System Parameters Research Articles

Theoretical and experiment optimization research of a frequency up-converted piezoelectric energy harvester based on impact and magnetic force

Harvesting environmental vibrations to power electronic components is an essential approach for addressing the power supply challenge in MEMS. However, conventional vibration energy harvesting systems frequently suffer from limited frequency bandwidth and high-frequency deficiencies. This paper proposes a novel up-frequency structure for piezoelectric vibration energy harvesting (VEH) that relies on both nonlinear magnetic force and piecewise linear force. The proposed VEH’s nonlinear dynamic characteristics are analyzed theoretically, and an experimental prototype machining and vibration test platform are constructed. Theoretical and experimental results are compared and analyzed by conducting basic experiments and key parameter optimization experiments. The research results demonstrate that the proposed VEH can efficiently harvest vibration energy in low-frequency and wide-band environments. Regarding the system parameters, higher vibration acceleration results in increased output voltage and wider working frequency bandwidth. Reducing the gap distance enhances piecewise linear vibration, which broadens the working frequency bandwidth. Furthermore, the proposed VEH’s ability to harvest low-frequency vibrations can be enhanced by reducing the magnet distance, thereby reducing the linear resonance frequency of the system. The findings of this study offer valuable insights for advancing the engineering application of MEMS self-power supply technology.

Dynamics, periodic orbits of a novel four-dimensional hyperchaotic system with hidden attractors

In recent years, the investigation of systems featuring hidden attractors and coexisting attractors has garnered significant attention. This paper presents a novel four-dimensional (4D) hyperchaotic system devoid of equilibrium points, achieved by formulating an equation without a solution or constructing a system without fixed points. Due to the complex shape of this attractor, a novel coding method is utilized to establish symbol dynamics using eight letters. The proposed system exhibits highly intricate dynamics, including variations in topological structure with alterations in system parameters, as well as an exploration and discussion of four types of coexisting attractors. Our extensive practice has led us to propose a new conjecture: hyperchaotic systems with parameters close to the bifurcation point frequently display multistable states. Furthermore, the unstable periodic orbits with different topological lengths in the hidden hyperchaotic attractor are calculated systematically using the variational method. Additionally, the DSP circuit implementation is employed to validate the numerical simulation results for this new 4D system. Finally, adaptive synchronization is successfully realized within the system, thereby confirming its feasibility.

A hybrid fractional order LMS algorithm for power system harmonic estimation

This article proposes a novel multi-innovation fractional order least mean square algorithm for estimating harmonic parameters in power systems with unknown amplitudes and phases. The algorithm incorporates fractional order calculus with variable initial values, which, in contrast to the fixed initial value approach, overcomes the non-locality issue, and converges to its true extreme value. In addition, by extending scalar innovation term to a vector form, the algorithm fully utilizes innovation within a fixed window to improve computational accuracy. The convex combination between integer order and novel fractional order ensures that the performance of the algorithm will not change significantly with the fractional order. Finally, the effectiveness is verified by numerical simulations and comparisons.

Hand eczema: a review of clinical, dermoscopic and histological features.

