Signal Recognition Method Research Articles

A new spectral measure of complexity and its capabilities for detecting signals in noise

This article is devoted to the improvement of signal recognition methods based on the information characteristics of the spectrum. A discrete function of the normalized ordered spectrum is established for a single window function included in the DFT. Lemmas on estimates of entropy, imbalance and statistical complexity in processing a time series of independent Gaussian quantities are proved. New concepts of one-dimensional and two-dimensional spectral complexities are proposed. The theoretical results obtained were verified by numerical experiments, which confirmed the effectiveness of the new information characteristic when detecting a signal mixed with white noise at low signal-to-noise ratios.

Modern risk assessment of the influences of natural and military man-made factors on the state of the historical buildings of the Kyiv-Pechersk Lavra

During its thousand-year history, the Kyiv-Pechersk Lavra has experiences many disasters. At the time when the fight against religion was part of the state ideology, the cathedral and monastery were closed, and its countless treasures were confiscated. Even in the turbulent 1930s, when the bolsheviks destroyed dozens of unique temples and churches in Kyiv, the Assumption Cathedral remained intact. Black days for the age-old shrine came during the German occupation in the fall of 1941. First, the shrine was looted, and many material and artistic valuables were taken. In November 1941, a series of explosions destroyed the main temple of the Kyiv-Pechersk Lavra, which had served as a tomb for the Kiev princes. In February 2022, the war had started again in our country, and modern scientific technology allows us to assess the impact of military events on buildings of Kyiv-Pechersk Lavra from viewpoint of the level of seismic hazard assessment, which have lasted in Ukraine for more than two years, because Kyiv-Pechersk Lavra is listed as a UNESCO World Heritage Site. The article presents the results of the analysis of the operation of devices for various purposes ― from the recording of seismic-acoustic waves, which have a high frequency of natural oscillations, to low-frequency ones, which are recorded by a laser inclinometer. The article also presents the results of modern automated data processing using various methods ― from the spectral method of signal recognition to the modern autopicker method of determining the time of arrival of waves AIC, which allows you to clearly determine the arrival time of vibration waves of various origins ― from the formation of cracks in historical buildings of the Kyiv-Pechersk Lavra to vibration waves of technical and natural origin.

Signal recognition based on transfer learning for Φ-OTDR fiber optic distributed disturbance sensor

Abstract Traditional classification models for optical fiber vibration signals can achieve good recognition results when there are a large number of samples. However, it is difficult to obtain sufficient training samples in real scenes. A method of Φ-OTDR sensor signal recognition based on VGGish transfer learning is proposed in this paper. Firstly, a selection indicator is designed to filter out vibration signals from sparse signals. Then, the VGGish network pretrained for sound classification is used for transfer learning as a feature extractor for optical fiber vibration signals. This method was applied to classify 6 kinds of optical fiber vibration signals. When the number of training samples is reduced to 480 samples, the method can still achieve 84.17% classification accuracy. Compared to the method of training from scratch, this proposed method reduces training time by 73.9%.

Research on Surface Processing Method of Pulse Transmission Signal of Amplitude-Modulated Drilling Fluid in 10,000-m Deep Wells

Conventional Measurement-While-Drilling (MWD) technology is unable to function statically at the predicted temperatures of deep formations exceeding 200 °C in wells reaching depths of 10,000 m. It is limited to measuring downhole engineering parameters through purely mechanical means, such as inclination. However, the accurate long-distance transmission of drilling fluid pulse signals poses a significant bottleneck, restricting the application of these mechanical measurement methods. To address these issues, this paper develops and designs an algorithm to identify and analyze the amplitude characteristics of deep well mud signals. By employing a signal coding algorithm, a signal processing analysis method, and a signal feature recognition algorithm based on grey correlation degree, we construct a signal recognition method capable of decoding mud amplitude encoded signals. Key techniques such as filtering, smoothing, and feature extraction are utilized in the signal processing, and the proposed method’s effectiveness is verified through the analysis of collected signals. Furthermore, long-distance simulation analysis software is developed to evaluate waveform distortion during extended transmission, confirming the feasibility of the recognition algorithm. Laboratory experiments demonstrate that this algorithm can accurately recognize and demodulate signals generated by mechanical inclinometer structures, providing a novel decoding method for signal transmission in deep and ultra-deep wells.

