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In this paper, stochastic optimisation of CVaR is applied to maintain risk-averse viability and improve resilience of a supply chain under propagated disruptions. In order to establish the risk-averse boundaries on supply chain viability space, two stochastic optimisation models are developed with the two conflicting objectives: minimisation of Conditional Cost-at-Risk and maximisation of Conditional Service-at-Risk. Then, the risk-averse viable production trajectory between the two boundaries is selected using a stochastic mixed integer quadratic programming model. The proposed approach is applied to maintain the supply chain viability in the smartphone manufacturing and the results of computational experiments are provided. The findings indicate that when the decision-making is more risk-aversive, the size of the viability space between the two boundaries is greater. As a result, more room is available for selecting viable production trajectories under severe disruptions. Moreover, the larger is viability space, the higher is both worst-case and average resilience of the supply chain. Risk-neutral, single-objective decision-making may reduce the supply chain viability. A single-objective supply chain optimisation which moves production to the corresponding boundary of the viability space, should not be applied under severe disruption risks to avoid greater losses. [ABSTRACT FROM AUTHOR]

Incorporating decision-makers' risk preferences into data-driven newsvendor models and developing machine learning methods to solve the models are the challenging problems addressed in this study. To consider different distributions and decision-makers' different risk preferences for the two losses of the total cost newsvendor model, the symmetrical, the partial symmetrical and the asymmetrical CVaR criteria are introduced. The regularisation, the primal-dual approach and the kernels in support vector machines are used to transform the data-driven risk-averse newsvendor problems under the CVaR criterion into the convex quadratic programming problems with good theoretical properties. Computational experiments are conducted on a real-world dataset. The models under the partial symmetrical and the asymmetrical CVaR criteria obtained good performances, but that under the symmetrical CVaR criterion suffered the underfitting problem. Two factors including the degrees of risk aversion for the two losses in the total cost newsvendor model and the empirical errors of data-driven models affect order decisions. The degrees of risk aversion for the two losses have anti-directional effects on order quantities. The introduction of asymmetrical CVaR criterion paves a new way to reveal the effects of different risk references for different losses on order decisions, and has the potential to improve newsvendor decisions. [ABSTRACT FROM AUTHOR]

Rock‐climbing robots have significant potential in fieldwork and planetary exploration. However, they currently face limitations such as a lack of stability and adaptability on extreme terrains, slow locomotion, and single functionality. This study introduces a novel multimodal and adaptive rock‐climbing robot (MARCBot), which addresses these limitations through spiny grippers that draw inspiration from morpho‐functionalities observed in beetles, arboreal birds, and hoofed animals. This hybrid bioinspired design enables high attachment strength, passive adaptability to different terrains, and quick attachment on rock surfaces. The multimodal functionality of the gripper allows for attachment during climbing and support during walking. A novel control strategy using dynamics and quadratic programming (QP) optimizes attachment wrench distribution, reducing cost‐of‐transport by 20.03% and 6.05% compared to closed‐loop inverse kinematic (CLIK) and virtual model control (VMC) methods, respectively. MARCBot achieved climbing speeds of 0.15 m min−1 on a vertical discrete rock surface under gravity and trotting speeds of up to 0.21 m s−1 on various complex terrains. It is the first robot capable of climbing on rock surfaces and trotting in complex terrains without the need for switching end‐effectors. This study highlights significant advancements in climbing and multimodal locomotion for robots in extreme environments. [ABSTRACT FROM AUTHOR]

This research tries to integrate fuzzy neural networks with penalty function to address the quadratic programming based on the mean‐variance Markowitz portfolio model. The fuzzy quadratic programming problem with penalty function consists of the lower, central, and upper models. The models utilize fuzzy neural networks to solve the models. The proposed method has been implemented on the six leading stocks in the Pakistan Stock Exchange. The approach identifies the ideal portfolios for potential investors in the Pakistan Stock Exchange. Data of the six popular stocks trading on the stock exchange from January 2016 to October 2020 are taken into consideration. The optimizers are RMSprop, Momentum, Adadelta, Adagrad, Adam, and gradient descent, respectively. The findings of all the optimizers at all three phases (lower, central, and upper) agree on identifying the optimal investment portfolios for investors. The optimizers recommend investing in either one of the two categories. The first group recommends investing in the FFC, ARPL, and UPFL portfolios. The second group recommends LUCK, AGTL, and IGIHL. The first group tends to enhance return, variability, and risk. It is a high‐risk group. The second group aims to reduce return variability while lowering risk. It is a risk‐averse group. It is evident that all of the optimizers recommend investing in FFC, ARPL, and UPFL, with the exception of the Adam and Adadelta optimizers, which recommends investment in IGIHL, AGTL, and LUCK. RMSprop, Momentum, Adagrad, and gradient descent increase variability, risk, and returns. Adam proves the best optimizer, then RMSprop, and finally, Adagrad. Adam, Adadelta, and RMSprop are sensitive, whereas momentum and gradient descent are irresponsive to fuzzy uncertain data. The percent improvement in the objective is 0.59% and 0.18% for the proposed Adagrad and Adadelta, respectively. [ABSTRACT FROM AUTHOR]

Twin Support Vector Machine (TSVM) transforms a single large quadratic programming problem (QPP) in support vector machine (SVM) into two smaller QPPs by finding two non-parallel classification hyperplanes, so that its computational time is reduced to a quarter of what the traditional SVM takes. However, TSVM ignores the data distribution of class, which makes TSVM sensitive to noise. In this paper, a fuzzy twin support vector machine based on centered kernel alignment (FTSVM-CKA) is proposed to solve the problem that TSVM is sensitive to noise. Firstly, a feature-weighted kernel function is constructed by using the information gain, and it is applied to the calculation of the centered kernel alignment (CKA). This assigns greater weight to strongly correlated features, emphasizing their classification importance over weakly correlated features. Secondly, the CKA method is utilized to derive a heuristic function for calculating the dependency between samples and their corresponding labels, which assigns fuzzy membership to different samples. Based on this, a fuzzy membership assignment strategy is proposed that can effectively address the sensitivity of TSVM to noise. Thirdly, this strategy is combined with TSVM to propose the FTSVM-CKA model. Moreover, this study employs a coordinate descent strategy with shrinking by active set to tackle the computational complexity arising from high-dimensional inputs. This can effectively accelerate the training speed of the model while ensuring classification performance. In order to evaluate the performance of FTSVM-CKA, this study conducts experiments designed on artificial and UCI datasets. The results demonstrate that FTSVM-CKA can efficiently and quickly solve binary classification problems with noise. [ABSTRACT FROM AUTHOR]

