Research on grounding grid corrosion detection based on hybrid artificial intelligence algorithm

2022 ◽  
pp. 1-22
Author(s):  
Haize Hu ◽  
Yunyi Li ◽  
Mengge Fang ◽  
Feiyu Hu ◽  
Zhanpeng Rong
Keyword(s):  
Artificial Intelligence ◽  
Detection Method ◽  
Short Circuit ◽  
Short Circuit Current ◽  
The Other ◽  
Reference Object ◽  
Grounding Grid ◽  
Intelligence Algorithm ◽  
Diagnosis Rate ◽  
Artificial Intelligence Algorithm

As an important part of substation, grounding grid is the main approach to release short-circuit current. Grounding grid is in the complex electromagnetic compund,and with increasely being operated, it is easily corroded for various reasons, resulting in short-circuit current not being discharged normally. It is difficult to detect the grounding grid without excavation, because it is generally buried underground. Therefore, it is very important to accurately detect the grounding grid without excavation. In this paper, a grounding grid detection method based on artificial intelligence hybrid algorithm is proposed. In order to verify the accuracy of the detection method, the grounding grid model is established by using electromagnetic transient simulation software ATP-EMTP. According to the ATP-EMTP simulation model, the node potential of each point of the grounding grid is detected as the reference object for verification. In order to remove the randomness of the simulation results, the average value of 20 tests was used as the corrosion diagnosis result. The results show that the missed diagnosis rate of the proposed in paper was 2.1%, which was reduced by 12.1%, 7.1% and 7.5% respectively compared with the other three algorithms. At the same time, the misdiagnosis is 2.1%, which is reduced by 10%, 6.2% and 12.9% respectively for the other three algorithms. In sum, the corrosion leakage diagnosis rate and misdiagnosis rate of the proposed artificial intelligence algorithm are lower than those of the other three optimization algorithms, and have higher accuracy and stability in corrosion diagnosis.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

scholarly journals Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 14
Author(s):  
Mei Dong ◽  
Hongyu Wu ◽  
Hui Hu ◽  
Rafig Azzam ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Monitoring System ◽  
Slope Instability ◽  
Mountainous Area ◽  
Probabilistic Forecasting ◽  
Intelligence Algorithm ◽  
Good Prediction Accuracy ◽  
Artificial Intelligence Algorithm ◽  
Steep Slopes

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

ATP stimulates Ca2+ release from a rapidly exchanging pool in cultured rat epididymal cells

1993 ◽  
Vol 264 (6) ◽  
pp. C1388-C1394 ◽  
Author(s):  
A. Y. Leung ◽  
H. L. Tai ◽  
P. Y. Wong
Keyword(s):  
Short Circuit ◽  
Short Circuit Current ◽  
The Other ◽  
Free Solution ◽  
Tetraacetic Acid ◽  
Cl Secretion ◽  
Transient Rise ◽  
Extracellular Compartment ◽  
Dose Dependent Fashion ◽  
Dose Dependent

A study was carried out to investigate an ATP-sensitive Ca2+ pool in rat epididymal cells and its role in transepithelial Cl- secretion. In normal buffered solution containing 2.5 mM free Ca2+, ATP triggered single calcium spikes in a dose-dependent fashion. In nominally Ca(2+)-free solution, the peaks of successive Ca2+ spikes diminished after repeated ATP stimulations. Addition of Sr2+ (2.5 mM) to Ca(2+)-free solution after ATP stimulation did not cause changes in fluorescence signals. However, in the presence of Sr2+, ATP gave rise to apparent repetitive Ca2+ spikes of similar magnitudes after repeated stimulations. Increasing the time of exposure in Ca(2+)-free solution containing 50 microM ethylene glycol-bis(beta-amino-ethyl ether)-N,N,N',N'-tetraacetic acid rapidly decreased the intracellular Ca2+ concentration ([Ca2+]i) response to subsequent ATP stimulation. On the other hand, increasing the time of exposure in Sr(2+)-containing solution in Ca(2+)-depleted cells rapidly increased the apparent [Ca2+]i response to subsequent ATP stimulation. These observations suggested the existence of a Ca2+ pool that was rapidly exchanging with the extracellular compartment. Apical application of ATP elicited a transient rise in short-circuit current across the epididymal epithelium in a dose-dependent fashion, and the response was reduced by prior stimulation with thapsigargin. Ca2+ released from a rapidly exchanging ATP-sensitive store might stimulate Cl- secretion in the epididymis, thereby maintaining the electrolyte contents and fluidity of the epididymal microenvironment.

scholarly journals Artificial Intelligence Algorithm-Based Ultrasound Image Segmentation Technology in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lianhua Zhang ◽  
Zhiying Jia ◽  
Xiaoling Leng ◽  
Fucheng Ma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Node Metastasis ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.

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