Hand eczema (HE) is a prevalent inflammatory skin condition, impacting 15-20% of individuals, with a notable incidence rate of approximately 7.3 cases per 1000 person/years. This condition exhibits significant gender-based variability, with a higher prevalence in females. The clinical presentation of HE is characterized by pruritic erythematous, edematous, weeping plaques, vesicles, and/or bullae, showcasing considerable heterogeneity. A literature search was conducted across multiple databases, including Medline, Pubmed, Scopus, and the Cochrane Library. The search was conducted using the following key words and medical subject heading (MeSH) terms: "hand," "eczema," "dermatitis," "dermoscopy," and "histology," employing the Boolean term "AND" to combine the research terms for optimal search precision. PRISMA algorithm has been used for article screening. The search scope included manuscripts published up to October 1, 2023. Up to 50% of HE cases are associated with atopic dermatitis, emphasizing the complex interplay between various dermatological conditions. Common subtypes of HE include irritant contact dermatitis (ICD), allergic contact dermatitis (ACD), atopic hand eczema (AHE), and protein contact dermatitis/contact urticaria (PCD). The chronic nature of HE presents a substantial management challenge, often underestimated, leading to delayed treatment and potential progression to chronic hand eczema (CHE). Beyond individual health implications, HE exerts a profound impact on occupational, domestic, social, and psychological aspects, establishing itself as the most prevalent occupation-related skin disease. This paper seeks to establish a comprehensive classification system for HE, integrating clinical, dermoscopic, and histological elements. Dermoscopy, specifically, proves instrumental in distinguishing HE from palmar psoriasis, revealing characteristic features such as yellow scales and irregular vessels. Histopathological findings underscore the dynamic changes observed from acute to chronic stages, while challenges in differentiating hyperkeratotic HE from psoriasis underscore the necessity for a holistic diagnostic approach. Accurate diagnosis and effective management of HE necessitate a holistic perspective that recognizes the inherent complexities of this inflammatory skin disease. By providing a multidimensional classification system, incorporating clinical, dermoscopic, and histological parameters, this paper aimed to contribute to a more nuanced understanding of HE and facilitate improved approaches to its diagnosis and treatment.

An almost p-standard system of parameters and approximately Cohen–Macaulay modules

An almost p-standard system of parameters and approximately Cohen–Macaulay modules

REACT: Autonomous intrusion response system for intelligent vehicles

Autonomous and connected vehicles are rapidly evolving, integrating numerous technologies and software. This progress, however, has made them appealing targets for cybersecurity attacks. As the risk of cyber threats escalates with this advancement, the focus is shifting from solely preventing these attacks to also mitigating their impact. Current solutions rely on vehicle security operation centers, where attack information is analyzed before deciding on a response strategy. However, this process can be time-consuming and faces scalability challenges, along with other issues stemming from vehicle connectivity. This paper proposes a dynamic intrusion response system integrated within the vehicle. This system enables the vehicle to respond to a variety of incidents almost instantly, thereby reducing the need for interaction with the vehicle security operation center. The system offers a comprehensive list of potential responses, a methodology for response evaluation, and various response selection methods. The proposed solution was implemented on an embedded platform. Two distinct cyberattack use cases served as the basis for evaluating the system. The evaluation highlights the system’s adaptability, its ability to respond swiftly, its minimal memory footprint, and its capacity for dynamic system parameter adjustments. The proposed solution underscores the necessity and feasibility of incorporating dynamic response mechanisms in smart vehicles. This is a crucial factor in ensuring the safety and resilience of future smart mobility.

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable Sr Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

A novel comprehensive method for weak signal blind detection based on adaptive quasi-periodic potential stochastic resonance and its application in bearing fault detection

In order to solve the common output saturation of stochastic resonance systems and the limitation of classical index SNR for blind detection, a novel adaptive quasi-periodic potential stochastic resonance blind detection method is proposed. First, a model of quasi-periodic potential stochastic resonance (QPPSR) possessing infinite steady state is constructed and analyzed for its structure change pattern. The superior performance of the model is verified by using the fourth-order Runge–Kutta algorithm. Secondly, the mechanism of QPPSR is analyzed using the probability flow method, which reveals the relationship between system parameters and performance. Again, a novel comprehensive blind detection index (CBDI) is exquisitely constructed to make up for the shortcomings of each indicator. Finally, CBDI and QPPSR are constructed into an adaptive blind detection system and applied to bearing fault detection. The results analyzed by experiments verify the good engineering application prospect of CBDI-QPPSR.

