Time series prediction method based on Convolutional Autoencoder and LSTM

Zhao, Xia; Han, Xiao; Su, Weijun; Yan, Z.

doi:10.1109/cac48633.2019.8996842

Cited by 20 publications

(7 citation statements)

References 9 publications

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“…Following the splicing of the final fitting results, the data near the intersection point can be cleaned so that every value on the X-axis has a unique correspondence with a value on the Y-axis. In this manner, the final fitting result can be approximately treated as a special one-dimensional signal; that is, the fitting result can be regarded as one-dimensional time-series data for the input in the following method [27].…”

Section: Contour Data Splicingmentioning

confidence: 99%

A Small Fishing Vessel Recognition Method using Transfer Learning based on Laser Sensors

Zheng

Yuan

Liu

2022

Preprint

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The management of small vessels has always been key to maritime administration. This paper presents a novel method for recognizing small fishing vessels based on laser sensors. Using four types of small fishing vessels as targets, a recognition method for small fishing vessels based on Markov transition field (MTF) time-series images and VGG-16 transfer learning is proposed. In contrast to conventional methods, this study uses polynomial fitting to obtain the contours of a fishing vessel and transforms one-dimensional vessel contours into two-dimensional time-series images using the MTF coding method. The VGG-16 model is used for the recognition process, and migration learning is applied to improve the results. The UCR time-series public dataset is used as a transfer learning dataset for the MTF time-series image encoding. The experiment demonstrates that the proposed method exhibits higher accuracy and performance than 1D-CNN and other general neural network models, and the highest accuracy rate is 98.92%.

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Section: Contour Data Splicingmentioning

confidence: 99%

A Small Fishing Vessel Recognition Method using Transfer Learning based on Laser Sensors

Zheng

Yuan

Liu

2022

Preprint

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“…CNN is mainly a neural network composed of convolutional layers and pooling layers, in which the convolutional layer acts as a filter, and the pooling layer is responsible for extracting invariant features. The convolutional autoencoder (CAE) [21][22][23] (5)…”

Section: Convolutional Autoencodermentioning

confidence: 99%

Signal processing of Internet of Vehicles based on intelligent interference

Hou

Deng

et al. 2022

EURASIP J. Adv. Signal Process.

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With the rapid development of in-vehicle electronic technology and artificial intelligence, Internet of Vehicles (IoV) technology, as an effective integration of the two, greatly reduces the probability of road traffic accidents. However, the current IoV system is not perfect for the control process of abnormal vehicles. Therefore, to strengthen the management and control of abnormal vehicles in the IoV, it is extremely necessary to propose a method for interfering with IoV signals. Among the current popular intelligent interference methods, most of them rely on the prior knowledge of the signal. However, prior knowledge is difficult to obtain in practical applications. Therefore, in view of the shortcomings of the current communication interference technology, this study introduces intelligent interference into the signal processing technology of the IoV. When the service provider identifies abnormal signals, the intelligent interference method can be used to achieve precise interference for a single target and then realize the control of the IoV signals. This study proposes an interference waveform generation technology based on convolutional autoencoders. The convolutional autoencoder was used to change the features on the fully connected layer to generate an interference waveform that is very similar to the received signal waveform. The simulation results show that the interference waveform generation technology proposed in this study can make the bit error rate (BER) reach 38.4% within the signal-to-interference ratio (SIR) from − 10 to − 15 dB.

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“…CAE can learn feature representations by jointly considering the spatial and temporal correlation of features. Then, the learnt feature representations can be used as model input for a classifier such as LSTM network [36] . Technically, the main difference between CAE and AE lies in the network structure for encoder and decoder.…”

Section: Introductionmentioning

confidence: 99%