Slave Model Research Articles

Stochastic Differential Games of Carbon Emission Reduction in the Four-Tier Supply Chain System Based on Reference Low-Carbon Level

This paper takes corporate social responsibility goodwill and consumers’ reference low-carbon level as endogenous variables of joint carbon emission reduction in the “supplier–manufacturer–retailer–consumer” supply chain system. The joint carbon emission reduction strategies of this four-tier system are analyzed from a dynamic perspective by considering random factors that affect the endogenous variables. Three stochastic differential games are proposed to examine the mechanism between each player, namely the cooperative model, Nash non-cooperative model, and Stackelberg master–slave model. Compared to the Nash non-cooperative game, the manufacturer/supplier-led Stackelberg master–slave game leads to Pareto improvement in the profits of the entire supply chain system and each player. The cooperative game demonstrates the highest expected emission reduction and corporate social responsibility goodwill, but also the highest variance. More importantly, the reference low-carbon level embraces consumers’ subjective initiative in the dynamic of carbon emission reduction. This level is an internal benchmark used to compare against the observed low-carbon level. This paper provides a theoretical foundation for strategic decision-making in emission reduction, contributing to sustainable development. By addressing environmental, economic, and social sustainability, it promotes climate action through carbon reduction strategies and offers policy recommendations aligned with the Sustainable Development Goals.

Adaptive Impedance Control for Teleoperation with Event-Triggered Controller

Uncertainty in the master–slave model is one of the primary factors affecting the transparency of teleoperation systems, and congestion in the master–slave communication network also greatly influences the performance of the teleoperation system. This paper proposes a combined framework of adaptive and impedance control to address the uncertainty in the master–slave model and achieve smooth operation at the slave end. Building upon this linear model, an event-triggered mechanism is designed using Lyapunov functions, with dynamic online adjustment of the triggering threshold parameters. Following the completion of the aforementioned research, control objectives are established to validate the performance of the teleoperation control system proposed in this paper. Finally, simulation verification is conducted in the Matlab/Simulink environment.

Estimation of Temperature Distribution in the Brain Using the Temperatures of Blood Inflow and Outflow for Brain Temperature Control

Brain cells can be damaged by higher temperature in the brain, and brain temperature distribution monitoring system is required from the perspective of brain preservation. Brain tissue can be further damaged by direct measurement of temperature using some sensors. Therefore, indirect methods of measuring temperature are required. In this study, we propose a method for estimating the average metabolic rate of the entire brain based on the difference between the observed and estimated blood flow temperatures that can be measured during selective brain hypothermia. Our mathematical simulator can calculate the brain temperature distribution based on the estimated average metabolic rate, taking into account other parameters like heat transfer, metabolic heat of individual tissues, heat‐washout by blood flow, and environmental temperature. The algorithm for estimating the metabolic rate of mathematical master model substituted as a human head is confirmed using the mathematical slave model on the simulator. It is possible to present the temperature distribution by estimating metabolic heat production in the brain, considering the equilibrium between heat wash‐out by the blood flow and heat transfer. In addition, an appropriate initial value of brain temperature enables the accurate temperature management enough for clinical use even in the process of temperature estimation. The development of our proposed method will make it possible to construct a non‐invasive monitoring system that presents the estimated temperature distribution in the brain instead of the conventional invasive method. The new system is expected to be utilized in a various treatments related to some brain disorders. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Dynamics of chaotic and hyperchaotic modified nonlinear Schrödinger equations and their compound synchronization

We present in this paper four versions of chaotic and hyperchaotic modified nonlinear Schrödinger equations (MNSEs). These versions are hyperchaotic integer order, hyperchaotic commensurate fractional order, chaotic non-commensurate fractional order, and chaotic distributed order MNSEs. These models are regarded as extensions of previous models found in literature. We also studied their dynamics which include symmetry, stability, chaotic and hyperchaotic solutions. The sufficient condition is stated as a theorem to study the existence and uniqueness of the solutions of hyperchaotic integer order MNSE. We state and prove another theorem to test the dependence of the solution of hyperchaotic integer order MNSE on initial conditions. By similar way, we can introduce the previous two theorems for the other versions of MNSEs. The Runge-Kutta of the order 4, the Predictor-Corrector and the modified spectral numerical methods are used to evaluate the numerical solutions for integer, fractional and distributed orders MNSEs, respectively. We calculate numerically using the Lyapunov exponents the intervals of parameters of the purposed models at which hyperchaotic, chaotic and stable solutions are exist. The MNSEs have an important role in many fields of science and technology, such as nonlinear optics, electromagnetic theory, superconductivity, chemical and biological dynamics, lasers and plasmas. The compound synchronization for these chaotic and hyperchaotic models is investigated. We state its scheme using the tracking control technique among three integer commensurate and non-commensurate orders as the derive models and one distributed order as a slave model. We presented and proved a theorem that provides us with the analytical formula for the control functions which are required to achieve compound synchronization. The analytical results are supported by numerical calculations and agreement is found.

