scholarly journals Research on Classification of Substation Background Information for Monitoring

2019 ◽  
Vol 136 ◽  
pp. 01023
Author(s):  
Wang Yang ◽  
Li Heng-xuan ◽  
E Shi-ping ◽  
Zhang Kan-jun
Keyword(s):  
High Efficiency ◽  
Background Information ◽  
Electrical System ◽  
Information Monitoring ◽  
Electrical Grids ◽  
Efficiency And Effectiveness

The substations are important parts of modern electrical grids. The monitoring of background information in substations should be carefully managed to keep the smoothly operating of the electrical system. However, there are still some problems in present substations. As a result, some problems can not be handled efficiently and effectively. In order to improve the robustness of information monitoring of substations, this study first analyses the typical problems in the substations. Afterwards, several ways are proposed to handle these problems. As all the information in the substations are standardized, classified and automatically diagnosed, the occurred problems can be smoothly solved with high efficiency and effectiveness.

An Application and Research of Gray-Relation for Color Classification in Dyeing Textile

2013 ◽  
Vol 694-697 ◽  
pp. 2881-2885
Author(s):  
Hai Yan Wang ◽  
Jian Xin Zhang
Keyword(s):  
System Analysis ◽  
High Efficiency ◽  
Information Management System ◽  
Space Distribution ◽  
Color Classification ◽  
Uniform Sampling ◽  
Forecast Precision ◽  
Internal Instability ◽  
The Stability

Dyeing textile’s information management system is the basis of accurate classification of color， machine studying methods have became a popular area of research for application in color classification. Traditional classification methods have high efficiency and are very simple ， but they are dependent on the distribution of sample spaces. If the sample data properties are not independent， forecast precision will been affected badly and internal instability will appear. An application of Gray-Relation for dyeing textile color classification has been designed， which offsets the discount in mathematical statistics method for system analysis. It is applicable regardless of variant in sample size， while quantizing structure is in agreement with qualitative analysis. On the basis of theoretical analysis， Dyeing textile color classification was conducted in the conditions of random sampling、 uniform sampling and stratified sampling. The experimental results proofs that by using Gray-Relation， dyeing textile color classification does not need to be dependent on sample space distribution， and increases the stability of classification.

scholarly journals Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2362 ◽  
Author(s):  
Alexander E. Hramov ◽  
Vadim Grubov ◽  
Artem Badarin ◽  
Vladimir A. Maksimenko ◽  
Alexander N. Pisarchik
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
High Efficiency ◽  
Brain Activity ◽  
Blood Oxygenation ◽  
Hemispheric Lateralization ◽  
Motor Execution ◽  
Functional Near Infrared Spectroscopy

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

Research on Information Distribution Optimization of Multi-Federation Interconnected Tactical Communication Simulation Training

2011 ◽  
Vol 460-461 ◽  
pp. 381-387
Author(s):  
Jian Shi Zhang ◽  
Zhi Yi Fang
Keyword(s):  
Communication Network ◽  
Large Scale ◽  
Simulation Training ◽  
High Efficiency ◽  
Information Distribution ◽  
Distribution Problem ◽  
Training Process ◽  
State Data ◽  
Interconnected Structure

Multi-federation interconnected structure is the support structure that adapts to large-scale tactical communication network joint training. It needs to organize the distribution and transmission of various kinds of data effectively to assure high efficiency training. Aiming at the information distribution problem of multi-federation interconnected tactical communication network simulation training process, the single federation structure and multi-federation interconnected structure were brought out. Based on the region discerption of training scale and data classification of data, aiming at the characteristics of network traffic, networking parameters and operation state data, the specific solutions were proposed, so that the information distribution in the simulation training was optimized

scholarly journals Analysis and classification of tem¬perature measurements during melting and casting of alloys using neural networks

2020 ◽  
Vol 63 (10) ◽  
pp. 856-861
Author(s):  
A. V. Fedosov ◽  
G. V. Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Human Factor ◽  
High Efficiency ◽  
Thermal Conditions ◽  
Second Stage ◽  
Three Stages ◽  
Fully Automatic ◽  
Artificial Neural

The article considers the issues of monitoring the thermal conditions of alloys melting and casting at foundries. It is noted that the least reliable method is when the measurement and fixing the temperature is assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, and the values are recorded automatically. This method assumes implementation of an algorithm for automatic classification of temperature measurements based on an end-to-end array of data obtained in the production stream. The solving of this task is divided into three stages. Preparing of raw data for classification process is provided on the first stage. On the second stage, the task of measurement classification is solved by using neural network principles. Analysis of the results of the artificial neural network has shown its high efficiency and degree of their correspondence with the actual situation on the work site. It was also noted that the application of artificial neural networks principles makes the classification process flexible, due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the obtained results. Correctly performed data classification provides an opportunity not only to assess compliance with technological discipline at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of alloys melting and casting with minimal costs for melting monitoring.

A Lightweight SE-YOLOv3 Network for Multi-Scale Object Detection in Remote Sensing Imagery

2021 ◽  
Author(s):  
Lifang Zhou ◽  
Guang Deng ◽  
Weisheng Li ◽  
Jianxun Mi ◽  
Bangjun Lei
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Computational Cost ◽  
Image Data ◽  
Detection Accuracy ◽  
Data Sets ◽  
One Stage ◽  
Efficiency And Effectiveness ◽  
Spatial Pyramid Pooling ◽  
Feature Augmentation

Current state-of-the-art detectors achieved impressive performance in detection accuracy with the use of deep learning. However, most of such detectors cannot detect objects in real time due to heavy computational cost, which limits their wide application. Although some one-stage detectors are designed to accelerate the detection speed, it is still not satisfied for task in high-resolution remote sensing images. To address this problem, a lightweight one-stage approach based on YOLOv3 is proposed in this paper, which is named Squeeze-and-Excitation YOLOv3 (SE-YOLOv3). The proposed algorithm maintains high efficiency and effectiveness simultaneously. With an aim to reduce the number of parameters and increase the ability of feature description, two customized modules, lightweight feature extraction and attention-aware feature augmentation, are embedded by utilizing global information and suppressing redundancy features, respectively. To meet the scale invariance, a spatial pyramid pooling method is used to aggregate local features. The evaluation experiments on two remote sensing image data sets, DOTA and NWPU VHR-10, reveal that the proposed approach achieves more competitive detection effect with less computational consumption.

Energy Management Strategies to Improve Electrical Networks Using Storage Systems

2016 ◽  
pp. 63-75
Author(s):  
Juan Aurelio Montero-Sousa ◽  
Luis Alfonso Fernández-Serantes ◽  
José-Luis Casteleiro-Roca ◽  
Xosé Manuel Vilar-Martínez ◽  
Jose Luis Calvo-Rolle
Keyword(s):  
Energy Management ◽  
Storage Systems ◽  
Management Strategies ◽  
Electrical Networks ◽  
Systems Management ◽  
Electrical System ◽  
Peak Shaving ◽  
Electricity Storage ◽  
General Terms

The successive energy crises, usually linked to the rising prices of oil, bring about new topics of the energy systems management in general terms. Over all, the electrical system is one of these cases. In addition, a greater concern for environmental issues has introduced, to a greater or lesser extent, the generation from renewable sources in the electrical system. In this context, the possibility of developing and using electricity storage systems would manage mismatches between generation and demand at electricity networks, making them more efficiently. In this research, we propose a number of possible strategies based on technical peak shaving and valley filling. The tool is used as energy storage systems in general terms, regardless of the accumulation technique used. The classification of strategies essentially serves two criteria: optimization service and increased profitability.

Energy Management Strategies to Improve Electrical Networks Using Storage Systems

2017 ◽  
pp. 1500-1514
Author(s):  
Juan Aurelio Montero-Sousa ◽  
Luis Alfonso Fernández-Serantes ◽  
José-Luis Casteleiro-Roca ◽  
Xosé Manuel Vilar-Martínez ◽  
Jose Luis Calvo-Rolle
Keyword(s):  
Energy Management ◽  
Storage Systems ◽  
Management Strategies ◽  
Electrical Networks ◽  
Systems Management ◽  
Electrical System ◽  
Peak Shaving ◽  
Electricity Storage ◽  
General Terms

The successive energy crises, usually linked to the rising prices of oil, bring about new topics of the energy systems management in general terms. Over all, the electrical system is one of these cases. In addition, a greater concern for environmental issues has introduced, to a greater or lesser extent, the generation from renewable sources in the electrical system. In this context, the possibility of developing and using electricity storage systems would manage mismatches between generation and demand at electricity networks, making them more efficiently. In this research, we propose a number of possible strategies based on technical peak shaving and valley filling. The tool is used as energy storage systems in general terms, regardless of the accumulation technique used. The classification of strategies essentially serves two criteria: optimization service and increased profitability.

scholarly journals Research and Application of One-key Sequence Control Technology for Substations

2019 ◽  
Vol 136 ◽  
pp. 01022
Author(s):  
Ge Li-Qing ◽  
Wang Jian-Feng ◽  
Teng Jing-Yu ◽  
Yang Ming
Keyword(s):  
Decision Support ◽  
Background Information ◽  
Control Technology ◽  
Technology System ◽  
Management Efficiency ◽  
Electrical Grids ◽  
Effectiveness And Efficiency ◽  
The One ◽  
Reasonable Use ◽  
Overall Effectiveness

The substations are important parts of modern electrical grids. In this sense, it is necessary to enhance the management efficiency and robustness of the substations. The one-key sequence control technology system could simultaneously control several subsystems and make use of their functions to automatically operate the substations. In this study, three subsystems, i.e., monitoring subsystems, error analysis subsystem, and decision support subsystem are designed in the one-key sequence control technology system. All the background information from the substations is monitored, checked, detected to find the potential threats. The decision support subsystem provides suggestive ways to handle these problems. Therefore, through the reasonable use of the one-key sequence control technology, the overall effectiveness and efficiency of the substations can be enhanced. With the development of artificial intelligence technologies, the one-key sequence control technology system can be further improved with more powerful functions.

scholarly journals Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2067
Author(s):  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs ◽  
João Gilberto Mendes dos Reis ◽  
Raquel Baracat Tosi Rodrigues da Silva
Keyword(s):  
Decision Tree ◽  
High Efficiency ◽  
Rating Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Environmental Efficiency ◽  
Classification Model ◽  
Bayes Algorithm ◽  
J48 Decision Tree

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

Fetal cardiotocography monitoring using Legendre neural networks

2019 ◽  
Vol 64 (6) ◽  
pp. 669-675 ◽  
Author(s):  
Abdulaziz Alsayyari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Uterine Contraction ◽  
High Efficiency ◽  
Fetal Monitoring ◽  
Data Sets ◽  
Legendre Series ◽  
Electronic Fetal Monitoring ◽  
A New Technique

Abstract A new technique for electronic fetal monitoring (EFM) using an efficient structure of neural networks based on the Legendre series is presented in this paper. Such a structure is achieved by training a Legendre series-based neural network (LNN) to classify the different fetal states based on recorded cardiotocographic (CTG) data sets given by others. These data sets consist of measurements of fetal heart rate (FHR) and uterine contraction (UC). The applied LNN utilizes a Legendre series expansion for the input vectors and, hence, has the capability to produce explicit equations describing multi-input multi-output systems. Simulations of the proposed technique in EFM demonstrate its high efficiency. Training the LNN requires a few number of iterations (5–10 epochs). The applied technique makes the classification of the fetal state available through equations combining the trained LNN weights and the current measured CTG record. A comparison of performance between the proposed LNN and other popular neural network techniques such as the Volterra neural network (VNN) in EFM is provided. The comparison shows that, the LNN outperforms the VNN in case of less computational requirements and fast convergence with a lower mean square error.

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