Search Results (341)

Based on the principle of symmetry, we propose a variable step-size FxLMS algorithm with double nonlinear functions cooperative coupling (DNVSS-FxLMS), aiming to optimize the contradiction between convergence rate and steady-state error in the active pressure pulsation control system of hydraulic systems. The algorithm innovatively couples two types of nonlinear mechanisms (rational-fractional and exponential-function-based), constructing a refined error-step mapping relationship to achieve a balance between rapid convergence and low steady-state error. Simulation experiments were conducted considering the complex time-varying operating environment of a simulation-based hydraulic system. The results demonstrate that, when the system undergoes unstable random changes, the DNVSS-FxLMS algorithm converges at least twice as fast as traditional and existing variable step size algorithms, while reducing steady-state error by 2–5 dB. The proposed DNVSS-FxLMS algorithm exhibits significant advantages in convergence rate, steady-state error reduction, and tracking capability, providing a highly efficient and robust solution for real-time active control of hydraulic system pressure pulsation under complex operating conditions. Full article

This study proposes a hybrid approach combining threshold-based algorithm and deep learning to detect four major gait events—initial contact (IC), toe-off (TO), opposite initial contact (OIC), and opposite toe-off (OTO)—using only a smartphone’s built-in inertial sensor placed in the user’s pocket. The algorithm enables estimation of spatiotemporal gait parameters such as cadence, stride length, loading response (LR), pre-swing (PSw), single limb support (SLS), double limb support (DLS), and swing phase and symmetry. Gait data were collected from 20 healthy individuals and 13 hemiparetic stroke patients. To reduce sensitivity to sensor orientation and suppress noise, sum vector magnitude (SVM) features were extracted and filtered using a second-order Butterworth low-pass filter at 3 Hz. A deep learning model was further compressed using knowledge distillation, reducing model size by 96% while preserving accuracy. The proposed method achieved error rates in event detection below 2% of the gait cycle for healthy gait and a maximum of 4.4% for patient gait in event detection, with corresponding parameter estimation errors also within 4%. These results demonstrated the feasibility of accurate and real-time gait monitoring using a smartphone. In addition, statistical analysis of gait parameters such as symmetry and DLS revealed significant differences between the normal and patient groups. While this study is not intended to provide or guide rehabilitation treatment, it offers a practical means to regularly monitor patients’ gait status and observe gait recovery trends over time. Full article

This paper primarily investigates the response and failure behavior of 6063-T5 aluminum alloy square tubes with varying outer side lengths under symmetric curvature-controlled cyclic bending in different bending directions. The response is characterized by the moment–curvature relationship and the variation in the outer side length with respect to curvature, whereas failure is characterized by the relationship between the controlled curvature and the number of cycles required to initiate buckling. The outer side lengths studied are 20 mm, 30 mm, 40 mm, and 50 mm, and the bending directions considered are 0°, 22.5°, and 45°. The moment–curvature curves exhibited cyclic hardening, and stable loops were formed for all outer side lengths and bending directions. An increase in the outer side length resulted in a higher peak bending moment, while a greater bending direction led to a slight increase in the peak bending moment. For a fixed bending direction, the curves representing the variation of the outer side length (defined as the change in length divided by the original length) with respect to curvature displayed symmetry, serrated features, and an overall increasing trend as the number of cycles increased, irrespective of the specific outer side length. In addition, increasing either the outer side length or altering the bending direction led to a larger variation in the outer side length. As for the relationship between curvature and the number of cycles required to initiate buckling, the data for each bending direction and each of the four outer side lengths formed distinct straight lines on a double-logarithmic plot. Based on the experimental observations, empirical equations were developed to characterize these relationships. These equations were then used to predict the experimental data and showed excellent agreement with the measured results. Full article

