System Parameters Research Articles

Knacks of marine predator heuristics for distributed energy source-based power systems harmonics estimation

The power system incorporates renewable energy resources into the main utility grid, which possesses low or no inertia, and these systems generate harmonics due to the utilization of power electronic equipment. The precise and effective assessment of harmonic characteristics is necessary for maintaining power quality in distributed power systems. In this paper, the Marine Predator Algorithm (MPA) that mimics the hunting behavior of predators is exploited for harmonics estimation. The MPA utilizes the concepts of Levy and Brownian motions to replicate the movement of predators as they search for prey. The identification model for parameter estimation of harmonics is presented, and an objective function is developed that minimizes the difference between the real and predicted harmonic signals. The efficacy of the MPA is assessed for different levels of noise, population sizes, and iterations. Further, the comparison of the MPA is conducted with a recent metaheuristic of the Reptile Search Algorithm (RSA). The statistical analyses through sufficient autonomous executions established the accurate, stable, reliable and robust behavior of MPA for all variations. The substantial enhancement in estimation accuracy indicates that MPA holds great potential as a strategy for estimating harmonic parameters in distributed power systems.

Design and performance optimization of a novel CAES system integrated with coaxial casing geothermal utilization

At present, the heat storage sub-system in the adiabatic compressed air energy storage system generally has low heat storage density and limited heat storage temperature, resulting in mediocre power generation efficiency of the system. To address these issues, this paper proposes a novel approach for coupling the energy storage system with geothermal energy utilization. This study innovatively uses coaxial casing wells to extract geothermal energy and to preheat compressed air in front of the expander. Detailed numerical investigations and dynamic thermodynamic analysis are carried out to evaluate system performance. Sensitivity and optimization are analyzed to identify the operational and design parameters of the proposed system. In addition, an economic analysis is conducted to estimate the investment and payback of the energy storage system. The results show that geothermal energy utilization can directly and significantly enhance the electric, heating and cooling energy output of compressed air energy storage system. During one 24-hours operation cycle, the electricity output is 104.45GJ, the heating supply output is 81.59 GJ, and the cooling supply output is 16.69 GJ. The RTE and energy efficiency are 64.26 % and 122.68 %, respectively. Economic analysis shows that the static payback period is 12.17 years, and the net present value can reach 3.46 million USD. It can be concluded that geothermal energy plays a greater role in energy storage systems than geothermal power production alone. The compressed air energy storage system combined with geothermal energy addresses current issues and leads to improved system performance.

Mortality risk factors for crush syndrome after an earthquake in Türkiye: Do systemic inflammatory parameters play any role?

The aim of our study is to assess the prognostic impact of the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) on 28-day mortality in patients admitted to the intensive care unit with crush syndrome following the Kahramanmaraş earthquake in Türkiye. A total of 63 adult patients with crush syndrome admitted to the intensive care unit after the earthquake were enrolled in this study. The medical records of the patients were examined using follow-up forms and the hospital data system. The mean age of the patients was 38.9±17.3 years, and the median time under debris was 31.5 hours. The 28-day mortality rate was 27%. In univariate generalized estimating equations (GEE) and other analyses, variables that are significant (or candidate variables) between 28-day mortality groups included age as a biological factor. These variables were included in the multivariate GEE model. The effects of continuous renal replacement therapy (CRRT), serum sodium concentration, Sequential Organ Failure Assessment (SOFA) score, and PLR on mortality were statistically significant. Elevated SOFA scores, the necessity for CRRT, increased serum sodium levels, and decreased PLR values are associated with increased 28-day mortality in patients with crush syndrome after an earthquake.

Characterization of Position Synchronous Control System for Double Hydraulic Cylinder

Background: There are some problems in the synchronous control system of electrohydraulic servo double hydraulic cylinders, such as uncertainty of system parameters, time variation, difference between two subsystems, and unbalanced load disturbance during operation. The above problems cause the output responses of the two cylinders to be inconsistent, resulting in the situation that the output flows of the two cylinders are not synchronized. Objective: Further improve the accuracy of synchronous control while reducing the cost of using synchronous control system. Methods: The AMEsim simulation model of electro-hydraulic servo dual-cylinder synchronous control system is established, and based on this model, the dynamic characteristics, coupling characteristics, and synchronization characteristics of the double-cylinder system are analyzed theoretically and simulated. Results: Analyze and verify the influence of various factors on the synchronization of double hydraulic cylinders, and summarize the influence law of the change of different factors on the output and synchronization accuracy of the double hydraulic cylinder system. Conclusion: This study analyzes the influence of various factors on the synchronization of double cylinders by adjusting the parameters, and summarizes the influence law of the change of different factors on the output and synchronization accuracy of the double cylinder system.

