Research on grounding grid corrosion detection based on hybrid 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.

Application of Artificial Intelligence in an Unsupervised Algorithm for Trajectory Segmentation Based on Multiple Motion Features

With the development of the wireless network, location-based services (e.g., the place of interest recommendation) play a crucial role in daily life. However, the data acquired is noisy, massive, it is difficult to mine it by artificial intelligence algorithm. One of the fundamental problems of trajectory knowledge discovery is trajectory segmentation. Reasonable segmentation can reduce computing resources and improvement of storage effectiveness. In this work, we propose an unsupervised algorithm for trajectory segmentation based on multiple motion features (TS-MF). The proposed algorithm consists of two steps: segmentation and mergence. The segmentation part uses the Pearson coefficient to measure the similarity of adjacent trajectory points and extract the segmentation points from a global perspective. The merging part optimizes the minimum description length (MDL) value by merging local sub-trajectories, which can avoid excessive segmentation and improve the accuracy of trajectory segmentation. To demonstrate the effectiveness of the proposed algorithm, experiments are conducted on two real datasets. Evaluations of the algorithm’s performance in comparison with the state-of-the-art indicate the proposed method achieves the highest harmonic average of purity and coverage.

Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

Based on the hourly visibility data, visibility and its changes during 2010–2020 at monthly and annual time scales over 47 international airports in China are investigated, and nine artificial-intelligence-based hourly visibility prediction models are trained (hourly data in 2018–2019) and tested (hourly data in 2020) at these airports. The analyses show that the visibility of airports in eastern and central China is at a poor level all year round, and LXA (in Lhasa) has good visibility all year round. Airports in south and the northwest China have better visibility from May to October and poorer visibility from November to April. In all months, the increasing visibility mainly occurs in the central, northeast and coastal areas of China, while decreasing visibility mainly appears in the western and northern parts of China. In spring, summer and autumn, the changes difference between east and west is particularly obvious. This East–West distribution of trends is obviously different from the North–South distribution shown by the mean. For all airports, good visibility mainly occurs from 14:00–18:00 p.m. Beijing Time, while poor visibility mainly concentrates from 22:00 p.m. to 12:00 p.m. the next day, especially between 3:00–9:00 a.m. Our proposed artificial intelligence algorithm models can be reasonably used in airport visibility prediction. In particular, most algorithm models have the best results in the visibility prediction over HFE (in Hefei) and SJW (in Shijiazhuang). On the contrary, the worst forecast results appear at LXA and LHW (in Lanzhou) airports. The prediction results of airport visibility in the cold season (October–December) are better than those in the warm season (May–September). Among the algorithm models, the prediction performance of the RF-based model is the best.

Optimization Analysis of Knowledge Label Personalized Dynamic Recommendation System Based on Artificial Intelligence Algorithm

There are many p roblems and shortcomings in traditional 3D modeling tech of environment design, and there is a great room for amelioration in reconstruction method and reconstruction precision. As an intelligent means integrating computer, info network and big data, CAD can create a more experiential and interactive environment design atmosphere. Based on this, this paper first analyses the concept and connotation of 3D modeling of environmental design, then studies the computer-aided environment design, and finally gives the 3D modeling tech of computer-aided environmental design.

Clone Selection Artificial Intelligence Algorithm-Based Positron Emission Tomography-Computed Tomography Image Information Data Analysis for the Qualitative Diagnosis of Serous Cavity Effusion in Patients with Malignant Tumors

This study aimed to investigate the application of positron emission tomography- (PET-) computed tomography (CT) image information data combined with serous cavity effusion based on clone selection artificial intelligence algorithm in the diagnosis of patients with malignant tumors. A total of 97 patients with PET-CT scanning and empirically confirmed as serous cavity effusion were retrospectively analyzed in this study. The clone selection artificial intelligence algorithm was applied to register the PET-CT images, and the patients were rolled into a benign effusion group and a malignant effusion group according to the benign and malignant conditions of the serous cavity effusion. Besides, the causes of patients from the two groups were analyzed, and there was a comparison of their physiological conditions. Subsequently, CT values of different KeV, lipid/water, water/iodine, and water/calcium concentrations were measured, and the differences of the above quantitative parameters between benign and malignant serous cavity effusion were compared, as well as the registration results of the clone algorithm. The results showed that the registration time and misalignment times of clonal selection algorithm (13.88, 0) were lower than those of genetic algorithm (18.72, 8). There were marked differences in CT values of 40–60 keV and 130–140 keV between the two groups. The concentrations of lipid/water, water/iodine, and water/calcium in basal substances of the malignant effusion group were obviously higher than the concentrations of the benign effusion group ( P < 0.05 ). Benign and malignant effusions presented different manifestations in PET-CT, which was conducive to the further diagnosis of malignant tumors. Based on clone selection artificial intelligence algorithm, PET-CT could provide a new multiparameter method for the identification of benign and malignant serous cavity effusions and benign and malignant tumors.

Artificial Intelligence Identified Resilient and Vulnerable Female Rats After Traumatic Stress and Ethanol Exposure: Investigation of Neuropeptide Y Pathway Regulation

Post-traumatic stress disorder (PTSD) is initiated by traumatic-stress exposure and manifests into a collection of symptoms including increased anxiety, sleep disturbances, enhanced response to triggers, and increased sympathetic nervous system arousal. PTSD is highly co-occurring with alcohol use disorder. Only some individuals experiencing traumatic stress develop PTSD and a subset of individuals with PTSD develop co-occurring alcohol use disorder. To investigate the basis of these individual responses to traumatic stress, single prolonged stress (SPS) a rodent model of traumatic stress was applied to young adult female rats. Individual responses to SPS were characterized by measuring anxiety-like behaviors with open field and elevated plus maze tests. Rats were then allowed to drink ethanol under an intermittent two bottle choice procedure for 8 weeks, and ethanol consumption was measured. An artificial intelligence algorithm was built to predict resilient and vulnerable individuals based on data from anxiety testing and ethanol consumption. This model was implemented in a second cohort of rats that underwent SPS without ethanol drinking to identify resilient and vulnerable individuals for further study. Analysis of neuropeptide Y (NPY) levels and expression of its receptors Y1R and Y2R mRNA in the central nucleus of the amygdala (CeA), basolateral amygdala (BLA), and bed nucleus stria terminalis (BNST) were performed. Results demonstrate that resilient rats had higher expression of Y2R mRNA in the CeA compared with vulnerable and control rats and had higher levels of NPY protein in the BNST compared to controls. The results of the study show that an artificial intelligence algorithm can identify individual differences in response to traumatic stress which can be used to predict subsequent ethanol drinking, and the NPY pathway is differentially altered following traumatic stress exposure in resilient and vulnerable populations. Understanding neurochemical alterations following traumatic-stress exposure is critical in developing prevention strategies for the vulnerable phenotype and will help further development of novel therapeutic approaches for individuals suffering from PTSD and at risk for alcohol use disorder.