Search

• The model of a cable driven parallel robot with multi-type actuators is derived and then verified by ADAMS software. • New trajectory planning algorithms for cable driven robot are developed by direct method schemes. • Direct shooting and collocation are adapted to the cable parallel robot dynamic in path planning. • The focus in trajectory planning is on cable constraints to satisfy control and smoothness. • An experimental platform is constructed to support direct shooting results. Cable tensions in cable robots make trajectory planning more complicated than in rigid-link robots. Since cables can only pull but not push, the cable tensions must be kept positive for a cable-driven system to maintain control. In this paper direct methods of trajectory planning including direct collocation and direct shooting are proposed to solve the two-point boundary value problem trajectory planning. Minimizing a robot's actuator force under cable constraints including positive forces and preventing severe changes in tensions are considered in this problem. The cable-driven robot consists of a 3-cable and a pneumatic cylinder, which betters robot tensionability. The direct methods are solved by the sequential quadratic programming algorithm and then compared with the GPOPS-II software package. The results designate that the direct methods of trajectory planning propose substantial benefits in satisfying essential continuity and smoothness of resulted profiles for a cable-driven robot with a multi-type actuation system. Experimental results confirm the numerical data, additional supporting the findings. [ABSTRACT FROM AUTHOR]

This paper proposes a method for dynamically controlling the working slope configuration in seasonal stripping open-pit coal mines. Constructing a bench group working slope model calculates the cyclic advancement time of the stripping working slope. A mathematical optimization model is then established to minimize the transport work of the overburden, and a Sequential Quadratic Programming (SQP) algorithm is employed to solve it. The method is applied to the Shengli West No. 2 open-pit coal mine, and the results show that the number of benches in the bench group is 5. The working bench width is 89 m, and the working slope advancement meets the stripping and mining engineering continuity requirements while minimizing the transport work of the overburden. The maximum advancement speed of the working slope under this configuration is 320 m/year. The paper also analyzes the working slope's maximum advancement speed variation with changes in the bench combination form and cyclic advancement distance. [ABSTRACT FROM AUTHOR]

The present work is to exploit the Gudermannian neural network (GNN) using the global competency of genetic algorithm (GA) and quick local refinements of sequential quadratic programming approach (SQPA), i.e., GNN-GA-SQPA for the nonlinear economic and environmental system. The differential form of the nonlinear system depends upon three classes, system capability of industrial elements, implementation cost of control values and a new diagnostics technical elimination cost. An error-based fitness function is constructed using the differential system and then optimized by using the hybrid competency of the GA-SQPA. Ten numbers of neurons, a merit Gudermannian function, and the suitable weight vectors are presented in the neural network construction. The accuracy of the GNN-GA-SQPA is assessed through the comparisons and the negligible performances of absolute error. The statistical observations using single and multiple trials validate the stability of the scheme. [ABSTRACT FROM AUTHOR]

Thermal unit commitment (UC) is a nonlinear combinatorial optimization problem that minimizes total operating costs while considering system load balance, on/off restrictions and other constraints. Successfully solving the thermal UC problem contributes to a more reliable power system and reduces thermal costs. This paper presents an exact mixed-integer quadratic programming (EMIQP) method for large-scale thermal UC problems. EMIQP revolutionizes the landscape by seamlessly translating the intricate nonlinear combinatorial optimization problem of UC into an exact mixed-integer quadratic formulation. This approach also elegantly reimagines on/off constraints as mixed-integer linear equations, employing both the sum and respective approaches. Our case studies unequivocally demonstrate the exceptional prowess of the EMIQP method, consistently securing the global optimum. Moreover, the mathematical-based EMIQP method produces identical results at each run, which is extremely important for UC in the real world. [ABSTRACT FROM AUTHOR]

We prove local quadratic convergence of the sequential quadratic programming (SQP) method for an optimal control problem of tracking type governed by one time step of the Euler-Lagrange equation of a time discrete regularized fracture or damage energy minimization problem. This lower-level energy minimization problem contains a penalization term for violation of the irreversibility condition in the fracture growth process and a viscous regularization term. Conditions on the latter, corresponding to a time step restriction, guarantee strict convexity and thus unique solvability of the Euler Lagrange equations. Nonetheless, these are quasilinear and the control problem is nonconvex. For the convergence proof with L∞ localization of the SQP-method, we follow the approach from Tröltzsch [SIAM J. Control Optim.38 (1999) 294–312], utilizing strong regularity of generalized equations and arguments from Hoppe and Neitzel [Optim. Eng.22 (2021)] for L2-localization. [ABSTRACT FROM AUTHOR]

Although some ensemble clustering approaches have been developed in recent years to improve the quality of the clustering, but lack of a median fuzzy partition-based consensus function that considers more participate reliable fuzzy clustering, remains unsolved problem. In this paper, we convert the median fuzzy partition problem into an optimization problem based on the reliability-based co-association matrix that minimizes distances between coassociation matrix of final clustering and co-association matrix of base-clustering in the ensemble. The optimization problem is a constrained nonlinear objective function and we solve it by sparse sequential quadratic programming (SSQP). Experimental results reveal the effectiveness of the proposed approach rather than the state-of-the-art methods in the qualityterms on various standard datasets. [ABSTRACT FROM AUTHOR]

