Time-domain Sequence Research Articles

De-noising magnetotelluric data based on machine learning

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.

TFCSRec: Time–frequency consistency based contrastive learning for sequential recommendation

Sequential recommendation aims to predict future user interactions by analyzing dynamic patterns within their historical behavior sequences. Deep neural networks have recently become popular for learning representations of these sequences in the time domain. However, representing users’ intentions in the time domain faces challenges such as noise in interactions and sparsity of data. Contrastive learning and representation learning in the frequency domain can mitigate these issues from different perspectives. In this paper, to fully integrate time-domain sequence representations, frequency-domain sequence representations and contrastive learning based on them, we propose a model called Time–Frequency Consistency based contrastive learning for Sequential Recommendation (TFCSRec). TFCSRec utilizes a time-domain encoder with a fully connected network and a filter network to extract high-order features and catch pure sequential patterns. Then, a learnable frequency-domain encoder with a recurrent neural network is designed to capture sequential characteristics in the frequency-domain space. Finally, TFCSRec combines a recommendation task and two contrastive learning tasks to optimize the two user representation encoders. Its contrastive learning is designed to minimize a contrastive regularization loss and a time–frequency consistency loss, which for the first time is constructed directly on the time-domain sequence representation and the frequency-domain sequence representation. Experiments on five benchmark datasets show that the proposed TFCSRec model outperforms other sequential recommendation models based on deep neural networks.

Analysis on the Effect of Phase Noise on the Performance of Satellite Communication and Measurement System

An oscillator is a key component of a satellite communication measurement and control system, performing symmetry precisely as a time frequency reference. At the same time, the phase noise index has a close coupling relationship with the overall performance of the entire system, while persistently breaking the symmetry property of the oscillator during work. It is very important to study and reasonably allocate the phase noise index. According to the theoretical formula of phase noise, this paper analyzes the power law spectral model in the frequency domain and the noise jitter characteristics in the time domain. Using the carrier tracking loop in the measurement system, the frequency domain transfer model of phase noise is established, and typical analysis results are given. A discrete fractional integration algorithm is proposed, which can generate the phase noise time domain sequence under the given power law spectral model coefficients. The proposed algorithm is more realistic compared with the previous numerical calculation method, and has sufficient accuracy compared with the results of the instrument. After frequency domain conversion, the RMS deviation between the simulated noise sequence in the frequency domain and the measured single sideband power spectral density is less than 2.5 dB, indicating that the phase noise sequence can reflect the frequency domain characteristics more completely. A communication measurement simulation system is built, and a discrete sequence simulation analysis method combining frequency domain and time domain is provided, and the coupling relationship of key indicators such as phase noise, thermal noise, communication data rate, modulation method and bit error rate is synthesized. The results show that the BER of the QPSK/BPSK communication system will not be significantly reduced if the phase jitter RMS caused by the phase noise is less than 5 degrees, so 5 degrees can be used as a reference for the decomposition of the carrier SSB phase noise index. The simulation results have been successfully applied to a satellite inter-satellite link system, which has universal practical application value.

Tool Wear State Recognition Based on One-Dimensional Convolutional Channel Attention.

Tool wear state recognition is an important part of tool condition monitoring (TCM). Online tool wear monitoring can avoid wasteful early tool changes and degraded workpiece quality due to later tool changes. This study incorporated an attention mechanism implemented by one-dimensional convolution in a convolutional neural network for improving the performance of the tool wear recognition model (1DCCA-CNN). The raw multichannel cutting signals were first preprocessed and three time-domain features were extracted to form a new time-domain sequence. CNN was used for deep feature extraction of temporal sequences. A novel 1DCNN-based channel attention mechanism was proposed to weigh the channel dimensions of deep features to enhance important feature channels and capture key features. Compared with the traditional squeeze excitation attention mechanism, 1DCNN can enhance the information interaction between channels. The performance of the model was validated on the PHM2010 public cutting dataset. The excellent performance of the proposed 1DCCA-CNN was verified by the improvement of 4% and 5% compared to the highest level of existing research results on T1 and T3 datasets, respectively.

