scholarly journals Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

Predictive Artificial Neural Network Laboratory Fatigue Endurance Limit Model for Asphalt Concrete Pavements Based on the Volumetric Properties and Loading Conditions

2021 ◽  
pp. 036119812199965
Author(s):  
Mayzan M. Isied ◽  
Mena I. Souliman ◽  
Waleed A. Zeiada ◽  
Nitish R. Bastola
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Asphalt Concrete ◽  
Endurance Limit ◽  
Volumetric Properties ◽  
Void Content ◽  
Loading Conditions ◽  
Ann Model ◽  
Artificial Neural ◽  
Fatigue Endurance

Asphalt concrete healing is one of the important concepts related to flexible pavement structures. Fatigue endurance limit (FEL) is defined as the strain limit under which no damage will be accumulated in the pavement and is directly related to asphalt healing. Pavement section designed to handle a strain value equivalent to the endurance limit (EL) strain will be considered as a perpetual pavement. All four-point bending beam fatigue testing results from the NCHRP 944-A project were extracted and utilized in the development of artificial neural network (ANN) EL strain predictive model based on mixture volumetric properties and loading conditions. ANN model architecture, as well as the prediction process of the EL strain utilizing the generated model, were presented and explained. Furthermore, a stand-alone equation that predicts the EL strain value was extracted from the developed ANN model utilizing the eclectic approach. Moreover, the EL strain value was predicted utilizing the new equation and compared with the EL strain value predicted by other prediction models available in literature. A total of 705 beam fatigue lab test data points were utilized in model training and evaluation at ratios of 70%, 15%, and 15% for training, testing, and validation, respectively. The developed model is capable of predicting the EL strain value as a function of binder grade, temperature, air void content, asphalt content, SR, failure cycles number, and rest period. The reliability of the developed stand-alone equation and the ANN model was presented by reasonable coefficient of determination (R2) value and significance value (F).

scholarly journals A novel model for predicting fatty liver disease by means of an artificial neural network

2020 ◽  
Author(s):  
Yi-Shu Chen ◽  
Dan Chen ◽  
Chao Shen ◽  
Ming Chen ◽  
Chao-Hui Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Liver Disease ◽  
Diagnostic Accuracy ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Health Check ◽  
Ann Model ◽  
Artificial Neural ◽  
Input Variables

Abstract Background The artificial neural network (ANN) emerged recently as a potent diagnostic tool, especially for complicated systemic diseases. This study aimed to establish a diagnostic model for the recognition of fatty liver disease (FLD) by virtue of the ANN. Methods A total of 7,396 pairs of gender- and age-matched subjects who underwent health check-ups at the First Affiliated Hospital, College of Medicine, Zhejiang University (Hangzhou, China) were enrolled to establish the ANN model. Indices available in health check-up reports were utilized as potential input variables. The performance of our model was evaluated through a receiver-operating characteristic (ROC) curve analysis. Other outcome measures included diagnostic accuracy, sensitivity, specificity, Cohen’s k coefficient, Brier score, and Hosmer-Lemeshow test. The Fatty Liver Index (FLI) and the Hepatic Steatosis Index (HSI), retrained using our training-group data with its original designated input variables, were used as comparisons in the capability of FLD diagnosis. Results Eight variables (age, gender, body mass index, alanine aminotransferase, aspartate aminotransferase, uric acid, total triglyceride, and fasting plasma glucose) were eventually adopted as input nodes of the ANN model. By applying a cut-off point of 0.51, the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908 [95% confidence interval (CI), 0.901–0.915]—significantly higher (P < 0.05) than that of the FLI model (0.881, 95% CI, 0.872–0.891) and that of the HSI model (0.885; 95% CI, 0.877–0.893). Our ANN model exhibited higher diagnostic accuracy, better concordance with ultrasonography results, and superior capability of calibration than the FLI model and the HSI model. Conclusions Our ANN system showed good capability in the diagnosis of FLD. It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.

Damage Detection of Offshore Platform Mooring Line Using Artificial Neural Network

2018 ◽  
Author(s):  
Djoni E. Sidarta ◽  
Jim O’Sullivan ◽  
Ho-Joon Lim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Total Mass ◽  
Offshore Platform ◽  
Mooring Line ◽  
Ann Model ◽  
Station Keeping ◽  
Mooring Lines ◽  
Artificial Neural

Station-keeping using mooring lines is an important part of the design of floating offshore platforms, and has been used on most types of floating platforms, such as Spar, Semi-submersible, and FPSO. It is of great interest to monitor the integrity of the mooring lines to detect any damaged and/or failures. This paper presents a method to train an Artificial Neural Network (ANN) model for damage detection of mooring lines based on a patented methodology that uses detection of subtle shifts in the long drift period of a moored floating vessel as an indicator of mooring line failure, using only GPS monitoring. In case of an FPSO, the total mass or weight of the vessel is also used as a variable. The training of the ANN model employs a back-propagation learning algorithm and an automatic method for determination of ANN architecture. The input variables of the ANN model can be derived from the monitored motion of the platform by GPS (plus vessel’s total mass in case of an FPSO), and the output of the model is the identification of a specific damaged mooring line. The training and testing of the ANN model use the results of numerical analyses for a semi-submersible offshore platform with twenty mooring lines for a range of metocean conditions. The training data cover the cases of intact mooring lines and a damaged line for two selected adjacent lines. As an illustration, the evolution of the model at various training stages is presented in terms of its accuracy to detect and identify a damaged mooring line. After successful training, the trained model can detect with great fidelity and speed the damaged mooring line. In addition, it can detect accurately the damaged mooring line for sea states that are not included in the training. This demonstrates that the model can recognize and classify patterns associated with a damaged mooring line and separate them from patterns of intact mooring lines for sea states that are and are not included in the training. This study demonstrates a great potential for the use of a more general and comprehensive ANN model to help monitor the station keeping integrity of a floating offshore platform and the dynamic behavior of floating systems in order to forecast problems before they occur by detecting deviations in historical patterns.

scholarly journals Artificial neural network model for precipitation forecast over Western Himalaya using satellite images

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 83-90
Author(s):  
PIYUSH JOSHI ◽  
M.S. SHEKHAR ◽  
ASHAVANI KUMAR ◽  
J.K. QUAMARA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
India Meteorological Department ◽  
Western Himalaya ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ann Model ◽  
Skill Scores ◽  
Artificial Neural

Kalpana satellite images in real time available by India meteorological department (IMD), contain relevant inputs about the cloud in infra-red (IR), water vapor (WV), and visible (VIS) bands. In the present study an attempt has been made to forecast precipitation at six stations in western Himalaya by using extracted grey scale values of IR and WV images. The extracted pixel values at a location are trained for the corresponding precipitation at that location. The precipitation state at 0300 UTC is considered to train the model for precipitation forecast with 24 hour lead time. The satellite images acquired in IR (10.5 - 12.5 µm) and WV (5.7 - 7.1 µm) bands have been used for developing Artificial Neural Network (ANN) model for qualitative as well as quantitative precipitation forecast. The model results are validated with ground observations and skill scores are computed to check the potential of the model for operational purpose. The probability of detection at the six stations varies from 0.78 for Gulmarg in Pir-Panjal range to 0.95 for Dras in Greater Himalayan range. Overall performance for qualitative forecast is in the range from 61% to 84%. Root mean square error for different locations under study is in the range 5.81 to 8.7.

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