Optimal Regulation Research Articles

Analisis Faktor-Faktor Yang Mempengaruhi Fungsi Di Tempat Kerja Pada Perawat

Background: The workplace function of nurses is an important thing that must be paid attention to by hospitals. Nurses certainly have an important function in their work in terms of health services. Employees should be able to adapt and be respected in their work environment. However, some people experience problems adapting to their work environment, resulting in low performance. Nurses' decreased function at work due to high workloads makes it difficult to control one's own behavior. This results in the implementation of the work not being optimal.Objective: This study was conducted to examine the factors that influence workplace function in nurses Method: This research is a quantitative research with a survey design. Research data was collected using a questionnaire from respondents who work as nurses in hospitals. The sampling technique used purpose sampling technique and the sample size was 200 nurses. This research uses SEM (Structural Equation Modeling) analysis using AMOS and regression analysis using SPSS. Results: Based on the results of the analysis that has been carried out, the research results show: self-regulation capacity has a significant effect on functioning at work, self-regulation capacity has a significant effect on self-destruction, self-regulation capacity has a significant effect on job craftsmanship, self-destruction has a significant effect on functioning at work. workplace, work craft has a significant influence on function in the workplace. Burnout does not moderate the relationship between self-regulatory capacity and functioning at work so it does not have a significant influence. Conclusion: Increased functioning in the workplace for nurses is influenced by optimal self regulation capacity in nurses, self-destruction which can be increasingly minimized, work craftsmanship which increases in nurses.

Incremental reinforcement learning and optimal output regulation under unmeasurable disturbances

In this paper, we propose novel data-driven optimal dynamic controller design frameworks, via both state-feedback and output-feedback, for solving optimal output regulation problems of linear discrete-time systems subject to unknown dynamics and unmeasurable disturbances using reinforcement learning (RL). Fundamentally different from existing research on optimal output regulation problems and RL, the proposed procedures can determine both the optimal control gain and the optimal dynamic compensator simultaneously instead of presetting a non-optimal dynamic compensator. Moreover, we present incremental dataset-based RL algorithms to learn the optimal dynamic controllers that do not require the measurements of the external disturbance and the exostate during learning, which is of great practical importance. Besides, we show that the proposed incremental dataset-based learning methods are more robust to a class of measurement noises with arbitrary magnitudes than routine RL algorithms. Comprehensive simulation results validate the efficacy of our methodologies.

Effective Knowledge Dissemination Modeling and Regulation in Blended Learning Networks

Blended learning networks (BLNs) based on the integration of online learning networks and offline learning environments provide new opportunities and platforms for people to acquire and update useful knowledge and carry out all kinds of learning activities anytime and anywhere. Effective modeling and regulation of the knowledge dissemination process can accurately grasp its dissemination process, promote knowledge innovation and collaborative sharing among learners, and accelerate the maximization of knowledge dissemination. However, it is a challenge to establish a comprehensive dynamics model and adopt the optimal regulation for the knowledge dissemination process under the constraints of a limited budget in large-scale BLNs with diverse learners. To this end, we first explore the evolution process of knowledge dissemination in BLNs and the blended learning interaction process of learners. Based on the system dynamics modeling theory, a dynamics model of knowledge dissemination is established. Second, two kinds of effective regulation strategies are proposed. We establish an optimal regulation system intending to maximize the dissemination of knowledge and use the optimal control theory to tackle the optimal solution distribution of regulation strategies. Then, we propose a knowledge dissemination regulation task allocation method based on the collaborative participation of users, and the reverse auction theory is used to quickly solve the task allocation scheme while ensuring performance. Finally, we demonstrate the effectiveness of proposed models and methods through extensive simulation experiments based on real datasets.

New Results in Cooperative Adaptive Optimal Output Regulation

New Results in Cooperative Adaptive Optimal Output Regulation

Should product‐specific advertisement be regulated in pharmaceutical markets?

