Cost Function Research Articles

Assessing CESM2 Clouds and Their Response to Climate Change Using Cloud Regimes

Abstract The Community Earth System Model, version 2 (CESM2), has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k-means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late twenty-first century in four shared socioeconomic pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

Optimal control in models of virus propagation

Based on the SEIRD model, we consider that when multiple viruses of different virulence coexist, the more virulent virus will reinfect nodes already infected by the less virulent virus, which we call here Superexposed. Based on the state transitions, the corresponding differential equations and cost functions are established, then building the corresponding optimal control problem, where the vaccine efficiency and drug efficiency are controlled variables. This nonlinear optimal control problem is solved by Pontryagin’s maximum principle to finding the structure of the optimal control strategies. Based on the definition of the basic regeneration number, yielding the R0 value for the model, then discussed the final epidemic size. In the numerical analysis section, we validate the accuracy of the structure, fitting the behavior of each state and the effect of different parameter values.

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm

Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334 m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.

Modelling a Response of Complex-Phase Steel at High Strain Rates

In this article, a response of the complex-phase high-strength steel SZBS800 was modelled by considering the strain-rate influence. The material’s response was first measured with a series of standard tensile tests at lower strain rates. Higher strain rates were achieved using the unconventional test of shooting the ball into flat specimens. A viscoplastic formulation of the Cowper–Symonds material model was applied to consider the strain-rate effects. The parameters SIGY, p, and C of the material model were estimated using a step-wise procedure. First, rough estimates of the three parameters were obtained from the tensile tests using the grid search method. Then, the parameters p and C were fine-tuned using the reverse engineering approach. With the help of explicit dynamic simulations and all the experimental data, a multi-criteria cost function was defined and applied to obtain a smooth response function for the parameters p and C. Its optimum was determined by a real-valued genetic algorithm. The optimal values of the estimated parameters model the material response well, although a domain of optimum candidates spans two orders of magnitude for the parameter p and a few orders of magnitude for the parameter C.

Securing Internet-of-Medical-Things networks using cancellable ECG recognition

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Data-driven economic MPC with asymptotic stability and strong duality verification using Hankel matrix

We consider the problem of dynamic regulation with an economic cost function to control unknown linear systems, in which improving the economic performance and guaranteeing the stability of economical optimal equilibrium point are control objectives. A data-driven economic MPC scheme is presented using measured input-output trajectories without a prior system identification step. Our method uses Hankel matrices which include one input-output data trajectory for prediction in economic MPC, while persistently exciting of the input generating the data is needed. One of the novelties of the presented framework is directly verifying the strong duality property from input-output trajectory with the general cost function, considered as the supply rate. This is used to find a Lyapunov function for data-driven economic MPC. Under the strong duality assumption, asymptotic stability of the economical optimal equilibrium point for the closed-loop system with terminal equality constraint is guaranteed. The proposed data-driven economic MPC approach needs only persistently exciting data trajectory along with an upper bound on the system order and need no model description and no online parameter estimation. The proposed scheme applicability compared to the existing model-based economic MPC and data-driven MPC is illustrated for continuous stirred tank reactor (CSTR) and a numerical example and the robustness of the proposed scheme is evaluated in the case of measurement noise, as well as nonlinear model for CSTR system.

A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space

This work presents a novel hybrid (physics- and data-driven) model for short-term (intra-day and day-ahead, 3h-24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points closest to the turbines. Here, with the aim to further capture the underlying atmospheric processes ruling the wind variability in the wind farm, the approach relies on drastically expanding the predictor space, composed of numerous meteorological variables throughout a large geographical domain, retrieved from a weather forecasting model (COSMO-1). An ad-hoc genetic algorithm that optimizes the selection of predictors is designed and combined with feed-forward artificial neural networks for its cost function evaluation. The introduced model is compared to multiple benchmark models in a 16-turbine wind farm in the Swiss Jura mountains. For +12h and +24h lead times, the new approach shows a root-mean squared error normalized to the installed wind farm capacity of 11% and 11.6%, respectively. These values entail ∼16% higher forecasting skill compared to state-of-the-art predictor frameworks. Results highlight the ability of the presented approach to systematically select as predictors different variables with a well-known impact on the wind farm performance, such as the turbulent kinetic energy or the vertical wind shear. Clustering the data according to the wind direction provides substantial benefit. In addition, it provides a better understanding of the attained improvement: largest performances occur in those wind directions affected by highly complex terrain. This indicates that the proposed model can be especially suitable for wind farms in complex terrain.

