Shock Signal Research Articles

Reversed self-steepening in Si3N4-organic hybrid slot waveguides with engineered nonlinearity

Abstract Dispersion of nonlinearity greatly influences both temporal and spectral evolutions for ultrashort pulses, although difficult to be tailored in a wide spectral range. Here, we show the hardly observable reversed self-steepening in an on-chip Si3N4-organic hybrid slot waveguide with sophisticated dispersion and an increased nonlinear coefficient with wavelength. An octave-spanning supercontinuum with significant red-shift spectral broadening and a rectangle-shaped pulse waveform with a sharp rising edge of 13 fs can be generated. We study the robustness of reversed self-steepening under different operating conditions and reveal its nonlinear dynamics. This deepens the understanding on the dispersion of nonlinearity and helps develop novel nonlinearity-engineered devices for on-chip optical shock formation, pulse shaping, and signal processing in the future.

Evaluation of the mechanical shock testing standards for electric vehicle batteries

The safety of Li-ion batteries (LIB) has become an important issue with the continuously increased use of electric vehicles (EV) in the world. In a survivable vehicle crash, when the vehicle needs to maintain structural integrity, crash-induced shock may damage EV's LIB. Therefore, an evaluation of commonly used mechanical shock test standards for EV battery module and pack is performed in this study against the crash-induced shock signals collected from National Highway Traffic Safety Administration (NHTSA) New Car Assessment Program (NCAP) tests. Various shock analysis methods including signal characteristics in time domain, power spectral density (PSD) and shock response spectrum (SRS) in frequency domain, and the acceleration/velocity-change diagram are used for the evaluation. It is found that most peak accelerations of NCAP shock signals significantly exceed the peak accelerations specified in shock testing standards. Crash-induced shocks cannot be fully represented by the half-sine pulse adopted in shock testing standards. In both time and frequency domains, the existing shock testing standards generally underestimate the severities of the crash-induced shocks, and therefore, are non-conservative. It also shows that the correct selection of a filter for the processing of the original crash-induced shock signal is crucial for the specification of EV battery shock environment and shock response analyses. The results obtained in this research can support the development of more reliable shock testing standards for EV batteries.

Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems

The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability.

Correlation between lumbo-pelvic shock attenuation and ankle range of motion of horse riders during extended trot: a pilot study

Abstract The lumbo-pelvic hip complex (LPHC) plays a pivotal role in equestrian sports, connecting the rider’s body to the horse’s back and influencing rider-horse interaction. Achieving an ideal ‘independence of seat’ characterised by balanced, elastic, and upright posture is representative of rider expertise. While previous studies have examined aspects of rider-horse coordination, the relationship between rider ankle range of motion (ROM) and shock attenuation at the LPHC remains mostly unexplored. Eight experienced dressage dyads participated, and their ankle ROM was measured using a goniometer while accelerometers recorded saddle and rider sacrum accelerations during 20 extended sitting trot strides. A strong positive correlation (r = 0.71, ) was observed between ankle ROM and saddle-rider’s sacrum increase in shock signal strength, indicating that greater ankle ROMs correlated to worse dampening capacities of experienced riders. Ankle ROM may have implications for the rider’s and horse welfare and performance. Understanding the role of ankle mobility provides valuable insights for coaching and training programs in equestrian sports.

The acoustic-vibration characteristics of cylindrical roller bearings with raceway failures: Simulation and experiment

This paper presents a mathematical model for cylindrical roller bearings, the precision noise test and simulation investigation are employed, the discussion focuses on the systematic investigation of the vibration characteristics, noise properties, and noise transmission attributes of defective bearings. The findings indicate that severe displacement oscillations of rollers and oil film oscillations can be observed due to the raceway fault, the average slip rate of rollers is seriously affected, resulting in a reduction in the operational stability of the cage. The noise shock is also introduced, which is primarily reflected in the mid-frequency range (670–8000 Hz) of the spectrum, leading to a deviation from the normal distribution of noise data points. This characteristic makes it challenging to detect noticeable shock signals when testing bearing noise at a distance. This research holds the promise of deepening comprehension of faulty bearing dynamics, thereby furnishing richer means for precise fault diagnosis.

