scholarly journals Inductive Power Transmission System for Electric Car Charging Phase: Modeling plus Frequency Analysis

2021 ◽  
Vol 12 (4) ◽  
pp. 267
Author(s):  
Naoui Mohamed ◽  
Flah Aymen ◽  
Mohammed Alqarni
Keyword(s):  
Power Transmission ◽  
Class Imbalance ◽  
Frequency Variation ◽  
Class Imbalance Problem ◽  
Inductive Power Transfer ◽  
Imbalance Problem ◽  
Summary Graph ◽  
Inductive Power Transmission ◽  
The Relationship ◽  
Inductive Power

The effectiveness of inductive power transfer (IPT) presents a serious challenge for improving the global recharge system performance. An electric vehicle (EVs) needs to be charged rapidly and have maximum power when it is charged with wireless technology. Based on various research, the performance of this recharge system is attached to several points and the frequency resonance is one of those parameters that can influence. In this paper, we try to explore the relationship between the obtained power and the signal input frequency for charging a lithium battery, solve the class imbalance problem and understand the maximum allowed frequency. To obtain the results, a mathematical model was first created to demonstrate the relationship, then the dynamic model was validated and tested using the Matlab Simulink platform. The performance of the worldwide wireless recharging system in terms of frequency variation is depicted in a summary graph.

scholarly journals Series-Series/Series Compensated Inductive Power Transmission System with Symmetrical Half-Bridge Resonant Converter: Design, Analysis, and Experimental Assessment

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2268 ◽  
Author(s):  
Jianfeng Hong ◽  
Mingjie Guan ◽  
Zaifa Lin ◽  
Qiu Fang ◽  
Wei Wu ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Power Transmission ◽  
Low Frequency ◽  
Resonant Converter ◽  
Inductive Power Transfer ◽  
Leakage Inductance ◽  
Analysis And Design ◽  
Converter Design ◽  
Inductive Power Transmission ◽  
Inductive Power

In order to compensate the large leakage inductance and improve the power transmission capacity, capacitors are widely used in inductive power transfer (IPT) systems, which results in high voltage or current stresses in the resonant tanks and limits higher volt-ampere (VA) rating of the transfer power, especially in medium and low frequency applications. This paper presents a symmetrical half-bridge resonant converter (SHRC) for series-series/series compensated IPT systems with detailed analysis and design. It operates at a relatively low frequency of 12.5 kHz, suitable for IGBT applications. The theoretical analysis shows that, compared with full-bridge resonant converter (FRC) for IPT, the symmetrical half-bridge resonant converter achieves a higher efficiency. Simulation and a prototype of 1500 W power output were built to verify the theoretical analysis. The experimental results show that the power loss of SHRC is 39.7 W while that of FRC is 79.4 W, which is consistent with the theoretical analysis. The global efficiency of the IPT based on the proposed converter is 91.6%.

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2803
Author(s):  
Rabeea Jaffari ◽  
Manzoor Ahmed Hashmani ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Loss Function ◽  
Class Imbalance ◽  
Power Line ◽  
Aerial Images ◽  
Class Imbalance Problem ◽  
Trade Off ◽  
Urban Scenes ◽  
Imbalance Problem ◽  
A Minor ◽  
Evaluation Parameters

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

scholarly journals Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

2021 ◽  
Author(s):  
Sayan Surya Shaw ◽  
Shameem Ahmed ◽  
Samir Malakar ◽  
Laura Garcia-Hernandez ◽  
Ajith Abraham ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Class Imbalance ◽  
Ring Theory ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Swarm Optimization ◽  
Imbalance Problem ◽  
Representative Samples ◽  
Selection Of

AbstractMany real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

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