Toward Load Identification Based on the Hilbert Transform and Sequence to Sequence Long Short-Term Memory

Le, Thi-Thu-Huong; Heo, Shinwook; Kim, Howon

doi:10.1109/tsg.2021.3066570

Cited by 78 publications

(30 citation statements)

References 37 publications

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“…CNN is an expert in processing image data but not the best option for passwords [28]. Traditional RNN suffers the problem of long-term dependencies due to gradient vanishing or gradient explosion, while LSTM [31,33] is a new type of RNN that adds gate units to address this problem. It has been applied in some text generation studies [34,35] and proved to be well suitable for handling sequential information.…”

Section: Proposed Modelmentioning

confidence: 99%

Password Guessing Based on GAN with Gumbel-Softmax

Zhou

et al. 2022

Security and Communication Networks

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Password guessing is an important issue in user security and privacy protection. Using generative adversarial network (GAN) to guess passwords is a new strategy emerging in recent years, which exploits the discriminator’s evaluation of passwords to guide the update of the generator so that password guessing sets can be produced. However, the sampling process of discrete data from a categorical distribution is not differentiable so that backpropagation does not work well. In this paper, we propose a novel password guessing model named G-Pass, which consists of two main components. The first is a new network structure, which modifies the generator from the convolutional neural network (CNN) to long short-term memory- (LSTM-) based network and employs multiple convolutional layers in the discriminator to provide more informative signals for generator updating. The second is Gumbel-Softmax with temperature control for training GAN on passwords. Experimental results show the proposed G-Pass outperforms PassGAN in password quality and cracking rate. Moreover, by dynamically adjusting one parameter during the training process, a trade-off between sample diversity and quality can be achieved with our proposed model.

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Section: Proposed Modelmentioning

confidence: 99%

Password Guessing Based on GAN with Gumbel-Softmax

Zhou

et al. 2022

Security and Communication Networks

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“…However, at present, most of the domestic electricity meters can only measure load power or energy consumption with low-frequency. To obtain high-frequency signals, the existing widely used smart electricity meters need to be transformed, resulting in a certain additional economic cost, which hinders their large-scale application and promotion [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Non-intrusive Load Monitoring Method Using Low Frequency Signal

Yuan

Jin

Wang

2023

Preprint

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Non-intrusive load monitoring(NILM) can identify the appliance categories, using time, work duration and the work states through data collected from a smart meter. With NILM technique,people can conduct the power supply and power consumption scientifically and reasonably so as to partially or completely solve the problem of imbalance between power supply and demand. The low-frequency non-invasive load state identification algorithm can identify the load using low-frequency signal without additional hardware facilities, so it has broad development prospects. However, it is difficult to improve the accuracy of low-frequency load identification because there is little information available. In this paper, a low-frequency non-invasive load state identification algorithm combining electrical switch state classification LSTM algorithm and maximum likelihood probability algorithm based on recursive reasoning is proposed. The load identification is divided into two steps, which reduces the overall difficulty and improves the accuracy of state identification. The experimental results on Ampsd2 data set show that the proposed algorithm can achieve better results than other NILM algorithms.

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“…The NILM methods based on high-frequency data use the characteristics of the load in the high-frequency data as the basis for load identification. Among them, literature [8] proposes to use current phase, amplitude and frequency as load features and then combine them with Long Short-Term Memory (LSTM) networks for load identification. Literature [9] uses voltage, active and reactive currents, Fourier transformed and coded to form a color signature, and then uses two-stream convolutional neural networks for load identification.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Monitoring Method Considering the Time-Segmented State Probability

Liu

Cheng

et al. 2022

IEEE Access

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Appliance-level data is a prerequisite for establishing friendly two-way interactions between customers and the power company, and this data is now mainly obtained by non-intrusive load monitoring. However, as the number of loads increases, the number of possible appliances state combinations tends to grow exponentially, leading to a significant increase in the time of load identification. In order to reduce the search range of the load state combinations and shorten the algorithm response time, a non-intrusive load monitoring method based on the time-segmented state probability is proposed in this paper. Firstly, the affinity propagation (AP) clustering algorithm is introduced to obtain the power templates of the load, and then the power templates are used to count the time-segmented state probabilities. Secondly, a number of appliance state matrices are generated using the probabilities, and the optimal matrix is selected by the function as the identification result of the appliance state. Finally, the performance of the algorithm is tested on the public NILM dataset and compared to several state-of-the-art techniques. The results illustrate that the proposed method achieves an accuracy of 96% for load state identification and 89% for power decomposition of the load, and is able to meet the real-time application requirements.

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Toward Load Identification Based on the Hilbert Transform and Sequence to Sequence Long Short-Term Memory

Cited by 78 publications

References 37 publications

Password Guessing Based on GAN with Gumbel-Softmax

Password Guessing Based on GAN with Gumbel-Softmax

A Novel Non-intrusive Load Monitoring Method Using Low Frequency Signal

Non-Intrusive Load Monitoring Method Considering the Time-Segmented State Probability

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