The Effect of Hydrogen as a Coolant on the Characteristics of Humidification-Dehumidification Desalination Systems

The air humidification-dehumidification (HDH) technique for water desalination can be useful in many water production applications. Researchers from all around the world have examined various implementations of this technology to improve it. The present research investigates the effect of three dehumidifier coolants on the system. These coolants include water, helium, and hydrogen. The impact of these coolants on the parameters of the humidification-dehumidification desalination system will be discussed. The investigation’s parameters are tested at various mass ratios, air flow rates, and air outlet heaters. The results show that when hydrogen is employed as a dehumidifier coolant, the gained output ratio (GOR) achieves its peak of 6.37 in the considered mass ratio range of 2.1 to 3. On the other hand, when hydrogen is utilized as a dehumidifier coolant, the system produces the maximum entropy, with the dehumidifier contributing the most. When the mass ratio changes from 2 to 3, the average entropy generation for the system using hydrogen in the dehumidifier increases by 3.8 and 2.9 times, respectively, compared to the average entropy generation for the system using water and helium. However, when hydrogen is used as a dehumidifier coolant, safety concerns must be addressed, as well as the size and cost of heat exchangers in comparison to water.

Covert Communications in Active-RIS-Aided NOMA Systems: Element Grouping or Not?

This paper investigates the impacts of element grouping on the covert communication performance of an active reconfigurable intelligent surface (ARIS)-aided uplink non-orthogonal multiple access (NOMA) system. Through element grouping, each element of the ARIS works in either the reflecting mode to reflect the information signal or the jamming mode to generate a jamming signal. Optimizing the NOMA transmit power, ARIS beamforming, and receive beamforming jointly is necessary to maximize the covert communication rate. To tackle the unsolvable covert communication rate maximization problem, we decouple the original problem into three sub-problems of optimizing the NOMA transmit power, ARIS beamforming, and receive beamforming, respectively. To tackle the mixed-integer non-linear programming for the element grouping, we introduce the arithmetic- and geometric-mean-based penalty term and apply the Dinkelbach transform to reformulate the optimization problem. Next, we propose an alternating optimization algorithm to optimize the system parameters. The numerical results demonstrate the effectiveness of the element grouping in improving the covert communication rate. However, the element grouping scheme achieves a lower covert communication rate performance compared to the scheme without element grouping, indicating that using element grouping for covert communications in the ARIS-aided uplink NOMA system is not the preferred option.

Parameter-dependent conditions for H ∞ stabilisation of polynomial parameter-varying system with delay

This paper considers the problems of achieving exponential stability and H ∞ stabilisation for polynomial parameter-varying system with time-varying delay. Unlike some existing linear parameter-varying results that treat system parameters as time invariant factors, this paper makes full use of the nonlinearity and time-varying characteristics of system through introducing a novel delay-and-parameter-dependent Lyapunov function. The main conclusions are discussed in three cases (system without input, system with disturbance input, system with disturbance input and control input). And the H ∞ stabilisation problem is solved by constructing a controller which is inserted with system parameters and their derivatives. The structures of Lyapunov function and controller lead to that the derived criteria for exponential stabilisation are expressed by state-and-parameter-dependent linear matrix inequalities, which are efficiently solved by sum-of-squares program. The effectiveness and superiority are verified through two numerical examples.

Gaussian Bistable Cascade Double Feedback Stochastic Resonance Weak Signal Enhancement Detection

Stochastic resonance (SR), as a nonlinear weak signal enhancement detection method, has found extensive applications in fields like fault diagnosis and biological information processing. However, further research has revealed that single stochastic resonance systems often fail to meet the requirements of practical engineering applications, leading to a growing interest in multi-system collaborative stochastic resonance. This article first constructs a cascaded SR system based on the Gaussian bistable model, and introduces the idea of feedback into the cascaded system. A Gaussian bistable cascaded dual feedback SR system is proposed to improve the output signal enhancement performance of the system. This system effectively utilizes the memory characteristics inherent in the output feedback and enables multi-level particle transitions and energy transfers in noisy input signals, thereby reducing noise and enhancing the extraction of weak signal features. Furthermore, the BSO optimization algorithm was utilized to optimize the system parameters of the model, resulting in an adaptive Gaussian bistable cascaded double feedback stochastic resonance (GBCDFSR) system. This system is applied to the processing of noisy periodic signals across various noise types. Simulation experiments and engineering applications show that the proposed model has stronger signal detection capabilities.

