scholarly journals ANN based Battery Health Monitoring - A Comprehensive Review

2020 ◽  
Vol 184 ◽  
pp. 01068
Author(s):  
Sai Vasudeva Bhagavatula ◽  
Venkata Rupesh Bharadwaj Yellamraju ◽  
Karthik Chandra Eltem ◽  
Phaneendra Babu Bobba ◽  
Naveenkumar Marati
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
The Other ◽  
Battery Management ◽  
Battery State Of Charge ◽  
Different Temperatures ◽  
Long Short Term Memory ◽  
Artificial Neural ◽  
Learning Machine ◽  
Error Percentage

The development of electric vehicles has bought a great revolution in the field of battery management as it deals with the health of the battery and also the protection of the battery. State of Charge (SoC) and State of Health (SoH) are the important parameters in determining the battery’s health. Advancements in Artificial Neural Networks and Machine Learning, a growing field in recent years has bought many changes in estimating these parameters. Access to huge battery data has become very advantageous to these methods. This manuscript presents an overview of different Artificial Neural Network techniques like Feedforward Neural Network (FNN), Extreme Learning Machine (ELM), and the Long Short Term Memory (LSTM). These techniques are trained with already existing data samples consisting of different values of voltages, currents at different temperatures with different charging cycles and epochs. The errors in each technique are different from the other as the constraints in one method are rectified using the other method to get the least error percentage and get the nearest estimate of the SoC and SOH. Each method needs to be trained for several epochs. This manuscript also presents a comparison of different methods with input parameters and error percentages.

scholarly journals INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

2020 ◽  
Vol 8 (5) ◽  
pp. 4047-4068
Author(s):  
Mehmet Hakan ÖZDEMİR ◽  
Murat İNCE ◽  
Batin Latif AYLAK ◽  
Okan ORAL ◽  
Mehmet Ali TAŞ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Solar Power ◽  
Short Term Memory ◽  
Renewable Energy Sources ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Renewable energy sources play an essential role in sustainable development. The share of renewable energy-based energy generation is rapidly increasing all over the world. Turkey has a great potential in terms of both solar and wind energy due to its geographical location. The desired level has not yet been reached in using this potential. Nevertheless, with the increase in installed power in recent years, electricity generation from solar energy has gained momentum. In this study, data on cumulative installed solar power in Turkey in the 2009-2019 period were used. Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods were selected to predict the cumulative installed solar power for 2020 with these data. The cumulative installed power was predicted, and the results were compared and interpreted.

scholarly journals A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

2021 ◽  
Vol 18 (13) ◽  
pp. 6801
Author(s):  
Junbeom Park ◽  
Seongju Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particulate Matter ◽  
Industrial Development ◽  
Short Term Memory ◽  
Model Performance ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

scholarly journals Multiple-Depth Soil Moisture Estimates Using Artificial Neural Network and Long Short-Term Memory Models

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2584
Author(s):  
Heechan Han ◽  
Changhyun Choi ◽  
Jongsung Kim ◽  
Ryan R. Morrison ◽  
Jaewon Jung ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Short Term Memory ◽  
Data Driven ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Accurate prediction of soil moisture is important yet challenging in various disciplines, such as agricultural systems, hydrology studies, and ecosystems studies. However, many data-driven models are being used to simulate and predict soil moisture at only a single depth. To predict soil moisture at various soil depths with depths of 100, 200, 500, and 1000 mm from the surface, based on the weather and soil characteristic data, this study designed two data-driven models: artificial neural networks and long short-term memory models. The developed models are applied to predict daily soil moisture up to 6 days ahead at four depths in the Eagle Lake Observatory in California, USA. The overall results showed that the long short-term memory model provides better predictive performance than the artificial neural network model for all depths. The root mean square error of the predicted soil moisture from both models is lower than 2.0, and the correlation coefficient is 0.80–0.97 for the artificial neural network model and 0.90–0.98 for the long short-term memory model. In addition, monthly based evaluation results showed that soil moisture predicted from the data-driven models is highly useful for analyzing the effects on the water cycle during the wet season as well as dry seasons. The prediction results can be used as basic data for numerous fields such as hydrological study, agricultural study, and environment, respectively.

scholarly journals Image Caption Generator using CNN-LSTM Deep Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 2968-2974
Author(s):  
Vaibhav Julakanti
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
The Other ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Individual ◽  
General Climate ◽  
Image Caption

Captioning pictures naturally is one of the significant aspects of the human visual framework. There are numerous benefits if there is a model which consequently inscription the scenes or climate encompassed by them and offers back the subtitle as a plain book. In this paper, we present a model dependent on CNN-LSTM neural organizations which naturally identifies the items in the pictures and creates inscriptions for the pictures. It utilizes Inception v3 pre-prepared model to play out the errand of distinguishing items and utilizations LSTM to produce the subtitles. It utilizes the method of Transfer Learning on pre-prepared models for the undertaking of item Detection. This model can perform two activities. The first is to recognize objects in the picture utilizing Convolutional Neural Networks and the other is to subtitle the pictures utilizing RNN based LSTM (Long Short Term Memory). It additionally utilizes a bar look for anticipating the inscriptions for example choosing the best words from the accessible corps. In this, we take top k expectations, feed them again in the model and afterward sort them utilizing the probabilities returned by the model. A portion of the product prerequisites of this undertaking is Tensor Flow V2.0, pandas, NumPy, pickle, PIL, OpenCV. A little GUI is made to transfer the picture to the model to create the inscription. The fundamental use instance of this undertaking is to help outwardly debilitated to comprehend the general climate and act as per that. The inscription age is one of the intriguing and centred fields of Artificial Intelligence which has numerous difficulties to survive. Inscription age includes different complex situations beginning from picking the dataset, preparing the model, approving the model, making pre-prepared models to test the pictures, identifying the pictures lastly producing the individual picture-based subtitles.

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