AbstractThis paper examines the optimal content regulation of direct‐to‐consumer advertisement (DTCA) by comparing two forms of DTCA—product‐specific and category‐specific—and identifies a key tradeoff which underlies this policy debate. Our analysis suggests that the optimal form of DTCA depends crucially on the cost effectiveness of DTCA and the market‐size distortion induced by DTCA. When the cost of advertisement is high, there often exists a Pareto‐improving policy choice: category‐specific DTCA is preferred when the market‐size distortion is more severe while produce DTCA is preferred when it is less so. As the cost decreases, however, a conflict emerges between pharmaceutical firms and patients: firms are worse off under product‐specific DTCA while patients are better off. We also find that the physician's reluctance to persuade misinformed patients can actually alleviate the market‐size distortion and hence be welfare‐enhancing.

FORMATION OF THE COST SYSTEM BASED ON THEIR OPTIMAL STRUCTURE

The functioning of business entities of the machine-building industry is closely related to the consumption in the process of manufacturing and selling products (goods, services) of a wide range of material, energy, labor, financial, and intellectual resources. Accordingly, the amounts incurred by the enterprise constitute a generalized monetary estimate of the cost of consumed production resources, and also reflect the optimal use of resources, the consumption of which is important for providing all the needs of the enterprise at the stage of production processes. Effective management influence at the stage of formation and implementation of expenses requires ensuring an equally significant degree of differentiation of the defined management system, as well as the justified application of norms and methods of solving management problems related to the optimal regulation of consumption norms. The basis of the integrated cost management system is a hierarchical system of performance indicators of the enterprise, which provides an opportunity to implement the principle of decentralization in the implementation of management decisions. The budgeting system creates prerequisites for attracting additional financial resources, finding channels for their mobilization, adequate centralization of cash flows, and regulates their rational use. The proposed algorithm for implementing an integrated cost management system is shown in Fig. 3.6. determines the procedure for implementing a set of systematized measures regarding the implementation of management technology, the system of intra-firm cost planning, which prescribes key regulations and management procedures.

Comparative analysis and FPGA realization of different control synchronization approaches for chaos-based secured communication systems.

Synchronization of the chaotic systems has attracted much attention in recent years due to its vital applications in secured communication systems. In this paper, an implementation and comparative analysis of two different control approaches for synchronization between two identical four-dimensional hyperchaotic systems is presented. The two control approaches are the Adaptive nonlinear controller and the linear optimal quadratic regulator LQR. To demonstrate the effectiveness of each controller, the numerical simulation is presented using Matlab/Simulink and the control law is derived. The performance of the proposed controllers is compared based on four factors; response time, squared error integration, energy applied from the controller, and cost function. To measure the robustness of the control approaches, the performance factors are compared when there is a change in system parameters and a variation in the initial conditions. Then the proposed synchronization methods are implemented on the FPGA platform to demonstrate the utilized resources on Field Programmable Gate Array (FPGA) hardware platform and the operation speed. Finally, to generalize the results of the comparison, the study is implemented for the synchronization of another secured communication system consisting of two identical three-dimensional chaotic. The experimental results show that the LQR method is more effective than the Adaptive controller based on the performance factors we propose. Moreover, the LQR is much simpler to implement on hardware and requires fewer resources on the FPGA.

On the psychological and economic foundations of forming private property relations in the economy for the purpose of their legal regulation

The paper considers the psychological and economic foundations of the formation of private property relations, which are based on the “sense of ownership” – the attitude to property as to one’s own, in the presence of an additional feature of a “sense of benefit”. The authors give a definition of property relations, which is based on psychological and economic foundations. The paper substantiates that private property under Russian law is a too generalized category – a single legal regime has been established for it, which does not correspond to the optimal legal regulation of property relations. The article substantiates the need to identify the subvarieties of private property, which have significant differences in psychological and economic motivation: classical, financial, social, collective and state-owned. Each subvariety of private property depends on the presence (absence) of a sense of ownership and a sense of benefit, as well as the presence (absence) of a certain level of property. Accordingly, civil legislation requires the legal consolidation of the subvarieties of private property, designating for each of them its own legal regime.