Study on crack propagation characteristics of rocks with different lateral pressure based on joint monitoring of DIC and AE

During the process of rock failure, the characteristics of crack propagation affect the fracture characteristics and macroscopic mechanical behavior of rocks, indirectly affecting the safety and stability of rock engineering. In order to study the evolution characteristics of cracks during rock failure under different lateral pressure, based on an improved digital image correlation (DIC) and acoustic emission (AE) signal recognition method, a visual biaxial servo loading device was developed to conduct biaxial compression tests on mudstone with prefabricated cracks of the same inclination angle. The research results indicate that the stages of crack propagation include microcracks propagation, crack tip formation, stable macroscopic cracks propagation, and unstable macroscopic cracks propagation. As the lateral pressure increased, the initiation frequency of cracks decreased, the quantity of propagation decreased, and the propagation path shortened, indirectly increasing the bearing strength of rocks. The initiation stress, peak stress, and elastic modulus of pre-cracked rocks with lateral pressure ≤ 2 MPa were lower than those of pre-cracked rocks with lateral pressure > 3 MPa, with the minimum reduction amplitude of 14.1%, 21.2%, and 12.6%, respectively. As the lateral pressure decreased, the dispersion of the AE main frequency distribution increased and accelerated its downward expansion. The surface temperature curves of rocks were prone to fluctuations and rapid upward evolution characteristics corresponding to crack tip formation and crack propagation, respectively. The research results provide theoretical and engineering references for the mining of weak coal seams.

A traffic and road signal recognition method through deep learning

Before deep learning became popular, some researchers used traditional computer vision algorithms, including support vector machines, decision trees, and random forests to deal with traffic and road signal recognition problems. These methods often require manual design of features and may not perform well when dealing with complex scenarios and changing conditions. Therefore, traffic and road signal recognition have witnessed a transformative shift with the advent of deep learning technologies. Convolutional neural networks (CNNs) developed from deep learning has shown prominent capabilities in accurately detecting and interpreting traffic signs and signals from images and videos. In this project, the whole method is mainly based on two parts, which refers to the process of signal-capturing and the detecting process. The CNN model used in the latter part consists of various function layers, including max-pooling layer, linear layer, 2D convolution layer and batch- normalization layer to modify accuracy. This model is trained and tested with Chinese Traffic Sign Dataset, with 58 categories of over 6000 pictures. With the assistance of OpenCV, the model may detect real-time traffic signals and convey messages to vehicles. After adjusting parameters in the CNN model, high probability tags are obtained during the process, with both accuracy and average loss labeled after each epoch. The results demonstrate that the proposed model achieved the testing accuracy of over 95 percent, after undergoing 200 epochs.

Cross-domain prototype similarity correction for few-shot radar modulation signal recognition

The new classes of radar signals are increasingly difficult to acquire under non-cooperative environments, which makes it difficult to support convolutional neural network training with limited labeled samples. The few-shot learning (FSL) methods have shown great performance in classification with limited labeled samples, but the FSL methods ignore that the class distributions between the new and original tasks are significantly different, resulting in a massive challenge in identifying new radar signals. To solve this problem, a few-shot radar modulation signal recognition method based on cross-domain prototype similarity correction (CDPSC) is proposed. Specifically, a residual feature tokenizer transformer (RFTT) model embedded with a pooling token generation block is designed to focus on the important features and improve the ability to represent samples. Meanwhile, the proposed domain prototype similarity mapping (DPSM) strategy adaptively learns the class mapping, reduces the inter-domain difference through feature distribution alignment, and effectively corrects the target domain prototypes. In addition, we introduce a sample prototype embedding (SPE) strategy in the training phase, which can reduce the intra-class distance and increase the inter-class distance. Experimental results demonstrate that the CDPSC method is superior to typical FSL methods in recognition accuracy under different sample numbers.

Research on Abnormal Starting Noise of Efficient Engine Caused by Abnormal Combustion Noise

