Distortion Phenomenon Research Articles

Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value.

Non-invasive identification of historical textiles and leather by means of external reflection FTIR spectroscopy

Identifying the fibres in historical textiles presents a complex challenge due to the wide variety of plant, animal and early synthetic materials that have been used. Traditionally, this identification process involves sampling followed by either microscopic examination or ATR-FTIR spectroscopy. However, there are instances when sampling is restricted due to the good condition or significant value of the object under analysis. Additionally, the presence of leather components alongside textiles can further complicate the identification. This paper proposes a novel non-invasive method for fibre identification based on External Reflection (ER) FTIR spectroscopy, which has been rarely applied to textiles or leather. The current research demonstrates that ER-FTIR spectrum is a viable tool for fibre identification on both recent and historical textiles. The non-invasiveness of the analytical approach enables a comprehensive investigation without compromising the number or position of samples. Respect to ATR-FTIR spectra, the ER-FTIR spectra frequently exhibit an amplification of certain diagnostic bands, facilitating the identification of the various fibres examined in this study (cotton, hemp, viscose, silk, wool, leather, polyamide, acrylic, polyester). The extended spectral range (7500–375 cm−1) which is provided by ER-FTIR spectrometry also contains extra bands in the near infrared region, which can provide key information for the discrimination due to the lack of distortion phenomena. The technique was applied to the characterisation of textile materials coming from a collection of 10 traditional Japanese samurai armours spanning from the 16th to the 20th century (Museo delle Culture, Lugano, Switzerland). For the first time, the results provided a comprehensive overview of the textiles utilized in Japanese armours across various historical periods. Overall, the appearance of materials in samurai armours reflects the evolution of armour-making techniques and the influence of socio-cultural factors throughout Japanese history. Synthetic and semi-synthetic materials were easily detected, revealing the occurrence of a past conservation treatment or the early adoption of modern man-made materials in the manufacturing of traditional armours. The approach outlined in this case study can be applied to textile collections of various kinds, offering a reliable mean to discern the yarn composition and detect non-original components. The method also appears as a valuable prescreening tool for designing a less intrusive yet more informative sampling strategy, should additional details about fibre type and dyeing be necessary.

In-situ analysis of cathode and anode impedances to probe the performance degradation of lithium–sulfur batteries

Improving the performance of Li-metal batteries with sulfur cathodes while maintaining high sulfur loading (>4 mg cm−2) and low electrolyte-to-sulfur ratio (<10 mg μL−1) is a significant challenge. These conditions often result in poor battery cycle life and sudden capacity failure during charge-discharge processes. To address this challenge, we investigate Li–S batteries in a three-electrode (3E) configuration, utilizing optimized materials and geometries for the reference electrode. This configuration allows us to independently track the in-situ variations in cathode and anode resistances, along with the electrode potential difference, unveiling the root causes of abrupt battery failure during cycling. Our 3E approach uncovers previously unnoticed distortion phenomena in the electrochemical impedance spectra of both the cathode and anode, providing essential indicators of imminent micro-short circuiting. Supported by simulations using the finite element method and equivalent circuit analysis software, these insights clarify that the primary reason for battery failure is the growth of Li dendrites and their subsequent contact with the cathode, rather than a sudden increase in cell impedance. By substituting traditional Li foil with a specially designed Li anode less susceptible to Li dendrite growth, we achieve reduced anode resistance and improved battery capacity and cycle life stability. This research reveals the potential of the 3E configuration for precise in-situ impedance monitoring, which is valuable for devising strategies to enhance cycle stability.

Suppression of vortex electromagnetic wave divergence by modulating the wave front dislocation line

Abstract When the beam is transmitted in the atmosphere, under the influence of atmospheric turbulence, the beam will occur energy attenuation, wavefront distortion and other phenomena, which will seriously affect the transmission and reception of information. Based on the PB phase principle enhances the focusing capability of the auto-focusing Airy Gaussian vortex beam by regulating the wavefront error curvature of the vortex beam, and then enhances the resistance of the beam against atmospheric turbulence. The results reveal the curvature of the wavefront error curve and the peak intensity of the light field of the beam can be changed by changing the beam polarization state parameters n and m in the focal plane. The auto-focusing Airy Gaussian vortex beam with the polarization state parameters m=1, n=4 increases the peak magnitude of the light field intensity in the focal plane of the auto-focusing Airy Gaussian vortex beam with the polarization state parameters m=0, n=0 to 1.99 times. Demonstrate that geometric phase tuning the auto-focusing Airy Gaussian vortex beams can enhance resistance to atmospheric turbulence. Furthermore, this paper extends this method to the field of millimeter wave. Under the transmission distance of 1km, the peak intensity of the initial vortex electromagnetic wave after the geometric phase adjustment is increased to 3.33 times, and the receiving radius of the target surface is reduced to 0.24 of the initial vortex electromagnetic wave. It shows that geometric phase-regulated vortex electromagnetic wave can suppress vortex electromagnetic wave divergence and have potential applications in wireless communication.

