Search Results (53)

The paper addresses the phenomena of negative effective mass and negative effective density emerging in systems driven by entropic elastic forces. The elasticity of polymers is, at least partially, of entropic origin, and it represents the tendency of a polymer to evolve into a more probable state, rather than into one of lower potential energy. Entropy forces are temperature-dependent; thus, the temperature dependence of the effective mass and effective density arises. The effect of the negative effective mass is a resonance effect, emerging in core–shell mechanical systems, which takes place when the frequency of the harmonic external force acting on a core–shell system connected by an ideal spring approaches from above to the eigen-frequency of the system. We address the situation when the ideal spring connecting the core to the shell is made from a polymer material, and its elasticity is of an entropic origin. The effective mass is calculated, and it is temperature-dependent. The chain of core–shell units connected with a polymer spring is studied. The effective density of the spring is temperature-dependent. Optical and acoustical branches of vibrations are elucidated. The negative mass and density become attainable under the variation of the temperature of the system. In the situation when only one of the springs demonstrates temperature dependence, entropic behavior is investigated. Exemplifications of the effect are addressed. Full article

Recordings of wind velocity and associated wind turbine (WT) power possess noise, owing to inaccurate sensor measurements, atmosphere conditions, working stops, and flaws. The measurements still contain noise even after purification, so the fit curve of the wind turbine power might be different from the datasheet. The model of wind turbine power (MWTP) is significant, owing to its utilization for predicting and managing the wind energy. There are two types of MWTP, namely the parametric and the non-parametric types. Parameter identification of the parametric MWTP can be treated as a high nonlinear optimization problem. The fitness function is to minimize the root average squared errors (RASEs) between the calculated and measured wind powers while subject to a set of parameter constraints. The non-parametric MWTP is identified through training through machine learning. In this article, machine learning, namely the support vector regression (SVR), is innovatively applied for the identification of the non-parametric MWTP. Additionally, the dynamic force and the eigen parameters of WTs at different wind velocities are studied theoretically. The theoretical model for analyzing the natural frequencies of WT is validated using two techniques, namely the finite element method and the Euler–Bernoulli beam theory. The simulations are executed using MATLAB. The SVR is assessed via the comparison of its results with those of three parametric MWTP, viz. the 5-, 6-parameter logistic functions, and the modified hyperbolic tangent. It can be affirmed that the SVR execution is excellent and can produce the non-parametric MWTP with a RASE less than other algorithms by 0.4% to 93.8%, with a small computation cost. Full article

This study presents an analytical and experimental approach to enhance cantilever-based piezoelectric energy harvesters by optimizing thickness distribution. Using a gradient projection algorithm within a state-space framework, the unimorph beam’s geometry is tailored while constraining the first natural frequency. The objective is to amplify axial strain within the piezoelectric layers, thereby increasing electric polarization and maximizing the conversion efficiency of mechanical vibrations into electrical energy. The steady-state response under harmonic base excitation at resonance was modeled to evaluate the harvester’s dynamic behavior against uniform-thickness counterparts. Results show that the optimized beam achieves significantly higher output voltage and energy harvesting efficiency. Simulations reveal effective strain concentration in regions of high piezoelectric sensitivity, enhancing power generation under resonant conditions. Two independent experimental setups were employed for empirical validation: a non-contact laser vibrometry system (Polytec 3D) and a first resonant base excitation setup. Eigenfrequencies matched within 5% using a Polytec multipath interferometry system, and constant excitation tests showed approximately 30% higher in optimal shapes electrical potential value generation. The outcome of this study highlights the efficacy of geometric tailoring—specifically, non-linear thickness shaping—as a key strategy in achieving enhanced energy output from piezoelectric harvesters operating at their fundamental frequency. This work establishes a practical route for optimizing unimorph structures in real-world applications requiring efficient energy capture from low-frequency ambient vibrations. Full article