We propose a parameter optimization method of multi-layer nested slit resonator (MNSR) for the converter transformer noise in a high voltage direct current (HVDC) converter station. With the optimization objective of achieving quasi-perfect sound absorption at the target frequencies and the structural parameters of MNSR as the optimization variables, the optimization model of the structural parameters of MNSR is established and then solved by the sequential quadratic programming algorithm (SQP). The theoretical and simulation results of the four validation cases show that the optimized MNSR can achieve quasi-perfect sound absorption at all target frequencies, which is consistent with the optimization objective. The parameter optimization design method provides an efficient method to design a dual-frequency quasi-perfect sound absorber for the converter transformer noise. [ABSTRACT FROM AUTHOR]

Online portfolio selection is attracting increasing attention in both artificial intelligence and finance communities due to its efficiency and practicability in deriving optimal investment strategies in real investment activities where the market information is constantly renewed every second. The key issues in online portfolio selection include predicting the future returns of risky assets accurately given historical data and providing optimal investment strategies for investors in a short time. In the existing online portfolio selection studies, the historical return data of one risky asset is used to estimate its future return. In this paper, we incorporate the peer impact into the return prediction where the predicted return of one risky asset not only depends on its past return data but also the other risky assets in the financial market, which gives a more accurate prediction. An adaptive moving average method with peer impact (AOLPI) is proposed, in which the decaying factors can be adjusted automatically in the investment process. In addition, the adaptive mean-variance (AMV) model is firstly applied in online portfolio selection where the variance is employed to measure the investment risk and the covariance matrix can be linearly updated in the investment process. The adaptive online moving average mean-variance (AOLPIMV) algorithm is designed to provide flexible investment strategies for investors with different risk preferences. Finally, numerical experiments are presented to validate the effectiveness and advantages of AOLPIMV. [ABSTRACT FROM AUTHOR]

We study properties of the convex hull of a set S described by quadratic inequalities. A simple way of generating inequalities valid on S is to take a nonnegative linear combinations of the defining inequalities of S. We call such inequalities aggregations. Special aggregations naturally contain the convex hull of S, and we give sufficient conditions for such aggregations to define the convex hull. We introduce the notion of hidden hyperplane convexity (HHC), which is related to the classical notion of hidden convexity of quadratic maps. We show that if the quadratic map associated with S satisfies HHC, then the convex hull of S is defined by special aggregations. To the best of our knowledge, this result generalizes all known results regarding aggregations defining convex hulls. Using this sufficient condition, we are able to recognize previously unknown classes of sets where aggregations lead to convex hull. We show that the condition known as positive definite linear combination together with hidden hyerplane convexity is a sufficient condition for finitely many aggregations to define the convex hull. All the above results are for sets defined using open quadratic inequalities. For closed quadratic inequalities, we prove a new result regarding aggregations giving the convex hull, without topological assumptions on S. [ABSTRACT FROM AUTHOR]

We study the problem of detecting infeasibility of large-scale linear programming problems using the primal-dual hybrid gradient method (PDHG) of Chambolle and Pock (2011). The literature on PDHG has mostly focused on settings where the problem at hand is assumed to be feasible. When the problem is not feasible, the iterates of the algorithm do not converge. In this scenario, we show that the iterates diverge at a controlled rate towards a well-defined ray. The direction of this ray is known as the infimal displacement vector v. The first contribution of our work is to prove that this vector recovers certificates of primal and dual infeasibility whenever they exist. Based on this fact, we propose a simple way to extract approximate infeasibility certificates from the iterates of PDHG. We study three different sequences that converge to the infimal displacement vector: the difference of iterates, the normalized iterates, and the normalized average. All of them are easy to compute, and thus the approach is suitable for large-scale problems. Our second contribution is to establish tight convergence rates for these sequences. We demonstrate that the normalized iterates and the normalized average achieve a convergence rate of O(1/k), improving over the known rate of O(1√k). This rate is general and applies to any fixed-point iteration of a nonexpansive operator. Thus, it is a result of independent interest since it covers a broad family of algorithms, including, for example, ADMM, and can be applied settings beyond linear programming, such as quadratic and semidefinite programming. Further, in the case of linear programming we show that, under nondegeneracy assumptions, the iterates of PDHG identify the active set of an auxiliary feasible problem in finite time, which ensures that the difference of iterates exhibits eventual linear convergence to the infimal displacement vector. [ABSTRACT FROM AUTHOR]

In this paper, an effective control and parameters identification scheme was proposed for high-precision trajectory tracking of space robotic manipulators. Our proposed control method employed a linear-extended-state-observer (LESO) based model predictive control (MPC) scheme. The feedback linearization approach was utilized to develop a model predictive controller. The optimization objective function was simplified to a standard quadratic programming (QP) problem for solution. To minimize the impact of the inaccuracy in the dynamics model, a LESO was designed to estimate the disturbance, and its convergence was demonstrated. In order to improve the accuracy of dynamic model, the physical consistency of inertial parameters was constrained using linear matrix inequality (LMI) techniques. Moreover, the dynamics parameters were estimated through semidefinite programming (SDP) techniques. To validate the effectiveness of the proposed control method, it was compared with existing controllers through simulations and experiments. The results demonstrated the superior performance of the proposed scheme, highlighting its potential for achieving high-precision trajectory tracking control in space robotic manipulators. • High-precision trajectory tracking control for space manipulator is investigated. • Feedback linearization based predictive model for MPC is established. • A LESO is designed and its convergence is proven. • A physical consistency parameters identification method is presented. • Both simulations and experiments are performed for validation. [ABSTRACT FROM AUTHOR]

