scholarly journals Comprehensive Model for Real Battery Simulation Responsive to Variable Load

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3209
Author(s):  
Gustavo Piske Fenner ◽  
Leonardo Weber Stringini ◽  
Camilo Alberto Sepulveda Rangel ◽  
Luciane Neves Canha
Keyword(s):  
Electric Circuit ◽  
Random Variable ◽  
Voltage Response ◽  
Variable Load ◽  
Good Prediction ◽  
Battery Voltage ◽  
Battery Model ◽  
Proposed Model ◽  
Electric Circuit Model

This paper proposes a battery voltage model that is suitable for variable operation. The model combines the features of the Kinetic Battery Model (KiBaM) and voltage model (VM), and it improves the accuracy and quality of the solution, addressing four characteristics of operation: charging, discharging, rest after charge, and rest after discharge. This model will be known as 4-KiVM and shows low impact on computational burden. The proposed model can keep track of the voltage even when the load is inverted or turned off. To calibrate and validate the model, a NASA-provided dataset was used composed of a battery with variable charges and discharges, simulating real applications. A metaheuristic method based on tabu search is used to extract constants from this dataset and validate this hybrid model. In addition, a comparison of performance of the 4-KiVM against KiBaM, VM, and the electric circuit model (ECM) was made, showing its advantages. The results of the simulations showed a good prediction of the battery voltage response and SOC prediction in random (variable) use.

High-Quality Fast QPOs from Magnetars: An Electric Circuit Model

2011 ◽  
Vol 20 (2) ◽  
Author(s):  
Alexander Stepanov ◽  
Valery Zaitsev ◽  
Esko Valtaoja
Keyword(s):  
High Frequency ◽  
Electric Circuit ◽  
Physical Parameters ◽  
Hot Electron ◽  
High Quality ◽  
Electron Positron ◽  
Quasiperiodic Oscillations ◽  
Electric Circuit Model ◽  
Very High

AbstractAn interpretation of high-frequency (20-2400 Hz) fast quasiperiodic oscillations of magnetars in terms of an equivalent electric circuit is given. Observed periods and the very high quality of quasi-periodic oscillations are explained. The oscillation source is represented as a hot electron-positron plasma in a ‘trapped fireball’, a system of current-carrying magnetic loops of various sizes. The physical parameters of a ‘trapped fireball’ in the flare of SGR 1806-20 (2004 December 27) are estimated.

scholarly journals Development and evaluation of a composite supercapacitor-based 12 V transient start–stop power system for vehicles: Modelling, design and fabrication scaling up

2020 ◽  
pp. 095765092093016
Author(s):  
Chunhong Lei ◽  
Richard Fields ◽  
Peter Wilson ◽  
Constantina Lekakou ◽  
Negar Amini ◽  
...  
Keyword(s):  
Power System ◽  
Electric Circuit ◽  
High Energy ◽  
Maximum Power ◽  
High Energy Density ◽  
High Power Density ◽  
Bottom Up ◽  
Application Specific ◽  
Electric Circuit Model

The study involves a bottom-up approach, from bottom cells to large supercapacitor pouch cells, encompassing the design, modelling and fabrication stages of the cells leading to a 12 V transient start–stop (TSS) power system for automotive applications. More specifically, the design of a large composite supercapacitor is presented, consisting of a high power density component and a high energy density component, hybridised at material level. The composition of the composite supercapacitor is optimised to be application-specific so that it satisfies a specified energy-to-maximum power ratio for the 12 V TSS system. The testing of the large composite supercapacitor pouch cells and the 12 V TSS system proves the validity of the bottom-up approach, validates the design and the proposed electric circuit model and its parameters, fitted according to experimental data of small laboratory cells and applied successfully to the large cells, and proves the high quality of the scaled-up fabrication processes. The 12 V TSS power system of seven large composite supercapacitor cells satisfies the set criteria of energy and maximum power for the specified duration, 15 Wh and 4.2 kW respectively, at a total mass of 3.94 kg, below the original set limit of 5 kg.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Key Determinants of Airline Loyalty Modeling in Thailand