Strategic Behavior and Optimal Inventory Level in a Make-to-Stock Queueing System with Retrial Customers

In this article, we consider a make-to-stock queueing system with retrial customers. Upon their arrival, customers make a decision to either join the system or not based on a reward–cost function. If customers join the retrial queue, they become repeat customers. Each repeat customer repeats their demand after an exponential amount of time until they have been successfully served. We explore the equilibrium strategies of customers in both the almost observable and unobservable cases. Furthermore, we also analyze the expected costs of the entire system based on the customers’ behavior in these two cases. Additionally, we determine the optimal inventory levels in both cases through numerical experiments.

Data-driven optimization for microgrid control under distributed energy resource variability

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

Optimization of a wind farm layout to mitigate the wind power intermittency

A multi-objective optimization method utilizing genetic algorithms is developed to optimize wind farm layout design with the dual objectives of enhancing the production of wind power and reducing hour-level intermittency in the generated power. This method introduces a novel metric for annual wind power intermittency based on the transition probability of wind conditions (i.e., speed and direction). The present multi-objective optimization method ensures that Pareto optimal layouts are evenly distributed according to multiple cost function values. To mitigate wind power intermittency, the spatial distribution of wake fields is strategically manipulated to counteract hourly fluctuations in wind conditions. In wind conditions favoring high power generation, it is found that increasing the overlap between wakes and turbines helps to minimize disparities in generated power compared to other wind conditions.

Hot‐spot aware multicost‐based energy‐efficient routing protocol for WBANs

SummaryWireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs.

Distributed finite-time optimization for networked Euler–Lagrange systems under a directed graph

In this paper, we investigate the finite-time distributed optimization problem for multiple Euler–Lagrange systems. A new distributed optimization control scheme is presented to achieve the state agreement in finite time while minimizing the sum of each agent’s local cost function. The proposed algorithm has the advantage of being able to achieve distributed finite-time optimization consensus under general unbalanced connected directed communication graphs. By virtue of finite-time Lyapunov theory and convex optimization, the finite-time convergence for the algorithm is analyzed. A numerical example is also presented to illustrate the effectiveness of the obtained results.

Dynamics analysis and optimal control of delayed SEIR model in COVID-19 epidemic

The coronavirus disease 2019 (COVID-19) remains serious around the world and causes huge deaths and economic losses. Understanding the transmission dynamics of diseases and providing effective control strategies play important roles in the prevention of epidemic diseases. In this paper, to investigate the effect of delays on the transmission of COVID-19, we propose a delayed SEIR model to describe COVID-19 virus transmission, where two delays indicating the incubation and recovery periods are introduced. For this system, we prove its solutions are nonnegative and ultimately bounded with the nonnegative initial conditions. Furthermore, we calculate the disease-free and endemic equilibrium points and analyze the asymptotical stability and the existence of Hopf bifurcations at these equilibrium points. Then, by taking the weighted sum of the opposite number of recovered individuals at the terminal time, the number of exposed and infected individuals during the time horizon, and the system cost of control measures as the cost function, we present a delay optimal control problem, where two controls represent the social contact and the pharmaceutical intervention. Necessary optimality conditions of this optimal control problem are exploited to characterize the optimal control strategies. Finally, numerical simulations are performed to verify the theoretical analysis of the stability and Hopf bifurcations at the equilibrium points and to illustrate the effectiveness of the obtained optimal strategies in controlling the COVID-19 epidemic.

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

Accurate estimation of battery metrics, such as state of health (SOH), is crucial for effective battery management systems (BMS) due to capacity degradation over time. This paper proposes a methodology to enhance battery capacity estimation accuracy by addressing uncertainties related to state of charge (SOC) estimation and measurement. The methodology employs the Neural Network Algorithm (NNA), an optimization algorithm inspired by artificial neural networks (ANNs). The NNA generates an initial population of pattern solutions and iteratively updates them using a weight matrix, bias operator, and transfer function operator. By combining the advantages of ANNs and optimization techniques, the NNA aims to find an optimal solution considering interdependent variables and incorporating global and local feedbacks. Leveraging the capabilities of the NNA, our objective is to identify the candidate that minimizes a specified cost function, ensuring up-to-date cell capacity through a memory forgetting factor. The algorithm's precision was validated using NASA's Prognostic Data, demonstrating outstanding performance by surpassing two aggressive algorithms in terms of accuracy. In the most severe case scenario, the algorithm achieved a peak error of less than 0.4%. Furthermore, the algorithm consistently demonstrated predictive performance measures that were superior to those of the compared algorithms.

Suppression of Initial Charging Torque for Electric Drive-Reconfigured On-Board Charger

This paper presents a new electric drive-reconfigured on-board charger and initial electromagnetic torque suppression method. This proposed reconfigured on-board charger does not need many components added to the original electric drive system: only a connector is needed, which is easy to add. Specifically, the inverter for propulsion is reconfigured as a buck chopper and a conduction path to match the reconfigured windings. Two of the machine phase windings serve as inductors, while the third phase winding is reutilized as a common-mode inductor. In addition, the initial charging torque is generated at the outset of the charging process, which may cause an instant shock or even rotational movement. In order to prevent vehicle movement, the reason for the charging torque and suppression method were analyzed. Further, predictive control of the model based on mutual inductance analysis was adopted, where the charging torque was directly used as a control object in the cost function. Finally, experimental performances were applied to verify the proposed reconfigured on-board charger under constant current and constant voltage charging.