Objective: Evaluate the application of ad valorem tariffs on beef imports in Mexico and their effects on production and consumption with 2020 data. Methodology: A non-linear programming (quadratic programming) partial equilibrium model was used, without considering storage. The national territory was divided into eight producer-consumer regions and two entry points for imports. Results: The model overestimated production (0.4%) and consumption (0.8%) with a Net Social Value (VSN) of 14,043 million Mexican pesos. Tariffs of 15, 25 and 35% reduced imports by 5.7, 8.5 and 12.5%; they increased production by 4.7, 6.2 and 8.8%; consumption and VSN decreased by 3.4, 5.6, 7.8% and 4.3, 5.9 and 7.8%. The policy favored producers and harmed consumers. Limitations: The base model assumes a free market, that is, a market without intervention; Also, the model does not consider the storage of the product. Conclusions: The results should be considered as policy recommendations when the government decides at some point, its application. [ABSTRACT FROM AUTHOR]

Positive instances are often significantly less than negative instances in real-world classification problems. However, positive categories are typically more relevant to the primary focus of categorization tasks. Moreover, obtaining labeled data is often expensive, and the majority of real-life data is unlabeled. Therefore, semi-supervised learning has become a popular approach for addressing imbalanced problems. Traditional support vector machines (SVMs) treat all samples equally and are not suitable for semi-supervised learning. To address this issue, a semi-supervised model called the fuzzy semi-supervised SVM (FS3VM) has been proposed. The FS3VM model uses the degree of entropy-based fuzzy membership to ensure the materiality of positive classes by assigning positive instances to relatively large degrees of fuzzy membership. After introducing the mainstream FS3VM model, the fundamental theory and methods of the model are discussed and expanded upon, including the FS3VM algorithm, which applies the Sequential Minimal Optimization (SMO) algorithm to the dual problem. The proposed FS3VM model is a smooth and continuous optimization problem, and its dual is a standard quadratic programming. Experimental results demonstrate that the proposed FS3VM model outperforms other compared learning algorithms. [ABSTRACT FROM AUTHOR]

The multi-objective integer programming problem often occurs in multi-criteria decision-making situations, where the decision variables are integers. In the present paper, we have discussed an algorithm for finding all efficient solutions of a multi-objective integer quadratic programming problem. The proposed algorithm is based on the aspect that efficient solutions of a multi-objective integer quadratic programming problem can be obtained by enumerating ranked solutions of an integer quadratic programming problem. For determining ranked solutions of an integer quadratic programming problem, we have constructed a related integer linear programming problem and from ranked solutions of this integer linear programming problem, ranked solutions of the original integer quadratic programming problem are generated. Theoretically, we have shown that the developed method generates the set of all efficient solutions in a finite number of steps, and numerically we have elaborated the working of our algorithm and compared our results with existing algorithms. Further, we have analyzed that the developed method is efficient for solving a multi-objective integer quadratic programming problem with a large number of constraints, variables and objectives. [ABSTRACT FROM AUTHOR]

Characterizing simultaneously diagonalizable (SD) matrices has been receiving considerable attention in recent decades due to its wide applications and its role in matrix analysis. However, the notion of SD matrices is arguably still restrictive for wider applications. In this paper, we consider two error measures related to the simultaneous diagonalization of matrices and propose several new variants of SD thereof; in particular, TWSD, TWSD-B, Tm, n-SD (SDO), DWSD, and Dm,n-SD (SDO). Those are all weaker forms of SD. We derive various sufficient and/or necessary conditions of them under different assumptions and show the relationships between these new notions. Finally, we discuss the applications of these new notions in, e.g., quadratically constrained quadratic programming and independent component analysis. [ABSTRACT FROM AUTHOR]

The current study presents a novel application of integrated intelligent computing solver for numerical treatment of second‐order prediction differential models by exploiting the continuous mapping of artificial neural network (ANN) models of differential operators, global/local search optimization competencies of combined genetic algorithms (GAs) and sequential quadratic programming (SQPs), that is, ANNGASQP. Neural network based differential models are arbitrary integrated to formulate merit function in mean squared error sense and merit function globally optimized with GAs aided with local refinements of SQP. The integrated neuro‐evolutionary ANNGASQP scheme is implemented on four different numerical examples of the prediction differential models for numerical solution to examine the precision, proficiency, and consistency. The comparison of proposed solutions through ANNGASQP for prediction differential models with available reference results indicate the good agreement with absolute errors around 10−6 to 10−8. The worth of ANNGASQP is further established through near optimal values of performance measures on statistical date for multiple trials. [ABSTRACT FROM AUTHOR]

In this paper, fixed-time convergent RNNs with logarithmic settling time are proposed for solving time-variant quadratic programming (QP), where the domain of attraction of the RNN models can be reasonably estimated a priori. Two novel activation functions are designed, and the exact expressions of the settling time functions for each given initial condition are given; and upper bounds of the settling times for any given initial condition can be obtained. In addition, the generic activation functions are constructed and analyzed to achieve logarithmic fixed-time settling. The RNNs based on the generic activation function are designed, which have a faster convergence rate and a fixed-time convergence property. By estimating the bound of the settling time function under given initial conditions in the bounded region, the semi-global fixed-time convergence of the RNN model is in turn established. It is shown that the predefined-time convergence of modified RNN models can be assured by adopting the inverse of the bound. Simulation results verify effectiveness of the proposed computing schemes for the time-variant QP problem solving and the repetitive motion planning of a redundant manipulator. [ABSTRACT FROM AUTHOR]

