scholarly journals Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil

2020 ◽  
Vol 19 (3) ◽  
pp. 1210
Author(s):  
Nur Badariah Ahmad Mustafa ◽  
N H Nik Ali ◽  
H. Zainuddin ◽  
Marizuana Mat Daud ◽  
Farah Hani Nordin
Keyword(s):  
Electric Field ◽  
Electrical Power ◽  
Major Axis ◽  
Dielectric Strength ◽  
Minor Axis ◽  
Transformer Oil ◽  
Support Vector ◽  
Electrical Power System ◽  
Feature Descriptors ◽  
Axis Length

Transformer is considered as one of the most important equipment in electrical power system networks. However, most problems occurred in transformer were related to the defects and weakness of the insulation systems. The oils used in transformer act as coolant and insulation purposes hence maintaining the dielectric strength of the transformer. In this work, electric field bridging pattern is observed from pre-breakdown and breakdown condition. The electric field bridging formation was recorded in the experimental setup and images were captured per frame. 193 images were randomly chosen from the whole video frames where 102 images were the pre-breakdown images and 91 images were the breakdown images. This system comprises of four stages: (i) a preprocessing stage to mark the electrodes tips and background subtraction; (ii) a segmentation stage to extract the electric field bridging formation in region of interest; (iii) a feature extraction stage to extract electric field bridging using feature descriptors, <em>area</em>, <em>minor-axis </em>and <em>major-axis length  </em> (iv) a classification stage to identify the pre-breakdown and breakdown condition. System performance was evaluated using support vector machine (SVM), <em>k</em>-nearest neighbour (<em>k</em>-NN) and random forest (RF) and SVM provided the most promising accuracy that was 99%. The results show that the combination of three feature descriptors, <em>area</em>, <em>minor-axis </em>and <em>major-axis length </em>are the best features combination in identifying the transformer oil condition. In future work, further studies will be conducted to investigate the pattern of pre- and post-breakdown due to some similarity found in image pattern. Due to that, more feature descriptors will be identified to find a unique pattern between pre- and post-breakdown condition

scholarly journals Automatic Tajweed Rules Recognition using K-Nearest Neighbour (K-NN)

2019 ◽  
Vol 8 (2S11) ◽  
pp. 552-557
Keyword(s):  
Classification Accuracy ◽  
Major Axis ◽  
Shape Descriptor ◽  
Processing Technique ◽  
Input Image ◽  
Minor Axis ◽  
Image Processing Technique ◽  
Nearest Neighbour ◽  
The People ◽  
Axis Length

Tajweed refers to a pronunciation rule for Al-Quran recitation in Islam. It acts as guidance for Muslims in reciting the Al-Quran in a correct manner. Yet, Tajweed rules could be complicated as it consists of various types of laws. It could also be confusing, and difficult to remember particularly for the people who have less knowledge in Tajweed rules. Thus, a study on automatic tajweed rules recognition using image processing technique is proposed. The scope of this study is limited to Idgham laws only. Initially, the input image went through the pre-processing process which includes four sub-processes which are greyscale conversion, binary conversion, thinning and flip, and word segmentation. Next, six attributes of shape descriptor which are major axis length, minor axis length, eccentricity, filled area, solidity, and perimeter were extracted from each input image. A technique of k-Nearest Neighbour (k-NN) is employed to recognize the two types of Idgham Laws which are Idgham Maal Ghunnah and Idgham Bila Ghunnah. The performance of the proposed study is evaluated to 180 testing images which returned 84.44% of classification accuracy. The outcome of this study is expected to recognize the Tajweed rules automatically and may assist the user on a proper recitation of Al-Quran.

scholarly journals Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Yang ◽  
Yao Dong ◽  
Yanhua Chen ◽  
Caihong Li
Keyword(s):  
New South ◽  
Electrical Power ◽  
New South Wales ◽  
Garch Model ◽  
Electricity Price ◽  
Support Vector ◽  
Ant Colony Optimization Algorithm ◽  
Electrical Power System ◽  
Combined Model ◽  
South Wales

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.