For four-channel photoplethysmograms (PPGs), this paper employs quaternion-valued medians as features for performing non-invasive blood glucose estimation. However, as the PPGs are contaminated by noise, the quaternion-valued medians are also contaminated by noise. To address this issue, principal component analysis (PCA) is employed for performing the denoising. In particular, the covariance matrix of the four-channel PPGs is computed and the eigen vectors of the covariance matrix are found. Then, the quaternion-valued medians of the four-channel PPGs are found and these quaternion-valued medians are represented as the four-channel real-valued vectors. By applying the PCA to these four-channel real-valued vectors and reconstructing the denoised four-dimensional real-valued vectors, these four-dimensional real-valued vectors are denoised. Next, these denoised four-dimensional real-valued vectors are represented as the denoised quaternion-valued medians. Compared to the traditional denoising methods and the traditional feature extraction methods that are performed in the individual channels, the quaternion-valued medians and the PCA are computed via fusing all of these four-channel PPGs together. Hence, the hidden relationships among these four channels of the PPGs are exploited. Finally, the random forest is used to estimate the blood glucose levels (BGLs). Our proposed PCA-based quaternion-valued medians are compared to the median of each channel of the PPGs and other features such as the time-domain features and the frequency-domain features. Here, the effectiveness and robustness of our proposed method is demonstrated using two datasets. The computer numerical simulation results indicate that our proposed PCA-based quaternion-valued medians outperform the existing quaternion-valued medians and the other features for performing non-invasive blood glucose estimation. Full article

In the field of electromagnetic countermeasures, suppressing mainlobe jamming represents a critical challenge requiring urgent resolution. Conventional polarization-based anti-jamming techniques, which fundamentally rely on obtaining pure jamming signals for prior parameter estimation, demonstrate limited effectiveness against co-frequency mainlobe suppression jamming. To tackle this problem, this paper proposes an innovative joint polarization-range-Doppler processing framework for airborne dual-polarized radar systems. Initially, we develop a polarized eigen-element surrogate technique to accurately estimate jamming polarization parameters, which demonstrates robust performance even under low jamming-to-signal ratio conditions. Subsequently, through Doppler compensation and range processing, we establish a combined feature projection method capable of reliably estimating target polarization from mixed signals containing target echoes, jamming, and noise. Then, leveraging the obtained polarization information, we construct an optimal target polarization projection filter. To comprehensively evaluate system performance, we introduce the novel metric of signal loss ratio, enabling rigorous analysis of the filter’s operational boundaries from dual perspectives: jamming suppression capability and target signal preservation. Extensive simulations across six distinct operational scenarios conclusively demonstrate the method’s superior performance, confirming its significant potential for practical implementation in engineering applications. Full article

In this paper, an equation describing nonlinear wave phenomena on the surface of magnetically active plasma in the approximation of the complete homogeneity of processes along the direction of the constant magnetic field is obtained. One of its solutions, in the form of a pulse having the shape of rapidly decaying oscillations with a changing period, is found to essentially depend on the magnitude of the magnetic field and shown to be approximately described by a specially selected analytical function. A detailed analytical analysis of the properties of another solitary wave formation existing under conditions of resonant coincidence of its carrier frequency with the corresponding value of its eigen surface oscillations in the considered cold semi-infinite plasma, in which a constant magnetic field is directed along its boundary, is also carried out. The conditions for the excitation of wave disturbances are determined, and analytical expressions that adequately describe the space–time structure of nonlinear waves are proposed. Full article

This study numerically investigated free vibration characteristics of functionally graded material (FGM) beams on Winkler–Pasternak three-parameter elastic foundations using the modified generalized differential quadrature (MGDQ) method. To compare the effects of different beam theories on the predicted frequency responses, an nth order generalized beam theory was employed to establish the governing equations of the system’s dynamic model within the Hamilton framework. As a pioneering effort, a MATLAB (version 2021a) computational program implementing the MGDQ method was developed to obtain the free vibration responses of foundation-supported FGM beams. Parametric analyses were conducted through numerical simulations to systematically examine the influences of various factors, including beam theories, damping coefficients, foundation stiffness parameters, boundary conditions, gradient indices, and span-to-thickness ratios, on the natural frequencies and damping ratios of FGM beams. The findings provide an essential theoretical foundation for dynamic characteristic analysis and functional design of foundation-supported FGM beam structures. Full article