Support-vector machines (SVMs) have been successfully employed to diagnose neurological disorders like epilepsy and sleep disorders via electroencephalogram (EEG) signal classification. However, EEG signals are susceptible to noise and outliers while recording. Thus, classification of EEG signals require efficient methods that is robust to noise and outliers. SVMs suffer in the presence of noise and outliers as it treats each sample to be of equal importance. Fuzzy membership function has been successful in dealing with noise and outliers. Fuzzy support vector machine uses fuzzy membership to give the appropriate weight to each sample for reducing the effect of noise and outliers. To reduce the effect of noise and outliers, intuitionistic fuzzy twin support vector machines (IFTWSVM) uses both membership and non-membership weights to subsidise the effect of outliers. Moreover, IFTWSVM solves smaller size quadratic programming problems which makes it comparatively efficient than fuzzy support vector machines. However, IFTWSVM suffers as (i) it involves the computation of matrix inverses which makes it intractable as the size of the data increases. (ii) it solves different problems for linear and nonlinear cases, and nonlinear case with linear kernel does not reduce to the linear case. Thus, it can only solve the approximate formulation not the exact formulation. To overcome these issues, we propose improved intuitionistic fuzzy twin support vector machine (IIFTWSVM). The proposed IIFTWSVM introduces different Lagrangian functions to avoid the computation of matrix inverses. Also, in nonlinear case, kernel trick is applied directly and hence, the exact formulation is solved. We employed the proposed IIFTWSVM for the classification of EEG signals. The experimental results and statistical analysis show that the proposed IIFTWSVM model is better compared to the given baseline models. Also, we evaluate the performance of the models on KEEL datasets to check the robustness of the proposed IIFTWSVM model. [ABSTRACT FROM AUTHOR]

Path planning of agricultural machinery is currently focused on static obstacle avoidance infield operation scenarios. The driving environment of the tractor is characterized by semi-structured roads with a width of 3-6 m. The tractor roads are relatively complex without identification lines. Many types of dynamic and static obstacles can be found, including the agricultural machinery temporarily parked on both sides of the road, various agricultural machinery, or other traffic participants traveling in the same or opposite direction, even crossing the tractor road. Large-scale agricultural machinery cannot perform the complex trajectory movements in the narrow and long path. It is highly required for the smoothness of the planned trajectory. At the same time, the speed planning is needed to avoid the dynamic obstacles. In this study, local path planning was proposed using quadratic programming, in order to fully meet the practical needs of safety, smoothness, and real-time planning of autonomous agricultural machinery in the scenario of tractor roads. Firstly, a finite state machine was used to define the driving mode of agricultural machinery for the obstacle categories. Secondly, a horizontal and vertical decoupling was adopted to reduce the redundancy for better timeliness. The behavior decision-making of trajectory and velocity was also designed using an improved dynamic state grid method. Thirdly, the planning starting point was designed with a control preview point to combine the planning and control systems. The discontinuity of the cycle trajectory was then effectively solved during planning. Finally, quadratic programming was used to optimize the sampling path. All planned trajectories, trajectory curvature, and velocities fully met the dynamic constraints. A penalty function of safety distance was introduced to solve the constraints exceeding the feasibility of a quadratic programming solution. That was the domain's upper limit. A series of vehicle tests were conducted in various driving environments, with a reference speed of 2 m/s. The results show that in the scenario of bypassing static obstacles, the average and maximum absolute curvature of the planned trajectory were 0.021, and 0.056 m−1, respectively, while the average and maximum absolute lateral error were 3.23, and 8.69 cm, respectively, and the distance from the agricultural machinery to the outer contour of the obstacle was greater than 0.76 m. Furthermore, the average and absolute error of planning speed were 0.08-0.12 m/s, and less than 0.38 m/s, respectively, while the acceleration variation range was -0.38- 0.44 m/s, in order to avoid the dynamic obstacles, such as driving in the opposite or same direction, or crossing tractor tracks. Therefore, there was the average and maximum time of 48, and 75 ms, respectively, after simulation with the planning period of 200 ms. The average time was reduced by 38 ms, whereas, the efficiency was improved by 44%, compared with the static state grid method. The findings can also provide technical support to the local path planning of agricultural machinery in the scenario of machine plowing roads. [ABSTRACT FROM AUTHOR]

To address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control. [ABSTRACT FROM AUTHOR]

In this paper, we propose a semidefinite relaxation based branch-and-bound algorithm to the unit-modulus constrained complex quadratic programming problem, which has broad applications in signal processing and wireless communications. Our research is motivated from the potential application of the magnitude least-square problem, which possesses the so-called block-arrow sparsity pattern. We discuss how to reduce the worst-case complexity of the proposed branch-and-bound algorithm by exploiting this special sparsity pattern. Numerical results are presented to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical lane-changing trajectory planner based on the least action principle for autonomous driving. Our approach aims to achieve reliable real-time avoidance of static and moving obstacles in multi-vehicle interaction scenarios on structured urban roads. Unlike previous studies that rely on subjective weight allocation and single weighting methods, we propose a novel trajectory planning strategy that decomposes the process into two stages: candidate trajectory generation and optimal trajectory decision-making. The candidate trajectory generation employs a path-velocity decomposition method, using B-spline curves to generate a multi-objective optimal lane-changing candidate path. Collision checking eliminates paths at risk of collision with static obstacles. Dynamic programming (DP) and quadratic programming (QP) are then used to plan the velocity of safe paths, generating candidate lane-changing trajectories based on curvature checking. The optimal trajectory decision-making process follows the decision mechanism of excellent drivers. We introduce a comprehensive evaluation function, the average action, which considers safety, comfort, and efficiency based on the least action principle. Feasible trajectories are ranked based on their average action, and the trajectory with the minimum average action and no collision risk with moving obstacles is selected as the tracking target. The effectiveness of the proposed method is validated through two common lane-changing scenarios. The results demonstrate that our approach enables smooth, efficient, and safe lane-changing while effectively tracking the planned velocity and path. This method offers a solution to local trajectory planning problems in complex environments and holds promising prospects in the field of autonomous driving. [ABSTRACT FROM AUTHOR]