Trunk distortion weakens the tree productivity revealed by half-sib progeny determination of Pinus yunnanensis

Twisted trunks are not uncommon in trees, but their effects on tree growth are still unclear. Among coniferous tree species, the phenomenon of trunk distortion is more prominent in Pinus yunnanensis. To expand the germplasm of genetic resources, we selected families with excellent phenotypic traits to provide material for advanced generation breeding. The progeny test containing 93 superior families (3240 trees) was used as the research material. Phenotypic measurements and estimated genetic parameters (family heritability, realistic gain and genetic gain) were performed at 9, 15, and 18 years of age, respectively. The genetic evaluation yielded the following results (1) The intra-family variance component of plant height (PH) was greater than that of the inter-family, while the inter-family variance components of other traits (diameter at breast height (DBH), crown diameter (CD), height under branches (HUB), degree of stem-straightness (DS)) were greater than that of the intra-family, indicating that there was abundant variation among families and potential for selection. (2) At half rotation period (18 years old), there was a significant correlation among the traits. The proportion of trees with twisted trunks (level 1–3 straightness) reached 48%. The DS significantly affected growth traits, among which PH and DBH were the most affected. The volume loss rate caused by twisted trunk was 18.06-56.75%, implying that trunk distortion could not be completely eliminated after an artificial selection. (3) The influence of tree shape, crown width, and trunk on volume increased, and the early-late correlation between PH, DBH and volume was extremely significant. The range of phenotypic coefficient of variation, genetic variation coefficient and family heritability of growth traits (PH, DBH, and volume) were 44.29-127.13%, 22.88-60.87%, and 0.79–0.83, respectively. (4) A total of 21 superior families were selected by the method of membership function combined with independent selection. Compared with the mid-term selection (18 years old), the accuracy of early selection (9 years old) reached 77.5%. The selected families’ genetic gain and realistic gain range were 5.79-19.82% and 7.12-24.27%, respectively. This study can provide some useful reference for the breeding of coniferous species.

Effect of rare earth element Y content on microstructure, magnetic properties, and electrochemical properties of the as-annealed FeCoNiAl0.2Yx high-entropy alloys

FeCoNi high-entropy alloy (HEA) is widely used in the aerospace and chemical industry. However, the strength and corrosion resistance of the alloy still need to be improved. In this paper, FeCoNiAl0.2Yx (x = 0, 0.05, 0.1, 0.2, and 0.3 in mole ratio) high entropy alloys with different contents of rare earth (RE) element yttrium (Y) were prepared by the vacuum arc melting method, and then the alloys were subjected to annealing treatment at 800 °C/2h. The microstructure, hardness, magnetism, and corrosion performance of FeCoNiAl0.2Yx HEAs in the annealed state with different contents of element Y were analyzed. The results show that the annealed FeCoNiAl0.2Yx HEAs without the addition of element Y and after the addition of element Y both were composed of a face-centered cubic phase. The alloy showed a dendritic structure inside. The element Y was solidly dissolved in the alloys to refine the microstructure of the alloys, and the dendrites were gradually refined with the addition of Y. The Y element caused the phenomenon of lattice distortion inside the alloys, which led to the increase in the alloys’ hardness. The alloy with x = 0.2 showed the greatest exchange of magnetic atoms and the alloy with x = 0.05 showed the lowest coercivity. The alloy with x = 0.1 showed the lowest self-corrosion current density, the broadest passivation zone, the densest dendrites, the smallest grain spacing, and the weakest tendency for intergranular corrosion. The present study shows that the introduction of element Y improves the microstructural morphology, hardness, magnetism, and corrosion properties of FeCoNiAl0.2Yx HEAs in the annealed state.

Analysis of aerodynamic and stealth characteristics of aircraft under the action of microwave plasma

This paper investigates the impact of plasma on the aerodynamic and stealth characteristics of hypersonic aircraft. Utilizing the finite volume method, the governing equations of the fluid mechanics were discretized, and the seven-element air chemical reaction model was employed for computations. A technique involving the alteration of the chemical reaction source term rate was employed to replicate the actual plasma concentration distortion phenomena induced by microwaves around hypersonic vehicles, enabling an analysis of the influence of plasma density distortion on the aerodynamic characteristics of these vehicles. In addition, the method of Piecewise Linear JE Recursive Convolution Finite-difference Time-domain (PLJERC-FDTD) is used to calculate the radar scattering cross-section of the target within both the initial and distorted flow fields. This allowed an examination of the variation in the aircraft’s stealth characteristics when exposed to distortion in an ionized environment.

