Strong Interference Conditions Research Articles

Fault Diagnosis of Pumped Storage Units—A Novel Data-Model Hybrid-Driven Strategy

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units.

The radial-tangential anisotropy of numerosity perception.

Humans can estimate the number of visually presented items without counting. In most studies on numerosity perception, items are uniformly distributed across displays, with identical distributions in central and eccentric parts. However, the neural and perceptual representation of the human visual field differs between the fovea and the periphery. For example, in peripheral vision, there are strong asymmetries with regard to perceptual interferences between visual items. In particular, items arranged radially usually interfere more strongly with each other than items arranged tangentially (the radial-tangential anisotropy). This has been shown for crowding (the deleterious effect of clutter on target identification) and redundancy masking (the reduction of the number of perceived items in repeating patterns). In the present study, we tested how the radial-tangential anisotropy of peripheral vision impacts numerosity perception. In four experiments, we presented displays with varying numbers of discs that were predominantly arranged radially or tangentially, forming strong and weak interference conditions, respectively. Participants were asked to report the number of discs. We found that radial displays were reported as less numerous than tangential displays for all radial and tangential manipulations: weak (Experiment 1), strong (Experiment 2), and when using displays with mixed contrast polarity discs (Experiments 3 and 4). We propose that numerosity perception exhibits a significant radial-tangential anisotropy, resulting from local spatial interactions between items.

Ultra-long-term spatiotemporal feature fusion network for bearing remaining life prediction under strong interference

Under high noise conditions and random impacts, which constitute strong interference, models often exhibit limited capability in capturing long-term dependencies, leading to lower accuracy in predicting the remaining useful life (RUL) of bearings. To address this issue, a spatiotemporal fusion network capable of ultra-long-term feature analysis is proposed to enhance the accuracy of bearing RUL prediction under substantial interference. This network utilizes a dilated convolution-based lightweight vision transformer encoder to extract spatial features reflecting the short-term degradation state of the bearing. Then, these features are sequentially fed into an adaptive tiered memory unit, based on the multiple attention mechanism and the neuron layering mechanism, to analyze temporal features indicative of long-term degradation. Subsequently, short-term spatial and long-term temporal features are fused for RUL prediction. To validate the robustness and predictive accuracy of the proposed approach under strong interference, a gearbox-rolling bearing accelerated platform is constructed, simulating high noise and random impact conditions. Experiments confirm the high robustness and predictive accuracy of the proposed method under strong interference conditions.

Novel Ramanujan Digital Twin for Motor Periodic Fault Monitoring and Detection

The signal-processing and intelligent diagnostic and monitoring methods based on motor current signature analysis (MCSA) for induction motors (IM) usually depend on preset parameters. Moreover, many of them have difficulty in achieving ideal health monitoring effect with strong noise interference and switching working conditions. To overcome these limitations, a novel digital twin architecture called the Ramanujan digital twin (RDT) is composed. This architecture uses the Ramanujan periodic transform (RPT) as its computational core to detect the potential fault signatures in each monitoring frame. The quantity of interest from IM will be selected and calibrated based on the Bayesian-updated driven calibration mechanism to construct the phenomenal simulation signals with high fidelity to the potential fault signatures. These signals will provide guidance information. The effectiveness and robustness of the RDT are validated through experimental cases.

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

To accurately and sensitively track the stator current of an induction motor (IM) and detect faults, stochastic resonance (SR) and the Teager energy operator (TEO) are combined to detect a fault in the residual stator current of a sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. First, a new reaching law is constructed to establish an SMO for better state tracking. Second, SR is used to absorb noise and amplify the detection residuals of the SMO, and the output results are estimated by the TEO in the time domain to achieve fault detection. Finally, the detection results of the IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of the stator current is processed by SR and the TEO, and the effectiveness of fault detection is enhanced.

