
Повний текст джерелаАнотація:
In this paper, a self-tuning PI speed controller based on diagonal recurrent neural network is (DRNN) investigated and simulated to increase the robustness of the direct torque control (DTC) scheme for three-phase low-power IM drive system using a Four Switch Three-Phase Inverter (FSTPI). The drive is subjected to different system inputs and disturbances, step changes in speed under different load conditions, abrupt loading at high speed and speed reversal. Furthermore, the robustness of the controller is evaluated by varying motor parameter, stator resistance and moment of inertia. A comparison of classical and self-tuning PI speed controllers was presented to determine the effectiveness of the proposed controller. It is concluded based on simulation results using Matlab/Simulink. that the self-tuning PI speed controller provides the best performance by reacting rapidly and adaptively.
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Ma, Qianli, Zhenxi Lin, Enhuan Chen, and Garrison Cottrell. "Temporal Pyramid Recurrent Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5061–68. http://dx.doi.org/10.1609/aaai.v34i04.5947.
Повний текст джерелаАнотація:
Learning long-term and multi-scale dependencies in sequential data is a challenging task for recurrent neural networks (RNNs). In this paper, a novel RNN structure called temporal pyramid RNN (TP-RNN) is proposed to achieve these two goals. TP-RNN is a pyramid-like structure and generally has multiple layers. In each layer of the network, there are several sub-pyramids connected by a shortcut path to the output, which can efficiently aggregate historical information from hidden states and provide many gradient feedback short-paths. This avoids back-propagating through many hidden states as in usual RNNs. In particular, in the multi-layer structure of TP-RNN, the input sequence of the higher layer is a large-scale aggregated state sequence produced by the sub-pyramids in the previous layer, instead of the usual sequence of hidden states. In this way, TP-RNN can explicitly learn multi-scale dependencies with multi-scale input sequences of different layers, and shorten the input sequence and gradient feedback paths of each layer. This avoids the vanishing gradient problem in deep RNNs and allows the network to efficiently learn long-term dependencies. We evaluate TP-RNN on several sequence modeling tasks, including the masked addition problem, pixel-by-pixel image classification, signal recognition and speaker identification. Experimental results demonstrate that TP-RNN consistently outperforms existing RNNs for learning long-term and multi-scale dependencies in sequential data.
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Gallicchio, Claudio, and Alessio Micheli. "Fast and Deep Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3898–905. http://dx.doi.org/10.1609/aaai.v34i04.5803.
Повний текст джерелаАнотація:
We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dynamical system (implemented through a recurrent neural network), and leverages a deep architectural organization of the recurrent units. Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. This can be viewed as a way to study the intrinsic power of the architecture of a deep GNN, and also to provide insights for the set-up of more complex fully-trained models. Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification.
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Chowanda, Andry, and Alan Darmasaputra Chowanda. "Recurrent Neural Network to Deep Learn Conversation in Indonesian." Procedia Computer Science 116 (2017): 579–86. http://dx.doi.org/10.1016/j.procs.2017.10.078.
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Rajesh, Sangeetha, and N. J. Nalini. "Musical instrument emotion recognition using deep recurrent neural network." Procedia Computer Science 167 (2020): 16–25. http://dx.doi.org/10.1016/j.procs.2020.03.178.
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Almiani, Muder, Alia AbuGhazleh, Amer Al-Rahayfeh, Saleh Atiewi, and Abdul Razaque. "Deep recurrent neural network for IoT intrusion detection system." Simulation Modelling Practice and Theory 101 (May 2020): 102031. http://dx.doi.org/10.1016/j.simpat.2019.102031.
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