Time-varying polytope distance (TVPD) problems are prevalent in scientific and engineering applications and can be transformed into time-varying quadratic programming (TVQP) problems with both equality and inequality constraints. Concurrently, the noise interferences during the solution process are non-negligible and challenging to eliminate. Although zeroing neural networks (ZNNs) perform well in solving various types of time-varying problems, they still fall short in the suppression of unbounded noises, such as linear noise. To address this limitation, this paper proposes an improving integration-enhanced ZNN (IIEZNN) model for accurately solving TVPD problems under noise environments. Compared with the existing ZNN models, the IIEZNN model has stronger inherent robustness. The stability and robustness of the IIEZNN model are guaranteed by rigorous theoretical analysis. Firstly, the effectiveness of the IIEZNN model is verified via two TVQP examples. Then, the IIEZNN model is generalized to TVPD problem solving and has excellent performance. Specifically, in solving the TVPD under linear noises, the residual error of the IIEZNN model converges to the order of 10 - 5 , which is much lower than that of the existing noise-tolerant ZNN model with an order of 10 - 1 . [ABSTRACT FROM AUTHOR]

In this article, a robust model predictive control method is investigated for settling the trajectory tracking problem of a bionic ankle–foot aided by a tensegrity mechanism. In order to achieve adaptive movement of the ankle–foot mechanism, a three-degrees-of-freedom spatial ankle–foot mechanism is designed by tensegrity, which is a spatial grid structure composed of springs and struts. Dynamic analysis is the basis of control algorithm research, and the dynamic model of the mechanism can be established by a Lagrangian equation. Then, a controller is proposed for tracking the trajectory of the ankle–foot mechanism under external disturbances. Combining rolling optimization and feedback correction, the controller can be defined as an optimization problem, by solving which the ankle–foot mechanism can be controlled to track the desired trajectory quickly. Furthermore, stability analysis is an essential part of predictive controller design, which can help to understand the operational mechanism of the control strategy. Numerical results demonstrate that the proposed approach improves trajectory tracking accuracy and avoids mechanism movement problems caused by disturbances. [ABSTRACT FROM AUTHOR]

Summary: This article addresses the safe tracking control problem of quadrotor unmanned aerial vehicles (QUAV) considering uncertain aerodynamics and unknown external disturbances. First, a robust adaptive backstepping controller is designed, where the bias of the control variable, that is, the tracking error of the attitude subsystem, is considered in the design process of the position subsystem. Meanwhile, the safe obstacle avoidance objective is realized by the quadratic programming (QP) method that combines the tracking controller with the exponential control barrier function (ECBF). Furthermore, an event‐triggered mechanism is used for the QP process to conserve computing resources. Simulation validates the feasibility and superiority of the proposed control scheme in presence of multi‐obstacle. [ABSTRACT FROM AUTHOR]

The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed consisting of two auxiliary neural networks derived from the neural network components of the dynamical system. Due to the presence of nonlinear activation functions in neural networks, we use quadratic constraints on the global sector to abstract the nonlinear activation functions in neural networks. By combining a quadratic constraint approach for the activation function with Lyapunov theory, the interval observer design problem is transformed into a series of quadratic and linear programming feasibility problems to make the interval observer operate with the ability to correctly estimate the system state with estimation errors within acceptable limits. The applicability of the proposed method is verified by simulation of the lateral vehicle control system. [ABSTRACT FROM AUTHOR]

Five-axis hybrid robots are more extensively used in the machining of complex parts, owing to their high dynamic performance and flexible attitude adjustment. Ball-end milling is widely employed in sculptured surface machining. The tool orientation can be adjusted for enhanced machining efficiency and superior cutting performance. However, hybrid robots' complex kinematic structures raise challenges to tool orientation planning. To generate a smooth toolpath that has good cutting performance and satisfies all the geometric and mechanical constraints, a tool orientation optimization model is presented. The weighted sum of the joint kinematic performance index and the effective tool diameter indexes are taken as the objective function. By constructing the robot workspace in advance, the computation in the generation of feasible tool orientation regions is reduced. The feasible orientation regions are further simplified as linear inequality constraints which are updated during iteration. In each iteration, the initial nonconvex optimization problem is locally approximated by a convex problem and solved by quadratic programming (QP). Simulations and experiments are conducted for validation of the proposed method. [ABSTRACT FROM AUTHOR]

A more flexible type of mixture autoregressive model, namely the Burr mixture autoregressive, BMAR model is studied in this article for modeling non linear time series. The model consists of a mixture of K autoregressive components with each conditional distribution of the component following a Burr distribution. The BMAR model enjoys some nice statistical properties which allow it to capture time series with: (1) unimodal or multimodal; (2) asymmetry or symmetry conditional distribution; (3) conditional heteroscedasticity; (4) cyclical or seasonal; and (5) conditional leptokurtic distribution. Sufficient and less restrictive conditions for the ergodicity of the BMAR model are derived and discussed. A more robust constrained optimization algorithm (EM – sequential quadratic programming method) is proposed for the non linear optimization problem. From the simulation studies carried out, the parameters estimation method showed satisfying results. The variance of the estimated parameters is also addressed with the missing information principle. Real datasets from two different fields of study are used to assess the performance of the BMAR model compared to other competing models. The comparison done in the empirical examples reveals the supremacy of the BMAR model in capturing the data behavior. [ABSTRACT FROM AUTHOR]

