Search Results (276)

The Korteweg–de Vries (KdV) equation is a nonlinear evolution equation that reflects a wide variety of dispersive wave occurrences with limited amplitude. It has also been used to describe a range of major physical phenomena, such as shallow water waves that interact weakly and nonlinearly, acoustic waves on a crystal lattice, lengthy internal waves in density-graded oceans, and ion acoustic waves in plasma. The KdV equation is one of the most well-known soliton models, and it provides a good platform for further research into other equations. The KdV equation has several forms. The aim of this study is to introduce and investigate a (2+1)-dimensional generalized fifth-order KdV equation with power law nonlinearity (gFKdVp). The research methodology employed is the Lie group analysis. Using the point symmetries of the gFKdVp equation, we transform this equation into several nonlinear ordinary differential equations (ODEs), which we solve by employing different strategies that include Kudryashov’s method, the $(G^{\prime }/G)$ expansion method, and the power series expansion method. To demonstrate the physical behavior of the equation, 3D, density, and 2D graphs of the obtained solutions are presented. Finally, utilizing the multiplier technique and Ibragimov’s method, we derive conserved vectors of the gFKdVp equation. These include the conservation of energy and momentum. Thus, the major conclusion of the study is that analytic solutions and conservation laws of the gFKdVp equation are determined. Full article

The aim of this study was to determine the connection between oscillations in operating pressure values and the appearance of various irregularities on machined surfaces. Such oscillations are a consequence of the high water pressure generated during abrasive water jet machining. Oscillations in the operating pressure values are periodic, namely due to the cyclic operation of the intensifier and the physical characteristics of water. One of the most common means of reducing this phenomenon is installing an attenuator in the hydraulic system or a phased intensifier system. The main hypothesis of this study was that the topography of a machined surface is directly influenced by the inability of the pressure accumulator to fully absorb water pressure oscillations. In this study, we monitored changes in hydraulic oil pressure values at the intensifier entrance and their connection with irregularities on the machined surface—such as waviness—when cutting aluminum AlMg3 of different thicknesses. Experimental research was conducted in order to establish this connection. Aluminum AlMg3 of different thicknesses—from 6 mm to 12 mm—was cut with different traverse speeds while hydraulic oil pressure values were monitored. The pressure signals thus obtained were analyzed by applying the fast Fourier transform (FFT) algorithm. We identified a single-sided pressure signal amplitude spectrum. The frequency axis can be transformed by multiplying inverse frequency data with traverse speed; in this way, a single-sided amplitude spectrum can be obtained, examined against the period in which striations are expected to appear (in millimeters). In the lower zone of the analyzed samples, striations are observed at intervals determined by the dominant hydraulic oil pressure harmonics, which are transferred to the operating pressure. In other words, we demonstrate how the machined surface topography is directly induced by water jet pressure frequency characteristics. Full article

Energy consumption has emerged as a critical design constraint in deploying high-performance neural networks, especially on edge devices with limited power resources. In this paper, a comparative study is conducted for two prevalent deep learning paradigms—convolutional neural networks (CNNs), exemplified by ResNet18, and transformer-based large language models (LLMs), represented by GPT3-small, Llama-7B, and GPT3-175B. By analyzing how the scaling of memory energy versus computing energy affects the energy consumption of neural networks with different batch sizes (1, 4, 8, 16), it is shown that ResNet18 transitions from a memory energy-limited regime at low batch sizes to a computing energy-limited regime at higher batch sizes due to its extensive convolution operations. On the other hand, GPT-like models remain predominantly memory-bound, with large parameter tensors and frequent key–value (KV) cache lookups accounting for most of the total energy usage. Our results reveal that reducing weight-memory energy is particularly effective in transformer architectures, while improving multiply–accumulate (MAC) efficiency significantly benefits CNNs at higher workloads. We further highlight near-memory and in-memory computing approaches as promising strategies to lower data-transfer costs and enhance power efficiency in large-scale deployments. These findings offer actionable insights for architects and system designers aiming to optimize artificial intelligence (AI) performance under stringent energy budgets on battery-powered edge devices. Full article

Anomaly detection methods for industrial control networks using multivariate time series usually adopt deep learning-based prediction models. However, most of the existing anomaly detection research only focuses on evaluating detection performance and rarely explains why data is marked as abnormal and which physical components have been attacked. Yet, in many scenarios, it is necessary to explain the decision-making process of detection. To address this concern, we propose an interpretable method for an anomaly detection model based on gradient optimization, which can perform batch interpretation of data without affecting model performance. Our method transforms the interpretation of anomalous features into solving an optimization problem in a normal “reference” state. In the selection of important features, we adopt the method of multiplying the absolute gradient by the input to measure the independent effects of different dimensions of data. At the same time, we use KSG mutual information estimation and multivariate cross-correlation to evaluate the relationship and mutual influence between different dimensional data within the same sliding window. By accumulating gradient changes, the interpreter can identify the attacked features. Comparative experiments were conducted on the SWAT and WADI datasets, demonstrating that our method can effectively identify the physical components that have experienced anomalies and their changing trends. Full article

Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for the coordinated operation of a virtual power plant cluster (VPPC). Considering the resource characteristics of different virtual power plants (VPPs) within a cooperative alliance, we propose a multi-VPP interaction and sharing architecture accounting for electricity–carbon interaction. An optimization model for VPPC is developed based on the asymmetric Nash bargaining theory. Finally, the proposed model is solved using an alternating-direction method of multipliers (ADMM) algorithm featuring an improved penalty factor. The research results show that P2P trading within the VPPC achieves resource optimization and allocation at a larger scale. The proposed distributed ADMM solution algorithm requires only the exchange of traded electricity volume and price among VPPs, thus preserving user privacy. Compared with independent operation, the total operation cost of the VPPC is reduced by 20.37%, and the overall proportion of new energy consumption is increased by 16.83%. The operation costs of the three VPPs are reduced by 1.12%, 20.51%, and 6.42%, respectively, while their carbon emissions are decreased by 4.47%, 5.80%, and 5.47%, respectively. In addition, the bargaining index incorporated in the proposed (point-to-point) P2P trading mechanism motivates each VPP to enhance its contribution to the alliance to achieve higher bargaining power, thereby improving the resource allocation efficiency of the entire alliance. The ADMM algorithm based on the improved penalty factor demonstrates good computational performance and achieves a solution speed increase of 15.8% compared to the unimproved version. Full article

In this study, we define the $k$-Cullen, $k$-Cullen–Lucas, and Modified $k$-Cullen sequences, and certain terms in these sequences are given. Then, we obtain the Binet formulas, generating functions, summation formulas, etc. In addition, we examine the relations among the terms of the $k$-Cullen, $k$-Cullen–Lucas, Modified $k$-Cullen, Cullen, Cullen–Lucas, Modified Cullen, $k$-Woodall, $k$-Woodall–Lucas, Modified $k$-Woodall, Woodall, Woodall–Lucas, and Modified Woodall sequences. The generating functions were derived and analyzed, especially for cases where Fibonacci numbers were assigned to parameter $k.$ Graphical representations of the generating functions and their logarithmic transformations revealed interesting growth trends and convergence behavior. Further, by multiplying the generating functions with exponential expressions such as $e^k$, we explored the self-similar nature and mirrored dynamics among the sequences. Specifically, it was observed that the Modified Cullen sequence exhibited a symmetric and inverse-like resemblance to the Cullen and Cullen–Lucas sequences, suggesting the presence of deeper structural dualities. Additionally, indefinite integrals of the generating functions were computed and visualized over a range of Fibonacci-indexed k values. These integral-based graphs further reinforced the phenomenon of symmetry and self-similarity, particularly in the Modified Cullen sequence. A key insight of this study is the discovery of a structural duality between the Modified Cullen and standard Cullen-type sequences, supported both algebraically and graphically. This duality suggests new avenues for analyzing generalized recursive sequences through generating function transformations. This observation provides new insight into the structural behavior of generalized Cullen-type sequences. Full article

Tempered fractional diffusion equations constitute a critical class of partial differential equations with broad applications across multiple physical domains. In this paper, the Crank–Nicolson method and the tempered weighted and shifted Grünwald formula are used to discretize the tempered fractional diffusion equations. The discretized system has the structure of the sum of the identity matrix and a diagonal matrix multiplied by a symmetric positive definite (SPD) Toeplitz matrix. For the discretized system, we propose a structure approximation-based preconditioning method. The structure approximation lies in two aspects: the inverse approximation based on the row-by-row strategy and the SPD Toeplitz approximation by the $\tau$ matrix. The proposed preconditioning method can be efficiently implemented using the discrete sine transform (DST). In spectral analysis, it is found that the eigenvalues of the preconditioned coefficient matrix are clustered around 1, ensuring fast convergence of Krylov subspace methods with the new preconditioner. Numerical experiments demonstrate the effectiveness of the proposed preconditioner. Full article