Heat stress-induced NO enhanced perylenequinone biosynthesis of Shiraia sp. via calcium signaling pathway

Perylenequinones (PQs) are natural photosensitizing compounds used as photodynamic therapy, and heat stress (HS) is the main limiting factor of mycelial growth and secondary metabolism of fungi. This study aimed to unravel the impact of HS-induced Ca2+ and the calcium signaling pathway on PQ biosynthesis of Shiraia sp. Slf14(w). Meanwhile, the intricate interplay between HS-induced NO and Ca2+ and the calcium signaling pathway was investigated. The outcomes disclosed that Ca2+ and the calcium signaling pathway activated by HS could effectively enhance the production of PQs in Shiraia sp. Slf14(w). Further investigations elucidated the specific mechanism through which NO signaling molecules induced by HS act upon the Ca2+/CaM (calmodulin) signaling pathway, thus propelling PQ biosynthesis in Shiraia sp. Slf14(w). This was substantiated by decoding the downstream positioning of the CaM/CaN (calcineurin) pathway in relation to NO through comprehensive analyses encompassing transcript levels, enzyme assays, and the introduction of chemical agents. Concurrently, the engagement of Ca2+ and the calcium signaling pathway in heat shock signaling was also evidenced. The implications of our study underscore the pivotal role of HS-induced Ca2+ and the calcium signaling pathway, which not only participate in heat shock signal transduction but also play an instrumental role in promoting PQ biosynthesis. Consequently, our study not only enriches our comprehension of the mechanisms driving HS signaling transduction in fungi but also offers novel insights into the PQ synthesis paradigm within Shiraia sp. Slf14(w).Key points• The calcium signaling pathway was proposed to participate in PQ biosynthesis under HS.• HS-induced NO was revealed to act upon the calcium signaling pathway for the first time.

Research on anti-vibration tension control based on self-coupling fractional-order PID

Constant tension control is essential for excellent winding quality. However, the system’s nonlinearity and external disturbance make it challenging to guarantee tension control accuracy with conventional control methods. Thus, a self-coupling fractional-order proportional–integral–derivative (SC-FOPID) control scheme combined with a disturbance observer is proposed to enhance the system’s anti-vibration performance. The fractional-order dynamic model of the unwinding roller and swing rod is established by analyzing the tension mechanism. Based on deliberate analysis and calculation, the vibration shock signal can be decomposed into periodic sinusoidal disturbance and bounded noise approximately. As such, an output-based anti-vibration method using a fractional-order model can be realized, where a back recursive disturbance observer is designed to estimate the periodic component. Simultaneously, the bounded noise exhibited in vibration can be attenuated by the SC-FOPID controller. The stability is guaranteed using the Lyapunov theorem, and the simulation results show the proposed method’s effectiveness in improving the tension control performance.

Insights into the Neuroprotective Potential of Epicatechin: Effects against Aβ-Induced Toxicity in Caenorhabditis elegans.

Medical therapies to avoid the progression of Alzheimer's disease (AD) are limited to date. Certain diets have been associated with a lower incidence of neurodegenerative diseases. In particular, the regular intake of foods rich in polyphenols, such as epicatechin (EC), could help prevent or mitigate AD progression. This work aims to explore the neuroprotective effects of EC using different transgenic strains of Caenorhabditis elegans, which express human Aβ1-42 peptides and contribute to elucidating the mechanisms involved in the effects of EC in AD. The performed assays indicate that this flavan-3-ol was able to reduce the signs of β-amyloid accumulation in C. elegans, improving motility and chemotaxis and increasing survival in transgenic strain peptide producers compared to nematodes not treated with EC. The neuroprotective effects exhibited by EC in C. elegans could be explained by the modulation of inflammation and stress-associated genes, as well as autophagy, microgliosis, and heat shock signaling pathways, involving the regulation of cpr-5, epg-8, ced-7, ZC239.12, and hsp-16 genes. Overall, the results obtained in this study support the protective effects of epicatechin against Aβ-induced toxicity.

Research on the Application of Intelligent Diagnosis of Major Equipment Failures in Coal Mines

Abstract Currently, the implementation of a coal mine production safety monitoring system, capable of real-time monitoring of the safety condition of large coal mine equipment and timely dispatch management, is crucial for the efficient and safe operation of coal mine enterprises. This paper designs the coal mine major equipment failure process based on the rolling bearing’s vibration mechanism, analyzes the major equipment’s failure signal using both time domain and frequency domain analysis methods, and employs the intelligent fuzzy control method to identify the major equipment failure. We have designed and practically applied a real-time, multi-threaded, and comprehensive fault diagnosis and prediction system for coal mine major equipment, combining information from coal mine safety, production, and operation. The results show that the normal state’s time-domain amplitude approximates (-0.2, 0.2), and the time-domain waveforms all display periodic shock signals when the bearing sustains damage. The faulty vibration amplitude of the inner ring is within 0.08 ms 2 and the faulty vibration amplitude of the outer ring is within 0.44 ms 2, and the diagnostic accuracy using the method of this paper reaches 97.2%. This study demonstrates the effectiveness of remote monitoring and fault analysis of major equipment in coal mines, which effectively improves the equipment start-up rate and ensures coal mine production safety.