A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response

In the context of national efforts to promote country-wide distributed photovoltaics (DPVs), the installation of distributed energy storage systems (DESSs) can solve the current problems of DPV consumption, peak shaving, and valley filling, as well as operation optimization faced by medium-voltage distribution networks (DN). In this paper, firstly, a price elasticity matrix based on the peak and valley tariff mechanism is introduced to establish a master–slave game framework for DN-DESSs under the DPV multi-point access environment. Secondly, the main model optimizes the pricing strategy of peak and valley tariffs with the objective of the lowest annual operating cost of the DN, and the slave model establishes a two-layer optimization model of DESSs with the objective of the maximum investment return of the DESSs and the lowest daily operating costs and call the CPLEX solver and particle swarm optimization algorithm for solving. Finally, the IEEE33 node system is used as a prototype for simulation verification. The results show that the proposed model can not only effectively reduce the operating cost of the distribution network but also play a role in improving the energy storage revenue and DPV consumption capacity, which has a certain degree of rationality and practicality.

Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces

One of the major challenges of coupled problems is to manage nonconforming meshes at the interface between two models and/or domains, due to different numerical schemes or domain discretizations employed. Moreover, very often complex submodels depend on (e.g., physical or geometrical) parameters, thus making the repeated solutions of the coupled problem through high-fidelity, full-order models extremely expensive, if not unaffordable. In this paper, we propose a reduced order modeling (ROM) strategy to tackle parametrized one-way coupled problems made by a first, master model and a second, slave model; this latter depends on the former through Dirichlet interface conditions. We combine a reduced basis method, applied to each subproblem, with the discrete empirical interpolation method to efficiently interpolate or project Dirichlet data across either conforming or non-conforming meshes at the domains interface, building a low-dimensional representation of the overall coupled problem. The proposed technique is numerically verified by considering a series of test cases involving both steady and unsteady problems, after deriving a posteriori error estimates on the solution of the coupled problem in both cases. This work arises from the need to solve staggered cardiac electrophysiological models and represents the first step towards the setting of ROM techniques for the more general two-way Dirichlet-Neumann coupled problems solved with domain decomposition sub-structuring methods, when interface non-conformity is involved.

A PID controller for synchronization between master-slave neurons in fractional-order of neocortical network model

Modeling of the biological neurons is a way to understand the architecture of neural networks of the brain. A complex brain network includes the synchronization between some groups of neurons. The dynamic behavior of interactions between groups of slave-master neurons in the neocortical network is unpredictable and challenging. The purpose of synchronizing a neural interaction is to reduce the synchronization error between the chaotic slave-master neurons. This paper uses a proportional–integral–derivative (PID) controller to synchronize master–slave neurons in the fractional-order of the neocortical network model based on dendritic spike frequency adaptation (DSFA) uncertainties and unknown disturbance effects. The purpose of this article is in two parts: First, we implemented the effect of previous states of the neuron conditions by fractional-order of the differential equations in the neocortical network model. Second, by synchronizing the FO neocortical master–slave model by PID controller, we investigated the connection strength of the complex network in chaotic point of view. The optimized PID coefficients and fractional-order were calculated using root mean square error (RMSE) criteria to control the membrane voltage synchronization. The chaotic behavior of the system was evaluated by numerical techniques such as attractor analysis and time series diagrams. The optimal RMSE value for master–slave neurons occurred at fractional-orders 0.89. It is shown that the synchronization of master–slave neurons improves over time, and eventually they are fully synchronized while the controller error is reduced.

Robust AC Transmission Expansion Planning Using a Novel Dual-Based Bi-Level Approach

The rapid integration of Renewable Energy Sources (RESs) strengthens the need for a power network that can robustly handle the system's uncertain scenarios. Thus, this paper proposes the first nonlinear novel dual based bi-level approach for robust AC Transmission Expansion Planning (TEP) plans with uncertainties in RES generations and loads. It utilizes a convex relaxation and is solved by Benders Decomposition (BD), where the master determines the robust AC TEP plan. The novel dual slave model for the second level of BD circumvents the issues in using the conventional conic dual theory and aids in the worst-case realization of uncertainties using additional novel constraints. The novel dual slave is solved using Interior Point Method (IPM), as it is not a mixed-integer problem. The proposed work also includes additional linear constraints to reduce the BD's slow convergence and direct the master towards optimality. The robustness of the AC TEP plans is verified by Monte-Carlo Simulation (MCS) of the actual nonlinear and non-convex AC Optimal Power Flow (OPF). The effect of the budget of uncertainty on the AC TEP plans is also investigated. A comparison with the results of a previous work reveals the superiority of the proposed work.