The Number Theoretic Transform (NTT) is a cornerstone for efficient polynomial multiplication, which is fundamental to lattice-based cryptographic algorithms such as CRYSTALS-Kyber—a leading candidate in post-quantum cryptography (PQC). However, existing NTT accelerators often rely on integer multiplier-based modular reduction techniques, such as Barrett or Montgomery reduction, which introduce significant computational overhead and hardware resource consumption. These accelerators also lack optimization in unified architectures for forward (FNTT) and inverse (INTT) transformations. Addressing these research gaps, this paper introduces a novel, high-speed NTT accelerator tailored specifically for CRYSTALS-Kyber. The proposed design employs an innovative shift-add modular reduction mechanism, eliminating the need for integer multipliers, thereby reducing critical path delay and enhancing circuit frequency. A unified pipelined butterfly unit, capable of performing FNTT and INTT operations through Cooley–Tukey and Gentleman–Sande configurations, is integrated into the architecture. Additionally, a highly efficient data handling mechanism based on Register banks supports seamless memory access, ensuring continuous and parallel processing. The complete architecture, implemented in Verilog HDL, has been evaluated on FPGA platforms (Virtex-5, Virtex-6, and Virtex-7). Post place-and-route results demonstrate a maximum operating frequency of 261 MHz on Virtex-7, achieving a throughput of 290.69 Kbps—1.45× and 1.24× higher than its performance on Virtex-5 and Virtex-6, respectively. Furthermore, the design boasts an impressive throughput-per-slice metric of 111.63, underscoring its resource efficiency. With a 1.27× reduction in computation time compared to state-of-the-art single butterfly unit-based NTT accelerators, this work establishes a new benchmark in advancing secure and scalable cryptographic hardware solutions. Full article

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes. Full article

Under the goal of “dual carbon”, the power market and carbon market are developing synergistically, which is strongly promoting the transformation of the power system in a clean and low-carbon direction. In order to realise the synergistic optimisation of multi-virtual power plants, economic and low-carbon operation, and the reasonable distribution of revenues, this paper proposes a multi-VPP power–carbon sharing operation optimisation strategy considering multiple uncertainties. Firstly, a cost model for each VPP power–carbon sharing considering the uncertainties of market electricity price and new energy output is established. Secondly, a multi-VPP power–carbon sharing operation optimisation model is established based on the Nash negotiation theory, which is then decomposed into a multi-VPP coalition cost minimisation subproblem and a revenue allocation subproblem based on asymmetric bargaining. Thirdly, the variable penalty parameter alternating directional multiplier method is used for the solution. Finally, an asymmetric bargaining method is proposed to quantify the contribution size of each participant with a nonlinear energy mapping function, and the VPPs negotiate with each other regarding the bargaining power of their electricity–carbon contribution size in the co-operation, so as to ensure a fair distribution of co-operation benefits and thus to motivate and maintain a long-term and stable co-operative relationship among the subjects. Example analyses show that the method proposed in this paper can significantly increase the revenue level of each VPP and reduce carbon emissions and, at the same time, improve the ability of VPPs to cope with uncertain risks and achieve a fair and reasonable distribution of the benefits of VPPs. Full article

In this study, the active vibration control (AVC) of a cantilever beam with an end mass is considered first and studied experimentally and through simulation. The Laplace transform method, Newmark method, and ANSYS are used for simulations. An impulse force applied to the mass and the velocity actuation applied to the base are assumed to be disturbance and controlling input, respectively. The displacement of the mass is taken as the feedback signal in simulations. Four strain gauges are located near the bottom point, connected with a Wheatstone bridge, and the output voltage of a load-cell amplifier (LCA) is used as the feedback signal in experiments. Strain feedback is considered in experiments because it is easy to implement, cost-effective, and can be used in applications. Experimental displacement signals obtained from the top of the beam are compared with the output signals from LCA and it is observed that they are approximately linearly dependent. Velocity input is generated with a servo motor-driven linear actuator in experiments. The closed loop control is achieved by a personal computer with an Adlink-9222 PCI DAQ card and a C program in the experiments. The integration of the closed loop control action into the transient solution with Newmark method and ANSYS is implemented in simulations. The input reference value is taken as zero for vibration control. The instantaneous value of the feedback signal at a time step is subtracted from zero to find the error signal value and the error value is multiplied by the control gain to calculate the controlling signal. The simulation results obtained with the Newmark method and ANSYS are in good agreement with the analytical results obtained with Laplace transform method. Simulation results are also in acceptable agreement with the experimental results for explaining the behavior of the success of AVC depending on the control gain, K_p. After verifying ANSYS solutions, the ANSYS procedure is applied to an aircraft wing as a real complex cantilever structure. The wing, with a length of 810.8 mm, 13 ribs with a length of 300 mm, and NACA 4412 airfoil, is considered in this study. It is observed that the AVC of real engineering structures can be simulated by integrating control action into transient solution in ANSYS. Full article

Accepting the premise that sociocultural anthropology is colonialist and Audre Lorde’s maxim that the master’s tools cannot remake the master’s house, I consider the value of a tool from outside the master’s house to reconstruct sociocultural anthropology. Intersectionality, variously known as a theory, a lens, or a metaphor, is rooted in U.S. Black women’s abolitionism of the mid-nineteenth century, which argued that rights-seeking efforts framed out Black women. The 1970s and 1980s brought increased attention, especially from Black American feminists, to the multiplying effects of the intersections of race, gender, and class. In 1989, the term intersectionality first appeared in print, and a theory was named. Since then, many fields of study and activism have embraced intersectionality. Edward Said posited that radical theories lose their edge when they travel outside their original context. I explore intersectionality’s travels to sociocultural anthropology—its chronology, advocates, and transformations. Although barely visible in much of sociocultural anthropology’s Whitestream, intersectionality has gained not only in numbers but also a stronger voice since its first published appearance in 2001. Nearly two centuries have passed since intersectionality’s origins in U.S. enslavement, but interlocking conditions of inequality pervade the world today, nurturing intersectionality’s radical ethos in sociocultural anthropology. Full article

Accurate and efficient white-spot defects detection for the surface of galvanized strip steel is one of the most important guarantees for the quality of steel production. It is a fundamental but “hard” small target detection problem due to its small pixel occupation in low-contrast images. By fully exploiting the low-rank and sparse prior information of a surface defect image, a Schatten-p norm-based low-rank tensor decomposition (SLRTD) method is proposed to decompose the defect image into low-rank background, sparse defect, and random noise. Firstly, the original defect images are transformed into a new patch-based tensor mode through data reconstruction for mining valuable information of the defect image. Then, considering the over-shrinkage problem in the low-rank component estimation caused by a vanilla nuclear norm and a weighted nuclear norm, a nonlinear reweighting strategy based on a Schatten $p$-norm is incorporated to improve the decomposition performance. Finally, a solution framework is proposed via a well-designed alternating direction method of multipliers to obtain the white-spot defect target image by a simple segmenting algorithm. The white-spot defect dataset from a real-world galvanized strip steel production line is constructed, and the experimental results demonstrate that the proposed SLRTD method outperforms existing state-of-the-art methods qualitatively and quantitatively. Full article

This paper investigates a backward stochastic linear quadratic control problem with an expected-type equality constraint on the initial state. By using the Lagrange multiplier method, the problem with a uniformly convex cost functional is first transformed into an equivalent unconstrained parameterized backward stochastic linear quadratic control problem. Then, under the surjectivity of the linear constraint, the equivalence between the original problem and the dual problem is proven by Lagrange duality theory. Subsequently, with the help of the maximum principle, an explicit solution of the optimal control for the unconstrained problem is obtained. This solution is feedback-based and determined by an adjoint stochastic differential equation, a Riccati-type ordinary differential equation, a backward stochastic differential equation, and an equality, thereby yielding the optimal control for the original problem. Finally, an optimal control for an investment portfolio problem with an expected-type equality constraint on the initial state is explicitly provided. Full article

Fault diagnosis in power converters is essential for keeping electrical systems stable, efficient and long-lasting. Park’s Vector Transform, discrete wavelet transform, Artificial Neural Network, Fuzzy Logic and other methods are used to diagnose faults in the power converter in both single and multiple open switch situations. These methods are implemented on the digital signal processor or controller, which needs additional hardware and consumes more processing time. This paper presents a hardware-based open switch fault diagnostic method in a 3ϕ voltage source inverter to minimize fault diagnosis time and cost. An innovative hardware-based approach that utilizes a single neuron for open switch fault diagnosis in 3ϕ voltage source inverters was successfully implemented without using a digital signal processor or controller. A gradient descent algorithm calculates the weight and bias values of a single processing neuron. Furthermore, a high-speed multiplier and adder circuit seamlessly integrate with the single processing neuron, enabling rapid real-time fault diagnosis. This method is capable of diagnosing single and multiple switch open circuit faults in switching devices under variable load conditions at different frequencies. The proposed system ensures good effectiveness and resistivity, detecting faults in less than one cycle with low implementation effort and no tuning or threshold dependence. It achieves 98% accuracy, 96% precision and 95% recall, with a 2% false positive rate. Unlike traditional methods, it eliminates DSP/controller dependency by using a single neuron-based processing circuit, reducing cost and improving real-time fault diagnosis in three-phase voltage source inverters. Full article