Modeling New Nature of Extraction and State Identification of Vibration Shock Signals From Hydroelectric Generating Units Using LCGSA Optimized RBF Combined With CEEMDAN Sample Entropy

Modeling New Nature of Extraction and State Identification of Vibration Shock Signals From Hydroelectric Generating Units Using LCGSA Optimized RBF Combined With CEEMDAN Sample Entropy

Refined composite hierarchical bidirectional diversity entropy and its application in fault type diagnosis and fault degree diagnosis of diesel engine fuel injectors

The performance of a diesel engine is closely related to the injector, and the fuel pressure shock signal for monitoring the state of the injector always contains multiple natural oscillation modes at different levels. Therefore, it is very necessary to detect the dynamic changes of the injector signal from multiple levels. Multi-scale Diversity Entropy (MDE) is a recent commonly used method of information entropy. However, due to its inherent defects, it limits the wide application of MDE in fault feature extraction. Therefore, this paper proposes a new nonlinear dynamics method, called the Refined Composite Hierarchical Bidirectional Diversity Entropy (RCHBDE). The ability of RCHBDE to extract signal complexity changes was verified through simulation signals, which is superior to MDE and multi-scale bidirectional diversity entropy (MBDE). On this basis, this article proposes a diagnostic framework for fuel injector fault type and degree based on RCHBDE and Seagull optimized Support Vector Machine. We apply the proposed method to the injector failure test data. The results show that RCHBDE can better reflect the dynamic change process of the injector fault signal than MDE and MBDE. Moreover, compared with existing methods, the proposed method in this paper improves the accuracy and efficiency of fault diagnosis.

Heartbeat and respiration monitoring based on FBG sensor network

A fiber Bragg grating (FBG)-based acquisition method for heartbeat and respiration was designed for portable home-based cardiopulmonary physiological parameter detection. The ballistocardiogram (BCG) is susceptible to noise interference during the acquisition process. In order to improve the accuracy of heart rate and respiration rate solving, the adaptive K-value-based variational modal decomposition (K-VMD) algorithm is proposed. First, the algorithm obtains the optimal solution by calculating the difference frequency of the last component of the neighboring K-values. Second, a threshold value is set according to the frequency variation. Again, the optimal K value is automatically selected according to the threshold. The problem of over-decomposition or under-decomposition can be solved by using the optimal K-value decomposition. Finally, heart rate and respiration rate are estimated using the power spectrum based on Welch's method. In 15 comparison experiments, the K-VMD algorithm was in perfect agreement with the ECG signal 13 times, and the other three conventional methods were 9, 7, and 5 times, respectively. The maximum error of the K-VMD algorithm was 1 bpm, and the other three conventional methods were 2 bpm, 3 bpm, and 3 bpm, respectively. The maximum error in the respiratory rate detection of this algorithm in the position of the diaphragm is ±0.2 bpm. It can be seen that the present algorithm can effectively reduce noise interference and improve monitoring accuracy. And it can provide data support for obtaining the characteristic waveform of cardiac shock signals and respiratory signals. It has certain practical applications.

Electric shock feature extraction method based on adaptive variational mode decomposition and singular value decomposition

AbstractThis paper proposes a feature extraction method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) for electric shock fault‐type identification. The AVMD algorithm is utilized to adaptively decompose the electric shock signal into intrinsic mode components, each containing distinct frequency information. Subsequently, the correlation coefficient is employed to extract the intrinsic mode component with amplitudes greater than or equal to 0.1 ( ≥ 0.1). Feature extraction is then performed using SVD on the ≥ 0.1 intrinsic mode component, based on its maximum singular value and singular entropy. This approach effectively overcomes the limitation of the traditional VMD that necessitates manual K value setting. Moreover, it achieves dimensionality reduction and feature extraction of the intrinsic mode components through SVD, resulting in enhanced computational efficiency and fault identification accuracy. Extensive simulations demonstrate the remarkable recognition rates of electric shock fault types in animals and plants using the proposed AVMD‐SVD method, achieving a recognition rate as high as 99.25%. Comparative performance analysis further verifies the superiority of AVMD‐SVD over similar empirical mode decomposition‐SVD feature extraction techniques.