An Improved Voltage Sag Monitor Placement Method by using Simple Probability Reach Area Matrix

Aim: A novel optimization model is introduced in this paper to achieve monitor placements of voltage sag. Background: Considering the uncertainty of system parameters, calculation errors and measurement errors, the method of MRA is not suitable. Objective: The contradiction between the number of monitors and observation areas can be coordinated to achieve voltage sag observation with fewer monitor quantities. Method: A new generalized approach defined as a simple probability reach area matrix (SPMRA) is proposed. Result: SPMRA method is simulated by the IEEE-39 bus system, which reveals that monitor placement quantities can be reduced effectively under probabilistic observability. Conclusion: SPMRA method is a general method for monitor placements of voltage sag. This method not only performs the functions of conventional MRA methods, but also addresses the challenges of errors and conflicts between observation requirements and installation costs.

A fuzzy-PID controller for load frequency control of a two-area power system using a hybrid algorithm

This paper presents the use of a new hybrid optimization approach known as particle swarm optimization and grey wolf optimizer (PSO-GWO) for improving frequency stability load frequency control (LFC) in tow-area power systems. The approach consists in optimizing the fuzzy proportional-integral-derivative (fuzzy-PID) controller parameters with meta-heuristic hybrid algorithm: PSO-GWO. This technique allows to have dynamic responses with the least possible frequency deviation in very short response times. The approach proposes to controls the tie-line power and the frequency deviation in the considered two-area power systems under variable perturbation in load and changing of system parameters in order to evaluate its effectiveness. The suggested hybrid algorithm-based fuzzy-PID controller is compared with various widely used control methods in the literature such as PID controller and algorithms such as PSO and GWO in order to evaluate its effectiveness and its robustness. Through the simulations carried out on MATLAB/Simulink, the proposed PSO-GWO fuzzy-PID and the objective function exhibit improved performance, achieving minimal objective values. The proposed technique proved to be quite powerful tool in the resolution of problems related to electrical power systems, particularly in load frequency control.

Performance Analysis of Lake Water Cooling Coupled with a Waste Heat Recovery System in the Data Center

Data centers (DCs) require continuous cooling throughout the year and produce a large amount of low-grade waste heat. Free cooling and waste heat recovery techniques are promising approaches to reduce DC energy consumption. Although previous studies have explored diverse waste heat utilization strategies, there is a significant gap in combining waste heat recovery with lake water cooling in DCs. Therefore, this study proposed a system integrating lake water cooling with waste heat recovery for DCs. To evaluate the energy-saving performance of the suggested system, the influence of waste heat recovery locations and volumes has been investigated. An analysis of the improvement in system parameters is also conducted. The study’s findings highlight that targeted recovery of waste heat from sources like chilled water or air in server rooms can significantly reduce the cooling energy demand of the system. The results show that recovering heat from the return air of IT equipment can yield a remarkable power usage effectiveness (PUE) and coefficient of performance (COP) of 1.19 and 10.17, and the energy consumption of the cooling system is reduced to 10.06%. Moreover, the outcomes reveal the potential for substantial energy savings of up to 26.05% within the proposed system by setting the chilled water and air supply temperatures to 16 and 20 °C, respectively.

Adaptive Iterative Learning Tracking Control for Nonlinear Teleoperators with Input Saturation

Addressing input saturation, external disturbances, and uncertain system parameters, this paper investigates the position tracking control problem for bilateral teleoperation systems with a time delay communication channel. Based on a composite energy function, we propose an adaptive iterative learning control (AILC) method to achieve the objective of position tracking under the alignment condition. This extends the existing research on the control of nonlinear teleoperation systems with time delay. The saturation constraint property of the Softsign function ensures that no state of the system exceeds its constraints. The controller learns to simultaneously deal with the uncertainty of system parameters online, reject external disturbances, and eliminate positional errors along the time and iteration axes. All signals in the system for any constant time delay are proved to be bounded. Ultimately, the performance of the proposed controller is further verified through numerical simulations.

