scholarly journals Development of a daily PM<sub>10</sub> and PM<sub>2.5</sub> prediction system using a deep long short-term memory neural network model

2019 ◽  
Vol 19 (20) ◽  
pp. 12935-12951 ◽  
Author(s):  
Hyun S. Kim ◽  
Inyoung Park ◽  
Chul H. Song ◽  
Kyunghwa Lee ◽  
Jae W. Yun ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Transport Model ◽  
Initial Conditions ◽  
Current System ◽  
Prediction System ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

Abstract. A deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. The structural and learnable parameters of the newly developed system were optimized from iterative model training. Independent variables were obtained from ground-based observations over 2.3 years. The performance of the particulate matter (PM) prediction LSTM was then evaluated by comparisons with ground PM observations and with the PM concentrations predicted from two sets of 3-D chemistry-transport model (CTM) simulations (with and without data assimilation for initial conditions). The comparisons showed, in general, better performance with the LSTM than with the 3-D CTM simulations. For example, in terms of IOAs (index of agreements), the PM prediction IOAs were enhanced from 0.36–0.78 with the 3-D CTM simulations to 0.62–0.79 with the LSTM-based model. The deep LSTM-based PM prediction system developed at observation sites is expected to be further integrated with 3-D CTM-based prediction systems in the future. In addition to this, further possible applications of the deep LSTM-based system are discussed, together with some limitations of the current system.

scholarly journals Development of daily PM<sub>10</sub> and PM<sub>2.5</sub> prediction system using a deep long short-term memory neural network model

2019 ◽  
Author(s):  
Hyun S. Kim ◽  
Inyoung Park ◽  
Chul H. Song ◽  
Kyunghwa Lee ◽  
Jae W. Yun ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Transport Model ◽  
Initial Conditions ◽  
Current System ◽  
Prediction System ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

Abstract. A deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. The structural and learnable parameters of the newly developed system were optimized from iterative model trainings. Independent variables were obtained from ground-based observations over 2.3 years. The performance of the particulate matter (PM) prediction LSTM was then evaluated by comparisons with ground PM observations and with the PM concentrations predicted from two sets of 3-D chemistry-transport model (CTM) simulations (with and without data assimilation for initial conditions). The comparisons showed, in general, better performance with the LSTM than with the 3-D CTM simulations. For example, in terms of IOAs (index of agreements), the PM prediction IOAs were enhanced from 0.36–0.78 with the 3-D CTM simulations to 0.62–0.79 with the LSTM-based model. The deep LSTM-based PM prediction system developed at observation sites is expected to be further integrated with 3-D CTM-based prediction systems in the future. In addition to this, further possible applications of the deep LSTM-based system are discussed, together with some limitations of the current system.

scholarly journals Estimation of Bladder Pressure and Volume from the Neural Activity of Lumbosacral Dorsal Horn Using a Long-Short-Term-Memory-based Deep Neural Network

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Milad Jabbari ◽  
Abbas Erfanian
Keyword(s):  
Neural Network ◽  
Neural Activity ◽  
Short Term Memory ◽  
Field Potential ◽  
Bladder Function ◽  
Bladder Pressure ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

AbstractIn this paper, we propose a deep recurrent neural network (DRNN) for the estimation of bladder pressure and volume from neural activity recorded directly from spinal cord gray matter neurons. The model was based on the Long Short-Term Memory (LSTM) architecture, which has emerged as a general and effective model for capturing long-term temporal dependencies with good generalization performance. In this way, training the network with the data recorded from one rat could lead to estimating the bladder status of different rats. We combined modeling of spiking and local field potential (LFP) activity into a unified framework to estimate the pressure and volume of the bladder. Moreover, we investigated the effect of two-electrode recording on decoding performance. The results show that the two-electrode recordings significantly improve the decoding performance compared to single-electrode recordings. The proposed framework could estimate bladder pressure and volume with an average normalized root-mean-squared (NRMS) error of 14.9 ± 4.8% and 19.7 ± 4.7% and a correlation coefficient (CC) of 83.2 ± 3.2% and 74.2 ± 6.2%, respectively. This work represents a promising approach to the real-time estimation of bladder pressure/volume in the closed-loop control of bladder function using functional electrical stimulation.

scholarly journals Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2826 ◽  
Author(s):  
Ma ◽  
Wu ◽  
Zhang ◽  
Wu ◽  
Jeon ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Good Effect ◽  
Prediction System ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Term Variation ◽  
Long Short Term Memory

In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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