High precision and low complexity control algorithm plays an important role in the developing of the end-effector instrumentation of different robot manipulators. In order to reduce the kinetic energy and the high-speed drift phenomenon of the repetitive motion tracking task, the robot manipulator needs to calibrate its configuration. In this paper, we formulate the configuration calibration of the robot manipulator for the repetitive motion task as a future quadratic programming optimization problem constrained with equality constraints, which is also regarded as a fundamental problem in artificial intelligence and modern control engineering. Zhang neural network, which is a canonical method, can be adopted to deal with the continuous form of the future optimization problem, named as temporally dependent quadratic programming problem with equality constraints. In order to overcome the issue of temporally dependent inverse computing, a novel Zhang neural network model and its uncertain disturbance tolerant model, which are termed as filtered reciprocal-kind Zhang neural network model and uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network model, respectively, are proposed by integrating the energy-type cost function and Zhang neural network design formula for solving the temporally dependent quadratic programming problem with equality constraints in this paper. Based on the Euler discrete formula and the models, the discrete filtered reciprocal-kind Zhang neural network and the discrete uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network algorithms are proposed for solving the future quadratic programming problem with equality constraints and the robot manipulator configuration calibration problem of repetitive motion. The convergence properties of the reciprocal-kind Zhang neural network model and its corresponding uncertain disturbance tolerant model are obtained by Lyapunov stability theory of nonlinear system and its corresponding perturbed system, while the convergence property of the filtered reciprocal-kind Zhang neural network model is analyzed by the limit thinking. For the repetitive motion task, three experiments for solving the configuration calibration problem of PUMA560, Kinova Jaco2, and Franka Emika Panda robot manipulators are performed to illustrate the effectiveness, robustness and superiority of our proposed discrete filtered reciprocal-kind Zhang neural network algorithms. • A novel configuration calibration scheme of the robot manipulators is formulated. • An inverse-free Zhang neural network algorithm is proposed. • The proposed algorithm is robustness under uncertain disturbance environment. [ABSTRACT FROM AUTHOR]

Novel methods for finding the optimal controls of new types of fractional optimal control problems with Riemann–Liouville performance indices and systems comprised of subsystems with Caputo derivatives are introduced. Pure fractional quadratic optimal control problems are modeled as quadratic programming (QP) by using a new idea and a state-control parameterization method. After formulating each linear or nonlinear type, its QP model is derived by which the QP solver in MATLAB can be used to obtain the solutions. There is no need for such operations as defining costate variables, deriving optimality conditions, etc. New concepts such as fractional boundary constraints and Riemann–Liouville isoperimetric constraints, are introduced. Multiple problems in different scenarios are investigated and numerous graphs and numerical results are presented. Pure fractional linear control problems with Riemann–Liouville performance indices and fractional systems are modeled as linear programming (LP) without discretization for the first time. Using the LP solver in MATLAB, the optimal solutions of the fractional/integer linear control problems such as bang–bang (or On–Off) and minimum fuel optimal control systems are obtained. Fractional types of the real-world problems such as container cranes and drug scheduling of cancer chemotherapy, are studied. • Pure multi-order fractional optimal control problems are presented. • QP and LP models of fractional optimal control problems are introduced. • LP method is introduced and used for the first time to solve pure fractional linear control problems such as Example 9. • Fractional order problems such as Examples 4C3 (Case 3), 5, 7, 8, and 10 cannot be solved by many of the existing methods. • New concepts such as fractional constraints are introduced. [ABSTRACT FROM AUTHOR]

The optimal control design of the boiler-turbine system is vital to ensure feasibility and high responsiveness over desired load variations. Using the traditional linear control techniques realization of this task is difficult, as the boiler-turbine mechanism has strong nonlinearities. Besides, environmental and economic concerns have replaced existing tracking control ones as the primary concerns of advanced power plants. Thus, this study proposes an optimal economic model predictive controller (EMPC) scheme for this unit on the basis of the input/output feedback linearization (IOFL) method. By employing the IOFL method, this unit is decoupled into a new linearized model that is utilized for developing the suggested optimal IOFL EMPC technique. The proposed control scheme is formulated in an economic quadratic programming form that considers the input-rate and input limits of the unit for optimal economic performance. In addition, an adaptive iterative algorithm is utilized for constraints mapping with guaranteeing a feasible solution in a finite number of steps without violation of original constraints over the entire predictive horizon. The outcomes of the simulation show that the suggested optimal IOFL EMPC scheme offers an improved dynamic and economic output performance over fuzzy hierarchical MPC, fuzzy EMPC, and nonlinear EMPC techniques during various load variations. • An optimal EMPC scheme based on IOFL method is proposed for boiler-turbine unit. • The IOFL EMPC scheme is formulated in an economic QP form including constraints. • An adaptive iterative algorithm ensures feasible solution without limits violation. • The IOFL EMPC scheme enhances dynamic economic performance for boiler-turbine unit. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office 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.)

Purpose: We propose and evaluate multiphoton parallel transmission (MP‐pTx) to mitigate flip angle inhomogeneities in high‐field MRI. MP‐pTx is an excitation method that utilizes a single, conventional birdcage coil supplemented with low‐frequency (kHz) irradiation from a multichannel shim array and/or gradient channels. SAR analysis is simplified to that of a conventional birdcage coil, because only the radiofrequency (RF) field from the birdcage coil produces significant SAR. Methods: MP‐pTx employs an off‐resonance RF pulse from a conventional birdcage coil supplemented with oscillating z$$ z $$‐directed fields from a multichannel shim array and/or the gradient coils. We simulate the ability of MP‐pTx to create uniform nonselective brain excitations at 7 T using realistic B1+$$ {\mathrm{B}}_1^{+} $$ and ΔB0$$ \Delta {\mathrm{B}}_0 $$ field maps. The RF, shim array, and gradient waveform's amplitudes and phases are optimized using a genetic algorithm followed by sequential quadratic programming. Results: A 1 ms MP‐pTx excitation using a 32‐channel shim array with current constrained to less than 50 Amp‐turns reduced the transverse magnetization's normalized root‐mean‐squared error from 29% for a conventional birdcage excitation to 6.6% and was nearly 40% better than a 1 ms birdcage coil 5 kT‐point excitation with optimized kT‐point locations and comparable pulse power. Conclusion: The MP‐pTx method resembles conventional pTx in its goals and approach but replaces the parallel RF channels with cheaper, low‐frequency shim channels. The method mitigates high‐field flip angle inhomogeneities to a level better than 3 T CP‐mode and comparable to 7 T pTx while retaining the straightforward SAR characteristics of conventional birdcage excitations, as low‐frequency shim array fields produce negligible SAR. [ABSTRACT FROM AUTHOR]