Drawing Inspiration from Nature: Trinitarian Strategies for Designing Polyoxometalates and Metal-Organic Framework-Based Biomimetic Microhoneycomb Electromagnetic Wave-Absorbing Materials.

Trinitarian designs in the morphology, components, and microstructure remain challenging for advanced electromagnetic wave absorption (EMWA) materials with light weight, strong absorption, and well-defined structure-function relationships. Herein, a series of X-doped MoS2/Cu9S5 with multilevel honeycomb structures (X-MoS2/Cu9S5 MHs, X = P, Si, Ge) were designed by space-confined growth and in situ sulfidation of a polyoxometalate-based metal-organic framework. X-MoS2/Cu9S5 MHs possess low density, high surface area, and abundant cation-cuprum and anion-sulfur double vacancies (VCu and VS) simultaneously that are unmatched by conventional EMWA materials. Also, the systematic investigation of the doping effect of various polyoxometalate heteroatoms on VCu and VS in the microhoneycomb has been conducted. Experimental results and density functional theory calculations reveal that the excellent EMWA performance (-56.21 dB) results from the synergistic effect of morphology design, component optimization, and vacancy regulation. This study not only provides an important opportunity to understand a morphology-component-microstructure strategy in electromagnetic wave absorption but also builds a noteworthy bridge between bioinspired engineering and microscale absorbers.

Optimal Regulation Performance of MIMO Networked Time-delay Systems With Limited Bandwidth and Interference Signals

Optimal Regulation Performance of MIMO Networked Time-delay Systems With Limited Bandwidth and Interference Signals

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

Abstract The objective of this paper is to develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network. It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network. For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed. In an event-triggered system, when an error signal exceeding a specified time-varying threshold it indicates the occurrence of a typical event. The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed. Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs. A simulation example is finally used to verify the effectiveness of such a control strategy can enhance the system performance.

Foliar application of plant growth regulators for enhancing heavy metal phytoextraction efficiency by Sedum alfredii Hance in contaminated soils: Lab to field experiments

The phytoremediation efficiency of plants in removing the heavy metals (HMs) might be influenced by their growth status and accumulation capacity of plants. Herein, we conducted a lab-scale experiment and a field try out to assess the optimal plant growth regulators (PGRs) including indole-3-acetic acid (IAA)/brassinolide (BR)/abscisic acid (ABA) in improving the phytoextraction potential of Sedum alfredii Hance (S. alfredii). The results of pot experiment revealed that application of IAA at 0.2 mg/L, BR at 0.4 mg/L, and ABA at 0.2 mg/L demonstrated notable potential as optimal dosage for Cd/Pb/Zn phytoextraction in S. alfredii. The findings of subcellular level of Cd/Pb/Zn in leaves showed that IAA (0.2 mg/L), BR (0.4 mg/L) or ABA (0.2 mg/L) promoted the HMs storage in the soluble and cell wall fraction, therefore contributing HMs subcellular compartmentation. In addition, application of PGRs notably enhanced the antioxidant system (SOD, CAT, POD, APX activities) while reducing lipid peroxidation (MDA content) in S. alfredii, consequently improving HMs tolerance and growth of S. alfredii. Moreover, the results of field trial showed that application of BR, IAA, or ABA+BR substantially improved the growth of S. alfredii by inducing plants biomass and augmenting the levels of photosynthetic pigment contents. Notably, ABA+BR noticed the highest theoretical biomass by 42.9 %, followed by IAA (41.6 %), and BR (36.4 %), as compared with CK. Additionally, ABA+BR treatment showed effectiveness in removing the Cd by 103.4 %, while BR and IAA led to a significant increase of Pb and Zn removal by 239 % and 116 %, respectively, when compared with CK. Overall, the results of this study highlights that the foliar application of IAA, BR, or ABA+BR can serve as viable strategy to boosting phytoremediation efficiency of S. alfredii in contaminated soil by improving the biomass and metal accumulation in harvestable parts.