MICROPROCESSOR DEVICE FOR OBTAINING BIOELECTRIC INFORMATION

The purpose of the study is to develop a microprocessor device for receiving, displaying, recording the electrocardiogram and its further processing. Physical methods of biopotentials studying generated by human tissues and organs make it possible to obtain information about the state of physiological processes and reflect the functional state of the human body. Scientific studies of the cardiovascular system, which directly affects the average life expectancy are of particular importance today. There is still great interest in the methods of analysis of electrocardiograms in modern medical research aimed at solving diagnostic problems. The solution of this problem is implemented on the concept of a virtual device, which is possible due to the use of software environment LabVIEW which is widely used in industry and research laboratories as a standard tool for data collection and device management.
 The main aspects of development are considered, key nodes of the device are described and the radio element base is offered, the algorithm of operation of the device software is described. The module for recording bioelectric potentials (ECG) uses a specialized chip from Analog Devices AD8232. The chip is an interface module for monitoring heart rate with one leading and is designed to receive, amplify and filter weak biosignals in conditions of strong interference.
 The hardware basis of the device in this work is the hardware and software complex Arduino Nano based on the microcontroller ATmega168, which is used only for collecting input data, their conversion and transmission to a PC. The control of the device and the logic of its operation of the device is determined by VP LabVIEW. The hardware and software features of the device allow the processing of diagnostic information by the method of phasography, in which the standard cardiogram is replaced by a phase portrait of the cardio signal. This significantly expands the diagnostic capabilities, increases the sensitivity and specificity of ECG diagnostics. The developed experimental sample of the virtual single-channel cardiograph combines modern technologies of instrument making industry and software processing of the received diagnostic information and can be applied as a laboratory board in educational process of special disciplines mastering on the educational and professional program "Micro- and nanosystem engineering".

FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems

The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment. However, owing to the restrictions on the prior information and channel conditions, these existing algorithms cannot perform well under strong interference and noncooperative communication conditions. To overcome these defects, this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated, and vectors are stitched to form two-dimensional FOLMS, which is used as the input of the deep learning-based model. Then, the multi-scale dilated convolution is used to extract the details of images at different scales, and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally, the concatenate fusion, residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types. Simulation experiments show that the average recognition probability of the proposed method at −12 dB is higher than 98%. Compared with the existing algorithms, the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addition, the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise, which is more suitable for non-cooperative communication systems than the existing algorithms.

Underwater bubble escape volume measurement based on passive acoustic under noise factors: Simulation and experimental research

Passive acoustic methods are suitable for long-term seabed gas leak monitoring with high accuracy under the condition of low cost and simple experimental equipment. However, the impact of noise in complex underwater environments poses great challenges for measurements. In this paper, aiming at the quantitative detection problem, a theoretical model of the bubble generation process by underwater gas escape is established, and the mechanism of bubble acoustic signal generation is clarified. The model identification of bubble volume oscillation is realized through the complementary ensemble empirical mode decomposition (CEEMD) method. Then continuous bubbles are segmented by the normalized energy method to achieve a high-precision measurement of volume inversion. A measurement prototype is developed and tested in the lake with multiple noises caused by seawater disturbance, sea wind, and ships. The results showed that the measurement errors could be kept within 10% even under the condition of strong noise interference.

Speed Interference Suppression for PD Radar Based on Adaptive Dictionary

Random pulse initial phase (RPIP) signal is a kind of agility waveform which is commonly used in pulse Doppler (PD) radar. Although RPIP has the merit of restraining velocity deception jamming effectively, its efficiency is restricted under the condition of strong interference. To make the RPIP signal fully play the anti-jamming performance, this paper proposed a speed interference suppression method based on adaptive dictionar that separates the target echo from the strong jamming signal with good sparsity. First, the prior knowledge of strong interference signal is obtained by the technique of peak detection which is combined with the dual channel processing. Second, the quasi-Karhunen-Loeve transform (Q-KLT) basis of interference signal is constructed based on the prior knowledge, and the approximate Q-KLT basis of target signal is constructed by the way of dictionary learning, and those signals can be obtained from the adaptive dictionary by the algorithm of base tracking (BP). Finally, the effectiveness of the proposed method is verified by numerical simulation, which proves that the method can ensure a lower Doppler sidelobe in the strong interference scene, which confirmed that it has a good anti-velocity deception performance.

A Novel Method for Adaptive Beamforming under the Strong Interference Condition

A Novel Method for Adaptive Beamforming under the Strong Interference Condition

Detection of weld groove edge based on multilayer convolution neural network

To detect the weld groove edge interfered by noise, a lightweight multilayer convolution neural network is designed. The network can extract multi-layer features, which improve the resolution of weld groove edge position detection and have strong anti-interference ability. The position of weld groove edge is recognized and located by generating prior box region. The global detection and location of weld groove edge is transformed into confidence prediction and location coordinate value regression in local prior box region, which ensures the accuracy and robustness. The training data set covers different interference noise and groove deformation to improve the generalization ability of the network under different interference conditions. The experiments show that the designed network has good detection accuracy under the condition of strong interference, with an average error of 0.238 mm and a maximum error of 0.702 mm. The processing time is 0.09 s, which shows a good application prospect in field welding.

Design of 2D LiDAR and camera fusion system improved by differential evolutionary PID with nonlinear tracking compensator

An improved 2D LiDAR and camera fusion system is proposed for the 3D reconstruction of unknown environments. It combines the advantages of dense 2D point cloud and rich color image, adopting a differential evolutionary nonlinear tracking PID to control the pitching motion of LiDAR and camera accurately. The quadratic polynomial transition function is used to optimize the pitching trajectory. The environment was scanned by the system and converted into a 3D colored point cloud by the data fusion algorithm. The experimental results show: the proposed PID control algorithm can accurately control the pitching motion with a small average error (0.0267°) and significantly reduce the point cloud inhomogeneity (0.00698); the processing time for converting each 2D point cloud into the 3D point cloud is about 0.6 ms; combined with the data fusion algorithm, the system can obtain the dense colored 3D point cloud; compared with binocular camera, depth camera and 3D LiDAR under the condition of strong light interference, the fusion system outperforms, with the reconstruction object errors of distance, length and width of 0.23%, 0.17% and 0.46% respectively. In conclusion, the system can obtain homogeneous, and dense colored 3D point cloud in real time while ensuring stable refresh frame rate.

Experimental investigation into simultaneous and sequential propagation of multiple closely spaced fractures in a horizontal well

Stage- and cluster-spacings have been increasingly reduced to place multiple closely spaced hydraulic fractures (HFs) in horizontal wells. However, whether these tightly-spacing fractures can be effectively created under the condition of strong stress interference remains unclear, especially with the lack of relevant experimental investigations. In this study, a novel experimental process was proposed to model the simultaneous and sequential propagation of multiple closely spaced fractures. Rock splitting and 3D reconstruction technology were used to characterize HF geometries. The effects of the number of clusters per stage, stage spacing, and net pressure in the previously created fractures on the propagation geometries were investigated in detail. The results show that closely spaced perforation clusters in a stage tend to be unevenly and asymmetrically initiated. In the simultaneous propagation of two closely spaced HFs, one dominant fracture may occur, but the suppressed fracture will likely coalesce with the predominant fracture. Simultaneously propagating fractures tend to create multiple sub-fractures by communicating bedding planes (BPs), which is accompanied by a serious oscillation in the injection pressure. In sequential propagation, the observed phenomenon demonstrat that the net pressure in a previously created fracture can alter the stress field near the perforation cluster in the subsequent stage. Affected by the local stress conditions, the subsequent fracture would probably initiate and propagate at a nonorthogonal angle to the horizontal wellbore and intersect with the previously created fracture. The experimental findings provide both experimental proof of the interference mechanism of simultaneous and sequential fracture propagation and a basis for the optimum design of hydraulic fracturing.

强干扰条件下矿井电梯井井壁损伤精细探测

强干扰条件下矿井电梯井井壁损伤精细探测

Influence of photoelectric conversion noise on closed-loop performance of adaptive SMF coupling device

The single-mode fiber (SMF) adaptive coupling device can efficiently and stably couple the space laser into SMF, which plays an important role in the fiber-based free space optical communication (FSOC) technology. Therefore, a novel corrector named adaptive fiber coupler (AFC) is developed and successfully used in the adaptive SMF coupling applications. However, in the FSOC system under long-range turbulent atmosphere, the closed loop performance of AFC will be seriously disturbed by the photoelectric conversion noise. This problem is studied in depth in this paper. The operational principle of the photoelectric conversion noise is analyzed, and the corresponding evaluation index isgiven. Furthermore, The numerical simulation experiments are conducted to study the specific influence of the photoelectric conversion noise. The results show that the averaged closed-loop coupling efficiency, control accuracy, and control bandwidth of AFC are seriously affected. According to the results, the empirical formula is given. This formula can be used to calculate the optical and electrical parameters that the AFC device should meet under the condition of strong noise interference. The theoretical and simulation results in this paper can provide a theoretical basis for designing the AFC device under long-range turbulent atmosphere.

К вопросу о выборе метода анализа переходных процессов для развития теории временного разрешения. Ретроспективный аналитический обзор

One of the fundamental and central problems of radio engineering and communication theory was and remains the scientific problem of finding ways to potentially reduce the frequency band occupied by the signal, as well as reducing the power of transmitting devices, which provide the necessary speed and reliability of information transfer, that is, the problem of increasing the specific bandwidth. One of the key forms of solving this problem is the transition to the reception of information messages in conditions of strong intersymbol interference. Sufficiently encouraging results in this direction are shown by the theory of resolution time presented in the papers [1-5] for APSK-N- and PSK-n- signals. At the same time, as a mathematical apparatus in these works, it is used as a mathematical apparatus in the field of optimization and analysis of transient processes. At the same time, as shown in these papers, the correct choice of the mathematical apparatus for the analysis of transient processes is of paramount importance. At present, due to the constant growth of the volume of transmitted information, more and more attention is paid to the issues of the possibility of increasing the transmission speed due to the use of the transmission mode "above the Nyquist rate". According to the theory of resolution time, which has been developing quite rapidly recently, it is required to analyze the transient processes in linear selective systems (LSS). In this case, LSS makes it possible to implement frequency selective properties of real communication channels in the channel model. At the same time, the question of choosing a method for analyzing the transient process is quite acute, since it primarily determines the complexity of estimating capacity procedure. This paper presents a retrospective, analytical review of methods for analyzing transient processes in LSS and substantiates the need to apply the method of slowly varying amplitudes S.I. Evtyanov and its developing. On its basis, the simplest and most convenient method for analyzing transient processes was chosen as applied to phase radio engineering systems for transmitting information.

