scholarly journals Forecast of Corona Losses using Grey Wolves on Variant Load Condition

2020 ◽  
Vol 8 (5) ◽  
pp. 3127-3134
Keyword(s):  
Meteorological Data ◽  
Load Condition ◽  
Learning Approach ◽  
Suggested Approach ◽  
Dynamic Choice ◽  
Heat Temperature ◽  
Overhead Lines ◽  
Effective Learning ◽  
Proposed Model ◽  
Corona Losses

Depending upon material science law and guidelines, the factors of overhead lines in network, electric and meteorological data recorded the essential relationships with the corona losses and the states of climate are normally spotted. Corona losses occurred to be characterized just by weighting components for particular states of climate of average twelve Bulgarian territories showing components of complete transmission lattice. A determining unit imply on a measurable procedure worried on an hourly premise corona shortfall is inspected and proposed to have the option to decrease the lopsided characteristics costs. This casing work includes a proposed model of learning for limiting the corona losses. The deficiencies of suggested approach lessen in each situation since it speaks to an effective learning adaptation and at the hour of testing it productively figure heat/temperature giving an extraordinary effect to fundamentally losses decrease. The fundamental purpose for the improvement of learning approach is that the learning approach sums up the information however expectation approach utilizes dynamic choice and disregards past information execution.

scholarly journals Estimating evapotranspiration using the complementary relationship and the Budyko framework

2017 ◽  
Vol 8 (4) ◽  
pp. 771-790 ◽  
Author(s):  
Homin Kim ◽  
Jagath J. Kaluarachchi
Keyword(s):  
Root Mean Square Error ◽  
Vegetation Cover ◽  
Meteorological Data ◽  
Mean Square ◽  
Limited Data ◽  
Complementary Relationship ◽  
Proposed Model ◽  
The Usa ◽  
Dry Conditions ◽  
The Subject

Abstract Several models have been developed to estimate evapotranspiration. Among those, the complementary relationship has been the subject of many recent studies because it relies on meteorological data only. Recently, the modified Granger and Gray (GG) model showed its applicability across 34 diverse global sites. While the modified GG model showed better performances compared to the recently published studies, it can be improved for dry conditions and the relative evaporation parameter in the original GG model needs to be further investigated. This parameter was empirically derived from limited data from wet environments in Canada – a possible reason for decreasing performance with dry conditions. This study proposed a refined GG model to overcome the limitation using the Budyko framework and vegetation cover to describe relative evaporation. This study used 75 eddy covariance sites in the USA from AmeriFlux, representing 36 dry and 39 wet sites. The proposed model produced better results with decreasing monthly mean root mean square error of about 30% for dry sites and 15% for wet sites compared to the modified GG model. The proposed model in this study maintains the characteristics of the Budyko framework and the complementary relationship and produced improved evapotranspiration estimates under dry conditions.

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

scholarly journals Design Analysis of a Dual Rotor Permanent Magnet Machine driven Electric Vehicle

2018 ◽  
Vol 152 ◽  
pp. 03004
Author(s):  
Mohd Izzat Bin Zainuddin ◽  
CV Aravind
Keyword(s):  
Magnetic Flux ◽  
Permanent Magnet ◽  
Flux Density ◽  
Direct Current ◽  
Load Condition ◽  
Magnetic Flux Density ◽  
Torque Density ◽  
Proposed Model ◽  
Outer Rotor ◽  
Electric Bike

Electric bike in urban countries such as Europe and China commonly used the brushless direct current machine (BLDC) as it able to produce high torque to transport the user from one place to another. However, BLDC torque density can’t be improving due to limitation magnetic flux generated by the permanent magnet. Therefore, the performance of electric bike can’t be improved. Outer rotor BLDC machine design able to improve the torque density of the motor due to increase radius of the motor which can be explained by simple physics equation (Torque = Force x radius). However, an outer rotor machine only generates constant speed, which is not suitable for operating under tractive load condition, especially electric bike. The proposed model is a new novel of double layer outer rotor BLDCPM machine which able to amplify the magnetic flux density and improve the torque density of the machine. The mutual magnetic coupling between the inner and outer rotor of the proposed model increase the magnetic flux intensity as both of them acts as individual parts. Thus, the magnetic flux generated by both rotors are double which resulted in improving the performance of the E-bike. Designing parameters and analysing the performance of the proposed 2D model is done using FEA tools. Evaluation of the conventional and proposed model by comparing torque performance, magnetic flux density and motor constant square density. Other than that, speed torque graph also is evaluated to justify either it can operate similarly to ICE engine with gears. Two model is designed which is Single Outer Rotor Brushless Direct Current (SORBLDC) and Double Outer Rotor Brushless Direct Current (DORBLDC) operated with the same cases of 27 Amp current supplied to it and operate under various speed from 500 rpm to 2000 rpm. The average torque produce by the conventional and proposed model are 2.045439 Nm and 3.102648 Nm. Furthermore, improvement of the proposed model to conventional model in terms of motor constant square density by 24.92%. Therefore, the proposed model able to improve the magnetic flux by amplifying which resulted to increase the torque density of the machine. Furthermore, the speed-torque graph of the proposed machine shows similarity with speed torque graph of ICE engine. Thus, the proposed machine is suitable to operate for bike application

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

A Regional Zenith Tropospheric Correction Model Based on the Guangxi CORS Network

2013 ◽  
Vol 353-356 ◽  
pp. 3438-3443
Author(s):  
Li Long Liu ◽  
Liang Ke Huang ◽  
Teng Xu Zhang ◽  
Miao Zhou ◽  
Chao Long Yao
Keyword(s):  
Interpolation Method ◽  
Meteorological Data ◽  
Correction Model ◽  
New Model ◽  
Model Based ◽  
Proposed Model ◽  
New Models ◽  
Using Data ◽  
The Relationship ◽  
Tropospheric Delays