Bubble Formation in a DC Electric Field

2008 ◽  
Author(s):  
Feng Chen ◽  
Yaozu Song ◽  
Yao Peng
Keyword(s):  
Aspect Ratio ◽  
Field Strength ◽  
Electric Field ◽  
Electric Field Strength ◽  
Bubble Growth ◽  
Growth Process ◽  
Major Axis ◽  
Minor Axis ◽  
Electric Stress ◽  
Dc Electric Field

The effect of a DC electric field on the formation and the characteristics of a nitrogen bubble injected from an orifice were studied experimentally and theoretically. This study was the first to divide the bubble growth process into four stages (waiting, expansion, deformation and detachment) according to the variation of the bubble shape in order to analyze the bubble behavior in the electric field. During the waiting stage, the waiting interval decreases significantly as the electric field strength rises. In the expansion stage, the minor axis reaches a maximum that decreases with increasing the electric field strength. Within the deformation stage, the major axis achieves its maximum and so does the aspect ratio. As the electric field strength rises, both the maximums of the major axis and the aspect ratio increase. At the detachment stage, as the electric field strength is intensified, the major axis lengthens, the minor axis shortens and the aspect ratio lengthens. From the waiting stage to the detachment stage, the effect of the electric field on the major axis of the bubble is marginal, while with increasing the electric field strength, the minor axis decreases distinctly and thus the aspect ratio increases. To employ the four-stage model, the bubble growth process was analyzed in detail under the electric field. The electric stress exerted on the bubble surface was calculated. The results show that the electric stress compresses the bubble equator and elongates the poles of the bubble, causing the bubble to elongate along the electric field direction.

scholarly journals Advantages of petrosectomy for superficial temporal artery to superior cerebellar artery bypass based on three-dimensional distance measurements using cadaver heads

2021 ◽  
Author(s):  
Kenji Uda ◽  
Kuniaki Tanahashi ◽  
Takashi Mamiya ◽  
Fumiaki Kanamori ◽  
Kinya Yokoyama ◽  
...  
Keyword(s):  
Superficial Temporal Artery ◽  
Three Dimensional ◽  
Major Axis ◽  
Temporal Artery ◽  
Minor Axis ◽  
Field Size ◽  
Superior Cerebellar Artery ◽  
Operative Field ◽  
Axis Length ◽  
Optimal Approach

AbstractSuperficial temporal artery (STA) to superior cerebellar artery (SCA) bypass is usually performed via the subtemporal approach (StA), anterior transpetrosal approach (ApA), or combined petrosal approach (CpA), but no study has yet reported a quantitative comparison of the operative field size provided by each approach, and the optimal approach is unclear. The objective of this study is to establish evidence for selecting the approach by using cadaver heads to measure the three-dimensional distances that represent the operative field size for STA–SCA bypass. Ten sides of 10 cadaver heads were used to perform the four approaches: StA, ApA with and without zygomatic arch osteotomy (ApA-ZO− and ApA-ZO+), and CpA. For each approach, the major-axis length and the minor-axis length at the anastomosis site (La-A and Li-A), the major-axis length and the minor-axis length at the brain surface (La-B and Li-B), the depth from the brain surface to the anastomosis site (Dp), and the operating angles of the major axis and the minor axis (OAa and OAi) were measured. Shallower Dp and wider operating angle were obtained in the order CpA, ApA-ZO+, ApA-ZO−, and StA. In all parameters, ApA-ZO− extended the operative field more than StA. ApA-ZO+ extended La-B and OAa more than ApA-ZO−, whereas it did not contribute to Dp and OAi. CpA significantly decreased Dp, and widened OAa and OAi more than ApA-ZO+. ApA and CpA greatly expanded the operative field compared with StA. These results provide criteria for selecting the optimal approach for STA-SCA bypass in light of an individual surgeon’s anastomosis skill level.

scholarly journals Computer Vision-based Human Activity Recognition for Elderly Home Care

2019 ◽  
Vol 9 (1S) ◽  
pp. 299-303
Keyword(s):  
Computer Vision ◽  
Elderly People ◽  
Orientation Angle ◽  
Major Axis ◽  
Gaussian Mixture ◽  
Minor Axis ◽  
Video Sequences ◽  
Novel Approach ◽  
Human Object ◽  
Axis Length

Video-based monitoring of elderly people at home receives more attention in recent days. In this paper, we propose a novel approach to develop smart monitoring system for elderly people using computer vision techniques. Gaussian Mixture Model (GMM) based algorithm is used for background and foreground separation inorder to track the activities of human object. The minimum bounding box of the human object is traced and features like major axis length, minor axis length and orientation angle are extracted. The proposed approach is evaluated on the video sequences of fall dataset.

Power Load Prediction Based on a Hybrid Forecast Algorithm

2013 ◽  
Vol 448-453 ◽  
pp. 2516-2519
Author(s):  
Min Zou ◽  
Huan Qi Tao
Keyword(s):  
Wavelet Transform ◽  
Support Vector Machines ◽  
Electrical Power ◽  
Least Square ◽  
Support Vector ◽  
Load Prediction ◽  
Electrical Power System ◽  
Power Load ◽  
Vector Machines ◽  
Standard Support

Power load prediction is an important task for the electrical power system. The nonstationary, nonlinear and volatile characteristics of power load data make more difficult for the accurate load prediction. This paper presents a hybrid forecast algorithm based on wavelet transform and support vector machines for power load prediction. The hybrid algorithm firstly decomposed the load series to several subseries with obvious tendency by wavelet transform. Then these subseries are forecasted with least square support vector machines (LS-SVM), an extension of standard support vector machines, respectively. Finally these forecast results were reconstructed as the prediction of original power load series. The effective simulation results of above algorithm were testified based on a sample load series.

scholarly journals Study of the effect of cellulose fibers on the electrical strength of transformer oil

Vestnik IGEU ◽  
2020 ◽  
pp. 23-33
Author(s):  
O.S. Melnikova ◽  
M.V. Prusakov ◽  
A.A. Zholobov
Keyword(s):  
Electric Field ◽  
Breakdown Voltage ◽  
Breakdown Strength ◽  
Dielectric Strength ◽  
Cellulose Fibers ◽  
Electrode System ◽  
Transformer Oil ◽  
Electrical Strength ◽  
Abrupt Decrease ◽  
Transformer Insulation

The electrical strength of transformer oil is the first parameter in transformer insulation tests. Such tests are carried out in a standard discharger according to the values of breakdown voltage. An abrupt decrease in electrical strength occurs when oil is contaminated with mechanical impurities. The greatest influence on the electric field is exerted by highly conductive cellulose fibers. The field between the electrodes may be severely distorted bya «bridge» of such fibers. At the same time, the influence of such particles is not taken into account in the tests. The problem is to experimentally determine the effect of such impurities on the breakdown strength. Thereby, this research poses and solves the problem of determining the dielectric strength of transformer oil in a standard discharger in the presence of cellulose fibers.To simulate electric field strengths, the ANSYS software package has been used. The basis of the 3D model was a standard measuring cell for determining breakdown voltage, which takes into account the boundary conditions in the form of a cube in which the electrode system is located, and the values of the electric field strength in the center of the electrode system.The electric field tension between the electrodes has been calculated, taking into account the influence of increased conductivity of cellulose fibers. It has been found that the electrical strength of oil gaps of moistened fibers with a length of more than 200 μm is significantly reduced, which is not taken into account when testing transformer oil for breakdown in a standard cell. This leads to inaccuracy in determining the electric strength of transformer oil in existing equipment.The results of the study can be used by operational services to improve the assessment of the quality of transformer oil used in power transformers as insulation. The results also can be used to study the mechanisms of electrophysical processes occurring in liquid dielectrics in the presence of fibers.

