Review on deep learning applications in frequency analysis and control of modern power system

Zhang, Yi; Shi, Xiaohan; Zhang, Hengxu; Cao, Yongji; Terzija, Vladimir

doi:10.1016/j.ijepes.2021.107744

Cited by 163 publications

(67 citation statements)

References 137 publications

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“…It is to first calculate the breast ultrasound gray level L , then use the OSTU algorithm to calculate the threshold, then get the image foreground and background, then use OSTU on the background to calculate another threshold again while keeping the background unchanged, and finally use the threshold segmentation of the entire image; so far, the experiment is over. Finally, the algorithm model is applied to clinical data for experimental verification [ 33 ].…”

Section: Application Experiments Of Artificial Intelligence Nuclear Medicine Automated Imagesmentioning

confidence: 99%

Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis

Sun

Yuan

2022

Journal of Healthcare Engineering

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In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence nuclear medicine automated images in tumor diagnosis. This paper studies the methods to improve the accuracy of the segmentation algorithm from the perspective of boundary recognition and shape changeable adaptive capabilities, studies the active contour model based on boundary constraints, and proposes a superpixel boundary-aware convolution network to realize the automatic CT cutting algorithm. In this way, the tumor image can be cut more accurately. The experimental results in this paper show that the improved algorithm in this paper is more robust than the traditional CT algorithm in terms of accuracy and sensitivity, an increase of about 12%, and a slight increase in the negative prediction rate of 3%. In the comparison of cutting images of malignant tumors, the cutting effect of the algorithm in this paper is about 34% higher than that of the traditional algorithm.

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Section: Application Experiments Of Artificial Intelligence Nuclear Medicine Automated Imagesmentioning

confidence: 99%

Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis

Sun

Yuan

2022

Journal of Healthcare Engineering

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“…In the past few decades, a variety of mathematical computational approaches have been implemented in different research fields such as fluid mechanic engineering [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], chemical engineering , electrical engineering , robotics and automation , urban planning engineering [73][74][75][76], petroleum engineering [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], energy engineering [95,96], mathematics [97][98][99][100][101][102][103][104], environmental engineering [105][106]…”

Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

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“…For example, the ongoing scientific discussion on the topic of XAI (explainable artificial intelligence) shows that this is one of the core problems of deep learning [ 59 , 60 , 61 ]. This problem is a direct result of model complexity, which in turn results from the application of artificial intelligence to complex tasks in complex environments [ 62 , 63 ]. In this context, however, the question is not only to what extent a task can be automated, but also to what extent it should be automated [ 64 , 65 ].…”

Section: Resultsmentioning

confidence: 99%

Sources of Risk of AI Systems

Steimers

Schneider

2022

IJERPH

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Artificial intelligence can be used to realise new types of protective devices and assistance systems, so their importance for occupational safety and health is continuously increasing. However, established risk mitigation measures in software development are only partially suitable for applications in AI systems, which only create new sources of risk. Risk management for systems that for systems using AI must therefore be adapted to the new problems. This work objects to contribute hereto by identifying relevant sources of risk for AI systems. For this purpose, the differences between AI systems, especially those based on modern machine learning methods, and classical software were analysed, and the current research fields of trustworthy AI were evaluated. On this basis, a taxonomy could be created that provides an overview of various AI-specific sources of risk. These new sources of risk should be taken into account in the overall risk assessment of a system based on AI technologies, examined for their criticality and managed accordingly at an early stage to prevent a later system failure.

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Review on deep learning applications in frequency analysis and control of modern power system

Cited by 163 publications

References 137 publications

Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis

Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

Sources of Risk of AI Systems

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