In this paper, the relationship between zenith tropospheric delays and the altitude of stations is analyzed using the EGNOS tropospheric correction model. The new model (EHT model) is proposed for estimating zenith tropospheric delays from regional CORS data without meteorological data. The proposed model is compared with the direct interpolation method and the remove-restore method using data from Guangxi CORS. The results show that the new models significantly improve the calculated precision.

scholarly journals A Critical Review on Blended Learning Versus Traditional Lecture Method

2021 ◽  
Vol 13 (2) ◽  
pp. 62-76
Author(s):  
Muhammad Hafeez
Keyword(s):  
Blended Learning ◽  
Critical Review ◽  
Learning Strategy ◽  
Research Articles ◽  
Learning Approach ◽  
Review Of Literature ◽  
Traditional Lecture ◽  
Effective Learning ◽  
Lecture Method ◽  
Information And Communication

From the beginning of 21st century, the leaning stratigies have been changed from traditional to information and communication based. A critical review of published articles about blended and traditional leaning stratigies has been conducted to highlight the importance and significance of both learning stratigies. Thirty-six (36) research articles published in various databases in various disciplines have been selected for review.  The review of literature showed that in most of the studies, the blended learning strategy proved to be more effective learning strategy against the traditional lecture method. From thirty-six published articles reviewed, twenty-five studies showed a statistically more significance value in blended learning approach for academic achievement, critical and creative skills in various disciplines. So, on the basis of this study, it is strongly recommended that blended learning strategy must be applied to achieve high academic and professional results.    

scholarly journals Culturally Responsive Madurese Language Learning

2021 ◽  
Vol 13 (2) ◽  
pp. 1314-1321
Author(s):  
Ahmad Faizi ◽  
Djoko Saryono ◽  
Muakibatul Hasanah ◽  
Nurcha sanah
Keyword(s):  
Language Learning ◽  
Culturally Responsive ◽  
Social Situation ◽  
Cultural Knowledge ◽  
Language Teachers ◽  
Local Culture ◽  
Learning Approach ◽  
Effective Learning ◽  
Cultural Experiences ◽  
Direct Interpretation

Learning efficiency highly relies on the implemented learning approach. The Madurese language (BM) learning is a social situation that stores cultural diversity reflected from students’ background. Meanwhile, culturally responsive learning facilitates effective learning that accommodates students’ cultural differences. This study investigates students’ knowledge and cultural experiences in a classroom, primarily those related to Probolinggo society’s local culture, using descriptive qualitative approach. The data were obtained through observation and interview with some Madurese language teachers. The data, in the form of excerpts, were analyzed using direct interpretation technique. The findings are associated with social, moral, and art cultural knowledge and experience related to local culture during the Madurese language learning. Various differences have been observed between students who are speaking Madurese and other languages. Their distinctive knowledge and experiences induce different opinion, behavior, and attitude, along with perspective toward art, in the class. Integrating students’ local culture related experiences present learning independence.

scholarly journals Deep Learning and Autoregressive Approach for Prediction of Time Series Data

2021 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Akhter Mohiuddin Rather
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Statistical Models ◽  
Time Complexity ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach ◽  
Proposed Model ◽  
Artificial Neural

Fractional This paper proposes a deep learning approach for prediction of nonstationary data. A new regression scheme has been used in the proposed model. Any non-stationary data can be used to test the efficiency of the proposed model, however in this work stock data has been used due to the fact that stock data has a property of being nonlinear or non-stationary in nature. Beside using proposed model, predictions were also obtained using some statistical models and artificial neural networks. Traditional statistical models did not yield any expected results; artificial neural networks resulted into high time complexity. Therefore, deep learning approach seemed to be the best method as of today in dealing with such problems wherein time complexity and excellent predictions are of concern.

scholarly journals Probabilistic prediction for the ampacity of overhead lines using Quantile Regression Neural Network

2020 ◽  
Vol 185 ◽  
pp. 02022
Author(s):  
Xu Jin ◽  
Fudong Cai ◽  
Mengxia Wang ◽  
Yang Sun ◽  
Shengyuan Zhou
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Transmission Lines ◽  
Meteorological Data ◽  
Overhead Line ◽  
Probabilistic Prediction ◽  
Overhead Lines ◽  
Overhead Transmission Lines ◽  
Planning And Control ◽  
And Control

The ampacity of overhead transmission lines play a key role in power system planning and control. Due to the volatility of the meteorological elements, the ampacity of an overhead line is timevarying. In order to fully utilize the transfer capability of overhead transmission lines, it is necessary to provide system operators with accurate probabilistic prediction results of the ampacity. In this paper, a method based on the Quantile Regression Neural Network (QRNN) is proposed to improve the performance of the probabilistic prediction of the ampacity. The QRNN-based method uses a nonlinear model to comprehensively model the impacts of historical meteorological data and historical ampacity data on the ampacity at predictive time period. Numerical simulations based on the actual meteorological data around an overhead line verify the effectiveness of the proposed method.

scholarly journals Image Based Classification of Rumor Information from the Social Network Platform

2021 ◽  
Vol 38 (5) ◽  
pp. 1413-1421
Author(s):  
Vallamchetty Sreenivasulu ◽  
Mohammed Abdul Wajeed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
End Users ◽  
Web Based ◽  
Suggested Approach ◽  
Text Input ◽  
Social Network Data ◽  
Proposed Model ◽  
Spam Filters ◽  
Network Platform

Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.

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