Spatial Selectivity of Plasma Sheath Channel at Millimeter Wave Band for Hypersonic Vehicle

Millimeter wave is a potential way to overcome the blackout problem. However, the plasma sheath is axially nonuniform, and it has different effects on millimeter wave at different axial positions. In this article, the influences of the spatially inhomogeneous time-varying plasma sheath on millimeter waves are investigated. The electron density model is established, the time-domain sequence of the random channel is calculated, and the channel statistical parameters at different axial positions are extracted. The results show that the millimeter wave channel is spatially selective. Millimeter waves can transmit through the plasma sheath with less attenuation, multiplicative interference, and nonlinear effects at any position of the vehicle except the nose. When the antenna position is properly selected, the attenuation and multiplicative interference intensity can be further reduced. This indicates that millimeter wave communication technology is the most promising way to overcome the blackout problem and accomplish diverse communication tasks of a hypersonic vehicle.

Improvement of College English Teaching Model under the Background of Information Technology

In order to improve the effect of college English teaching reform, this paper combines information technology to improve the college English teaching mode. According to the digital processing requirements of English teaching resources, in order to make the loop establishment circuit time of automatic gain control independent of the input signal, this paper uses a logarithmic amplifier to detect it. Moreover, this paper combines the SC algorithm and the SC algorithm based on the time-domain PN sequence to propose a new frame synchronization algorithm. The algorithm has precise frame synchronization points and reduces the usage of hardware resources. Finally, this paper constructs a college English teaching system based on information technology. The experimental research results show that the college English teaching system based on information technology proposed in this paper has a good teaching effect.

A memristive chaotic system with rich dynamical behavior and circuit implementation

In this paper, a novel four-dimensional autonomous chaotic system based on memristive diode bridge is presented, and it consists of a simple linear oscillation and a memristor. According to the mathematical model of the system, the equilibrium point is analyzed, and the phase portraits, time-domain sequences, bifurcation diagrams and Lyapunov exponent spectrums are numerically simulated. The rich dynamical behavior of proposed system is investigated. It includes multistability, offset boosting and chaotic bursting. In addition, the circuit simulation and Field-Programmable Gate Array (FPGA) hardware experiment are carried out to verify the feasibility of the system. Then the application of proposed system in chaotic image encryption is presented. By carrying out some security performance analyses, we show that the proposed system has good security performance.

A Music Emotion Classification Model Based on the Improved Convolutional Neural Network.

Aiming at the problems of music emotion classification, a music emotion recognition method based on the convolutional neural network is proposed. First, the mel-frequency cepstral coefficient (MFCC) and residual phase (RP) are weighted and combined to extract the audio low-level features of music, so as to improve the efficiency of data mining. Then, the spectrogram is input into the convolutional recurrent neural network (CRNN) to extract the time-domain features, frequency-domain features, and sequence features of audio. At the same time, the low-level features of audio are input into the bidirectional long short-term memory (Bi-LSTM) network to further obtain the sequence information of audio features. Finally, the two parts of features are fused and input into the softmax classification function with the center loss function to achieve the recognition of four music emotions. The experimental results based on the emotion music dataset show that the recognition accuracy of the proposed method is 92.06%, and the value of the loss function is about 0.98, both of which are better than other methods. The proposed method provides a new feasible idea for the development of music emotion recognition.

Optical Neural Network Based on Synthetic Nonlinear Photonic Lattices

We reveal that a synthetic photonic lattice based on coupled optical loops can be utilized as a feed-forward neural network for processing of optical pulse sequences in time domain. As a proof of concept, we train the optical system to restore an initial shape of the pulse train from the signal distorted due to linear dispersion in a fiber-optic link. We also show efficient training of the optical network with an intrinsic Kerr-type nonlinearity for the realization of target nonlinear transmission functions and inference functionality for the discrimination of different pulse sequences. The theoretical modeling is performed under practical conditions and can guide future experimental realizations.