Design of High-Reliability Low-Dropout Regulator Combined with Silicon Controlled Rectifier-Based Electrostatic Discharge Protection Circuit Using Dynamic Dual Buffer

Overshoot and undershoot caused by the current load impact the accuracy of the required output voltage and circuit performance. The transient response issue in existing low-dropout (LDO) regulators is a dynamic specification that must be addressed at the design stage. This transient response is influenced by system parameters such as stability and gain. The LDO regulator suggested in this study is designed to minimize the change in output voltage by considerably enhancing the gain using a dynamic dual buffer structure. A dynamic dual buffer is utilized to effectively control undershoot and overshoot. Under the conditions that the input voltage range is from 3.3 to 4.5 V, the maximum load current is 300 mA, the output voltage is 3 V, and the output of the proposed LDO regulator with the dynamic dual buffer structure has undershoot and overshoot voltages of 41 mV and 31 mV, respectively. That is, the output voltage of the proposed LDO regulator effectively provided and discharged an additional current suited for the undershoot/overshoot conditions to enhance the transient response characteristics. Furthermore, the electrostatic discharge (ESD) robustness characteristics of the proposed LDO regulator improved because of the silicon-controlled rectifier underlying the ESD protection device embedded in the output node and power line.

3D Chaotic Nonlinear Dynamic Population-Growing Mathematical System Modeling with Multiple Controllers

Modeling, stabilization, and identification processes are significant stages in the process of developing knowledge about chaotic dynamical systems which entail the effective prediction depending on the degree of uncertainty toleration in the forecast, accuracy of the current state to be measured as well as a time scale resting on the dynamics of the system. Control of under-activated dynamical systems has been considered substantially, and it is for periods and is currently developing in various domains such as biology, data analysis, computing systems, and so forth. Dynamic systems of growing population signifies a model describing the way a population evolves over time during which population goes through major life events, split into discrete time periods. The size of the population at a given time period is determined by the rate of growth as well as other related factors. Most progress has been made in model-based control theory, which has drawbacks when the system under consideration is exceedingly complicated, and no model can be constructed. Accordingly, a 3D-discrete and dynamic human population growth system with many controllers is proposed by examining the stability and symmetry of controller system clarifications. The symmetric stability control results are presented by considering a special parametric dynamic system in its coefficients besides suggesting periodic functional coefficients in terms of sin and cos functions. The controllers have the ability to reduce population growth rate unpredictability or enhance system stability under various external conditions. The unique and very effective strategies in relevant domains could provide a deeper understanding of their impact as well as the theoretical or technological innovations thereof. These controllers are capable of reducing population growth rate unpredictability or improving system stability under various external conditions, and applicable strategies in the relevant domains can provide profound comprehension over the impact along with the theoretical as well as technological advancements.

Pilot Assignment for Cell Free Massive MIMO Systems: A Successive Interference Cancellation Approach

In the contemporary era, considerable attention has been directed towards exploring cell-free massive multiple-input multiple-output (CF-M-MIMO) systems. This novel network paradigm has gained prominence as a potential solution for tackling the persistent challenge of inter-cell interference prevalent in traditional cellular MIMO networks. This study investigates a pilot assignment approach based on Successive Interference Cancellation (SIC) aimed at alleviating pilot contamination issues inherent in CF-M-MIMO systems. Through comprehensive numerical analyses and simulations, we demonstrate the efficacy and enhanced performance of the SIC-based approach competed with common random and greedy pilot assignment strategies. The proposed methodology addresses the critical challenge of pilot contamination, a phenomenon that severely impacts system performance and spectral efficiency. By iteratively decoding signals and canceling interference, the SIC algorithm optimizes pilot assignments, resulting in improved data rates and more efficient resource utilization. Our findings underscore the robustness and scalability of the SIC-based scheme across diverse system parameters and deployment scenarios, affirming its potential as a promising solution for enhancing the performance of CF-M-MIMO systems. Overall, this study contributes valuable insights into the design and optimization of pilot assignment strategies, offering a pathway for further research and development in the field of CF-M-MIMO systems.

Optimal drift rate control and two-sided impulse control for a Brownian system with the long-run average criterion

Abstract In this paper, we consider a joint drift rate control and two-sided impulse control problem in which the system manager adjusts the drift rate as well as the instantaneous relocation for a Brownian motion, with the objective of minimizing the total average state-related cost and control cost. The system state can be negative. Assuming that instantaneous upward and downward relocations take a different cost structure, which consists of both a setup cost and a variable cost, we prove that the optimal control policy takes an $\left\{ {\!\left( {{s^{\ast}},{q^{\ast}},{Q^{\ast}},{S^{\ast}}} \right),\!\left\{ {{\mu ^{\ast}}(x)\,:\,x \in [ {{s^{\ast}},{S^{\ast}}}]} \right\}} \right\}$ form. Specifically, the optimal impulse control policy is characterized by a quadruple $\left( {{s^{\ast}},{q^{\ast}},{Q^{\ast}},{S^{\ast}}} \right)$ , under which the system state will be immediately relocated upwardly to ${q^{\ast}}$ once it drops to ${s^{\ast}}$ and be immediately relocated downwardly to ${Q^{\ast}}$ once it rises to ${S^{\ast}}$ ; the optimal drift rate control policy will depend solely on the current system state, which is characterized by a function ${\mu ^{\ast}}\!\left( \cdot \right)$ for the system state staying in $[ {{s^{\ast}},{S^{\ast}}}]$ . By analyzing an associated free boundary problem consisting of an ordinary differential equation and several free boundary conditions, we obtain these optimal policy parameters and show the optimality of the proposed policy using a lower-bound approach. Finally, we investigate the effect of the system parameters on the optimal policy parameters as well as the optimal system’s long-run average cost numerically.