Adaptive Local Flaw Detection Based on Magnetic Flux Leakage Images With a Noise Distortion Effect for Steel Wire Ropes

Steel wire rope (SWR) is widely used in industrial scenarios because of its high strength and toughness. To avoid accidents that local flaws (LFs) can cause, SWRs should be inspected regularly. Magnetic flux leakage (MFL) image detection, which is a common SWR inspection method, is inevitably influenced by shaking noise and strand noise. Although noise-reduction methods for these two types of noise have been proposed, the phenomenon of noise distortion has not received enough attention. This paper investigates the noise distortion phenomenon on the MFL image and finds that the distorted noise severely affects the performance of existing noise-feature-oriented (NFO) denoising methods. To solve this problem, we adopt a target-feature-oriented (TFO) denoising method. Specifically, the removal of noise is avoided, instead, an LF-enhancement-based denoising process is proposed. Moreover, to localize LFs in denoised images, a three-stage adaptive localization method mainly based on disjoint region analysis is proposed. The experiment results show that the proposed TFO denoising method significantly improves the denoising performance for images that distorted noise influences. In addition, the proposed adaptive localization method improves the intelligence of the localization process and has better localization performance for images with distorted noise.

Research on denoising the temperature monitoring signal of a distributed optical fiber downhole cable

Aiming at the noise mixed in the downhole cable skin temperature signal measured by the BOTDR distributed fiber optic sensing temperature measurement system, a new improved wavelet threshold function is proposed, and denoising experiments are completed based on the MATLAB platform for the measured cable skin temperature signal. The denoising results of several threshold function methods are compared and analyzed from the aspects of graphical intuitive comparison as well as quantitative evaluation indicators. The results show that the improved threshold function denoising fiber optic temperature monitoring signal has a higher SNR and lower RMSE, there is no obvious distortion phenomenon, and the denoising performance is significantly improved compared with the traditional threshold function, which has a high application value.

Hysteresis measurement and generic modeling of magnetostrictive materials under high-frequency excitation and high-intensity bias field

The applied DC (direct-current) bias fields can cause the changes of hysteresis and loss properties of magnetostrictive alloys materials, which further affect the output characteristics of electromagnetic devices. This study investigates the impact of high-frequency AC (alternating-current) excitation and high intensity DC bias magnetic field on hysteresis characterization for different magnetostrictive materials. To obtain the hysteresis properties of materials, a generic dynamic magnetic measured system is established, which can test the low-permeability magnetic materials with a DC bias strength range of 0–20000 A/m at AC excitation frequency range of 1–9 kHz. The tested results show that the Terfenol-D, Fe83Ga17, and Fe30Co70 exhibit different DC bias sensitivities, which cause the largest deviation (31.68 %) of losses compared with unapplied DC bias field. To better predict these changes, a generic high-frequency dynamic Jiles-Atherton (J-A) model with the coupling AC excitation and DC bias field is proposed. The AC excitation and DC bias related terms are introduced to characterize distortion and ellipticity phenomenon for these hysteresis loops. A combined method of Simulated Annealing Particle Swarm Optimization-Linear Decreasing Inertia Weight (SA-PSO-LDIW) algorithm and Neural Network (NN) are proposed to identify the introduced relevant parameters. This paper solves the problem of high-frequency hysteresis measurement and loss calculation for low permeability and high electrical impedance magnetostrictive rods-shaped material which can help guide the high frequency application for magnetostrictive materials.

High-frequency distortion phenomenon and solution of low frequency vibration sensor

This paper analyzes the difference between several acceleration over-limit faults, and finds out the cause of false acceleration fault of wind turbines, which is because the low-frequency vibration sensor used by the unit is interfered with the high-frequency signal generated by the friction of the yaw system and causes the sensor output signal distortion. The performance of different manufacturers’ sensors under different installation positions is reproduced on the spot. A cost-effective solution to the high frequency distortion problem (installing a physical low-pass filter) is developed, and the comparative test results before and after its application are demonstrated experimentally.