Effect of Slow-Release Urea Partial Replacement of Soybean Meal on Lactation Performance, Heat Shock Signal Molecules, and Rumen Fermentation in Heat-Stressed Mid-Lactation Dairy Cows.

This study aimed to assess the effects of partially substituting soybean meal in the diet with slow-release urea (SRU) on the lactation performance, heat shock signal molecules, and environmental sustainability of heat-stressed lactating cows in the middle stage of lactation. In this study, 30 healthy Holstein lactating dairy cattle with a similar milk yield of 22.8 ± 3.3 kg, days in milk of 191.14 ± 27.24 days, and 2.2 ± 1.5 parity were selected and randomly allocated into two groups. The constituents of the two treatments were (1) basic diet plus 500 g soybean meal (SM) for the SM group and (2) basic diet plus 100 g slow-release urea and 400 g corn silage for the SRU group. The average temperature humidity index (THI) during the experiment was 84.47, with an average THI of >78 from day 1 to day 28, indicating the cow experienced moderate heat stress conditions. Compared with the SM group, the SRU group showed decreasing body temperature and respiratory rate trends at 20:00 (p < 0.1). The substitution of SM with SRU resulted in an increasing trend in milk yield, with a significant increase of 7.36% compared to the SM group (p < 0.1). Compared to the SM group, AST, ALT, and γ-GT content levels were significantly increased (p < 0.05). Notably, the levels of HSP-70 and HSP-90α were significantly reduced (p < 0.05). The SRU group showed significantly increased acetate and isovalerate concentrations compared with the SM group (p < 0.05). The prediction results indicate that the SRU group exhibits a significant decrease in methane (CH4) emissions when producing 1 L of milk compared to the SM group (p < 0.05). In summary, dietary supplementation with SRU tended to increase the milk yield and rumen fermentation and reduce plasma heat shock molecules in mid-lactation, heat-stressed dairy cows. In the hot summer, using SRU instead of some soybean meal in the diet alleviates the heat stress of dairy cows and reduces the production of CH4.

Reference velocity demodulation method for accelerometer shock testing based on enhanced CEEMD and threshold correction

High-G accelerometers are critical for measuring high shock signals and must be calibrated to improve measurement accuracy. A laser Doppler velocimeter (LDV) is required to calibrate a high-G accelerometer to provide a high-precision reference velocity. The LDV signal must be demodulated to obtain the velocity. However, the phase method is susceptible to noise interference, while the conventional periodic distribution method is challenging to demodulate and severely affected by signal oscillations. We propose a novel periodic distribution method based on enhanced complementary ensemble empirical mode decomposition (CEEMD) and threshold correction to demodulate the LDV signal. First, the LDV signal is processed with CEEMD to obtain multiple intrinsic mode functions (IMFs) and the residual. Next, each IMF is partially zeroed to obtain the noise-reduced LDV signal. Then, the over-threshold peak of the noise-reduced LDV signal is calculated. Finally, the demodulated velocity of the LDV signal is obtained by correcting the noise-reduced LDV signal according to the over-threshold peak point and calculating all the zero points. Simulation and experimental results show that the proposed method outperforms the phase method based on enhanced CEEMD and the periodic distribution method based on enhanced CEEMD and can significantly reduce noise interference. The results show that the proposed method can accurately demodulate the LDV signal to obtain a highly accurate reference velocity, improving the reliability of accelerometer shock testing.

Predictive model and physical interpretation of asymmetric features in pyroshock signals

In aerospace engineering, electronic devices can be easily damaged under pyroshock environments, and therefore, shock testing is required. The shock response spectrum (SRS) is the widely adopted specification tool to compare shock severities between field pyroshocks and testing shocks. However, two shocks with almost the same SRS but different temporal features could still lead to large differences in the failure of a device. The test specification should consider temporal features of shocks in order to be more representative of the field shocks. During shock propagation, shock signal evolves from an exponentially decaying shape to an asymmetric shape with an increased initial rise stage, which is characterized by the temporal moment or asymmetric basis functions. This study explains the evolution mechanism of shock asymmetry based on elastic wave propagation theory and proposed a predictive model. The theoretical foundation for the application of signal asymmetry in pyroshock characterization, device failure analysis and SRS reconstruction is provided.