scholarly journals lakemorpho: Calculating lake morphometry metrics in R

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1718 ◽  
Author(s):  
Jeffrey Hollister ◽  
Joseph Stachelek
Keyword(s):  
Software Tool ◽  
Major Axis ◽  
R Package ◽  
Minor Axis ◽  
Lake Morphometry ◽  
Shoreline Development ◽  
Lake Ecology ◽  
Digital Elevation ◽  
Scale Modelling ◽  
Axis Length

Metrics describing the shape and size of lakes, known as lake morphometry metrics, are important for any limnological study. In cases where a lake has long been the subject of study these data are often already collected and are openly available. Many other lakes have these data collected, but access is challenging as it is often stored on individual computers (or worse, in filing cabinets) and is available only to the primary investigators. The vast majority of lakes fall into a third category in which the data are not available. This makes broad scale modelling of lake ecology a challenge as some of the key information about in-lake processes are unavailable. While this valuable in situ information may be difficult to obtain, several national datasets exist that may be used to model and estimate lake morphometry. In particular, digital elevation models and hydrography have been shown to be predictive of several lake morphometry metrics. The R package lakemorpho has been developed to utilize these data and estimate the following morphometry metrics: surface area, shoreline length, major axis length, minor axis length, major and minor axis length ratio, shoreline development, maximum depth, mean depth, volume, maximum lake length, mean lake width, maximum lake width, and fetch. In this software tool article we describe the motivation behind developing lakemorpho, discuss the implementation in R, and describe the use of lakemorpho with an example of a typical use case.

scholarly journals Rice Grain Classification using Multi-class Support Vector Machine (SVM)

2019 ◽  
Vol 8 (3) ◽  
pp. 215
Author(s):  
Shafaf Ibrahim ◽  
Nurul Amirah Zulkifli ◽  
Nurbaity Sabri ◽  
Anis Amilah Shari ◽  
Mohd Rahmat Mohd Noordin
Keyword(s):  
Support Vector Machine ◽  
Brown Rice ◽  
Major Axis ◽  
Extraction Process ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Support Vector ◽  
Rice Grain ◽  
Axis Length

<span>Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.</span>

scholarly journals Neoangiogenesis and microvascular density in myelodysplastic syndrome – a single center experience

2019 ◽  
Vol 2 (2) ◽  
pp. 40-50
Author(s):  
Anita Skrtic ◽  
Njetocka Gredelj-Simec ◽  
Ika Kardum-Skelin ◽  
Eva Lovric ◽  
Darija Muzinic ◽  
...  
Keyword(s):  
High Risk ◽  
Major Axis ◽  
Risk Groups ◽  
Microvascular Density ◽  
Minor Axis ◽  
Control Group ◽  
Bone Marrow Biopsies ◽  
Single Center Experience ◽  
Axis Length ◽  
Network Transformation

Angiogenesis has a significant part in the pathogenesis of hematological malignancies, such as leukemia and myelodysplastic syndromes (MDS). We evaluated the relationship between morphometric, morphological and clinical features of MDS. Blood vessels of 31 newly diagnosed MDS bone marrow biopsies were immunohistochemically analyzed using CD34 and compared with 8 controls and 13 chronic myelomonocytic leukemias (CMML). MDS were categorized into three risk groups: low-, intermediate- and high-risk MDS. Microvascular density (MVD) and major and minor axis length were analyzed using digital image analysis. Overall, MDS had significantly higher MVD and lower minor axis values than the control group and CMML. High-risk MDS had significantly higher MVD compared to the controls, while all MDS risk groups had lower minor axis values than the control group. Increased minor and major axis values were prognostic predictors of shorter overall survival in all MDS risk groups and CMML patients. In conclusion, angiogenesis presents one of the essential factors in MDS pathogenesis and progression characterized by descriptive marrow microvascular network transformation. The size-related features are powerful indicators of survival in MDS patients.

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