Guaranteed cost tracking control of constrained input nonlinear uncertain systems using event-based ADP

This article investigates the guaranteed cost robust tracking control problem of nonlinear systems subjected to input constraint and unmatched uncertainty. The event-based adaptive dynamic programming (ADP) approach is utilized to address this problem. First, the tracking error and reference trajectory are combined to form an augmented uncertain system. Then, by decomposing the uncertainty into the matched and unmatched parts, the original tracking problem is converted into the optimal regulation problem of an auxiliary system. The cost function for the auxiliary system is defined, and the associated Hamilton–Jacobi–Bellman (HJB) equation is solved using a single critic neural network (NN). Moreover, a novel event-triggering rule is formulated, and it is shown that the designed event-based controller guarantees that the tracking error is uniformly ultimately bounded. The derivation of event-based guaranteed cost and its relation with the time-based counterpart is presented. The exclusion of the infamous Zeno behavior is guaranteed. The uniform ultimate boundedness of the critic weight estimation error is shown. Finally, the effectiveness of the proposed event-triggered ADP method is illustrated through simulations of the spring–mass–damper system and Van der Pol’s oscillator with unmatched uncertainty.

Hybrid Iteration ADP Algorithm to Solve Cooperative, Optimal Output Regulation Problem for Continuous-Time, Linear, Multiagent Systems: Theory and Application in Islanded Modern Microgrids With IBRs

This paper proposes a novel adaptive dynamic programming (ADP) algorithm, named hybrid iteration (HI), to solve the cooperative, optimal output regulation problem (CO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> RP) for continuous-time, linear, multi-agent systems. Unlike traditional ADP algorithms, i.e., policy iteration (PI) and value iteration (VI), HI does not need an initial stabilizing control policy required by PI. At the same time, it maintains a faster convergence rate compared with VI. First, a model-based HI algorithm is proposed to solve the CO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> RP. Based on the proposed HI algorithm, a data-driven, adaptive, optimal controller is developed to solve the cooperative, adaptive, optimal output regulation problem without using any information about the physics of the system. Instead, the states/input information collected along the trajectories of the dynamic system is employed. The proposed data-driven HI is applied to the adaptive, optimal secondary voltage control (also known as voltage restoration control) of an islanded modern microgrid based on inverter-based resources. Compared with the VI and PI algorithms, comparative simulation results demonstrate that the proposed HI approach is significantly able to save the convergence time of the central processing unit (also known as CPU) deployed, reduce the number of learning iterations, and remove the requirement of the initial stabilizing control policy. Comparative experiments reveal the practicality and superiority of the proposed methodology.

Refined Algorithms for Adaptive Optimal Output Regulation and Adaptive Optimal Cooperative Output Regulation Problems

Refined Algorithms for Adaptive Optimal Output Regulation and Adaptive Optimal Cooperative Output Regulation Problems

Modulation of soil aeration and antioxidant defenses with hydrogen peroxide improves the growth of winter wheat (Triticum aestivum L.) plants

Soils with subsurface drip irrigation are often characterized by hypoxia, which could impair the aerobic respiration of roots and the productive potential of crops. Irrigation with hydrogen peroxide oxygenated water (HPOW) is one way to improve soil aeration in the rhizosphere. However, few studies have focused on exploring the interaction of HPOW with soil aeration and productivity of crops. The current work aimed to investigate the effects of four levels of HPOW [0 ppm H2O2 solution (tap water) as a control (H0), 600 ppm, 800 ppm and 1000 ppm H2O2 solution (H600, H800 and H1000)] on irrigation water properties, soil aeration, antioxidant defenses, growth biomarkers and yield components in winter wheat (Triticum aestivum L.) plants. Results confirmed that dissolved oxygen (DO) concentrations in H600, H800 and H1000 were significantly increased by 52.10%–126.59% and surface tension coefficients were significantly decreased by 17.72%–31.60% as compared to H0. DO concentrations in soil solutions irrigated with H600, H800 and H1000 were significantly elevated by 9.78%–15.68% as compared to H0, and the maximum value equal to 7.44 mg L−1 was found at a soil depth of 20 cm. Further, H600 and H800 reduced concentrations of malondialdehyde and H2O2 in leaves, increased accumulation of proline and activities of antioxidant enzymes, and resulted in an enhancement in grain yield of 15.28% and 25.23%, respectively. Application of H1000 resulted in lower growth biomarkers and yield components due to oxidative stress induced by decreased activities of antioxidant enzymes and accumulation of proline as well as elevated levels of lipid peroxidation as compared to H600 and H800. The optimal regulation was achieved with the H800 treatment. Overall, HPOW improved the plants growth by modulating soil aeration and antioxidant defenses which enhanced tolerance to hypoxic stress.

Indefinite Robust Linear Quadratic Optimal Regulator for Discrete-Time Uncertain Singular Markov Jump Systems.

The robust LQ optimal regulator problem for discrete-time uncertain singular Markov jump systems (SMJSs) is solved by introducing a new quadratic cost function established by the penalty function method, which combines the penalty function and the weighting matrices. First, the indefinite robust optimal regulator problem for uncertain SMJSs is transformed into the robust optimal regulator problem with positive definite weighting matrices for uncertain Markov jump systems (MJSs). The transformed robust LQ problem is settled by the robust least-squares method, and the condition of the existence and analytic form of the robust optimal regulator are proposed. On the infinite horizon, the optimal state feedback is obtained, which can guarantee the regularity, causality, and stochastic stability of the corresponding optimal closed-loop system and eliminate the uncertain parameters of the closed-loop system. A numerical example and a practical example of DC motor are used to verify the validity of the conclusions.

Ripple-Response Modeling and Design-Oriented Analysis of Output Common Power Flow Regulation in Bipolar SIDO DC–DC Converters

Compared with unipolar SIDO DC-DC converters, the output common power flow in the bipolar SIDO DC-DC converters affects lots of system characteristics in high-frequency (HF) region, which poses great challenges for the modeling and design of the bipolar SIDO converters. In this paper, a ripple-response model of the bipolar SIDO DC-DC converter is proposed to capture the HF characteristics by analyzing the response nature and the sampling process of the multi-piecewise current ripple. Based on the derived model, the design-oriented analysis with the change of some key parameters is presented to guide the system optimal design and power flow regulation. Then, the compensators are optimized to improve the dynamical performance and the HF stability simultaneously. Furthermore, the cross-regulation of the SIDO converter is explored via the common- and differential-mode transfer functions. Particularly, the effect of the output common power flow on the cross-regulation is revealed by identifying the frequency-domain coupling point. These analysis results help to suppress the cross-regulation significantly. Finally, the effectiveness of the model and analysis is verified by experimental results. These results show that the proposed method is more suitable for the bipolar SIDO converters to improve power flow regulation in comparison with the existing ones.

Improvement of Thermoelectric Performance of SnTe by Energy Band Optimization and Carrier Regulation

Improvement of Thermoelectric Performance of SnTe by Energy Band Optimization and Carrier Regulation

Research on energy storage capacity analysis and short-term load prediction method for new energy high penetration system

The uncertainty of high penetration new energy is strong. After the proportion of electric units is reduced, the flexible resources are suppressed, and the system-level energy storage can effectively relieve the frequency modulation pressure. In this paper, an energy storage capacity analysis method is proposed for new energy high permeability system. The short-term load is predicted by quantile regression analysis, so as to obtain the optimal peak regulation and frequency modulation frequency in operation, so as to further analyze the relationship between new energy permeability, capacity and energy storage power. The results show that the analytical method proposed in this paper can determine the demand capacity of the new energy high permeability system.