Emerging robotic systems with compliant characteristics, incorporating nonrigid links and/or elastic actuators, are opening new applications with advanced safety features, as well as improved performance and energy efficiency in contact tasks. However, the complexity of such systems poses challenges in modeling and control due to their nonlinear nature and model variations over time. To address these challenges, the paper introduces Locally Weighted Projection Regression (LWPR) and its online learning capabilities to keep the model of compliant actuators accurate and enable the model-based controls to be more robust. The approach is experimentally validated in Cartesian position and stiffness control for a 4 DoF planar robot driven by Variable Stiffness Actuators (VSA), whose real-time implementation is supported by the Sequential Least Squares Programming (SLSQP) optimization approach. [ABSTRACT FROM AUTHOR]

This article tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each directed-acyclic-graph (DAG) having a minimum inter-arrival period for its activation requests and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with Dynamic Voltage and Frequency Scaling (DVFS) capabilities, including big.LITTLE Arm architectures and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by minimizing power consumption, respecting precise timing constraints, maximizing the applications' slack, respecting given power consumption constraints, or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs.-hardware implementation choice for tasks that can be hardware accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals. [ABSTRACT FROM AUTHOR]

Multi-instance learning (MIL) handles data that is organized into sets of instances known as bags. Traditionally, MIL is used in the supervised-learning setting for classifying bags which contain any number of instances. However, many traditional MIL algorithms do not scale efficiently to large datasets. In this paper, we present a novel primal–dual multi-instance support vector machine that can operate efficiently on large-scale data. Our method relies on an algorithm derived using a multi-block variation of the alternating direction method of multipliers. The approach presented in this work is able to scale to large-scale data since it avoids iteratively solving quadratic programming problems which are broadly used to optimize MIL algorithms based on SVMs. In addition, we improve our derivation to include an additional optimization designed to avoid solving a least-squares problem in our algorithm, which increases the utility of our approach to handle a large number of features as well as bags. Finally, we derive a kernel extension of our approach to learn nonlinear decision boundaries for enhanced classification capabilities. We apply our approach to both synthetic and real-world multi-instance datasets to illustrate the scalability, promising predictive performance, and interpretability of our proposed method. [ABSTRACT FROM AUTHOR]

Approaches which "pool adjacent violators" are very simple and efficient methods to solve isotonic regression problems. We extend this type of algorithms to include non-uniform interval constraints in the complete order case. We prove correctness and linear computational complexity of a resulting approach. We also show that a straightforward implementation in C outperforms more general solvers on a sequence of specifically designed problem instances. [ABSTRACT FROM AUTHOR]

Ratings are frequently used to evaluate and compare subjects in various applications, from education to healthcare, because ratings provide succinct yet credible measures for comparing subjects. However, when multiple rating lists are combined or considered together, subjects often have missing ratings, because most rating lists do not rate every subject in the combined list. In this study, we propose analyses on missing value patterns using six real-world data sets in various applications, as well as the conditions for applicability of imputation algorithms. Based on the special structures and properties derived from the analyses, we propose optimization models and algorithms that minimize the total rating discordance across rating providers to impute missing ratings in the combined rating lists, using only the known rating information. The total rating discordance is defined as the sum of the pairwise discordance metric, which can be written as a quadratic function. Computational experiments based on real-world and synthetic rating data sets show that the proposed methods outperform the state-of-the-art general imputation methods in the literature in terms of imputation accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a solution approach for the transportation problem (TP) in an uncertain environment using trapezoidal intuitionistic fuzzy numbers (TrIFNs). We introduces a new approach to solving Transportation Problems (TP) in uncertain environments, focusing on the Trapezoidal Fermatean Fuzzy Number (TrFFN). The Fermatean Fuzzy TP (FFTP) treats transportation cost, supply, and demand as TrFFN, offering superior performance and feasibility compared to existing methods. It also highlights its advantages in uncertain environments. Using standard LP algorithms, the IFTP is converted into a deterministic linear programming (LP) problem. The quadratic fuzzy transportation problem is a significant problem in operations research and optimization, involving finding the best feasible solution for a transportation problem with quadratic function cost coefficients. The paper presents a new method for solving the fully generalized quadratic fuzzy transportation problem under an ambiguous environment, considering uncertainties and fuzziness inherent in real-world transportation scenarios. The paper also addresses the minimum spanning tree (MST) problem, which involves a graph with either trapezoidal or triangular fuzzy numbers assigned to each arc length. The graded mean integration representation of fuzzy numbers is used to solve these problems. The paper proposes an alternative undefined outranking method by extending the Elimination Et Choix Traduisant La Realite (ELECTRE 1) Method method to consider uncertain, imprecise, and linguistic assessments a group of decision-makers provides. The report addresses the gap in the Electre 1 literature for problems involving conflicting systems of criteria, uncertainty, and imprecise information. [ABSTRACT FROM AUTHOR]