A light-fueled self-rolling unicycle with a liquid crystal elastomer rod engine

Active materials hold substantial potential for self-sustaining motion, however, the dependency on movable light or extensive area illumination in current structures often restricts the design and practical deployment of such active machines. In this study, we report a novel zero-energy-mode, self-rolling unicycle equipped with a liquid crystal elastomer rod engine, which fueled by a fixed, small-area light source. By employing an elaborate dynamic model for liquid crystal elastomer, we derive the lateral curvature of the liquid crystal elastomer rod and the driving moment necessary for the unicycle's self-rolling. The study results demonstrate that the self-rolling of the unicycle is driven by a moment induced by the shift in the center of gravity of the curved liquid crystal elastomer rod. Our numerical simulations indicate the presence of a supercritical Hopf bifurcation point delineating the transition between the self-rolling mode and the static mode within the unicycle system. Additionally, the rolling velocity of the unicycle relies on a handful of key system parameters, notably including light intensity, light penetration depth, length of the liquid crystal elastomer rod, rolling friction coefficient, total mass of the unicycle, and wheel radius of the unicycle. The experimental validation of a self-rolling unicycle with zero-energy-mode has been conducted. The self-rolling unicycle constructed in this paper has excellent characteristics of simple structure, zero-energy-mode, horizontal constant light source, and small area light source, providing valuable insights for the utilization of photoresponsive liquid crystal elastomer rods in soft robotics, medical devices, energy harvesting systems, and actuators.

Priority-based scheduling in residential energy management systems integrated with renewable sources using adaptive Salp swarm algorithm

With the remarkable growth and implementation of communication technology, sensors, and measurement equipment in the Smart Grid (SG) environment, demand side management (DSM) and demand response (DRs) can be easily implementable in residential energy systems integrated with renewable energy sources (RES). Looking at this perspective, this paper suggests an intelligent and dynamic load-priority-based scheduling optimal smart residential energy management system (REMS). The objectives to achieve through priority-based scheduling in the case of a residential energy management system are multi-focussed in terms of peak load reduction, consumer choice of consumption according to priority basis, and cost-effectiveness towards electricity price savings. The issues related to uncertainties with RES due to environmental dependency must be incorporated into the DSM. A single objective discrete formulation based on the Adaptive Salp Swarm Algorithm (ASSA) has been done on modelling and optimizing the crucial system parameters for scheduling, ideally the operation of residential appliances, along with the sources and prioritized-based loads available. System constraints, consumer priorities, energy source availability, uncertainties, and objectives are considered in the formulation to justify the approach that is feasible in real-time conditions. To enhance the search capabilities of SSA, the control parameters vary optimally in both the exploration and exploitation stages of searching. Comparative results with genetic algorithms (GA), particle swarm optimization (PSO), and conventional SSA are presented in different cases, such as (1) traditional homes without REMS, (ii) smart homes with REMS (iii) smart homes using REMS with RES.

Effect of extrusion cooking on physical and thermal properties of instant flours: a review

The production of instant flour constitutes a fast-expanding sector, and, this is an innovative area, that is being modified adjusting continually its methodologies to enhance production efficiency, optimizing its resources, fostering innovation in its applications, and increasing its economic income. Among the methods widely cited for precooked flours production are spray drying, drum drying, and extrusion cooking, the latter emerging as a high-potential and versatile solution to produce such commodities. In this regard, a comprehensive understanding of the extrusion process, its mechanical principles, and its effects on the physical characteristics of extruded raw materials is necessary. Analyzing process parameters (specifical mechanical energy and mean residence time) is essential to achieve the desired outcomes. Furthermore, it was analyzed the effect of the process modification conditions (temperature, screw speed, and moisture content) on the physical characteristics of the extruded instant flours. This review offers insights into the most reported system parameters as Specifical Mechanical Energy (SME), Pressure, Torque and, physical properties assessed in different instant flour obtained by extrusion such as Water Absorption Index (WAI), Water Solubility Index (WSI), Swelling Power (SP), Rehydration Capacity, Wetting Capacity, Sinking, Dispersibility, Pasting properties, Thermal properties, and Color. The review summarized and discussed the behavior of the hygroscopic properties and the water affinity of different instant flours obtained by extrusion.

