Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Wang, Junfei; Srikantha, Pirathayini

doi:10.1109/tsg.2021.3062722

Cited by 33 publications

(12 citation statements)

References 35 publications

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“…The connection weight parameter w and the threshold h of the model are preset and cannot be changed. The connection weight parameter w and the threshold h of the perceptron can be automatically corrected by training [ 14 ]. Although the perceptron has the ability to learn, it cannot solve the simple linear inseparable distribution problem in the two-dimensional plane.…”

Section: Deep Learning Algorithm For the Layout Of Urban Social Facil...mentioning

confidence: 99%

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Huang

2022

Computational Intelligence and Neuroscience

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The spatial layout and optimization of social facilities for sports are related to many factors such as urban economy, transportation, population, and urban planning. With the rapid development of artificial intelligence today, deep learning came into being. It also provides a lot of methods for our in-depth research on it. To solve the problem of unbalanced layout of urban social facilities for sports and the difficulty in meeting the needs of residents under the current background, this paper adopts the methods of M-P model, loss function, and activation function. Taking Hangzhou, a sports city that is about to host the Asian Games, as an example, it carried out an experiment to optimize the layout of urban public sports services. Through various tests and data, it divides the accessibility level of social facilities for sports in Hangzhou into four levels: high accessibility point (1.0-1.5), high accessibility point (0.5-1.0), the accessibility is average (0.2-0.5), and the accessibility is poor (<0.2). It also concluded that the areas with high accessibility (1.0-1.5) are Shangcheng District and Binjiang District, and the layout of sports facilities is optimal. According to the minimum facility point model, Xiaoshan District and Yuhang District need to add 6 large-scale social facilities for sports. Through this experiment, this paper can also provide a reference for other urban layouts. This has significance for the optimization of sports facilities and the optimization of public facilities.

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Section: Deep Learning Algorithm For the Layout Of Urban Social Facil...mentioning

confidence: 99%

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Huang

2022

Computational Intelligence and Neuroscience

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“…Yang et al analyzed the construction project cost risk management theory [5]. Wang and Srikantha used quantitative evaluation methods to construct a fuzzy comprehensive evaluation model of engineering project cost risk, thereby, quantitatively assessing the impact of risk factors on the cost of highway engineering projects [6]. Wang et al used Monte Carlo simulation and crystal ball software to analyze the risk level of engineering project cost [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Zhong

Yin

2022

Journal of Control Science and Engineering

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In order to effectively control the cost risk of power grid construction projects, the author proposes a cost risk management system for power grid construction projects based on the fuzzy clustering algorithm. The system introduces the fuzzy clustering maximum tree algorithm, and by constructing a mathematical model, combined with the empirical analysis, the key risk factors in the cost risk of power grid construction projects are identified. Through the analysis, it can be concluded that the key risks in the cost risk of power grid construction projects are the planning risks of infrastructure projects, the research risks of infrastructure projects, and the cost risks of infrastructure projects. Experimental results show that combined with expert experience and the actual situation of power grid engineering, the classification result at threshold λ = 0.785 becomes more realistic. At this λ level, 6 risk factors are grouped into 4 categories as follows: class I x 1 , x 2 , x 4 , class II x 3 , class III x 5 , and class IV x 6 . Through research, the identification of key risks can enable project managers to control the cost of power grid construction projects, targeted, so that risks can be minimized and investment returns can be improved.

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“…Deep learning is a complex machine learning algorithm that has achieved far more results in speech and image recognition than previous related technologies [3]. Deep learning has achieved a lot in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, voice, recommendation and personalization technologies, and other related elds [4]. Deep learning makes machines imitate human activities such as audio-visual and thinking, and solves many complex pattern recognition problems, which makes great progress in artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

Wang

2022

Mathematical Problems in Engineering

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Ideological and political education can improve people’s ideological and moral character and psychological quality, improve social governance, and promote the harmonious development of society. However, the current data of ideological and political education have the trend of mass and diversification. In order to improve the effect of ideological and political education, this paper proposes an analysis method of the historical process of cultural self-confidence of ideological and political education based on in-depth learning. By preprocessing the ideological and political education data to obtain text keywords, the principal component analysis method is improved to reduce the dimension of the ideological and political education data, and the vector space model is used to complete the text balance processing of the reduced dimension data. The data sensitivity after dimensionality reduction is analyzed by data training, the historical text data mining model is constructed to extract data features, and the bidirectional recurrent neural network is used to complete data extraction. The semantic features of the extracted data are obtained by using the forward and reverse feature generation methods, and the historical process of the cultural confidence of ideological and political education is analyzed by using the two-way generation method. The experimental results show that the accuracy rate of the historical process analysis method of cultural self-confidence in ideological and political education based on deep learning is 95%, the recall rate is 94%, and the degree of collaborative performance is good.

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Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Cited by 33 publications

References 35 publications

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

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