Variations in stratospheric atmospheric circulation significantly impact tropospheric weather and climate. Understanding these variations not only aids in better prediction of tropospheric weather and climate but also provides guidance for the development and flight trajectories of stratospheric aircraft. Our understanding of the stratosphere has made remarkable progress over the past 100 years. However, we still lack a comprehensive perspective on large-scale patterns in stratospheric circulation, as the stratosphere is a typical complex system. To address this gap, we employed the eigen microstate approach (EMA) to revisit the characteristics of zonal wind from 70–10 hPa from 1980 to 2022, based on ERA5 reanalysis data. Our analysis focused on the three leading modes, corresponding to variations in the strength of the quasi-biennial oscillation (QBO) and the stratospheric atmospheric circulations in the Arctic and Antarctic, respectively. After filtering out high-frequency components from the temporal evolutions of these modes, a significant 11-year cycle was observed in the Antarctic stratospheric atmospheric circulation mode, potentially linked to the 11-year solar cycle. In contrast, the Arctic stratospheric atmospheric circulation mode showed a 5–6-year cycle without evidence of an 11-year periodicity. This difference is likely due to the timing of polar vortex breakdowns: the Antarctic polar vortex breaks up later, experiencing its greatest variability in late spring and early summer, making it more susceptible to solar radiation effects, unlike the Arctic polar vortex, which peaks in winter and early spring. The fourth mode exhibits characteristics of a Southern Hemisphere dipole and shows a significant correlation with the Antarctic stratospheric atmospheric circulation mode, leading it by about two months. We designed a linear prediction model that successfully demonstrated its predictive capability for the Antarctic polar vortex. Full article

Coastal protection solutions can be categorised as grey, hybrid or natural. Grey infrastructure includes artificial structures like dykes. Natural habitats like seagrasses are considered natural protection infrastructure. Hybrid solutions combine both natural and grey infrastructure. Evidence suggests that grey solutions can negatively impact the environment, while natural habitats prevent flooding without such adverse effects and provide many ecosystem services. New types of protective solutions, called biomimetic solutions, are inspired by natural habitats and reproduce their features using artificial materials. Few studies have been conducted on these new approaches. This study aims to quantify wave dissipation observed in situ above a biomimetic solution inspired by kelps, known for their wave-dampening properties. The solution was deployed in a full water column near Palavas-les-Flots in southern France. A one-month in situ experiment showed that the biomimetic solution dissipates around 10% of total wave energy on average, whatever the meteo-marine conditions. Wave energy dissipation is frequency-dependent: short waves are dissipated, while low-frequency energy increases. An anti-dissipative effect occurs for forcing conditions with frequencies close to the eigen mode linked to the biomimetic solution’s geometry, suggesting that resonance should be considered in designing future biomimetic protection solutions. Full article

The effects of global warming are far-reaching and can affect and threaten every aspect of human existence. Human activities such as burning fossil fuels and deforestation have mainly contributed to the emission of these greenhouse gases into the atmosphere. Construction activities and practices are one such human activity. Building services are the aspects of a building that ensure the occupants are comfortable and secure within the building. However, building services use resources such as energy and water to create such comfort. The 4.0 era of industry has introduced dimensions to Green Building Concepts and practices of creating structures and processes that are environmentally friendly, responsible and resource efficient. This research, therefore, seeks to develop a framework for the integration of Industry 4.0-related green concepts into services in commercial buildings. A cross-sectional survey design was adopted in this research to provide information concerning integrating green concepts into building services in commercial buildings in Nigeria. Data were collected with questionnaires from 106 built environment professionals who also use commercial buildings in the study area of Abuja. Statistical tools for frequency, percentage, mean score, relative important index (RII), independent samples t test, Mann–Whitney U test and the factor reduction method based on eigen values were used to analyze the data. The results indicated that quality indoor air conditioning is the most critical satisfaction parameter for users within commercial buildings, with an average mean score of 3.81. The aesthetic effect of installed services on the building façade and high-quality building services components was ranked high, with an average mean score of 3.33 for each. The results also indicated that the lack of relevant technology and inadequate training of service personnel hindered the growth of green building concepts in Nigeria. These factors had a mean score of 4.35 each. Professionals ranked energy efficient/saving bulbs, e.g., CFLs, remote controlled, sensored lights, natural daylight, solar photovoltaic panels and building management systems, as the most effective green components that can be incorporated into commercial buildings. These factors and others were combined to create a framework for integrating green concepts for services into commercial buildings. When the construction industry and government in Nigeria adopt this framework, it can promote more integration of green concepts into commercial building services. Full article