In this paper, a novel type of polarization-insensitive terahertz metal metasurface with cross-shaped holes is presented, which is designed based on the theory of bound states in continuous media. The fundamental unit of the metasurface comprises a metal tungsten sheet with a cross-shaped hole structure. A thorough analysis of the optical properties and the quasi-BIC response is conducted using the finite element method. Utilizing the symmetry-breaking theory, the symmetry of the metal metasurface is broken, allowing the excitation of double quasi-BIC resonance modes with a high quality factor and high sensitivity to be achieved. Analysis of the multipole power distribution diagram and the spatial distribution of the electric field at the two quasi-BIC resonances verifies that the two quasi-BIC resonances of the metasurface are excited by electric dipoles and electric quadrupoles, respectively. Further simulation analysis demonstrates that the refractive index sensitivities of the two quasi-BIC modes of the metasurface reach $404.5 \mathrm{G}\mathrm{H}\mathrm{z}/\mathrm{R}\mathrm{I}\mathrm{U}$ and $578.6 \mathrm{G}\mathrm{H}\mathrm{z}/\mathrm{R}\mathrm{I}\mathrm{U}$, respectively. Finally, the functional material PHMB is introduced into the metasurface to achieve highly sensitive sensing and detection of CO₂ gas concentrations. The proposed metallic metasurface structure exhibits significant advantages, including high sensitivity, ease of preparation, and a high Q-value, which renders it highly promising for a broad range of applications in the domains of terahertz biosensing and highly sensitive gas sensing. Full article

Stellar radiation simulation is critical in the space industry; however, with the current simulation methods, only a single color temperature and magnitude can be modulated at a time. Furthermore, star sensors rely on star observation tests for accurate calibration; this seriously restricts their development. This paper presents a novel star spectral information multi-partition mapping simulation method to closely simulate real sky star map information, thus replacing non-scenario-specific field stargazing experiments. First, using the stellar spectral simulation principle, a multi-partition mapping principle based on a digital micro-mirror device is proposed, and the theoretical basis of sub-region division is provided. Second, multi-component mapping simulation of stellar spectral information is expounded, and a general architecture for the same based on a double-prism symmetry structure is presented. Next, the influence of peak spectral half-peak width and peak interval on spectral simulation accuracy is analyzed, and a pre-collimated beam expansion system, multi-dimensional slit, and spectral splitting system are designed accordingly. Finally, a test platform is set up, and single-region simulation results and multi-region consistency experiments are conducted to verify the feasibility of the proposed method. Our method can realize high-precision simulation and independently control the output of various color temperatures and magnitudes. It provides a theoretical and technical basis for the development of star sensor ground calibration tests and space target detection light environment simulation. Full article

Large-scale integration of renewable energy introduces significant random perturbations to the power system, disrupting the symmetry and balance of active power, which complicates the stabilization of the system’s frequency. Inter-regional energy cooperation plays a crucial role in maintaining the symmetry and balance of the overall power system’s active power. However, when the power system area is expanded, automatic generation control (AGC) based on reinforcement learning faces the challenge of not being able to leverage the prior experience of the original system topology to train the new area, making it difficult to quickly develop an effective control strategy. To address these challenges, this paper proposes a novel data-driven AGC method that employs a multi-agent reinforcement learning algorithm with a learning to coordinate and teach reinforcement (LECTR) mechanism. Specifically, under the LECTR mechanism, when the power system region expands, agents in the original region will instruct the agents in the newly merged region by providing demonstration actions. This accelerates the convergence of their strategy networks and improves control accuracy. Additionally, the proposed algorithm introduces a double critic network to mitigate the issue of target critic network value overestimation in reinforcement learning, thereby obtaining higher-quality empirical data and improving algorithm stability. Finally, simulations are conducted to evaluate the method’s effectiveness in scenarios with an increasing number of IEEE interconnected grid areas. Full article

In continuous casting, fewer strand operations are sometimes required to match production schedules. However, the study of flow behavior and temperature distribution under fewer strand casting conditions remains insufficiently systematic, especially with regard to the grade casting process, which has not yet been explored. This study presents an innovative investigation of the grade transition process in a symmetrical 12-strand tundish under fewer strand casting conditions. Seven operational cases were analyzed: standard casting (the normal symmetric Case 0), individual closure of strands 1–6 (the asymmetric Cases 1–6), and simultaneous closure of strands 1–2 (the asymmetric Case 7). Notably, strand closures in Cases 5 and 6 significantly impair flow characteristics in their respective strands. The impact area temperature reaches approximately 1844 K (new heat) after 30 min of continuous casting. However, Case 6 exhibits persistent low-temperature regions near strands 5 and 6. The average transition billet lengths for Cases 0 to 7 are 72.41 m, 70.16 m, 70.30 m, 71.68 m, 72.95 m, 72.12 m, 76.35 m, and 65.45 m, respectively. Based on a comprehensive evaluation of flow dynamics, temperature uniformity, and transition billet length, Case 1 emerges as the most favorable single-strand closure strategy. Operational recommendations suggest avoiding strand closure patterns implemented in Cases 5 and 6 during reduced strand casting operations. Full article