2020 ◽  
Vol 12 (10) ◽  
pp. 4165 ◽  
Author(s):  
Dissakoon Chonsalasin ◽  
Sajjakaj Jomnonkwao ◽  
Vatanavongs Ratanavaraha
Keyword(s):  
Airline Industry ◽  
Structural Equation ◽  
Statistical Significance ◽  
Perceived Quality ◽  
Equation Modeling ◽  
Structural Factors ◽  
Proposed Model ◽  
Airline Passengers ◽  
Domestic Airline

The airline industry in Thailand has grown enormously over the past decade. Competition among airline companies to reach market share and profit has been intense, requiring strong strategic abilities. To increase the service quality of such companies, identifying factors related to the context of airlines is important for policymakers. Thus, this study aims to present empirical data on structural factors related to the loyalty of domestic airline passengers. Structural equation modeling was used to confirm the proposed model. The questionnaire was used to survey and collect data from 1600 airline passengers. The results indicate that satisfaction, trust, perceived quality, relationship, and image of airlines positively influenced loyalty with a statistical significance of α = 0.05. Moreover, the study found that expectation and perceived quality indirectly influenced loyalty. The findings provide a reference for airline operators to clearly understand the factors that motivate passenger loyalty, which can be used to develop the sustainability of marketing strategies and support competitiveness.

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1-19
Author(s):  
Sreelakshmy I. J. ◽  
Binsu C. Kovoor
Keyword(s):  
Patch Size ◽  
Image Inpainting ◽  
Qualitative Evaluation ◽  
Natural Images ◽  
External Information ◽  
Image Editing ◽  
Model Combining ◽  
Proposed Model ◽  
The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

scholarly journals Adaptive Reservation of Network Resources According to Video Classification Scenes

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1949
Author(s):  
Lukas Sevcik ◽  
Miroslav Voznak
Keyword(s):  
User Satisfaction ◽  
Quality Evaluation ◽  
Spatial Information ◽  
Service Providers ◽  
Video Quality ◽  
Subjective Assessment ◽  
Video Sequences ◽  
Network Resources ◽  
Proposed Model

Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

scholarly journals Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingli Wang ◽  
Huikuan Gu ◽  
Jiang Hu ◽  
Jian Liang ◽  
Sisi Xu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prediction Model ◽  
Volumetric Modulated Arc Therapy ◽  
Model Evolution ◽  
Prediction Ability ◽  
Knowledge Based ◽  
Proposed Model ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

The prediction of the cyclic mechanical behavior of stainless steel 304L at room temperature

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hala Messai ◽  
Salim Meziani ◽  
Athmane Fouathia
Keyword(s):  
Stainless Steel ◽  
Design Methodology ◽  
Room Temperature ◽  
Type Model ◽  
Good Prediction ◽  
304L Stainless Steel ◽  
Content Type ◽  
The Third ◽  
Chaboche Model

Purpose The purpose of this paper is to highlight the performance of the Chaboche model in relation to the database identification, tests with imposed deformations were conducted at room temperature on 304L stainless steel specimens. Design/methodology/approach The first two tests were performed in tension-compression between ±0.005 and ±0.01; in the third test, each cycle is composed of the combination of a compression tensile cycle between ±0.01 followed by a torsion cycle between ±0.01723 (non-proportional path), and the last, uniaxial ratcheting test with a mean stress between 250 MPa and −150 MPa. Several identifications of a Chaboche-type model were then performed by considering databases composed of one or more of the cited tests. On the basis of these identifications, the simulations of a large number of ratchet tests in particular were carried out. Findings The results present the effect of the optimized parameters on the prediction of the behavior of materials which is reported in the graphs, Optimizations 1 and 2 of first and second tests and Optimization 4 of the third test giving a good prediction of the increasing/decreasing pre-deformation amplitude. Originality/value The quality of the model's predictions strongly depends on the richness of the database used for the identification of the parameters.

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