scholarly journals 3D Modeling Technology Based On Computer Aided Environment Design

2022 ◽  
Vol 2146 (1) ◽  
pp. 012031
Author(s):  
Feng Li ◽  
Wenbing Xi ◽  
Xin Dai
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Mutual Influence ◽  
Power Grid ◽  
Distribution Network ◽  
Environment Design ◽  
Real Time Analysis ◽  
Multi Classification ◽  
Voltage Violation

Abstract Voltage violation of the distribution network greatly affects the power supply quality and the use’s power consumption experience. To better improve the voltage quality of the power grid, real-time analysis of voltage violation can helps power grid personnel to handle voltage violation instantly and efficiently though analyzing the attribute indicators on dis-tribution network lines. However, many studies are concerned only with the single voltage violation cause, and ignore the more complicated phenomenon of voltage violations. In this paper, we proposed a joint attributes based neural network multi-classification (JANN) model that take mutual influence between attributes from different nodes in the distribution network into account when voltage violations are detected. Concretely, we construct the set of joint attributes from each node in the distribution network though real-time monitoring of the power grid. Then the joint attribute based neural network model is constructed to analyze the voltage violation phenomenon, and determine the cause multi-classification of voltage violations. Experimental results show that the proposed (JANN) method can reach 95.79% F1-score rate on multi-classification of voltage violation causes.

A new hybrid model combining EMD and neural network for multi-step ahead load forecasting

2021 ◽  
pp. 1-16
Author(s):  
Hasmat Malik ◽  
Majed A. Alotaibi ◽  
Abdulaziz Almutairi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Hybrid Model ◽  
Storage System ◽  
Load Forecasting ◽  
Model Performance ◽  
Energy Storage System ◽  
Electric Load ◽  
Real Time Analysis ◽  
Model Combining

The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error).

scholarly journals Multi-Classification of Brain Tumor Images Using Deep Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69215-69225 ◽  
Author(s):  
Hossam H. Sultan ◽  
Nancy M. Salem ◽  
Walid Al-Atabany
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Deep Neural Network ◽  
Multi Classification

An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity

2019 ◽  
Vol 21 (9) ◽  
pp. 681-692
Author(s):  
Luis Francisco Barbosa-Santillán ◽  
María de los Angeles Calixto-Romo ◽  
Juan Jaime Sánchez-Escobar ◽  
Liliana Ibeth Barbosa-Santillán
Keyword(s):  
Neural Network ◽  
Enzymatic Activity ◽  
Real Time ◽  
High Throughput Screening ◽  
Cellulolytic Activity ◽  
Color Patterns ◽  
Cellulolytic Microorganisms ◽  
The Neural Network ◽  
Neural Network Classifiers

Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.

Sign in / Sign up

Export Citation Format

Share Document