This report discusses the impact of nanoparticles on glass-forming systems composed of a liquid crystalline (LC) mixture E7 and paraelectric BaTiO₃ particles ($d\approx 50 \mathrm{n}\mathrm{m}$, globular), tested via broadband dielectric spectroscopy. In the isotropic phase, critical changes in the dielectric constant are shown. They are related to the weakly discontinuous nature of the isotropic–nematic transition. In the nematic phase, two primary relaxation times/processes and DC electric conductivity are considered, down to the glass temperature $T_g$. The prevalence of portrayals via the ‘double exponential’ MYEGA equation and the critical & activated Drozd-Rzoska relation for dynamic properties are shown. For the primary loss curve, critical-like changes of its maximum (peak) are evidenced: ${\epsilon }_{peak}^{″}\propto 1/\left(T-T_g^{*}\right)$ for $T_g<T<\left(T_g+25 \mathrm{K}\right)$, where $T_g^{*}<T_g$ denotes the extrapolated singular temperature. Dielectric constant monitoring revealed the permanent arrangement of rod-like LC molecules by nanoparticles’ endogenic impact in the nematic phase. The heuristic model regarding this unique behavior is presented. It considers a hypothetical link between the glass transition and a hidden near-critical discontinuous phase transition, uniquely avoiding a symmetry change. The uniaxiality of LC molecules enables the detection of critical-like features when approaching the glass transition, hypothetically associated with a specific ‘amorphous’ phase transition. Full article

Photovoltaic systems are affected by light intensity, temperature, and radiation angle, which influence their efficiency. Accurate estimation of PV module parameters is essential for improving performance. This paper presents an improved optimization technique based on the White Shark Optimizer (WSO) algorithm to optimize key characteristics of the PV module, including current, voltage, series resistance, shunt resistance, and ideality factor. The proposed method incorporates opposition-based learning (OBL) and chaos theory to improve search efficiency. A critical aspect of PV module modeling is inherent symmetry in electrical and thermal characteristics, where balanced parameter estimation ensures uniform energy conversion efficiency. With the application of symmetrical search techniques during the process of optimization, the proposed method enhances convergence robustness and stability, ensuring consistent and precise results across different PV models. Experimental evaluations conducted on three PV models—Single Diode Model (SDM), Double Diode Model (DDM), and general photovoltaic modules—demonstrate that the proposed method outperforms existing metaheuristic techniques such as Jumping Spider Optimization (JSO), Harris Hawks Optimization (HHO), WOA, Gray Wolf Optimizer (GWO), and basic WSO. Key results show improvements in the Friedman rating by 8.1%, 10.79%, and 9.6% for the SDM, DDM, and PV modules, respectively. Additionally, the proposed method achieves superior parameter estimation accuracy, as evidenced by reduced RMSE values compared to the competing algorithms. This work highlights the importance of advanced optimization techniques in maximizing PV output power while maintaining symmetry in parameter estimation. By ensuring a balanced and systematic optimization approach, this study assists in the development of robust and efficient solutions for PV system modeling. Full article