With the flourishing development of deep learning, synthetic aperture radar (SAR) ship detection based on this method has been widely applied across various domains. However, most deep-learning-based detection methods currently only use the amplitude information from SAR images. In fact, phase information and time-frequency features can also play a role in ship detection. Additionally, the background noise and the small size of ships also pose challenges to detection. Finally, satellite-based detection requires the model to be lightweight and capable of real-time processing. To address these difficulties, we propose a multi-domain joint SAR ship detection method that integrates complex information with deep learning. Based on the imaging mechanism of line-by-line scanning, we can first confirm the presence of ships within echo returns in the eigen-subspace domain, which can reduce detection time. Benefiting from the complex information of single-look complex (SLC) SAR images, we transform the echo returns containing ships into the time-frequency domain. In the time-frequency domain, ships exhibit distinctive features that are different from noise, without the limitation of size, which is highly advantageous for detection. Therefore, we constructed a time-frequency SAR image dataset (TFSID) using the images in the time-frequency domain, and utilizing the advantages of this dataset, we combined space-to-depth convolution (SPDConv) and Inception depthwise convolution (InceptionDWConv) to propose Efficient SPD-InceptionDWConv (ESIDConv). Using this module as the core, we proposed a lightweight SAR ship detector (LSDet) based on YOLOv5n. The detector achieves a detection accuracy of 99.5 with only 0.3 M parameters and 1.2 G operations on the dataset. Extensive experiments on different datasets demonstrated the superiority and effectiveness of our proposed method. Full article

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%. Full article

The Finite Element Method (FEM) has emerged as a powerful tool for predicting the behavior of industrial products, including those with complex geometries or uncommon materials. Finite Element Analysis (FEA) is widely used to study structural vibration-related aspects such as stress, displacement, and velocity. Modal analysis, a standard technique for characterizing the vibrational behavior of structures, is essential for identifying resonance frequencies, optimizing component design, and assessing structural integrity. Finite Elements (FE) modal analysis enables engineers to evaluate numerically the modal parameters, whereas model order reduction (MOR) schemes are exploited to achieve a balance between computational efficiency and accuracy, enabling a more efficient solution for computing transient dynamic analysis. Assessing the accuracy and reliability of FE solutions is a crucial aspect of the design cycle, and model-updating procedures are commonly employed to maximize the correlation between measured and predicted dynamic behavior. This study investigated the accuracy and computational efficiency of linear, quadratic, and cubic hexahedral FE formulations for modal analysis and transient dynamic solutions. More specifically, the documented results demonstrate the profitable use of the eigenenergy norm obtained in eigen solutions as a valid predictor of the accuracy reported using either the time response assurance criterion (TRAC) or the frequency response assurance criterion (FRAC), measured in transient dynamic cases. Moreover, our results also highlight the superior computational efficiency of higher-order formulations for both the eigen and transient dynamic solutions. Full article

Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F₁-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system. Full article

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing custom-developed photogrammetry software, high-resolution images are processed to generate detailed 3D models, which are subsequently converted to STL files with precision. Adaptive meshing in COMSOL 5.3 Multiphysics, controlled through a MATLAB 2023 API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL’s advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining the desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency. Full article