Supporting load frequency control process by using adaptive model reference virtual inertia controller when connecting renewable energy plants

Virtual inertia control can be used in combination with conventional load frequency controllers to support the system's inertia against the attenuation caused by renewables. As fractional-order controllers add more degrees of freedom, leading to a better response and improved controller robustness, this work proposes the implementation of a developed fractional-order model reference adaptive virtual inertia controller when integrating renewables in combination with fractional-order ID-P with filter controller as secondary controllers. To validate the introduced controllers, their results have been compared to those of using integer and fractional-order PID controllers as secondary controllers, in combination with adaptive and fixed parameters virtual inertia controllers. The Artificial Rabbits heuristic algorithm is used for the purpose of tuning secondary controllers and fixed parameters virtual inertia controllers. The proposed combination of adaptive model reference virtual inertia control with fractional-order ID-P with a filter achieves the best response among all the studied combinations. Using the proposed combination shows a relative percentage improvement in the cost function value of 26% to 160% compared to all the studied combinations. Additionally, the proposed combination shows a maximum frequency overshoot value of 0.04 Hz and a minimum undershoot value of -0.06 Hz.

Real-time and hardware in the loop validation of electric vehicle performance: Robust nonlinear predictive speed and currents control based on space vector modulation for PMSM

The transport industry's environmental impact has prompted various stakeholders to focus more on electric vehicles as a potential solution. Extensive research on electric vehicle motor control reveals their complexity, highlighting the crucial role of the traction motor control mechanism being one of their essential subsystems. This study presents a novel approach to control the drive system of an electric vehicle's permanent magnet synchronous motor, using a robust non-linear model predictive control in a cascaded structure combined with a space vector modulation technique. The formulation of the robust non-linear model predictive control law involves optimizing a new cost function with the inclusion of an integral action in the controller to ensure zero steady-state error. An important aspect of this control approach is its ability to enhance robustness without needing information about external perturbations and parameter uncertainties. Moreover, the space vector modulation technique is designed to address the issue of current harmonic distortion by carefully selecting appropriate switching vectors. The appropriate selection for space voltage vectors for every sampling period maintains a consistent switching frequency. As a result, the SVM can enhance torque-rippled regulation and accomplish effective flux and torque control. The performance of the proposed control system is evaluated through a series of numerical simulations using MATLAB/Simulink. The simulation results are analyzed and compared to assess the effectiveness of the proposed control system in terms of tracking accuracy, disturbance rejection, robustness against parameter uncertainties, reduction of torque, and current ripples. To further validate the numerical simulation results, a hardware-in-the-loop (HIL) setup is established using the OPAL-RT platform. Furthermore, experimental validation on a PMSM utilizing the dSPACE DS1202 board is carried out, demonstrating the efficacy of the proposed control system. These findings validate the suggested control technique's robustness and efficiency.

Graph-based minimum error entropy Kalman filtering

When the Gaussian kernel function is chosen with a small kernel bandwidths, the minimum error entropy Kalman filter (MEEKF) exhibits excellent performance. However, further narrowing the kernel bandwidths leads to a degradation in performance. To address this issue, this paper proposed a Graph-based MEEKF (G-MEEKF) algorithm based on Graph Signal Processing (GSP) theory. The G-MEEKF algorithm addresses the problem by smoothing the errors using graph filtering and incorporating graph topology into the cost function. The convergence of the algorithm is theoretically analyzed and simulations demonstrate that G-MEEKF improves the performance of MEEKF under small kernel bandwidths. Furthermore, it is emphasized that the topology of the graph abstracted from the minimum error entropy (MEE) definition also influences the performance of the algorithm.

Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains.

Weighted information entropy‐based Kalman filter for outliers and structural noise

AbstractStructural noise and outliers are widely present in real‐world state estimate scenarios, and they significantly degrade the performance of most filtering algorithms based on minimum mean square error (MMSE) criterion. To address this problem, this paper first models structural noise and outliers as independent and piecewise identical distribution (IPID). Then, a minimum error weighted entropy‐based Kalman filter (MEWE‐KF) is proposed, where a new cost function is constructed by introducing a weight function related to error location distances in an original information space into the minimum error entropy (MEE) criterion. Further, the iterative formulations of the proposed filter are derived, and the computational complexity and the convergence are also analyzed. Simulation results show that the proposed filter with adaptive weights has the superior performance for suppressing structural noise and outliers.