Article Research on Abnormal Starting Noise of Efficient Engine Caused by Abnormal Combustion Noise Feng Huang 1, * , Xiaochun Zeng 1, Xili Wang 1, Xinqi Qiao 2, Yi Wang 1, Liang Fang 3, Yunhua Zhang 3, Dengcheng Liu 4, and Yonghong Deng 4 1 Jiangling Motors Co., Ltd., Nanchang 330001, China 2 Mechanical and Power Engineering College, Shanghai Jiao Tong University, Shanghai 200092, China 3 Automobile College, Tongji University, Shanghai 200092, China 4 Nanchang Automotive Institute of Intelligence & New Energy, Nanchang 330052, China * Correspondence: fhuang6@jmc.com.cn Received: 20 February 2024; Revised: 7 April 2024; Accepted: 9 May 2024; Published: 22 May 2024 Abstract: An abnormal noise problem was reported in a pickup truck equipped with a 2.0 L efficient engine during start-up. The noise was reproduced by the engine anechoic chamber bench for further analysis. NVH test (conducted through audio playback and signal filtering) showed that the noise had a signal of 1–3 kHz. Analysis through cylinder-by-cylinder oil-cutting method found that the abnormal noise completely disappeared after the fourth cylinder was cut off, indicating that the noise was related to the combustion of the fourth cylinder. The combustion characteristics of each cylinder were then analyzed in the starting condition through the Kibox combustion analyzer. The noise appeared after the first fuel injection of the fourth cylinder, and the maximum pressure rise rate was 42 Bar/°, confirming that the abnormal noise was combustion noise. Further tests on the physical fuel injection signal of each cylinder found that the fourth cylinder only had the main injection signal with no pilot injection signal. Further analysis of the synchronization signal recognition method of the model and the underlying logic of the fuel injection program showed that the synchronization signal was recognized for the first time in the starting process after the calculation moment of the 4th cylinder pre-injection program, resulting in the failure of the pre-injection calculation. Finally, the bottom fuel injection interrupt program was pushed back (60° to the crankshaft angle), ensuring smooth injection of the 4th cylinder pilot injection in the starting working condition, thus solving the abnormal noise without impacting engine performance.

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi‐Kernel Extreme Learning Machine

BackgroundIn the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution. New methodWe designed experiments involving three motor imagery tasks—wrist extension, wrist flexion, and wrist abduction—with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM). ResultsAfter EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %. Comparison with other methods and conclusionsWe compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.

Time-Frequency Aliased Signal Identification Based on Multimodal Feature Fusion.

The identification of multi-source signals with time-frequency aliasing is a complex problem in wideband signal reception. The traditional method of first separation and identification especially fails due to the significant separation error under underdetermined conditions when the degree of time-frequency aliasing is high. The single-mode recognition method does not need to be separated first. However, the single-mode features contain less signal information, making it challenging to identify time-frequency aliasing signals accurately. To solve the above problems, this article proposes a time-frequency aliasing signal recognition method based on multi-mode fusion (TRMM). This method uses the U-Net network to extract pixel-by-pixel features of the time-frequency and wave-frequency images and then performs weighted fusion. The multimodal feature scores are used as the classification basis to realize the recognition of the time-frequency aliasing signals. When the SNR is 0 dB, the recognition rate of the four-signal aliasing model can reach more than 97.3%.

Research on Sea State Signal Recognition Based on Beluga Whale Optimization-Slope Entropy and One Dimensional-Convolutional Neural Network.

This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization-slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization-slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization-slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition.

Optical fiber vibration signal recognition based on an efficient multidimensional feature extraction network.

In the field of optical fiber vibration signal recognition, one-dimensional signals have few features. People often used the shallow layer of a one-dimensional convolutional neural network (1D-CNN), which results in fewer features being learned by the network, leading to a poor recognition rate. There are also many complex algorithms and data processing methods, which make the whole signal recognition process more complicated. Therefore, an optical vibration signal recognition method based on an efficient multidimensional feature extraction network was proposed. Based on ResNet-50, efficient channel attention (ECA) was used to improve image features extraction ability, and a long short-term memory (LSTM) network was used to enhance the extraction of temporal features. Three different vibration signals were collected using a phase-sensitive optical time-domain reflectometry (Φ-OTDR) optical fiber sensing system. Vibration signals were converted into 128×128 grayscale images, which have more effective vibration information. The experimental results show that the three types of signals can be recognized and classified effectively by the network, and the average recognition rate is 98.67%.

Criteria for assessing the adequacy of image recognition methods and their verification using examples of artificial series of signals

The article discusses four criteria for assessing the adequacy of the most well-known image recognition methods. Verification of two of these criteria is carried out by empirical analysis using the example of the most well-known signal recognition methods, such as DTW, DDTW, as well as methods based on the Wavelet transform and Fourier transform. Two artificial sets of images are used as recognition objects, formed by uniformly shifting the base image both horizontally and vertically. In general, the goal of this research is to develop a new method for extracting recognition features using the example of the image of the State Emblem of the Republic of Azerbaijan. In the context of this study, a verification of a previously proposed signal recognition algorithm is carried out based on the artificial family of curves, for which the most accessible and acceptable method of displacement is established: horizontally or simultaneously horizontally and vertically.