Wireless Sensor Networks Master Slave based Farm Monitoring

Abstract: There are many tough situations where in it becomes harder to monitor our farm area manually. To check the water supply and wetness of soil, to detect occurrence of a fire accident, internist of light etc. Sometimes manual inspection may not be very precise, humans generally not intentionally do overlook. So instead of checking out manually we use Wireless Sensor Networks (WSN), that monitor our farm and update data on to the server, which can be monitored by sitting at home. This also helps us in continuous monitoring of our farm without any inconvenience. To achieve this, we are using wireless sensor networks Master Slave model. Where in, both master and slave are equipped with the sensors to measure the environment. Each master and slave are equipped with the smoke sensor, soil moisture sensor, LDR, DTH11 to monitor the surrounding environment. Mater node acts as a gate way i.e, (all) the slave nodes communicate to or transfer information to the master node. Mater node transmits the information on to the server. This communication between slave to master is done using ZigBee HC12 modules and Bluetooth module is used for the communication between master and the server. Here we are using a mobile application, Arduino Bluetooth controller to show the received data.

Chaotic Communication System with Symmetry-Based Modulation

Communication systems based on chaotic synchronization are gaining interest in the area of secure and covert data transmission. In this paper, a novel digital communication technique based on a coherent chaotic data transmission approach is proposed. In general, this technique resembles the well-known approach based on the modulation of nonlinearity parameters. The key idea of this study is to modulate a signal by varying not the system parameter but the symmetry coefficient in discrete chaotic models obtained by the special numerical integration method. For this purpose, the self-adjoint semi-implicit integration method of order 2 is used to obtain discrete master and slave models of the considered chaotic oscillator. The experimental results explicitly show that, like during parameter modulation, transmitting and receiving oscillators may completely synchronize only if the symmetry coefficients are equal in both systems. The architecture of the communication system based on the proposed modulation is presented. The practical applicability of the approach is confirmed by transmitting a test binary sequence between the transmitter and receiver models and preliminary benchmarking of the obtained communication system. Since the symmetry coefficient modulation does not significantly impact the chaotic behavior of the transmitting digital system, its better suitability for covert messaging was experimentally confirmed by comparing it with the parameter modulation technique.

A parameter-free framework for calibration enhancement of near-infrared spectroscopy based on correlation constraint

A new parameter-free framework for calibration enhancement (PFCE) was proposed for dealing with the near-infrared (NIR) spectral inconsistency and maintaining the prediction ability of the calibration model under different conditions. The calibration issues encountered in the maintenance with or without using standards, and even the enhancement between instruments have been thoroughly addressed. The general calibration maintenance/enhancement cases were formulated into non-supervised PFCE (NS-PFCE), semi-supervised PFCE (SS-PFCE), and full-supervised PFCE (FS-PFCE). The NS-PFCE made use of both the provided master and slave spectra of standard samples to construct a maintained calibration slave model by implementing a correlation constraint on the regression coefficients. The SS-PFCE and FS-PFCE methods integrated the slave spectra and reference information of standard samples at the same time into the slave spectral calibration, and thus a maintenance or enhancement model could be achieved for the slave spectra, in particular measured on different instruments. The use of dataset1 comprised of 655 pharmaceutical tablets measured on two NIR spectrometers and datset2 containing 117 plant leaf samples in two mesh sizes has demonstrated that the PFCE framework had a significant effect on enhancing the predictions of the slave spectra in the models. The root mean square errors of prediction (RMSEPs) of either active pharmaceutical ingredient (API) amount in tablets or reducing sugar content in plant leaf samples from the slave spectra approached to or were lower than those values predicted from the master spectra in the master models established with the partial least-squares (PLS) regression method. The advantage of PFCE was parameter-free and efficient. First, the method could be flexibly employed in scientific or applicative environment with no regard to the parameter specification. Second, the performance of NS-PFCE was comparable to the classical calibration maintenance methods, yet the SS-PFCE and FS-PFCE could enhance the prediction ability to a level widely considered as the upper boundary of classical calibration maintenance methods reached.The source code of the method is available at https://github.com/JinZhangLab/PFCE.