Study of Simulation Platform for BDS/INS/CNS Deep Integration Navigation

In this paper, INS/CNS (CINS) is integrated into a module, and then CINS and BDS are further combined to form a deep integrated BDS/INS/CNS navigation system, which can significantly improve the navigation, positioning, and attitude measurement accuracy under high dynamic and strong interference conditions. It has broad application prospects for specific users such as high-altitude long-endurance Unmanned Aerial Vehicle (UAV) and high-maneuvering glider. In order to verify and analyze the algorithm and performance of the deep integrated navigation system, the design and implementation of BDS/INS/CNS deep integrated navigation simulation platform are presented and the overall architecture, information flow, and the composition of each subsystem of the simulation platform are introduced. The simulation results show that, under high dynamic conditions, the position accuracy of the BDS/INS/CNS deep integrated navigation system is better than 1 m, the speed accuracy is better than 0.1 m/s, and the overall performance is better than the BDS/INS deep integrated navigation system. It also verifies the availability of the simulation platform, which has guiding significance for the next design of BDS/INS/CNS deep integrated navigation prototype.

An Infrared Small Target Detection Algorithm Based on Peak Aggregation and Gaussian Discrimination

Due to its inherent characteristics, infrared small target detection plays an important role in the field of image detection. In order to improve the detection accuracy of small infrared targets under complex sea background, by analyzing the difference between small targets and sea clutter, we propose an infrared small target detection algorithm based on the peak aggregation number and Gaussian discrimination. First we remove the background through local large value detection and extracts suspect targets. Then, the peak aggregation number of the suspected target is counted to eliminate most of the strong wave clutter and strong island edges. Finally, the small waves are eliminated by Gaussian discrimination. The experimental results show that our algorithm has good performance under strong noise interference and calm sea conditions.

On the Optimality of Interference Decoding Schemes for K-User Gaussian Interference Channels

The sum capacity of the general K-user Gaussian Interference Channel (GIC) is known only when the channel coefficients are such that treating interference as noise (TIN) is optimal. The Han-Kobayashi (HK) scheme is an extensively studied coding scheme for the K-user interference channel (IC). Simple HK schemes are HK schemes with Gaussian signaling, no time sharing and no private-common power splitting. The class of simple HK (S-HK) schemes includes the TIN scheme and schemes that involve various levels of interference decoding and cancellation at each receiver. For the 2-user GIC, simple HK schemes are sufficient to achieve all known sum capacity results—sum capacity under mixed, strong and noisy interference conditions. We derive channel conditions under which simple HK schemes achieve sum capacity for general K-user Gaussian ICs. For the K-user GIC, these results generalize existing sum capacity results for the TIN scheme to the class of simple HK schemes.

Designing an Image Encryption Scheme Based on Compressive Sensing and Non-Uniform Quantization for Wireless Visual Sensor Networks

Wireless visual sensor networks (WVSN) have been widely used to capture images in the fields of monitoring, intelligent transportation, and reconnaissance in recent years. Because of the wireless transmission mode and the huge amount of image data, major challenges in this application are frequent information stealing, big data problems, and harsh communication circumstances. Some encryption schemes based on compressive sensing (CS) and chaotic systems have been proposed to cope with these threats, but most of them are vulnerable against the chosen-plaintext attack (CPA). To remedy these defects, this paper designs a novel method based on non-uniform quantization (NQ). Then, in order to evaluate the true compression ratio (CR), our work takes into account limited data precision in cipher images, while most papers ignored this fact and calculated CR with the assumption of infinite data precision. Besides, to eliminate the periodic windows in the bifurcation diagram of the logistic map (LM), an optimized logistic map (OLM) is designed. Furthermore, simulation results prove that the performance of anti-jamming in the proposed cryptosystem is better than that in existing schemes under the condition of strong noise interference or severe data loss. In conclusion, the proposed method could improve the performance of security and anti-jamming for WVSN.