Two-stage precoding schemes have been developed to reduce the channel estimation overhead in FDD systems. By integrating user scheduling into these schemes, it becomes possible to meet the quality-of-service requirements of high-density wireless communication systems, despite the limitations on spatial resources and transmit power budget. In this paper, we propose a user scheduling and dynamic beam allocation method for neighbor-based joint spatial division multiplexing (N-JSDM) transmission. The user scheduling problem is formulated as a 0–1 quadratic programming problem to maximize effective spectral efficiency (ESE) using directional channel properties. To address the complexity issue, convex relaxation and linearization methods are employed to transform the problem into a 0–1 mixed integer linear programming, and a dimensionality reduction method is introduced. The proposed user scheduling-aided N-JSDM scheme reduces downlink training length and feedback of channel state information. Additionally, a dynamic configuration form is used for pre-beamforming matrix design based on user distribution, outperforming conventional approaches. Simulation results demonstrate higher ESE achieved by the proposed scheme compared to previous methods. [ABSTRACT FROM AUTHOR]

This study investigates the topology optimization problem using various optimization approaches, taking inspiration from the 99-line MATLAB code developed by Sigmund. The educational MATLAB code is based on the Solid Isotropic Material with Penalization (SIMP) model of the artificial material density method. The objective is to minimize the compliance function with a weight constraint, with the design variables being the densities of all elements. The aim is to identify a more efficient optimization technique as an alternative to the commonly used optimality criteria algorithm provided by other MATLAB built-in tools. Two types of optimization algorithms are examined: gradient-based methods such as Interior-Point, Sequential Quadratic Programming (SQP), and Active-Set, as well as metaheuristic methods including the Genetic Algorithm. The results are verified and validated by comparing them with existing literature, demonstrating good agreement. Performance assessments are conducted to compare the results obtained from these algorithms in terms of quality and computational efficiency. The numerical findings indicate that the interior-point method outperforms the other investigated methods, although the optimality criteria algorithm remains the most efficient for solving topology optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an exact algorithm for optimizing a linear fractional utility function over the efficient set of a multi objective integer quadratic problem. The algorithm is based on the "Branch and Cut" principle, which combines the branching process to ensure decision variables' integrity and efficient cuts built off the non-increasing gradients' directions of objective functions to eliminate inefficient integer solutions. The proposed approach accelerates the convergence to the efficient solution that optimizes the utility function. After presenting and describing the algorithm, a detailed didactic example is illustrated, followed by an experimental study to validate our approach and show computational costs. [ABSTRACT FROM AUTHOR]

The rational approximation is an important tool for establishing a dynamic analysis model in time domain for semi‐infinite water and soil. It accurately represents the interaction between the semi‐infinite medium and an engineering structure. Current research on rational approximation method focuses mainly on the establishment of time‐domain analysis models. However, the stability, accuracy, and calculation efficiency of the rational approximation model cannot be guaranteed simultaneously. Based on linear system control theory, this work decomposes the continuous‐time rational approximation (CRA) and the discrete‐time rational approximation (DRA) into a combination of first‐ and second‐order subsystems, and the stability boundaries for the identified parameters are established according to the stability conditions of each subsystem. A genetic algorithm and a sequential quadratic programming algorithm are used to construct a parameter identification method that can ensure stability in the time domain. Through parameter identification of complex frequency response functions, it is demonstrated that the method proposed in this work can guarantee the stability and accuracy simultaneously, and the calculation efficiency is also substantially improved over that of the existing methods. [ABSTRACT FROM AUTHOR]

Background: Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights. Results: To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein–protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering. Conclusion: ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git. [ABSTRACT FROM AUTHOR]

Portfolio optimization has been studied extensively by researchers in computer science and finance, with new and novel work frequently published. Traditional methods, such as quadratic programming, are not computationally effective for solving complex portfolio models. For example, portfolio models with constraints that introduce nonlinearity and non-convexity (such as boundary constraints and cardinality constraints) are NP-Hard. As a result, researchers often use meta-heuristic approaches to approximate optimal solutions in an efficient manner. This paper conducts a comprehensive review of over 140 papers that have applied evolutionary and swarm intelligence algorithms to the portfolio optimization problem. These papers are categorized by the type of portfolio optimization problem considered, i.e., unconstrained or constrained, and are further categorized by single-objective and multi-objective approaches. Furthermore, the various portfolio models used, as well as the constraints, objectives, and properties in which they differ, are also discussed in a detailed analysis. Based on the findings of the reviewed work, guidance for future research in portfolio optimization is given. Possible areas for future work include dynamic portfolio optimization, predictive pricing, the further investigation of multi-objective approaches. [ABSTRACT FROM AUTHOR]

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system's robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system's potential for real-world application. [ABSTRACT FROM AUTHOR]