Spatio-temporal task pricing for shared electric micro-mobility battery-swapping platform with reinforcement learning

Spatial crowdsourcing has emerged in shared electric micro-mobility platforms, compensating occasional drivers (ODs) per task of swapping micro-mobility batteries. As ODs autonomously select tasks only when satisfied with predetermined compensation and travel distance, a traditional uniform pricing strategy results in possible low task completion. To resolve the imbalance between ODs and tasks, this study introduces a spatio-temporal pricing strategy where task prices differ by region and time interval. Considering the daily variations in task distribution and OD availability, the goal is to minimise the platform costs equal to the sum of total OD wages and penalties for uncompleted tasks. The reinforcement learning approach with proximal policy optimisation (PPO) is implemented to generate real-time continuous task prices. A domain-specific masking technique is incorporated to improve the learning process by disregarding the data from inactive grids in loss calculations. Computational results show that the PPO agent strategically raises prices in regions with insufficient ODs according to the OD density level. Further comparison with the mixed integer programming model with perfect information on ODs' willingness-to-accept parameters demonstrates the superior capability of our algorithm in navigating the uncertainties of OD task acceptance. A sensitivity analysis provides insights into the decision of system parameters.

Modeling the dynamic interaction between machine tools and their foundations

The performance of a machine tool is directly influenced by the characteristics of the floor, subsoil, and their interaction with the installed machine. Installing a machine tool in its operational environment poses a distinct challenge that bridges mechanical and civil engineering disciplines. This interdisciplinary issue is often overlooked within the individual separate disciplines. However, effectively addressing this challenge requires a comprehensive understanding of mechanical and civil engineering principles. To address this problem, the present study proposes a method for improved modeling of the dynamic properties of the machine tool by considering the foundation and the subsoil on which it is installed. The method is based on finite element modeling. Linear models of the system components and the connections between them were used. These, supplemented with damping expressed by complex stiffness, made it possible to determine the natural frequencies, mode shapes, and frequency response functions (based on which the transmissibilities were obtained). Based on the experimentally verified models of vertical and horizontal lathes, the sensitivity analysis aimed at estimating the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation was conducted. Thus, it was possible to identify those machine tool-support-foundation-subsoil system parameters that had the most significant impacts on the vibration's transmissibility. After analyzing the cases discussed, it became evident that the transmissibility of vibrations is primarily influenced by two key factors. First and foremost, the properties of the structural loop of the machine tool played a significant role. Additionally, the characteristics of the subsoil on which the foundation was situated emerged as a crucial determinant in the observed vibration transmissibility.

Floquet analysis perspective of driven light–matter interaction models

In this paper, we analyze the harmonically driven Jaynes–Cummings and Lipkin–Meshkov–Glick models using both numerical integration of time-dependent Hamiltonians and Floquet theory. For a separation of time scales between the drive and intrinsic Rabi oscillations in the former model, the driving results in an effective periodic reversal of time. The corresponding Floquet Hamiltonian is a Wannier–Stark model, which can be analytically solved. Despite the chaotic nature of the driven Lipkin–Meshkov–Glick model, moderate system sizes can display qualitatively different behaviors under varying system parameters. Ergodicity arises in systems that are neither adiabatic nor diabatic, owing to repeated multi-level Landau–Zener transitions. Chaotic behavior, observed in slow driving, manifests as random jumps in the magnetization, suggesting potential utility as a random number generator. Furthermore, we discuss both models in terms of a Floquet Fock state lattice.

Patient dose and associated exposure parameters in pelvic x-ray examinations: dependence on radiographic system.