Inventory rationing, admission control, and production capacity allocation in a make‐to‐stock/make‐to‐order manufacturing system

AbstractThis paper considers a manufacturing system in which products are produced in both make‐to‐stock (MTS) and make‐to‐order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non‐preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of this paper is to study a policy that coordinates inventory rationing, admission control, and production capacity allocation to maximize the system's profit. We formulate the problem as a Markov decision process model and identify the structure of optimal control policies. We investigate the effect of inventory rationing on the profit by comparing its performance to that of the system with a first‐come‐ first‐serve policy to allocate inventory to multiple demand classes and study the extent to which the benefit of inventory rationing can be affected by system parameter changes. We also propose a heuristic that manages control decisions from linear threshold functions. Our test results from numerical examples show that the average percentage difference between the optimal and heuristic policies is within 1.2%.

The Prognostic Significance of Pontine-White Matter Score in Primary Central Nervous System Lymphoma Patients

Background: Limited data exist on the significance of PET imaging and quantitative PET parameters in primary central nervous system (CNS) lymphoma due to its relative rarity. This study was conducted to investigate the prognostic value of a novel internal standardization indicator, the pontine-white matter (PW) score, in primary CNS lymphoma patients undergoing post-treatment 18F-FDG PET/CT and PET/MR imaging. Methods: From January 2014 to December 2022, eligible patients with primary CNS lymphoma who underwent post-treatment PET imaging were enrolled. Using the FDG uptake of the pons and white matter as an internal reference, the PW score was graded based on the metabolism of the post-therapeutic lesion for each patient, and its associations with patients’ prognosis were investigated. Results: In total, 41 patients with post-treatment PET/CT and 49 patients with post-treatment PET/MR imaging were enrolled. ROC curve analysis indicated that the PW score possessed robust discriminative ability in distinguishing patients with worse outcomes. Furthermore, a higher PW score was significantly correlated with and identified as an independent prognostic indicator for, worse prognosis in both the PET/CT and PET/MR cohorts. Conclusion: The study demonstrated that the PW score was an effective prognostic indicator for identifying post-treatment primary CNS lymphoma patients with worse outcomes.

Melting phenomena of self-organized magnetic structures investigated by variational autoencoder

The phase transition phenomenon is an important research topic in various physical studies. However, it is difficult to define the order parameters in many complex systems involving self-organized structures. We propose a method to define order parameters using a variational autoencoder network. To demonstrate these capabilities, we trained a deep learning network with a dataset composed of spin configurations in a chiral magnetic system at various temperatures. It removes thermal fluctuations from the input data and leaves the remaining structural information with a spin magnitude. We define an order parameter with magnitude of output spins and compare the results with those of conventional analysis. The comparison indicates similar results. Using the order parameter, the thermal properties of the chiral magnetic system were investigated by varying the physical parameters and data size.

A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic

This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements and enables the simultaneous estimation of transmission topology and epidemic factors. The use of the joint estimation scheme reduces the estimation error. The stability of equilibria and convergence behaviors of proposed dynamics are then analyzed. Furthermore, the sensitivity of the proposed model to population movements is evaluated in terms of the basic reproduction number. This article also rigorously investigates the effectiveness of non-pharmaceutical interventions via distributively controlling population flow in curbing virus transmission. It is found that the population flow control strategy reduces the number of infections during the epidemic.

A Comprehensive Review of the Smart Microgrids’ Modeling and Control Methods for Sustainable Developments

Estimation strategies and hierarchical control measures are required for the successful operations of microgrids. These strategies and measures monitor the processes within the control variables and coordinate the system dynamics. State-of-the-art frameworks and tools are built into innovative grid technologies to model different structures and forms of microgrids and their dynamic behaviors. Smart grids’ dynamic models were developed by reviewing different estimation strategies and control technologies. A Microgrid control system is made up of primary, secondary, and tertiary hierarchical layers. These architectures are measured and monitored by real-time system parameters. Different estimation schemes and control strategies manage microgrid control layers’ dynamic performances. The control strategies in the developed technologies dynamics were accessed in the grid environment. The control strategies were modeled for microgrids using six design layers: adaptive, intelligent, robust, predictive, linear, and non-linear. The estimation schemes were assessed using microgrid controllers’ modeling efficiency. Hierarchical control strategies were also developed to optimize the operation of microgrids. Hence, this research will inform policy-making decisions for monitoring, controlling, and safeguarding the optimal design strategies for modeling microgrids.