A Highly Parallelized Signal and Image Recognition Method Based on Spatial Optics

A Highly Parallelized Signal and Image Recognition Method Based on Spatial Optics

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

LPI Radar Signal Recognition Based on Feature Enhancement with Deep Metric Learning

Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they are susceptible to interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI radar signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain LPI signals are first transformed into time–frequency images via the Choi–Williams distribution. Then, we propose a feature enhancement network with attention-based dynamic feature extraction blocks to fully extract the fine-grained features in time–frequency images. Meanwhile, we introduce deep metric learning to reduce noise interference and enhance the time–frequency features. Finally, we construct an end-to-end classification network to achieve the signal recognition task. Experimental results demonstrate that our method obtains significantly higher recognition accuracy under a low signal-to-noise ratio compared with other baseline methods. When the signal-to-noise ratio is −10 dB, the successful recognition rate for twelve typical LPI signals reaches 94.38%.

Application of pipeline leakage detection based on distributed optical fiber acoustic sensor system and convolutional neural network

Underwater pipelines are exposed to harsh environments, including high salinity, multi-modal vortex corrosion, and severe wave interference. Their safety is essential for the development and transportation of marine energy. Therefore, real-time safety monitoring of long-distance energy pipelines is of great strategic importance for ensuring the safety of life and property and energy security. With the rapid development of energy development, the corrosion and leakage mechanisms of natural gas pipelines, as well as their identification and early warning, have become the focus of attention. Optical fiber sensing technology has been applied to various energy safety monitoring fields. However, the mechanism of sound source fluctuations in pipeline leakage and the mutual coupling mechanism between distributed optical fiber sensing technology and leakage sound waves are not yet clear. This paper establishes a model based on sound wave propagation and leakage noise response, derives a quadratic fitting relationship between pipeline pressure fluctuations and leakage orifices and a relationship between leakage noise source standard deviation and orifices, and proposes a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) permutation entropy underwater natural gas pipeline leakage signal recognition method based on distributed optical fiber acoustic sensing technology. The results of theoretical analysis are verified by experiments. It shows that the signal processing method of CEEMDAN permutation entropy is superior to traditional noise reduction methods, which can better preserve the features of the original signal; the radial basis function (RBF) neural network model can accurately identify four different leakage features with an accuracy of 88.15%, and its recognition stability and generalization ability are superior to convolutional neural network, backpropagation, and random forest. Therefore, the research results of this paper provide a new method for safety monitoring in the application of energy pipeline transportation engineering, and expand the potential application scenarios of distributed acoustic sensing sensor systems and RBF neural networks.

Efficient ship noise classification with positive incentive noise and fused features using a simple convolutional network

Ship noise analysis is a critical area of research in hydroacoustic remote sensing due to its practical implications in identifying ship direction, type, and even specific ship identities. However, the limited availability of data poses challenges in developing accurate ship noise classification models. Previous studies have mainly focused on small-sample learning approaches, resulting in complex network structures. Nonetheless, underwater robots often have limited computing power, making it essential to develop simpler recognition networks. In this paper, we address the issue of data scarcity by introducing positive incentive noise. We propose a CNN-based hydroacoustic signal recognition method that achieves comparable or superior performance to previous studies, using a simple network structure as a back-end decision system. We describe the feature extraction process using a dataset with added noise and compare the performance of various features. Additionally, we compare our proposed method with previous studies. Experimental results demonstrate that simple neural networks can achieve high performance and excellent generalizability without the need for complex network structures like adversarial learning models.

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

An Information Recognition and Time Extraction Method of Tracking a Flying Target with a Sky Screen Sensor Based on Wavelet Modulus Maxima Theory

Aiming at the problems of big noise, lots of false targets, and accurate time extraction while tracking a flying target in the signal from a sky screen sensor, a flying target recognition and time extraction method is proposed, based on wavelet transformation. The noisy signal output by the sky screen sensor is filtered with wavelet transformation to filter out some high-frequency components; the filter is designed to handle the signal time frequency characteristics of the flying target and noise. To improve the recognition efficiency of whether the signal includes tracking of the flight target, based on a two-class discriminant model, the wavelet Fisher discriminant method is used to construct the feature vector of the false target and the flying target signal, and the recognition method of the flying target signal is studied. According to the wavelet modulus maxima theory, the single target signal is isolated, and the time moment of the flying target passing through the detection screen is calculated. The velocities calculated based on the flying target signal recognition method proposed in this paper and based on the least-mean-squares algorithm of the traditional sky screen sensor velocity measurement system are compared with the net target velocity measurement system. The results show that the velocity data obtained by the method in this paper are closer to the true value of the target flight velocity, and the average error between the velocity value obtained by the method in this paper and the standard net target velocity measurement system is less than 0.954 m/s, which verifies the superiority of the method proposed in this paper.