A Discrete Control Method for the Unsignalized Intersection Based on Cooperative Grouping

The traditional signalized intersection is no longer able to meet the increasing demands of transportation. A method combining autonomous intersection management (AIM) and connected and automated vehicles (CAVs) is suggested for solving existing traffic problems. This study proposes a control method for unsignalized intersections based on cooperative grouping, which discretizes the continuous traffic flow into a series of small-scale vehicle groups. In addition, a Petri Net model with a progressive structure is proposed, for which the time domain sequence of each group is strictly defined. To realize the global control of CAVs, the whole process of vehicle movement is decomposed into two processes: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) adjustment on branches and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) coordination in the junction. All grouping conditions in the junction are clearly defined, and time windows are used for the discrete traffic flow modeling. Furthermore, a cooperative adaptive cruise control (CACC) model with bifurcations is proposed for grouping and following the control of vehicles on each branch. Simulation results show that vehicles are automatically grouped according to their initial positions, and no collision occurs during the whole process. Applied with the proposed method, the traffic capacity of a four-branch intersection exceeds 10000 veh/h under ideal conditions. The vehicles run more smoothly and show a better fuel economy than the other two methods.

An Improved Convolutional Neural Network for Three-Phase Inverter Fault Diagnosis

This article proposes an end-to-end method based on an improved convolutional neural network model for inverter fault diagnosis. First, transient time-domain sequence data under different faults are analyzed, and raw signals are taken as fault representations without manually selecting feature extraction methods. Second, the model can automatically learn and extract features in the input domain using stacked convolution layers with the wide first-layer convolution kernel and a global max pooling layer; thus, it eliminated the influence of expert experience. Finally, the fault diagnosis results of the three-phase voltage-source inverter are automatically obtained in the softmax layer. The proposed fault diagnosis method has superior recognition performance with mixed noise data and variable load data. Contrastive experiments show that the improved fault diagnosis model is effective than traditional machine learning and other deep learning methods.

Tri‐transformer Hawkes process via dot‐product attention operations with event type and temporal encoding

AbstractAsynchronous event sequences widely exist in the real world, such as social networks, electronic medical records, financial data, and genome analysis. For modeling asynchronous event sequences in the continuous time domain, point process has become the underpinning. In the initial research stage, Hawkes process is widely used because it can capture the self‐triggering and mutual triggering modes between different events in a variety of point process functions. In recent years, due to the development of neural networks, deep point process (also known as neural point process) can learn models with the stronger fitting ability and reduce the dependence on prior knowledge by using the powerful capacity of neural networks. The proposal of the transformer Hawkes process (THP) has led to a huge performance improvement, so a new climax of the transformer‐based deep Hawkes process is set off. However, THP does not make full use of the event and temporal information underlying the asynchronous event sequence, meanwhile, if we simply take the event type encoding and temporal encoding as the sequence encoding, a single transformer may suffer from learning bias. In order to circumvent these problems, we propose a tri‐transformer Hawkes process model (TTHP), in which the event and temporal information are introduced to the dot‐product attention operations as auxiliary information to form different multihead attention, respectively, and are utilized to build three heterogeneous learners. A series of well‐designed experiments on synthetic and real‐world datasets validate the effectiveness of the proposed TTHP.

Enabling Unique Word OFDM for Underwater Acoustic Communication

This letter presents a new scheme to solve the issue of high redundant energy in Unique Word Orthogonal Frequency Division Multiplexing (UW-OFDM). The proposed scheme, called Low-Redundant Energy UW-OFDM (LRE-UW-OFDM), is suitable for time-varied harsh channels such as the Underwater Acoustic (UWA) channel were inserting the UW data in the time domain leads to huge redundant energy and a dramatic deterioration in Bit Error Rate (BER) performance. The insertion of UW in the proposed LRE-UW-OFDM is done in frequency domain to be any desired sequences in time domain with constant redundant subcarriers energy regardless of the size and the location of data subcarriers. The proposed scheme is evaluated over a long-delay UWA channel demonstrating its outperformance against conventional schemes in terms of BER and mean symbol energy.

A SSTDR Methodology, Implementations, and Challenges.

Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our review includes both scientific litera-ture and selected patents. We also discuss future applications of SSTDR.