Technological differences between computed radiography (CR) and digital radiography (DR) systems can influence patient doses and exposure parameters in pelvic x-ray examinations. The presence of radiosensitive organs in the pelvic region underscores the need to optimize these parameters for both CR and DR systems. This prospective study aimed to compare the patient doses and exposure parameters for adult patients undergoing pelvic x-ray examinations using CR and DR systems, based on data from Sri Lanka. The study included data from 56 x-ray examinations, with 25 using CR and 31 using DR. Patient demographic characteristics and exposure parameters (kVp: kilovoltage peak, mAs: tube current-exposure time product) were recorded, and patient doses were measured in terms of the kerma-area product (PKA) using a PKA meter. Despite similar mean weight and body mass index (BMI), the CR systems showed significantly higher mean kVp (7.4%), mAs (16.4%), and PKA (29.7%) than the DR systems (CR - kVp: 73.2, mAs: 37.8, PKA: 2.29Gy cm2; DR - kVp: 67.8, mAs: 31.6, PKA: 1.61Gy cm2). The Mann-Whitney U test revealed statistically significant differences in PKA and kVp between the CR and DR systems (p < 0.05). Furthermore, even with lower patient weight and BMI, the mean mAs and PKA in this study were substantially higher than those reported in the literature for both CR and DR systems. These results suggest the need to optimize current mAs settings for the studied hospitals and introduce radiographic system-specific exposure parameters and reference dose levels for pelvic x-ray examinations in order to enhance patient protection.

Подходы к выбору индикаторов региональной бюджетной безопасности (на примере арктических регионов России)

The article deals with the approach to selection of regional budget security indicators in order to develop a system of parameters to assess the regional budget security. The approach is based on the principle of identifying the interests of economic agents. The satisfaction level of regional authorities, economic entities, and population is considered as a budget security indicator. The express analysis based on the materials from the Arctic regions of Russia indicated an imbalance of interests of economic agents, which is further confirmed by the decline in regional and population welfare due to reduction in regional product volume redistributed through budgets and by the decrease in businesses financial results in context of the changing world economy conditions. Balance of economic interests will be achieved by the increase in budget revenues and per capita incomes.

Dynamical analysis and event-triggered adaptive finite-time prescribed performance control of the FO coupled MEMS resonators

This paper explores the dynamic behaviors and control method of weakly coupled micro-electro-mechanical system (MEMS) resonators with fractional-order (FO) dynamics. In the dynamics analysis session, Lyapunov exponents, bifurcation theory, and phase diagrams are used to analyze the effects of system parameters, coupling stiffness, and FO on the complex dynamical behaviors such as periodicity, pseudo-periodicity, and chaos oscillations. To suppress chaotic oscillations, a dynamic event-triggered finite-time prescribed performance controller is proposed. The unknown nonlinear functions are approximated through the interval type-3 fuzzy system (IT3FS) with an adaptive law. A novel finite-time prescribed performance function (finite-time PPF) is constructed to establish constrained boundaries for tracking errors. Compared with the existing PPFs, the proposed approach allows for more flexible setting of the convergence boundary before reaching steady-state. Subsequently, the constrained tracking errors are mapped to an unconstrained form using a nonlinear transformation function, whether the error constraint is symmetric or asymmetric. To circumvent the “explosion of complexity” arising from backstepping design process, a tracking differentiator (TD) is utilized. Additionally, to alleviate strain on communication resources, an event-triggered mechanism is devised to update control signals. The trigger threshold of the control signals can be adaptively adjusted based on the values of the Lyapunov function and the dynamic auxiliary variables. This control method guarantees finite-time convergence for all signals of the system. Finally, extensive simulations are performed to verify the effectiveness of the proposed algorithm.

Load Frequency Control Strategy of Interconnected Power System Based on Tube DMPC

Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive Control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness.

Least Squares Estimation of Multifactor Uncertain Differential Equations with Applications to the Stock Market

Multifactor uncertain differential equations are powerful tools for studying dynamic systems under multi-source noise. A key challenge in this study is how to accurately estimate unknown parameters based on the framework of uncertainty theory in multi-source noise environments. To address this core problem, this paper innovatively proposes a least-squares estimation method. The essence of this method lies in constructing statistical invariants with a symmetric uncertainty distribution based on observational data and determining specific parameters by minimizing the distance between the population distribution and the empirical distribution of the statistical invariant. Additionally, two numerical examples are provided to help readers better understand the practical operation and effectiveness of this method. In addition, we also provide a case study of JD.com’s stock prices to illustrate the advantages of the method proposed in this paper, which not only provides a new idea and method for addressing the problem of dynamic system parameter estimation but also provides a new perspective and tool for research and application in related fields.