This paper presents a novel decomposition-based hybrid many-objective optimization method using particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms. In the proposed method, the objective function space decomposed into optimization sub-problems. In this respect, the origin of the coordinate systems of the scaled objective function space (i.e., ideal point) and uniformly distributed points in the scaled objective function space are used to generate reference directions. The scaling of the objective function space in each generation leads to the improvement of the accuracy of the ideal point, which is important because of its role in the generation of the reference directions. In the proposed method, optimization of all the reference directions performed simultaneously to increase the accuracy as well as the computational efficiency. In addition, the proposed method is based on the hybridization of multi-objective particle swarm optimization (MOPSO) using the reference direction and local fast and accurate search capabilities of the SQP to provide a computationally more efficient and accurate algorithm for solving many-objective optimization problems. In the proposed method, a new mutation operator was introduced into the MOPSO to enhance the computational performance, uniformity of the solution and to prevent the MOPSO from pre-convergence to the local Pareto front. The proposed method was compared with other many-objective optimization algorithms using several challenging DTLZ benchmark problems and real-world many-objective optimization problems involving 3 to 15 objective functions. The results show that the proposed method has a high diversity and very accurate Pareto solutions. In this respect, the proposed method outperforms other recently developed many-objective optimization algorithms used in this study. Based on the obtained results, the proposed method provides an effective way of solving many-objective optimization problems. [ABSTRACT FROM AUTHOR]

The goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOCUC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOCB was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified. [ABSTRACT FROM AUTHOR]

To alleviate the impact of air conditioning (AC) systems on the overall performance of electric vehicles (EVs) and realize optimal control over the AC system, an optimal energy-saving operating control strategy for AC systems was proposed in this work. The main goal of our approach was to minimize the power consumption under the premise of meeting load demands. The energy-saving optimization problem of AC systems was solved using the improved genetic algorithm (IGA) by establishing a steady-state model of a frequency-variable heat pump AC system. In the proposed IGA, sequential quadratic programming (SQP) was embedded as a local search scheme to enable high convergence rate and solving accuracy, as well as effectively solve the optimization problem of frequency-variable heat pump AC systems. According to optimization results, the energy-saving operation of an AC system can be realized by dynamically adjusting the compressor frequency, indoor-fans frequency, and outdoor-fans frequency according to the actual working conditions of the system. The proposed optimal control method for the frequency-variable heat pump AC system based on the IGA also provided a robust basis for the intelligent control of AC systems. Meanwhile, it would be a potential solution to extend the driving ranges of EVs. [ABSTRACT FROM AUTHOR]

In real‐world problems, data suffer from measurement errors, data staleness, and repeated measurements, which make data uncertain. To consider the uncertainty of data, the concept of giving each data feature as a multidimensional Gaussian distribution has been utilized. In 2021, support vector machines with an ϵ$$ \epsilon $$ insensitive zone pinball loss (UPinSVMs) for uncertain data classification was proposed, where ϵ$$ \epsilon $$ is a positive number. The UPinSVMs bring noise insensitivity, stability for resampling, and increased model sparsity. However, the value of ϵ$$ \epsilon $$ is known to be specified. In order to improve the performance, we combine the generalized pinball loss ((ϵ1,ϵ2)$$ {\epsilon}_1,{\epsilon}_2\Big) $$‐Mod‐Pin‐SVM) into the uncertain classification, term as UGPinSVMs, where ϵ1,ϵ2$$ {\epsilon}_1,{\epsilon}_2 $$ are two positive numbers. The generalized pinball loss is an optimal insensitive zone pinball loss and is an extension of existing loss functions that also addresses the issues of noise insensitivity and resampling instability. We solve the primal quadratic programming problems by transforming individuals into unconstrained optimization using an efficient stochastic gradient descent algorithm. More specially, we have introduced verified theorems that are related to our approaches and investigated scatter minimization. The results from several benchmark datasets show that our model outperforms the existing classifier in terms of accuracy and statistical analysis. Furthermore, the application of our framework to the crop recommendation dataset is also examined. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new nonlinear optimization model to solve semidefinite optimization problems (SDPs), providing some properties related to local optimal solutions. The proposed model is based on another nonlinear optimization model given by [S. Burer and R. Monteiro, A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization, Math. Program. Ser. B 95 (2003), pp. 329–357], but it has several nice properties not seen in the existing one. Firstly, the decision variable of the proposed model is a triangular low-rank matrix. Secondly, the existence of a strict local optimum of the proposed model is guaranteed under some conditions, whereas the existing model has no strict local optimum. In other words, it is difficult to construct solution methods equipped with fast convergence using the existing model. We also present some numerical results, showing that the use of the proposed model allows to deliver highly accurate solutions. [ABSTRACT FROM AUTHOR]

In civil engineering self‐weight loading usually represents the preponderant load case and shall therefore be considered in structural optimization. The present study investigates numerical topology optimization of structural stiffness with a bound on disposable amount of material. A customized topology optimization routine based on Sequential Quadratic Programming was built and is examined by means of known benchmark problems complemented by consideration of self‐weight. This study aims to highlight the relevance of consideration of self‐weight in resource efficient structural design by demonstrating its impact on the optimal structural layout. [ABSTRACT FROM AUTHOR]

We present SLEQP, an open source C‐package consisting of an active‐set method capable of solving large‐scale nonlinear programming (NLP) problems based on successive linear programming and equality constrained quadratic programming techniques. The method includes a feasibility restoration phase and second‐order corrections (SOCs), achieving robust practical performance. It has also been adapted for the special cases of unconstrained optimization, box‐constrained problems, and nonlinear least squares problems and contains interfaces to both Python and MATLAB. To demonstrate the performance of the package, we perform a computational study based on the well‐known CUTest suite of NLP problems. [ABSTRACT FROM AUTHOR]