We prove that the probability of “A or B”, denoted as p(A or B), where A and B are events or hypotheses that may be recursively dependent, is given by a “Hyperbolic Sum Rule” (HSR), which is relationally isomorphic to the hyperbolic tangent double-angle formula. We also prove that this HSR is Maximum Entropy (MaxEnt). Since this recursive dependency is commutative, it maintains the symmetry between the two events, while the recursiveness also represents temporal symmetry within the logical structure of the HSR. The possibility of recursive probabilities is excluded by the “Conventional Sum Rule” (CSR), which we have also proved to be MaxEnt (with lower entropy than the HSR due to its narrower domain of applicability). The concatenation property of the HSR is exploited to enable analytical, consistent, and scalable calculations for multiple hypotheses. Although they are intrinsic to current artificial intelligence and machine learning applications, such calculations are not conveniently available for the CSR, moreover they are presently considered intractable for analytical study and methodological validation. Where, for two hypotheses, we have p(A|B) > 0 and p(B|A) > 0 together (where “A|B” means “A given B”), we show that either {A,B} is independent or {A,B} is recursively dependent. In general, recursive relations cannot be ruled out: the HSR should be used by default. Because the HSR is isomorphic to other physical quantities, including those of certain components that are important for digital signal processing, we also show that it is as reasonable to state that “probability is physical” as it is to state that “information is physical” (which is now recognised as a truism of communications network engineering); probability is not merely a mathematical construct. We relate this treatment to the physics of Quantitative Geometrical Thermodynamics, which is defined in complex hyperbolic (Minkowski) spacetime. Full article

Background: Wearable inertial sensors are essential tools in biomechanics and sports science for assessing gait in real-world conditions. This study explored gender-based differences in biomechanical walking patterns among healthy Greek athletes using the BTS G-Walk system, focusing on key gait parameters to inform gender-specific training and rehabilitation strategies. Methods: Ninety-five healthy athletes (55 men, 40 women), aged 18 to 30 years, participated in this study. Each athlete performed a standardized 14 m walk while 17 biomechanical gait parameters were recorded using the BTS G-Walk inertial sensor. Statistical analyses were conducted using SPSS to assess gender differences and left–right foot symmetry. Results: No significant asymmetry was found between the left and right feet for most gait parameters. Men exhibited longer stride lengths (left: p = 0.005, Cohen’s d = 0.61; right: p = 0.009, Cohen’s d = 0.53) and longer stride and gait cycle durations (left: p = 0.025, Cohen’s d = 0.52; right: p = 0.025, Cohen’s d = 0.53). Women showed a higher cadence (p = 0.022, Cohen’s d = −0.52) and greater propulsion index (left: p = 0.001, Cohen’s d = −0.71; right: p = 0.001, Cohen’s d = −0.73), as well as a higher percentage of first double support (p = 0.030, Cohen’s d = −0.44). Conclusions: These findings highlight the impact of biological and biomechanical differences on walking patterns, emphasizing the need for gender-specific training and rehabilitation. The BTS G-Walk system proved reliable for gait analysis, with potential for optimizing performance, injury prevention, and rehabilitation in athletes. Future research should explore larger, more diverse populations with multi-sensor setups. Full article

While inertial measurement unit (IMU)-based systems have shown their potential in quantifying medically significant gait parameters, it remains to be determined whether they can provide accurate and reliable parameters both across various walking conditions and in healthcare settings. Using an IMU-based system we previously developed, with one IMU module on each subject’s heel, we quantify the gait parameters of 55 men and 46 women, all healthy and aged 40–65, in normal, dual-task, and fast walking conditions. We evaluate their intra-session reliability, and we establish a new reference database of such parameters showing good to excellent reliability. ICC(2,1) assesses relative reliability, while SEM% and MDC% assess absolute reliability. The reliability is excellent for all spatiotemporal gait parameters and the stride length (SL) symmetry ratio (ICC ≥ 0.90, SEM% ≤ 4.5%, MDC% ≤ 12.4%) across all conditions. It is good to excellent for the fast walking performance (FWP) indices of stride (Sr), stance (Sa), double-support (DS), and step (St) times; gait speed (GS); and the GS normalized to leg length (GS_n1) and body height (GS_n2) (ICC ≥ 0.91, |SEM%| ≤ 10.0%, |MDC%| ≤ 27.6%). Men have a higher swing time (Sw) and SL across all conditions. The following parameters are gender-independent: (1) Sa, DS, GS_n1, GS_n2; (2) the symmetry ratios of SL and GS, as well as Sa% and Sw% (representing Sa and Sw as percentages of Sr); and (3) the FWPs of Sr, Sa, Sw, DS, St, cadence, Sa% and Sw%. Our results provide reference values with new insights into gender FWP comparisons rarely reported in the literature. The advantages and reliability of our IMU-based system make it suitable in medical applications such as prosthetic evaluation, fall risk assessment, and rehabilitation. Full article