Quadratic programs with box constraints involve minimizing a possibly nonconvex quadratic function subject to lower and upper bounds on each variable. This is a well-known NP-hard problem that frequently arises in various applications. We focus on two convex relaxations, namely the reformulation–linearization technique (RLT) relaxation and the SDP-RLT relaxation obtained by combining the Shor relaxation with the RLT relaxation. Both relaxations yield lower bounds on the optimal value of a quadratic program with box constraints. We show that each component of each vertex of the RLT relaxation lies in the set { 0 , 1 2 , 1 } . We present complete algebraic descriptions of the set of instances that admit exact RLT relaxations as well as those that admit exact SDP-RLT relaxations. We show that our descriptions can be converted into algorithms for efficiently constructing instances with (1) exact RLT relaxations, (2) inexact RLT relaxations, (3) exact SDP-RLT relaxations, and (4) exact SDP-RLT but inexact RLT relaxations. Our preliminary computational experiments illustrate that our algorithms are capable of generating computationally challenging instances for state-of-the-art solvers. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of robust adaptive safety control of a class of nonlinear systems with parametric uncertainties. Two novel definitions, that is, robust adaptive control Lyapunov function (RACLF) and robust adaptive control barrier function (RACBF) are introduced in this article. Also, an estimation of unknown parameters is obtained based on multiple identification models, state predictors and a logic switching mechanism. The switching mechanism plays a vital role in enhancing the transient properties of nonlinear systems. Meanwhile, a safe motion framework for differential robots is provided based on the existence of parametric uncertainties in RACLF and RACBF, and a safe and stable controller is obtained by solving quadratic programming. Finally, simulation results of differential robot capable of safe motion in space are presented to verify the validity of the design method. [ABSTRACT FROM AUTHOR]

The current investigative exploration exemplifies the conceptualization of a novel design intelligent computing paradigm based on artificial neural networks (ANNs) by utilizing radial basis function (RBF) to analyze magnetohydrodynamic (MHD) Williamson nanofluid two-dimensional flow along a stretchable sheet under the effect of chemical reaction as well as thermal radiation in a porous medium. This newly designed technique is an amalgam of a well-known reliable global solver named genetic algorithms (GAs) and a swift convergence generated local solver named sequential quadratic programming (SQP) used in ANNs by taking RBF as a kernel function i.e. ANNs-RBF-GASQP solver. The PDEs demonstrating the current nanofluid problem flow are transformed into the system of non-linear ODEs through a relevant similarity transformation and subsequently solved using ANNs-RBF-GASQP solver to investigate thermohydraulic properties by manipulating the values of various system parameters present in the ODEs. Moreover, the simulation results show that increasing the heat source parameter leads to a significant decrease in temperature. Additionally, an increase in the porosity parameter causes a decrease in the velocity of nanofluid, as a higher value of porosity increases fluid permeability and greater resistance to flow. The efficacy of the suggested solver is scrutinized through various statistical and convergence analyses. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion In magnetic resonance imaging (MRI), maintaining a highly uniform main magnetic field (B0) is essential for producing detailed images of human anatomy. Passive shimming (PS) is a technique used to enhance B0 uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using linear programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming.In this work, we aimed to improve the efficacy of passive shimming that has the advantages of balancing field quality, iron usage, and harmonics in an optimal manner and leads to a smoother field profile.This study introduces a hybrid algorithm that combines particle swarm optimization with sequential quadratic programming (PSO‐SQP) to enhance shimming performance. Additionally, a regularization method is employed to reduce the iron pieces' weight effectively.The simulation study demonstrated that the magnetic field was improved from 462  to 3.6 ppm, utilizing merely 1.2 kg of iron in a 40 cm diameter spherical volume (DSV) of a 7T MRI magnet. Compared to traditional optimization techniques, this method notably enhanced magnetic field uniformity by 96.7% and reduced the iron weight requirement by 81.8%.The results indicated that the proposed method is expected to be effective for passive shimming. [ABSTRACT FROM AUTHOR]

Gradient-based neural dynamics (GND) models are a classical algorithm for solving optimization problems, but it has non-negligible flaws in solving dynamic problems. In this study, a novel GND model, namely the zeroing feedback gradient-based neural dynamics (ZF-GND) models, is proposed based on the original GND model for tracking down the exact solution of dynamic quadratic programming problem (DQP). Further, a nonlinear projection function is designed to accelerate the convergence of the model. An upper bound on the convergence time of the ZF-GND model is rigorously defined through theoretical analysis. The superior effect of the ZF-GND model in terms of convergence is verified through comparison experiments. Finally, an application of robot motion planning is introduced to verify the practicality of the ZF-GND model. [ABSTRACT FROM AUTHOR]

The study considers issues of elaborating and validating a technique of autonomous vehicle motion planning based on sequential trajectory and speed optimization. This method includes components such as representing sought-for functions by finite elements (FE), vehicle kinematic model, sequential quadratic programming for nonlinear constrained optimization, and Gaussian N-point quadrature integration. The primary novelty consists of using the inverse approach for obtaining vehicle trajectory and speed. The curvature and speed are represented by integrated polynomials to reduce the number of unknowns. For this, piecewise functions with two and three degrees of freedom (DOF) are implemented through FE nodal parameters. The technique ensures higher differentiability compared to the needed in the geometric and kinematic equations. Thus, the generated reference curves are characterized by simple and unambiguous forms. The latter fits best the control accuracy and efficiency during the motion tracking phase. Another advantage is replacing the nodal linear equality constraints with integral nonlinear ones. This ensures the non-violation of boundary limits within each FE and not only in nodes. The optimization technique implies that the spatial and time variables must be found separately and staged. The trajectory search is accomplished in the restricted allowable zone composed by superposing an area inside the external and internal boundaries, based on keeping safe distances, excluding areas for moving obstacles. Thus, this study compares two models that use two and three nodal DOF on optimization quality, stability, and rapidity in real-time applications. The simulation example shows numerous graph results of geometric and kinematic parameters with smoothed curves up to the highest derivatives. Finally, the conclusions are made on the efficiency and quality of prognosis, outlining the similarities and differences between the two applied models. [ABSTRACT FROM AUTHOR]

Disassembly line balancing (DLB) is a crucial optimization item in the recycling and remanufacturing of waste products. Considering the variations in the number of operators assigned to each station, this study investigates DLBs with six distinct station configurations: single-manned, multi-manned, single-robotic, multi-robotic, single-manned–robotic, and multi-manned–robotic setups. First, a unified mixed-integer programming (MIP) model is established for Type-I DLBs with each configuration to minimize four objectives: the number of stations, the number of operators, the total disassembly time, and the idle balancing index. To obtain more solutions, a novel bi-metric is proposed to replace the quadratic idle balancing index and is used in lexicographic optimization. Subsequently, based on the unified Type-I models, a unified MIP model for Type-II DLBs is established to minimize the cycle time, the number of operators, the total disassembly time, and the idle balancing index. Finally, the correctness of the established unified models and the effectiveness of the proposed bi-metric are verified by solving two disassembly cases of lighters and hairdryers, which further shows that the mathematical integration method of unified modeling has significant theoretical value for the multi-objective optimization of the DLBs with six distinct station configurations. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra's shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments. [ABSTRACT FROM AUTHOR]

The article addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a time allocation scheme, named DaTA, which is based on the Different Target Simultaneous Wireless Information and Power Transfer (DT-SWIPT) scheme such that the wireless station can share the remaining energy after transmission to the Hybrid Access Point (HAP) to those who have not transmitted to the HAP to minimize the energy consumption of the WPCN. In addition to proposing a new frame structure, the article also considers the Signal-to-Noise (SNR) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the article, the problem of minimization of energy consumption is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. The simulation results showed that the proposed scheme outperforms the related work in terms of energy consumption and energy efficiency. [ABSTRACT FROM AUTHOR]

In order to ensure the high smoothness of railway operation, it is necessary to test the track irregularity and issue a fine-tuning program for maintenance based on the measurement data. Among them, a way to automatically calculate the fine-tuning scheme according to the track deviation to meet the construction conditions (e. g. adjustment limit, bridge eccentricity, tunnel clearance, etc. ) is a difficult problem in track maintenance. In order to solve these problems, the fine-tuning scheme optimization problem was transformed into a quadratic programming model and the corresponding optimization parameters were set according to the actual working conditions in order to realize the automatic optimization. According to the definition of track irregularity, the optimized track deviation was a sufficiently smooth curve in the mileage direction, and the optimized deviation amount and its first-order, second-order and third-order change rates were taken as the optimization objective and given different weights, and the construction constraints were converted into the upper and lower limits of the optimized deviation amount, so that the track fine-tuning algorithm taking into account the constraints and limitations were realized. Compared with the traditional method based on cubic spline curve fitting, this method avoids the selection of spline nodes and can take into account the construction constraints, which improves the efficiency of optimization. In a railway line large machine tamping operation, the proposed method is used for automatic optimization operation, and the dynamic TQI of the track decreases by 28% on average after practical application, which verifies the correctness and feasibility of the method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set (VCTS) at a minimal but safe distance. To guarantee collision avoidance, the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking (EB) trajectories of two successive units during the whole EB process. In this case, the minimal safety distance is usually numerically calculated without an analytic formulation. Thus, the constrained VCTS control problem is hard to address with space-time separation, which is still a gap in the existing literature. To solve this problem, we propose a Distributed Economic Model Predictive Control (DEMPC) approach with computation efficiency and theoretical guarantee. Specifically, to alleviate the computation burden, we transform implicit safety constraints into explicitly linear ones, such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently. For theoretical analysis, sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC, employing compatibility constraints, tube techniques and terminal ingredient tuning. Moreover, we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS. Finally, experimental results demonstrate the performance and advantages of the proposed approaches. [ABSTRACT FROM AUTHOR]

The USDA Thrifty Food Plan (TFP) is a federal estimate of a healthy diet at lowest cost for US population groups defined by gender and age. The present goal was to develop a version of the TFP that was more tailored to the observed dietary patterns of self-identified Hispanic participants in NHANES 2013–16. Analyses used the same national food prices and nutrient composition data as the TFP 2021. Diet quality was measured using the Healthy Eating Index 2015. The new Hispanic TFP (H-TFP) was cost-neutral with respect to TFP 2021 and fixed at $186/week for a family of four. Two H-TFP models were created using a quadratic programming (QP) algorithm. Fresh pork was modeled separately from other red meats. Hispanic NHANES participants were younger, had lower education and incomes, but had similar or higher HEI 2015 scores than non-Hispanics. Their diet included more pulses, beans, fruit, 100% juice, grain-based dishes, and soups, but less pizza, coffee, candy, and desserts. The H-TFP market basket featured more pork, whole grains, 100% fruit juice, and cheese. The second TFP model showed that pork could replace both poultry and red meat, while satisfying all nutrient needs. A vegetarian H-TFP proved infeasible for most age–gender groups. Healthy, affordable, and culturally relevant food plans can be developed for US population subgroups. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs), which gained popularity recently, is facing the problem of reducing the cost of acquiring large datasets. Although a portion of the work combining GNN with active learning has been moderately successful, there are still some shortcomings in this research area. Most of the studies using clustering methods can only obtain local optima, and some of them suffer from the problem of difficult convergence. Moreover, the result of clustering is often undesired if the amount of data in each class is not balanced. Some methods obtain higher performance by combining multiple metrics, but are limited by the adverse effects of under-training the initial network. For these reasons, we propose an active learning framework based on quadratic programming in this paper. This framework transforms the sample sampling process into an optimal solution problem, which can obtain the global optimal solution and avoid the problem of hard convergence. Experimental results on several datasets demonstrate that the proposed method outperforms other baselines. [ABSTRACT FROM AUTHOR]

To solve to the problems of long planning times and low success rates in automatic parking in constrained parking lanes, a modified approach to path planning was proposed, which could improve the hybrid A* algorithm. Firstly, the parking path was divided into two parts : a forward pose adjustment segment and a reverse parking segment. A linear-arc constrained optimization model was established to plan the pose adjustment segment, while finding a suitable starting point for reverse parking. Subsequently, collision risk cost was introduced as an additional factor in the hybrid A* algorithm. The node expansion process was improved by checking whether the vehicle contour intersects with obstacle lines, enhancing the real-time performance and safety of the reverse parking segment. Finally, a cost function was designed with criteria such as path length, smoothness, and deviation, taking into account the vehicle's kinematic constraints. A quadratic programming approach was used to smooth the initial path, resulting in the final path. Simulation analysis was conducted using MATLAB to compare the modified algorithm with the original algorithm. The results showed that, in constrained parking lanes, the improved algorithm could produce a smooth, collision-free parking path with a reduced search time of 23.8% compared with the hybrid A* algorithm. Additionally, the obtained path was safer and more suitable for tracking control. [ABSTRACT FROM AUTHOR]

During pandemics, such as COVID-19, supplies of vaccines can be insufficient for meeting all needs, particularly when vaccines first become available. Our study develops a dynamic methodology for vaccine allocation, segmented by region, age, and timeframe, using a time-sensitive, age-structured compartmental model. Based on the objective of minimizing a weighted sum of deaths and cases, we used the Sequential Least Squares Quadratic Programming method to search for a locally optimal COVID-19 vaccine allocation for the United States, for the period from 16 December 2020 to 30 June 2021, where regions corresponded to the 50 states in the United States (U.S.). We also compared our solution to actual allocations of vaccines. From our model, we estimate that approximately 1.8 million cases and 9 thousand deaths could have been averted in the U.S. with an improved allocation. When case reduction is prioritized over death reduction, we found that young people (17 and younger) should receive priority over old people due to their potential to expose others. However, if death reduction is prioritized over case reduction, we found that more vaccines should be allocated to older people, due to their propensity for severe disease. While we have applied our methodology to COVID-19, our approach generalizes to other human-transmissible diseases, with potential application to future epidemics. [ABSTRACT FROM AUTHOR]

To improve the tracking accuracy and robustness of the path-tracking control model for intelligent vehicles under longitudinal and lateral coupling constraints, this paper utilizes the Kalman filter algorithm to design a longitudinal and lateral coordinated control (LLCC) strategy optimized by adaptive sliding mode control (ASMC). First, a three-degree-of-freedom (3-DOF) vehicle dynamics model was established. Next, under the fuzzy adaptive Unscented Kalman filter (UKF) theory, the vehicle state parameter estimation and road adhesion coefficient (RAC) observer were designed to estimate vehicle speed (VS), yaw rate (YR), sideslip angle (SA), and RAC. Then, a layered control concept was adopted to design the path-tracking controller, with a target VS, YR, and SA as control objectives. An upper-level adaptive sliding mode controller was designed using RBF neural networks, while a lower-level tire force distribution controller was designed using distributed sequential quadratic programming (DSQP) to obtain an optimal tire driving force. Finally, the control strategy was validated using Carsim and Matlab/Simulink software under different road adhesion coefficients and speeds. The findings indicate that the optimized control strategy is capable of adaptively adjusting control parameters to accommodate various complex conditions, enhancing the tracking precision and robustness of vehicles even further. [ABSTRACT FROM AUTHOR]

Surface reconstruction plays a pivotal role in various fields, including reverse engineering, and oil and gas exploration. However, errors in available data and insufficient surface morphology information often introduce uncertainty into the reconstruction. It is crucial to accurately characterize and visualize the uncertainty in surface reconstruction for risk analysis and planning further data collection. To this end, this paper proposes an uncertainty characterization method based on twin support vector regression. First, various modeling data are effectively integrated and the information contained in the high-confidence sample is efficiently utilized through the uncertainty interval generated by quantiles and upper/lower bound constraints. Second, well-path points are incorporated by imposing inequality constraints on the corresponding prediction points. Finally, in order to reduce computation time, the problem of uncertainty characterization is formulated as two smaller-scale quadratic programming. The results obtained from a real fault dataset and a synthetic dataset validate the effectiveness of the proposed method. When well data are available, the generated uncertainty envelopes are constrained by well data, which can partially mitigate reconstruction uncertainties. [ABSTRACT FROM AUTHOR]

The challenge of distributing molten iron involves the optimal allocation of blast furnace output to various steelmaking furnaces, considering the blast furnace's production capacity and the steelmaking converter's consumption capacity. The primary objective is to prioritize the distribution from the blast furnace to achieve a balance between iron and steel production while ensuring that the volume of hot metal within the system remains within a safe range. To address this, a constrained multi-objective nonlinear programming model is abstracted. A linear weighting method combines multiple objectives into a single objective function, while the Lagrange multiplier method addresses constraints. The proposed hybrid Archimedes optimization algorithm effectively solves this problem, demonstrating significant improvements in time efficiency and precision compared to existing methods. [ABSTRACT FROM AUTHOR]

Orthogonal Nonnegative Matrix Factorization (ONMF) with orthogonality constraints on a matrix has been found to provide better clustering results over existing clustering problems. Because of the orthogonality constraint, this optimization problem is difficult to solve. Many of the existing constraint-preserving methods deal directly with the constraints using different techniques such as matrix decomposition or computing exponential matrices. Here, we propose an alternative formulation of the ONMF problem which converts the orthogonality constraints into non-convex constraints. To handle the non-convex constraints, a penalty function is applied. The penalized problem is a smooth nonlinear programming problem with quadratic (convex) constraints that can be solved by a proper optimization method. We first make use of an optimization method with two gradient projection steps and then apply a post-processing technique to construct a partition of the clustering problem. Comparative performance analysis of our proposed approach with other available clustering methods on randomly generated test problems and hard synthetic data-sets shows the outperformance of our approach, in terms of the obtained misclassification error rate and the Rand index. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TSVM) is an effective machine learning tool for classification problems. However, TSVM classifier works on empirical risk principle only and also while training, each sample contributes equally, even if it is a noise or an outlier. It does not incorporate the uncertainties associated with data into modeling and hence its generalization ability declines. To address these issues, intuitionistic fuzzy regularized least square twin support vector machine having intuitionistic fuzzy network has been proposed in this paper. The non-parallel classifiers are obtained by solving two systems of linear equations only rather than the solution of two quadratic programming problems as in TSVM, which leads to speed up the training process. Moreover, the method follows both structural risk and empirical risk minimization principles. In order to de-escalate the effect of pollutant patterns, their contribution of the patterns into learning the decision function has been made according to their importance in the classification. The significance of the training patterns is measured in terms of intuitionistic fuzzy numbers based on their geometrical locations and surroundings. The method is further extended to find non-parallel decision planes in the feature space using nonlinear kernel function, which also gives rise to the solution of two systems of linear equations. To show the efficacy of the proposed method, computer simulations on fourteen standard and six big UCI datasets using linear and Gaussian kernels are performed and their results have been compared with well-established methods in the literature. The experimental results are represented in terms of accuracy, computational time, F-measure, sensitivity and specificity rates. The outcomes demonstrate that the proposed method outperforms the existing methods and is also feasible for big datasets. The comparison and statistical inferences using two non-parametric: Friedman and Nemenyi tests, conclude that the proposed approach is fast and yields better generalization. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TWSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TWSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TWSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with, we first introduce the basic theory of support vector machine, TWSVM and then focus on the various improvements and applications of TWSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects. [ABSTRACT FROM AUTHOR]

The article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances. [ABSTRACT FROM AUTHOR]

The conventional design of reinforced soil retaining structures (RSRS) assumes a dry or fully saturated backfill in a deterministic approach, which may not reflect real-field conditions accurately. While there are ongoing efforts to integrate partially saturated conditions, there is a noticeable lack of emphasis on simplifying the design processes associated with these conditions. This constraint restricts their practical application, particularly in the context of probabilistic scenarios. This study employed the collocation-based stochastic response surface method to present a simple probabilistic method for the seismic stability analysis of reinforced soil retaining structures in partially saturated conditions. To improve accuracy, the horizontal slice is modified to include the effects of cohesion and suction stress in no-flow and infiltration conditions with the least number of assumptions. For practical applicability, the whole process is made efficient by using the nonlinear constrained sequential quadratic programming. To elaborate, a probabilistic parametric study is conducted on a typical reinforced soil structure with silty sand backfill, a recommended material for its construction. The randomness associated with each system, that is, soil, hydraulic parameters, and seismicity, is considered. The results emphasize the need to consider the probabilistic aspect while designing the reinforced soil structures, especially when dealing with partially saturated conditions. The research promotes the adoption of simplified probabilistic methods to encourage field engineers to re-evaluate designs that were originally calculated using deterministic methods. Practical Applications: When designing the soil retaining structures, engineers usually assume the soil is either completely dry or completely wet. However, this may not match real-world conditions. While there are efforts to account for partially saturated conditions, the methods are often complicated. This makes it hard to use them in real-life situations, especially when considering the chance of earthquakes. This becomes more complicated when it comes to probabilistic design that involves considering uncertainties and variations in soil and environmental conditions. Even though engineers have been trying out new ways of predicting things in engineering, the industry is still a bit unsure about using these methods fully in their designs. This may render the structures prone to failure in case of an uncertain situation. This study uses a straightforward method to analyze the safety of soil retaining structures including the realistic scenarios of earthquake occurrences and uncertainty in soil and environmental conditions. The results encourage the incorporation of pragmatic scenarios for safe designing of soil retaining structures. The practical application of this study lies in the fact that a special emphasis has been laid to make the employed methodologies simple, accurate yet efficient so that field engineers can be encouraged to reassess their designs using the proposed practical probabilistic approach. [ABSTRACT FROM AUTHOR]

This study proposes an efficient Newton-type method for the optimal control of switched systems under a given mode sequence. A mesh-refinement-based approach is utilised to discretise continuous-time optimal control problems (OCPs) and formulate a nonlinear program (NLP), which guarantees the local convergence of a Newton-type method. A dedicated structure-exploiting algorithm (Riccati recursion) is proposed to perform a Newton-type method for the NLP efficiently because its sparsity structure is different from a standard OCP. The proposed method computes each Newton step with linear time-complexity for the total number of discretization grids as the standard Riccati recursion algorithm. Additionally, the computation is always successful if the solution is sufficiently close to a local minimum. Conversely, general quadratic programming (QP) solvers cannot accomplish this because the Hessian matrix is inherently indefinite. Moreover, a modification on the reduced Hessian matrix is proposed using the nature of the Riccati recursion algorithm as the dynamic programming for a QP subproblem to enhance the convergence. A numerical comparison is conducted with off-the-shelf NLP solvers, which demonstrates that the proposed method is up to two orders of magnitude faster. Whole-body optimal control of quadrupedal gaits is also demonstrated and shows that the proposed method can achieve the whole-body model predictive control (MPC) of robotic systems with rigid contacts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to efficiently plan trajectories for industrial robots, where total trajectory time is reduced while limiting the robot end-effector acceleration to a safe threshold, which guarantees an object to be transported safely without damage or spills. We also propose to solve the optimization problem in two steps by utilizing both the quadratic programming algorithm and sparse sequential quadratic programming method. The proposed two-step procedure empirically proves that computing time of solving the optimization problem is significantly reduced. Effectiveness of the proposed approach was verified in Drake, where the obtained results are highly promising. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [ABSTRACT FROM AUTHOR]

An event-triggered neural adaptive cooperative control is proposed for the towing system (TS) with model parameter uncertainties and unknown disturbances. Different from ordinary multi-vessel formation control, the tugs and unactuated offshore platform in the TS are connected together by towlines, and the resultant tension of the towlines serves as the actual drag force for the platform. Initially, based on the radial basis function neural network (RBFNN), an adaptive RBFNN is designed to compensate unknown disturbances and model parameter uncertainties of the TS, and we use minimal learning parameter (MLP) algorithm to reduce the online learning parameters of adaptive RBFNN. Combined with dynamic surface technology and event-triggered control (ETC) mechanism, an event-triggered neural adaptive virtual controller is designed to obtain the desired drag force of the platform. According to the quadratic programming algorithm, the desired drag force is allocated as the desired tensions of towlines. Subsequently, the desired towline length and the desired position information of the tugs are obtained sequentially through the towline model and the position relationship between the tugs and the platform. Then, according to the desired positions of tugs, an event-triggered neural adaptive distributed cooperative controller is designed for achieving the multi-tug towing of the offshore platform. The ETC mechanism is introduced to reduce the communication burden within the TS and the execution frequency of the tugs' thrusters. Finally, the stability of the closed-loop system is proven using the Lyapunov theory, and the ETC mechanism proves that no Zeno behavior occurs. The effectiveness of the ETC mechanism and the MLP-based adaptive RBFNN on the controllers of TS is verified through simulations and comparison analysis. [ABSTRACT FROM AUTHOR]

The intensity and structure of the secondary flows have significant influences on the turbine cascade losses. Combining with the extension method of Non-linear Programming by Quadratic Lagrangian (NLPQLP), two endwall parametric methods respectively based on the B-spline surface and Fourier series are used to optimize the aerodynamic losses of a turbine cascade. The optimization processes are based on Computational Fluid Dynamics (CFD) analysis and two final designs are obtained by different methods. The endwall profile generated by the B-spline surface method (EW-B1) appears to relatively reduce the total pressure loss by 16.58% and the secondary kinetic energy coefficient (C SKE ) by 27.08%, while the endwall profile generated by the Fourier series method (EW-F1) relatively reduces the loss by 16.18% but increases C SKE by 8.99%. The radial movement of upper Passage Vortex (PV) and the weakening of the pressure branch of the Horseshoe Vortex (HVps) are confirmed to be the reasons of loss reduction in EW-B1, while the movement of PV is the main reason for EW-F1 to decrease the total pressure loss. In EW-B1, the weakening of HVps has a much more significant effect on the decrease in loss, which is affected by the non-axisymmetric shapes upstream of the vortex trajectory and the drop in C SKE generation near the throat. [ABSTRACT FROM AUTHOR]

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This paper develops a mathematical framework that aims to control the temperature and rotational speed in the activation process of a gas turbine in an optimal way. These controls influence the deformation and the stress in the component due to centripetal loads and transient thermal stress. An optimal control problem is formulated as the minimization of maximal von Mises stress over a given time and over the whole component. To find a solution for this, we need to solve the linear thermoelasticity and the heat equations using the finite element method. The results for the optimal control as functions of the rotation speed and external gas temperature over time are computed by sequential quadratic programming, where gradients are computed using finite differences. The overall outcome reveals a significant reduction of approximately 10%, from 830 N/mm² to 750 N/mm², in von Mises stress by controlling two parameters, along with the temporal separation of physical control phenomena. [ABSTRACT FROM AUTHOR]

As the environmental aspects become increasingly important, the disassembly problems have become the researcher's focus. Multiple criteria do not enable finding a general optimization method for the topic, but some heuristics and classical formulations provide effective solutions. By highlighting that disassembly problems are not the straight inverses of assembly problems and the conditions are not standard, disassembly optimization solutions require human control and supervision. Considering that Reinforcement learning (RL) methods can successfully solve complex optimization problems, we developed an RL-based solution for a fully formalized disassembly problem. There were known successful implementations of RL-based optimizers. But we integrated a novel heuristic to target a dynamically pre-filtered action space for the RL agent (dlOptRL algorithm) and hence significantly raise the efficiency of the learning path. Our algorithm belongs to the Heuristically Accelerated Reinforcement Learning (HARL) method class. We demonstrated its applicability in two use cases, but our approach can also be easily adapted for other problem types. Our article gives a detailed overview of disassembly problems and their formulation, the general RL framework and especially Q-learning techniques, and a perfect example of extending RL learning with a built-in heuristic. [ABSTRACT FROM AUTHOR]

We consider the algorithm by Ferson et al. (Reliab Comput 11(3):207--233, 2005) designed for solving the NP-hard problem of computing the maximal sample variance over interval data, motivated by robust statistics. The formulation can be written as a nonconvex quadratic optimization problem with a specific structure. First, we design a new version of the algorithm improving its original time bound O (n 2 · 2 ω) to O (n log n + n · 2 ω) , where n is the size of input data and ω is the clique number in a certain intersection graph. Then, we treat input data as random variables and introduce a natural probabilistic data generating model. We get 2 ω = O (n 1 / log log n) on average. This results in average computing time O (n 1 + ϵ) for ϵ > 0 arbitrarily small, which may be considered as surprisingly good average-time complexity for solving an NP-hard problem. We also prove the following tail bound on the distribution of computation time: hard instances, forcing the algorithm to compute in time 2 Ω (n) , occur rarely, with probability tending to zero at rate e - n log log n . The main result admits a smoothed-complexity interpretation: the average computing time can be bounded by n 1 + O (1 / σ) log log n , where σ measures the dispersion of the distribution of data perturbation. [ABSTRACT FROM AUTHOR]