Research on Optimization Strategies of Algorithmic Models for Digital Visual Communication in Cultural Heritage Protection

Abstract With the development of digital technology, digital visual communication has more diversified forms in cultural heritage protection. This paper proposes to design a series of digital commemorative collection tickets for the Dong painting based on the theme. In the visual aesthetic evaluation, the optimization strategy of the digital visual communication algorithms model for cultural heritage protection is evaluated. In the optimization of image enhancement, based on the Retinex algorithm, we propose a single-scale algorithm that achieves the edge enhancement and dynamic range compression of the image and a multi-scale algorithm that meets the requirements of enhancing the details of the image and preserving the color at the same time and introduces the color recovery factor C to avoid the phenomenon of color distortion of the picture. Based on the K-means algorithm and Gaussian mixture model, the optimization of the way of selecting the initial clustering center and determining the number of class clusters is done to improve the efficiency of color feature extraction. Based on the optimized algorithmic model, the evaluation score of the Dong painting product of intangible cultural heritage designed in this paper is 6.95, which is better than the other comparative products. And the total evaluation value of all design elements in the product is more than 80.

A Novel Friction Compensation Method Based on Stribeck Model With Fuzzy Filter for PMSM Servo Systems

Friction deteriorates speed and position control accuracy of high-precision machine tools and causes distortion phenomenon when the speed crosses zero. In this paper, a new compensation method based on static Stribeck model (SB) combined with fuzzy low-pass filter (SBFF) is studied for friction compensation of permanent magnet synchronous motor (PMSM) servo systems. First, models of the servo system, the Stribeck, and the LuGre are established. Then, it is theoretically derived that the hysteresis effect of the LuGre model (LG) can be equivalent to a low-pass filter term compared to the SB. Based on this, the relationship between the filter time constant and the motor reference speed can be derived. To further improve the friction compensation effect near zero speed and to weaken the delay generated by the filter during the non-zero-speed range, fuzzy logic is introduced to adjust the filter time constant according to the reference speed variation. Through simulations and experiments, compensation strategies based on the LG feedforward, SB feedforward, SBFF feedforward and SBFF feedback are compared, and the robustness of the proposed SBFF feedforward compensation method over entire speed range is verified as well.

Classification guided thick fog removal network for drone imaging: ClassifyCycle.

The foggy images captured by drones are nonuniform due to inhomogeneous distribution of fog in higher altitude, leading to the obvious fog thickness differences in the images. This paper proposes a classification guided thick fog removal network for drone imaging, termed ClassifyCycle. The drone images are input into the proposed classification module (ICLFn) to enhance the reliability of follow-up learning network. The style migration module (ISMn) is introduced to reduce the image distortion, such as hue artifact and texture distort. The proposed network ClassifyCycle does not require paired foggy and corresponding fog-free datasets, avoiding the phenomena of overexposure, distortion, color deviation and fog residue after defogging. Extensive experimental results show that the proposed ClassifyCycle network surpasses the state-of-the-art algorithms on synthetic and realistic drone images captured in thick fog weather.

Novel travelling wave fault location approach for overhead transmission lines

Travelling wave fault location approach for overhead transmission line is widely applied in actual power grid, which has better fault location accuracy comparing with conventional power frequency data based method. However, travelling wave fault location approach is highly related to the frequency distortion phenomenon of travelling wave propagation velocity, which includes two parts: (i) the distortion caused by reflection and refraction phenomena of transformer substation and (ii) the attenuation caused by distributed resistance of transmission line. Therefore, aiming to improve the fault location accuracy, the paper contributes to two aspects: (i) the complete theoretical analysis of the distortion phenomenon of travelling wave signal and (ii) the improved travelling wave fault location approach considering distortion phenomenon. Eventually, based on PSCAD/EMTDC simulation tool, a typical 500 kV case study model is established to verify the fault location accuracy of proposed travelling wave fault location approach under the influences of fault type issue, fault location issue and grounding resistance issue.

Synergistic lattice regulation of additive and interface engineering to realize high efficiency CsPbI2Br perovskite solar cell

The CsPbI2Br material has gained recognition as an exceptional candidate for both single- and multi- junction solar cells due to its remarkable thermal and light stability, along with its suitable band gap. However, despite these inherent advantages, CsPbI2Br perovskite solar cells (PSCs) still encounter significant energy losses, which impede the further enhancement of their efficiency. Although various approaches involving additives and interface engineering techniques improved device performance, the influence of these methods on the perovskite lattice is frequently overlooked. Herein, synergistic lattice regulation through the combination of potassium acetate (KAc) as electron interface layer and methylammonium chloride (MACl) as perovskite additive, was proposed. The MACl dopant effectively increases the crystallinity and grain size of CsPbI2Br film by retarding the crystallization rate of perovskite, resulting in the enhancement in short-circuit current density (Jsc) of PSCs. Unfortunately, unlike the behavior observed in inorganic–organic perovskites, the introduction of Cl‾ from MACl into the interstitial positions leads to lattice expansion in CsPbI2Br, resulting in reduced open-circuit voltage (Voc) and fill factor (FF). However, the incorporation of K+ replacing Cs+ effectively mitigates lattice distortion phenomena. Consequently, the CsPbI2Br PSCs, benefiting from the complementary effects of the MACl additive and KAc interfacial layer, exhibit an outstanding champion power conversion efficiency of 17.11 %. This research offers profound insights into the impact of ions introduced through additive and interface engineering on perovskite lattice stress.