A Micro-Doppler Frequency Ambiguity Resolution Method Based on Complex-Valued U-Net

In order to resolute the micro-Doppler frequency ambiguity caused by radar pulse repetition frequency not high enough (i.e., pulse dimension does not satisfy the requirement of Nyquist sampling theorem), this paper presents a micro-Doppler frequency ambiguity resolution method based on complex-valued U-net. The echo sequence is interpolated by zeros in the pulse dimension to increase the equivalent pulse repetition frequency, so that the echo sequence after zero interpolation contains the real micro-Doppler frequency; at the same time, some new frequency components are generated. The variation law of the echo sequence frequency after zero interpolation is analyzed. Then, the echo sequence in time domain after zero interpolation is transformed to the time-frequency domain by short-time Fourier transform (STFT). Finally, the time-frequency results can be segmented by the model, which is trained by complex-valued U-net to eliminate the redundant frequencies generated by zero interpolation; thus, the reconstruction of real micro-Doppler frequency is realized. Theoretical analysis and simulation results show that the proposed method can solve the problem of micro-Doppler frequency ambiguity. Compared with fully convolution network (FCN) and fully convolution residual network (FCRN), the proposed method has better performance and robustness.

Predictive Maintenance of Compressor Using ThingSpeak IoT Platform

Any instrument or machine that needs to be monitored and inspected on a regular basis to ensure its long life and proper maintenance. Machine condition monitoring in the time and frequency domain is unquestionably required to ensure reliability. Condition monitoring is a method of observing a machine's condition parameter (vibration, temperature and etc.), with the explicit objective of detecting a substantial change that could indicate the onset of a malfunction It's an important part of compressor predictive maintenance and lowering compressor downtime. In today's world, the Internet of Things (IoT) is the most effective approach and technology for continuously monitoring the state of any machine. We've employed MEMS sensors to detect misalignment, non-linearity, and other anomalies., Through the Wi-Fi module ESP8266, vibrations and temperature in compressors, as well as changes in signals, will be communicated to the cloud. These signals are in order in the temporal domain. The Fast Fourier transform is used to examine and convert the time domain sequence into the frequency domain.

A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems

This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller.

Side information-free low computational complexity TDSS-SLM transmission system

The authors propose the side information-free low-computational complexity Time Domain Sequence Superposition-Selected Mapping (TDSS-SLM) scheme for PAPR suppression. We adopt IFFT point reduction for transmitter and reception technique using pilot subcarriers for side information estimation. The simulation results show the TDSS-SLM transmitter achieves PAPR reduction of 2.3 dB which corresponds to the 1.5 times larger PAE than that in the conventional transmitter. In addition, the proposed receiver shows loss-less BER performances for both AWGN channel and two types of fading channels, while the computational complexity of the proposed receiver is nearly the same as the conventional OFDM receiver, which is less than 1/10 of the ML receiver.

Research on Optimal Algorithm for Time-domain Synthesis of Shock Response Spectrum Based on Continuous Second Derivative

Aerospace power supply and distribution products must predict the shock response based on the shock boundary conditions to determine the mechanical design safety factor. In this paper, the SRS of a typical product is used as an input condition, and a damped-sine discrete wave is used to synthesize an equivalent discrete time-domain sequence for simulation calculation. In order to improve the calculation efficiency, a low-sampling frequency time-domain sequence optimization algorithm based on continuous second derivative is proposed; through calculation comparison and verification, the algorithm can effectively improve the simulation operation efficiency, and the response prediction accuracy can meet the allowable requirements, which provides a new idea for products’ SRS calculation.

BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

The benign epilepsy with spinous waves in the central temporal region (BECT) is the one of the most common epileptic syndromes in children, that seriously threaten the nervous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram (EEG) spikes in the Rolandic area during the interictal period, that is an important basis to assist neurologists in BECT diagnosis. With this regard, the paper proposes a novel BECT spike detection algorithm based on time domain EEG sequence features and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The synthetic minority oversampling technique (SMOTE) is applied to address the spike imbalance issue in EEGs, and the bi-directional LSTM (BiLSTM) is trained for spike detection. The algorithm is evaluated using the EEG data of 15 BECT patients recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 85.75% precision, that generally outperforms several state-of-the-art spike detection methods.