Heat stress enhanced perylenequinones biosynthesis of Shiraia sp. Slf14(w) through nitric oxide formation.

Perylenequinones (PQs) are a class of natural polyketides used as photodynamic therapeutics. Heat stress (HS) is an important environmental factor affecting secondary metabolism of fungi. This study investigated the effects of HS treatment on PQs biosynthesis of Shiraia sp. Slf14(w) and the underlying molecular mechanism. After the optimization of HS treatment conditions, the total PQs amount reached 577 ± 34.56 mg/L, which was 20.89-fold improvement over the control. Also, HS treatment stimulated the formation of intracellular nitric oxide (NO). Genome-wide analysis of Shiraia sp. Slf14(w) revealed iNOSL and cNOSL encoding inducible and constitutive NOS-like proteins (iNOSL and cNOSL), respectively. Cloned iNOSL in Escherichia coli BL21 showed higher nitric oxide synthase (NOS) activity than cNOSL, and the expression level of iNOSL under HS treatment was observably higher than that of cNOSL, suggesting that iNOSL is more responsible for NO production in the HS-treated strain Slf14(w) and may play an important role in regulating PQs biosynthesis. Moreover, the putative biosynthetic gene clusters for PQs and genes encoding iNOSL and nitrate reductase (NR) in the HS-treated strain Slf14(w) were obviously upregulated. PQs biosynthesis and efflux stimulated by HS treatment were significantly inhibited upon the addition of NO scavenger, NOS inhibitor, and NR inhibitor, indicating that HS-induced NO, as a signaling molecule, triggered promoted PQs biosynthesis and efflux. Our results provide an effective strategy for PQs production and contribute to the understanding of heat shock signal transduction studies of other fungi.Key points• PQs titer of Shiraia sp. Slf14(w) was significantly enhanced by HS treatment.• HS-induced NO was first reported to participate in PQs biosynthetic regulation.• Novel inducible and constitutive NOS-like proteins (iNOSL and cNOSL) were obtained and their NOS activities were determined.

From safety to frustration: The neural substrates of inhibitory learning in aversive and appetitive conditioning procedures

Inhibitory associative learning counters the effects of excitatory learning, whether appetitively or aversively motivated. Moreover, the affective responses accompanying the inhibitory associations are of opponent valence to the excitatory conditioned responses. Inhibitors for negative aversive outcomes (e.g. shock) signal safety, while inhibitors for appetitive outcomes (e.g. food reward) elicit frustration and/or disappointment. This raises the question as to whether studies using appetitive and aversive conditioning procedures should demonstrate the same neural substrates for inhibitory learning. We review the neural substrates of appetitive and aversive inhibitory learning as measured in different procedural variants and in the context of the underpinning excitatory conditioning on which it depends. The mesocorticolimbic dopamine pathways, retrosplenial cortex and hippocampus are consistently implicated in inhibitory learning. Further neural substrates identified in some procedural variants may be related to the specific motivation of the learning task and modalities of the learning cues. Finally, we consider the translational implications of our understanding of the neural substrates of inhibitory learning, for obesity and addictions as well as for anxiety disorders.

Frequency Characteristic Measurement of High-G Accelerometers Based on Down-Step Response

High-G accelerometers are a critical component for the accurate measurement of high-impact signals. Each high-G accelerometer must be frequency calibrated to ensure the reliability of its measurements. However, the existing methods for measuring the frequency response of high-G accelerometers use sinusoidal shocks for excitation, which is not only complex but also prone to noise interference. To overcome this problem, we propose a down-step response method (DSRM) to measure the frequency characteristics of high-G accelerometers. First, the aluminum foam was selected as the cushion material for the shock test to generate the down-step shock signal. Then, the response signal of the high-G accelerometer under the down-step shock excitation was converted into a step response signal. Finally, the features in the step response signal were extracted to calculate the frequency characteristics of the accelerometer. Experimental results show that DSRM outperforms the conventional fast Fourier transform (FFT) and can significantly reduce noise interference while reducing the equipment requirements. The results demonstrate the proposed measurement method’s high reliability, stability, and convenience.

Time-frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery. Therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals is conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number and second penalty factor is proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) is established. A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search (HS) method. A multi-source shock simulation signal is established, and actual data on the valve fault are obtained through diesel engine fault experiments. The fault recognition rate of the intake and exhaust valve clearance is above 90% and the extraction accuracy of the shock start position is improved by 10°.