The transmit waveform with ambiguity function specifications in the range‐Doppler plane has been of great interest in traditional single antenna radar systems in recent years. This paper considers the design of waveform sets with a desired slow‐time ambiguity function for multiple‐input multiple‐output radar. Specifically, it is desirable that the ambiguity function has low sidelobes where clutter occurs, as this reduces the impact of the clutter on target detection. Instead of unimodular waveforms, the authors consider amplitude modulation signals and introduce the bound constraint on signal amplitudes into the optimisation for the consideration of transmitter efficiency. The design problem is formulated as minimising the weighted integrated sidelobes level of the ambiguity function over the range‐Doppler plane under bound constraints. The resulting problem has a highly non‐linear objective function and is constrained by bound constraints and quadratic equality (constant energy) constraints, making it difficult to solve. The authors develop an efficient algorithm based on the sequential quadratic programming technique and non‐linear conjugate gradient method to solve it. Simulation results show that the proposed algorithm is superior to the existing methods. [ABSTRACT FROM AUTHOR]

By optimizing the reconfigurable intelligent surface (RIS) reflection coefficients, the channel capacity of legitimate users can be increased, thereby enhancing the anti-jamming performance of communication systems. However, existing studies on RIS-assisted anti-jamming assume that there is no coupling between the RIS reflection coefficients and the incident angle of electromagnetic (EM) waves, which is quite unreasonable. Therefore, we consider the effect of the incident angle of EM waves on the reflection coefficients of the RIS and propose a communication anti-jamming scheme assisted by an RIS with angular response. Specifically, a problem is formulated to optimize the RIS reflection coefficients so that the legitimate signal is amplified, but the jamming signal is attenuated, thus enhancing the legitimate channel capacity. However, the coupling of the EM incident angle and the RIS reflection coefficients causes the problem to be non-convex. To tackle this problem, we equivalently transform the RIS reflection coefficients optimization problem into a quadratically constrained quadratic programming (QCQP) problem using the complex Taylor expansion and the multidimensional complex quadratic transform (MCQT) and solve it utilizing the alternating direction method of the multipliers (ADMM) algorithm. The simulation results reveal that, compared to other schemes supported by RIS without angular response, the proposed scheme is able to achieve a significant improvement in the anti-jamming performance. [ABSTRACT FROM AUTHOR]

Moving-target-defense (MTD) fundamentally avoids an illegal initial compromise by asymmetrically increasing the uncertainty as the attack surface of the observable defender changes depending on spatial-temporal mutations. However, the existing naive MTD studies were conducted focusing only on wired network mutations. And these cases have also been no formal research on wireless aircraft domains with attributes that are extremely unfavorable to embedded system operations, such as hostility, mobility, and dependency. Therefore, to solve these conceptual limitations, this study proposes normalized drone-type MTD that maximizes defender superiority by mutating the unique fingerprints of wireless drones and that optimizes the period-based mutation principle to adaptively secure the sustainability of drone operations. In addition, this study also specifies MF2-DMTD (model-checking-based formal framework for drone-type MTD), a formal framework that adopts model-checking and zero-sum game, for attack-defense simulation and performance evaluation of drone-type MTD. Subsequently, by applying the proposed models, the optimization of deceptive defense performance of drone-type MTD for each mutation period also additionally achieves through mixed-integer quadratic constrained programming (MIQCP) and multi-objective optimization-based Pareto frontier. As a result, the optimal mutation cycles in drone-type MTD were derived as (65, 120, 85) for each control-mobility, telecommunication, and payload component configured inside the drone. And the optimal MTD cycles for each swarming cluster, ground control station (GCS), and zone service provider (ZSP) deployed outside the drone were also additionally calculated as (70, 60, 85), respectively. To the best of these authors' knowledge, this study is the first to calculate the deceptive efficiency and functional continuity of the MTD against drones and to normalize the trade-off according to a sensitivity analysis with the optimum. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An input shaper-based model predictive control scheme is proposed for an overhead crane to suppress the swing of the payload. A control-oriented mathematical model of the crane is firstly derived based on the small angle hypothesis. Furthermore, three kinds of commonly used shapers are discussed and a zero vibration derivative shaper is selected for the crane. Then, a model predictive swing suppression controller is designed for the crane based on the selected shaper. Constraints on the control input and its slew rate are respected. Besides, the constraint on the swing angle of the payload is also respected as an output constraint. The constrained solution is given in the form of standard quadratic programming. The designed control scheme combines open-loop and closedloop control techniques, achieving accurate positioning of the trolley and good swing suppression of the payload. A series of simulation demonstrates the effectiveness and advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

Aiming at the instability problem of a four-wheel independent drive electric bus under high-speed conditions, this paper first designs a vehicle yaw stability controller based on a linear two-degree-of-freedom model and a linear quadratic programming (LQR) algorithm. A vehicle roll stability controller is then designed based on a linear three-degree-of-freedom model and a model predictive control algorithm (MPC). Moreover, a coordinated control rule based on the lateral load transfer rate (LTR) is designed for the coupled problem of yaw and roll dynamics. Finally, the effectiveness of the proposed control algorithm is verified by simulation. The obtained results show that when the vehicle is running at a high speed of 90 km/h, the stability control algorithm can control the yaw rate tracking error within 0.05 rad/s. In addition, the control algorithm can reduce the maximum amplitude of the side slip angle, the maximum value of the roll angle, the maximum value of the roll angular velocity, and the amplitude of the lateral acceleration by more than 96%, 81.1%, 65.0%, and 11.1%, respectively. [ABSTRACT FROM AUTHOR]