Probability density forecasting of wind power based on multi-core parallel quantile regression neural network

The large-scale utilization of wind energy brings severe challenges to the dispatching operation of power systems. Currently, the probability density prediction method combining quantile regression neural network (QRNN) with Epanechnikov kernel function is an excellent algorithm for wind power prediction, which can give the comprehensive probability distribution of future wind power and effectively quantify the uncertainty of wind power generation. However, existing probability density prediction methods process data sequentially in different quantiles, and computational time costs multiply with the increase of training data. It affects the practicality of the probability density prediction method. To overcome this issue, this paper proposes a multi-core parallel quantile regression neural network (MPQRNN) based on parallel master–slave (MS) model. The algorithm divides the complex prediction tasks at all quantiles into multiple parallel sub-tasks, which are independently run on different cores, so that performance advantages of the multi-core CPU can be fully utilized for improving the computational efficiency of the joint operation model. We compare four different scale sample sets under different process numbers. The influence of different CPU core numbers on the parallel performance of MPQRNN are analyzed by the algorithmic nature of speedup and parallel efficiency. To demonstrate the effectiveness of the proposed model, comparative experiments of other four traditional models are carried out on data sets. The simulation results demonstrate that the MPQRNN can not only improve the training efficiency of QRNN, but also obtain precise results of wind power forecasting, showing potential value and utility for complex power system.

Multi‐frequency averaging (MFA) model of a generic electric vehicle powertrain suitable under variable frequency of averaging developed for remote operability

Geographically distributed hardware-in-the-loop (HIL) testing has the potential to allow hybrid vehicle powertrain components (battery, motor drive, and engine) to be developed at geographically remote locations but tested concurrently and coupled. Inter-location internet communication links can allow non-ideal behaviour observed in a physical component in one location (e.g. an electrical drive) to be imposed on another physical component elsewhere (e.g. an ICE), and vice-versa. A key challenge is how to represent the behaviour of a remote, physical component under testing in a local HIL environment. Internet communications are too slow and unreliable to transmit waveforms in real-time and so one solution is to use a local ‘slave’ model whose behaviour and parameters are tuned based on observations at the remote location. This study proposes a multi-frequency averaging (MFA) slave model of an electric motor drive system for use in this application; it addresses a weakness in previously published work by extending the MFA model to variable frequency operation. The model was benchmarked against experimental operation (and its equivalent simulation model) in open-loop and closed-loop space vector pulse-width modulation control strategy, fixed and variable frequency operation. Results show significant reconciliation of model and experiment.

Analysis of asynchronous distributed multi-master parallel genetic algorithm optimization on CAN bus

Industrial optimization problems are usually difficult to solve due to complexity and high number of constraints. Evolutionary algorithms are a conventional method to solve these problems. However, many industrial applications are real-time or we need to find a feasible optima solution in a limited time. Parallel genetic algorithm is a method to utilize properties of the genetic algorithm and parallel processing and implementation of a fast evolutionary algorithm. Controller Area Network (CAN) protocol is widely used in various industries such as automotive, medical, aerospace. In this paper, we implement a multiple-population coarse-grained parallel genetic algorithm on CAN bus to improve speed and performance of the conventional genetic algorithm which is asynchronous distributed multi-master. Evaluation criteria such as speed up, efficiency, serial fraction and reliability are calculated for the proposed parallel processing which is used for optimization problem of five benchmark functions. And finally, this structure is compared with the master–slave model. The proposed structure is created conditions for improving network reliability with very low cost of communication.

Parallel multi-objective artificial bee colony algorithm for software requirement optimization

In incremental software development approaches, the product is developed in various releases. In each release, a set of requirements is proposed for the development. Usually, due to lack of funds, lack of time and dependency between requirements, there is no possibility to develop all the required requirements. There are two conflicting objectives for choosing an optimal subset of the requirements: increasing customer satisfaction and reducing development costs. This problem is known as the next release problem (NRP) and is categorized as an NP-hard problem. Unlike the standard version of the NRP, we formulate this problem as a restricted multi-objective optimization problem. There exist metaheuristic algorithms for solving this problem performed as serials. In this paper, we introduce a parallel algorithm based on the master–slave model in order to improve the quality of the solutions. Based on the criteria of multi-objective problems, the quality of the obtained solution is compared with several metaheuristic algorithms. Two scenarios and two different datasets are used for experiments. Results indicate that the proposed method in the first scenario would highly improve the quality of solutions. Moreover, the method reduces execution time significantly through improvement in the quality of the solution in the second scenario.

Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master–Slave Model

The aim of this paper was to develop a master–slave model with fluid-thermo-structure (FTS) interaction for the thermal fatigue life prediction of a thermal barrier coat (TBC) in a nozzle guide vane (NGV). The master–slave model integrates the phenomenological life model, multilinear kinematic hardening model, fully coupling thermal-elastic element model, and volume element intersection mapping algorithm to improve the prediction precision and efficiency of thermal fatigue life. The simulation results based on the developed model were validated by temperature-sensitive paint (TSP) technology. It was demonstrated that the predicted temperature well catered for the TSP tests with a maximum error of less than 6%, and the maximum thermal life of TBC was 1558 cycles around the trailing edge, which is consistent with the spallation life cycle of the ceramic top coat at 1323 K. With the increase of pre-oxidation time, the life of TBC declined from 1892 cycles to 895 cycles for the leading edge, and 1558 cycles to 536 cycles for the trailing edge. The predicted life of the key points at the leading edge was longer by 17.7–40.1% than the trailing edge. The developed master–slave model was validated to be feasible and accurate in the thermal fatigue life prediction of TBC on NGV. The efforts of this study provide a framework for the thermal fatigue life prediction of NGV with TBC.

Рабство как форма человеческого бытия (видение античных авторов в свете идей русских философов)

In the article views of antique authors of human aspect of a phenomenon of slavery are considered. The author recognizes that the importance of a similar research is connected with existence of archetypic layers in the most various cultures causing reproduction of a phenomenon of slave model of the personality and corresponding to it behavior forms. The attention to three aspects in understanding of the bases of existence of the slave as special human type, recorded by antique authors is paid. It, first, slavery “under the law” according to which the captured captives are on sale in slavery. Secondly, this slavery caused by the human nature connected with ethnic origin on a formula “the barbarian and the slave by the concept nature identical”. Thirdly, this slavery in spiritual sense generated by the nature of activity in which the person is engaged. The types of activity forming the free person is for them philosophical reflection as disinterested search of the truth and also, according to Aristotle, political activity. Views of antique authors correspond in the article to views of the Russian religious philosophers proceeding from other paradigm of the activity basis of free existence implanted in Christian outlook. Its essence is that the aspiration to the truth which is implemented in occupations by philosophy has to be directed deep valuable installation, which sense in aspiration to connection of the person good luck.

Balancing the Degree of Exploration and Exploitation of Swarm Intelligence Using Parallel Computing

The two basic search behaviors used in metaheuristic algorithms in general and swarm intelligence in particular are exploration and exploitation. Exploration refers to searching the unexplored area of the feasible region while exploitation refers to the search of the neighborhood of a promising region. The success of these algorithms highly depends on how these two search behaviors are balanced. Increasing the number of initial solutions indeed increase the performance of the algorithm over the expense of the runtime. Different exploration and exploitation adjustments are used in different metaheuristic algorithms. To well balance the degree of exploration and exploitation, which ultimately leads to finding good solutions, needs a careful implementation and sufficient runtime. However, the runtime is another issue which has been a bottleneck for different application, especially for high dimensional, highly constrained and expensive objective function cases. Using parallel computing to overcome this limitation has been a central research issue in the study of metaheuristic algorithms. The three basic parallelization approaches used are the parallel move model which is a master slave model where solutions will be distributed to different processors for updating, parallel multi start model which uses several updating methods for the solutions in parallel and move acceleration method, which refers to a parallel implementation with parallelizing the objective function. There is no specific parallelization used towards balancing of exploration and exploitation is mentioned. Hence, in this paper a parallel implementation of swarm intelligence in general and two algorithms, namely particle swarm optimization and prey predator algorithm, in particular will be discussed. The parallelization proposed is aimed to merge the parallel move model and the parallel multi start model with the aim of adjusting the degree of exploration and exploitation. Experimental study based on ten benchmark problems from constrained as well as unconstrained problems will be used for simulation. It will be shown that the parallelization with specific distribution of exploration and exploitation runs better and efficiently compared to the serial and also the parallel version without exporation/exploitation adjustment.

Lag synchronisation of master–slave dynamical systems via intermittent control

ABSTRACTIn this paper, the problem on lag synchronisation is investigated for a master–slave model. The intermittent control method is used to design controllers, lag synchronisation criteria are proposed. Together with some Lyapunov–Krasovskii functionals and effective mathematical techniques, several conditions are derived. An example is given to illustrate the effectiveness of the proposed methods.

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second, the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods.