scholarly journals Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

2017 ◽  
Vol 7 (3) ◽  
pp. 593
Author(s):  
Novizon Novizon ◽  
Zulkurnain Abdul-Malek ◽  
Aulia Aulia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Leakage Current ◽  
Thermal Image ◽  
Histogram Method ◽  
Good Tool ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Long Time ◽  
Artificial Neural

<p>Manual analysis of thermal image for detecting defects and classifying of condition of surge arrester take a long time. Artificial neural network is good tool for predict and classify data. This study applied neural network for classify the degree of degradation of surge arrester. Thermal image as input of neural network was segmented using Otsu’s segmentation and histogram method to get features of thermal image. Leakage current as a target of supervise neural network was extracted and applied Fast Fourier Transform to get third harmonic of resistive leakage current. The classification results meet satisfaction with error about 3%.</p>

Correlation between Third Harmonic Leakage Current and Thermography Image of Zinc Oxide Surge Arrester for Fault Monitoring Using Artificial Neural Network

2014 ◽  
Vol 554 ◽  
pp. 598-602 ◽  
Author(s):  
Yusuf Novizon ◽  
Abdul Malek Zulkurnain ◽  
Abdul Malek Zulkurnain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Leakage Current ◽  
Thermal Image ◽  
Critical Parameters ◽  
Thermal Images ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The ageing level of ZnO materials in gapless surge arresters can be determined by using either the traditional leakage current measurements or recently introduced thermal images of the arrester. However, a direct correlation between arrester thermal images and its leakage current (and hence the ageing level) is yet to be established. This paper attempts to find such a correlation using an artificial neural network (ANN). Experimental work was carried out to capture both the thermal images and leakage current of 120kV rated polymeric housed gapless arresters. Critical parameters were then extracted from both the thermal image and the leakage current, before being exported into the artificial neural network tool. Using the leakage current level, the conditions of the arrester are classified as normal, caution, and faulty. The ANN correctly classifies the ageing level using only the thermal image information with an accuracy of 97%, which is highly encouraging.

Artificial Neural Network in Operation Management Regarding Communication Issue

2021 ◽  
pp. 2303-2320
Author(s):  
Ayan Chatterjee ◽  
Susmita Sarkar ◽  
Mahendra Rong ◽  
Debmallya Chatterjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travelling Salesman Problem ◽  
Operation Management ◽  
Routing Problem ◽  
Important Concern ◽  
Management Area ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Communication issue in operation management is important concern in the age of 21st century. In operation, communication can be described based on major three wings- Travelling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and Transportation Problem (TP). Artificial Neural Network (ANN) is an important tool to handle these systems. In this chapter, different ANN based models are discussed in a comprehensive way. This chapter deals with how various approaches of ANN help to design the optimal communication network. This comprehensive study is important to the decision makers for the analytical consideration. Although there is a lot of development in this particular domain from a long time ago; but only the revolutionary contributed models are taken into account. Another motivation of this chapter is understanding the importance of ANN in the operation management area.

Prediction of Resilient Modulus for Hot Mix Asphalt Based on Artificial Neural Network

2011 ◽  
Vol 304 ◽  
pp. 18-23
Author(s):  
Chun Hua Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Hot Mix Asphalt ◽  
Resilient Modulus ◽  
Structure Design ◽  
Forecast Precision ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Resilient modulus of material is an important parameter for pavement structure design and analysis. However it is very tedious to get this parameter for hot mixture asphalt in laboratory. Moreover it takes long time to do experiments. In this paper, artificial neural network (ANN) is applied to predict to resilient modulus for hot mixture asphalt. A neural network model is constructed and trained plenty of times with selected test data until precision meets requirement. Then the model is used to predict resilient modulus for hot mix asphalt. Result of contrast prediction with test data shows that forecast precision is high. This provides a new method to predict resilient modulus for hot mixture asphalt.

Using Artificial Neural Network to Filter Spam for Chinese Mail

2011 ◽  
Vol 55-57 ◽  
pp. 762-766
Author(s):  
Shih Ming Pi ◽  
Hsiu Li Liao ◽  
Su Houn Liu ◽  
Ding Kang Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Text Classification ◽  
False Positives ◽  
The Internet ◽  
Network Approach ◽  
Good Tool ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Computing Speed

As the Internet developed, the problem of spam has become increasingly serious. Not only caused great distress to individuals, but also have a great business costs. With improvements in computing speed, neural network is becoming a very good tool for text classification. The purpose of this study is to conduct few experiments by using neural network approach for Chinese mails’ content. The result shows that neural network approach is effective for Chinese mails’ spam-identification and the adjustments of some parameters (the number of keywords, the number of nodes, and the number of categories) also increase the accurate rate, while reducing false positives.

Long-Term Sales Forecast Method Based on Grey Model and Artificial Neural Network

2013 ◽  
Vol 385-386 ◽  
pp. 1726-1729
Author(s):  
Yi Jun Wang ◽  
Hong Ying Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulation Experiment ◽  
Grey Model ◽  
Forecast Method ◽  
Sales Forecast ◽  
Long Time ◽  
Artificial Neural ◽  
The U.S

Long-term sales forecasting is a problem that has been focused on for a long time. In order to forecast the long-term sales of an industry or an enterprise accurately, a new method based on Grey Model and Artificial Neural Network is proposed in this paper. The effectiveness and feasibility of the proposed method is verified by simulation experiment using sales data of the manufacturing and trade industry provided by the U.S. government.

scholarly journals Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Features from Accelerated Segment Test (FAST)

2020 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Raden Sumiharto ◽  
Ristya Ginanjar Putra ◽  
Samuel Demetouw
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nutrient Content ◽  
Scale Invariant ◽  
Rice Field Soil ◽  
Long Time ◽  
The Mean ◽  
Artificial Neural ◽  
Phosphorus And Potassium ◽  
Scale Invariant Feature

Nutrient Content NPK is macro nutrient content that important for the growth of a plant. The measurement of NPK conducted periodically, but the measurement using laboratories test need relatively long time. This Research is conducted to determine the nutrient content of the soil, consisted of nitrogen, phosphor, and calcium (NPK) using digital image processing based on Features from Accelerated Segment Test (FAST) and backpropagation artificial neural network. The data sample in this research taken from the rice field soil in Daerah Istimewa Yogyakarta province where the soil taken at the length of 30 cm to 110 cm with l20 cm interval, and -30° to 30° degree with interval 10°. The model from this measurement system based on texture’s characteristic that extracted using Scale Invariant Feature Transform from soil’s image that already passed pre-processing process. The characteristic result will be the input from the artificial neural network with a variation on parameter’s model. The model tested for the purpose of knowing the influence the distance and degree where the image taken and the influence of parameter’s artificial neural network. The result from the research, is a accurate value of the measurement for each nutrient in the soil, nitrogen (94.86%), phosphor (58.93%) and calcium (63.57%), with the mean 72,46%. The corresponding result obtained from image taken with optimal height of 70 cm and degree 0o

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