The computational complexity of existing multi-symbol detection (MSD) algorithms grows exponentially as the observation intervals increase, resulting in difficulties in algorithm implementation for detecting pulse code modulation/frequency modulation (PCM/FM) signals, especially for multi-channel signals. To address the challenges, we proposed a low-complexity MSD algorithm based on the averaged matched filtering. The proposed algorithm groups the local reference signals based on the different importance levels of the middle and edge bits in the correlation operations and averages the edge bits, leading to a considerable decrease in matching branches. Furthermore, it leverages the phase symmetry, and the proposed algorithm retains half of the averaged local reference signals for the matching operation, thus further reducing the matching branches. The proposed algorithm reduces the storage of the local signals and correlation operations to one-eighth compared to the traditional MSD algorithm under different observation lengths. Additionally, based on the structure of multiplexed FIR filters, the proposed algorithm optimizes single-channel single-coefficient FIR filters into four-channel double-coefficient FIR filters, further reducing the hardware resource consumption by approximately 25%. The simulation results showed that the proposed algorithm achieved demodulation performance comparable to the traditional MSD algorithms while reducing the computational complexity by 87.5%. Compared to the decision-feedback MSD algorithm, it achieves higher demodulation gain with a 75% complexity reduction. The Field Programmable Gate Array (FPGA) platform implementation results showed that the proposed algorithm reduces hardware resource consumption by nearly 90% compared with the traditional algorithm, and the hardware demodulation performance loss is less than 1 dB compared with the simulation results. Full article

The development of the elastic balance area within the distribution network places greater demands on the interaction between sources and loads, which impacts the stability of the power system. While achieving symmetry in active power is essential for stable operation, it is challenging to attain perfection due to various disruptions that can exacerbate frequency and voltage instability. Additionally, due to the inherent resonance characteristics of LCL filters and the time-varying nature of weak grid line impedance, grid-connected inverters may interact with the grid, potentially leading to oscillation issues. A grid-forming inverter control method that incorporates resonance suppression is proposed to address these challenges. First, a control model for the grid-forming inverter based on the Virtual Synchronous Generator (VSG) is established, enabling the system to exhibit inertia and damping characteristics. Considering the interaction between the VSG grid-connected system and the weak grid, sequence impedance models of the VSG system, which feature voltage and current double loops within the αβ coordinate system, are developed using harmonic linearization techniques. By combining the impedance analysis method, the stability of the system under weak grid conditions is evaluated using the Nyquist criterion. The validity of the analysis is confirmed through simulations. Finally, in order to ensure the effectiveness and correctness of the simulation, an experimental prototype of an NPC three-level LCL grid-forming inverter is built, and the experimental results have verified that the system has good elastic support capability and resonance suppression capability in the elastic region. Full article

This study is focused on the fabrication and characterization of various dual waveguides through femtosecond (fs) laser irradiation of GeO₂-based glass samples. The objective of the present work is to develop diverse waveguide configurations, namely straight, S-bend and Y-shaped waveguides within GeO₂–PbO glasses embedded with silver nanoparticles, utilizing a double-guide platform, for photonic applications such as resonant rings and beam splitters. Enhanced guidance was observed with a larger radius of curvature (80 mm) among the two distinct S-bend waveguides produced. The maximum relative propagation loss was recorded for the S-bend waveguide with a 40 mm radius, while the minimum loss was noted for the Y-shaped waveguide. In the latter configuration, with an opening angle of 5° and a separation of 300 µm between the two arms, an output power ratio of 50.5/49.5 between the left and right arms indicated promising potential for beam splitter applications. During the study, the quality factor (M²) of the proposed architectures was measured and the 80 mm S-bend configuration presented the best symmetry between the x and y axes; in the case of the Y configuration the similarity between the M² values in both axes, for the first and second arms, indicates comparable light guidance. Full article