A Review on Intelligent Detection and Classification of Power Quality Disturbances:Trends,Methodologies,and Prospects

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more prominent.It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids.In order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse effects.This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly presented.Finally,comments on the future evolution of PQD detection techniques are given.Unlike the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

Research on Business Model Optimization of Artificial Intelligence Garbage Classification

In recent years,garbage classification and environmental protection are gradually becoming an important step in the construction of ecological civilization in China.However,the popularity and commercial value of the application of artificial intelligence trash cans in Beijing are not high at present.This article analyzes these problems one by one and propose solutions,hoping that the commercial value of artificial intelligence trash cans can be optimized and improved and to make the city greener.This paper uses the questionnaire method and the literature method to research and analyze the optimization of the business model of artificial intelligence in garbage classification.

Intelligent classification model of surrounding rock of tunnel using drilling and blasting method

Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience.To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human participation.An intelligent classification technique based on information technology and artificial intelligence could overcome these issues.In this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this study.Based on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock.This approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.

An improved fast fractal image compression using spatial texture correlation

This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.

Intelligent classification of lung malignancies using deep learning techniques

Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung diseases.To detect the location of the cancerous lung nodules,this work uses novel deep learning methods.The majority of the early investigations used CT,magnetic resonance and mammography imaging.Using appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer.All of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the patients.To address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were utilized.Design/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early stage.In certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly detected.Deep learning is a new,capable and influential approach for predicting medical images.In this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung nodules.Convolutional neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image dataset.Findings-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan images.According to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google Collaborator.Originality/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and DenseNet169.The results are validated by computing the confusion matrix for each network type.