Genetic and Molecular Evidence of a Tetrapolar Mating System in the Edible Mushroom Grifola frondosa.

Grifola frondosa is a valuable edible fungus with high nutritional and medicinal values. The mating systems of fungi not only offer practical strategies for breeding, but also have far-reaching effects on genetic variability. Grifola frondosa has been considered as a sexual species with a tetrapolar mating system based on little experimental data. In the present study, one group of test crosses and six groups of three-round mating experiments from two parental strains were conducted to determine the mating system in G. frondosa. A chi-squared test of the results of the test-cross mating experiments indicated that they satisfied Mendelian segregation, while a series of three-round mating experiments showed that Mendelian segregation was not satisfied, implying a segregation distortion phenomenon in G. frondosa. A genomic map of the G. frondosa strain, y59, grown from an LMCZ basidiospore, with 40.54 Mb and 12 chromosomes, was generated using genome, transcriptome and Hi-C sequencing technology. Based on the genomic annotation of G. frondosa, the mating-type loci A and B were located on chromosomes 1 and 11, respectively. The mating-type locus A coded for the β-fg protein, HD1, HD2 and MIP, in that order. The mating-type locus B consisted of six pheromone receptors (PRs) and five pheromone precursors (PPs) in a crossed order. Moreover, both HD and PR loci may have only one sublocus that determines the mating type in G. frondosa. The nonsynonymous SNP and indel mutations between the A1B1 and A2B2 mating-type strains and the reference genome of y59 only occurred on genes HD2 and PR1/2, preliminarily confirming that the mating type of the y59 strain was A1B2 and not A1B1. Based on the genetic evidence and the more reliable molecular evidence, the results reveal that the mating system of G. frondosa is tetrapolar. This study has important implications for the genetics and hybrid breeding of G. frondosa.

Research on the Application of Convolutional Neural Network Model in Night Surveillance Video Image Enhancement

With the popularization of professional digital imaging equipment, digital image processing is popularly used in many fields such as industrial production, video surveillance, intelligent transportation, remote sensing and monitoring, and plays a significant role. In low illumination environments such as cloudy days, nights, indoor and object occlusion, imaging devices often capture images with low brightness and contrast, severe loss of detail information, and a large amount of noise. Enhancing low illumination images can enhance their clarity, highlight the texture details of the scene, greatly enhance the quality of the image, and provide data quality assurance for completing tasks such as target recognition and tracking, image segmentation, etc. This paper proposes a low light image enhancement algorithm based on CNN model to address the problem of low brightness and unclear monitoring video images in nighttime scenes due to lighting conditions. This algorithm can effectively improve the quality of low light images and exhibit superiority on multiple public datasets. The algorithm proposed in this article not only effectively improves the brightness of the image, but also enhances the detail clarity of the image to a certain degree, and can avoid color distortion and halo phenomena to a certain degree.

Chinese personalised text‐to‐speech synthesis for robot human–machine interaction

AbstractSpeech interaction is an important means of robot interaction. With the rapid development of deep learning, end‐to‐end speech synthesis methods based on this technique have gradually become mainstream. Chinese deep learning‐based speech synthesis techniques suffer from problems such as unstable synthesised speech, poor naturalness and poor personalised speech synthesis, which do not satisfy some practical application scenarios. Hence, an F‐MelGAN model is adopted to improve the performance of Chinese speech synthesis. A post‐processing network is used to refine the Mel‐spectrum predicted by the decoder and alleviate the Mel‐spectrum distortion phenomenon. A phoneme‐level and sentence‐level combined module is proposed to model the personalised style of speakers. A combination of an acoustic conditioning network, speaker encoder network GCNet and feedback‐constrained training is proposed to solve the problem of poor personalised speech synthesis and achieve personalised speech customisation in Chinese. Experimental results show that the whole model can generate high‐quality speech with high speaker similarity for both speakers that appear in the training process and speakers that never appear in the training process.

Numerical and experimental analysis of the parameters affecting the distortion phenomenon in the welding of wind turbine tower flange

Numerical and experimental analysis of the parameters affecting the distortion phenomenon in the welding of wind turbine tower flange