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When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As enterprises increasingly rely on cloud-based Human Resource Management Systems (HRMS) deployed on the Hadoop platform, the optimization of weighting algorithms becomes imperative to enhance system efficiency. This paper addresses the complex challenge of load balancing in the cloud environment by proposing Effective Load Balancing Strategy (ELBS) a hybrid optimization approach that integrates both Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). The optimization objective involves the allocation of N jobs submitted by cloud users to M processing units, each characterized by a Processing Unit Vector (PUV). The PUV encapsulates critical parameters such as Million Instructions Per Second (MIPS), execution cost α, and delay cost L. Concurrently, each job submitted by a cloud user is represented by a Job Unit Vector (JUV), considering service type, number of instructions (NIC), job arrival time (AT), and worst-case completion time (wc). The proposed hybrid GA-GWO aims to minimize a cost function ζ, incorporating weighted factors of execution cost and delay cost. The challenge lies in determining optimal weights, a task addressed by assigning user preferences or importance as weights. The hybrid algorithm iteratively evolves populations of processing units, applying genetic operators, such as crossover and mutation, along with the exploration capabilities of GWO, to efficiently explore the solution space. This research contributes a comprehensive algorithmic solution to the optimization of weighting algorithms in enterprise HRMS on the cloud and Hadoop platform. The adaptability of the hybrid ELBS to dynamic cloud environments and its efficacy in handling complex optimization problems position it as a promising tool for achieving load balancing in HRMS applications. The proposed approach provides a foundation for further empirical validation and implementation in practical enterprise settings. [ABSTRACT FROM AUTHOR]

The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the used 3LNPC-SAPF topology, aims to ensure the seamless integration of a photovoltaic system into the three-phase four-wire grid while effectively mitigating grid harmonics, grid current unbalance, ensuring grid unit power factor by compensating the load reactive power, and allowing power sharing with the grid in case of an excess of generated power from the PV system, leading to overall high power quality at the grid side. This developed approach is based initially on the application of the four-wire instantaneous p-q theory for the identification of the reference currents that have to be injected by the 3LNPC-SAPF in the grid point of common coupling (PCC). Whereas, the 3LNPC is controlled based on using the finite control set model predictive control (FCS-MPC), which can be accomplished by determining the convenient set of switch states leading to the voltage vector, which is the most suitable to ensure the minimization of the selected cost function. Furthermore, the used topology requires a constant DC-link voltage and balanced split-capacitor voltages at the input side of the 3LNPN. Hence, the cost function is adjusted by the addition of another term with a selected weighting factor related to these voltages to ensure their precise control following the required reference values. However, due to the random changes in solar irradiance and, furthermore, to ensure efficient operation of the proposed topology, the PV system is connected to the 3LNPN-SAPF via a DC/DC boost converter to ensure the stability of the 3LNPN input voltage within the reference value, which is achieved in this paper based on the use of the maximum power point tracking (MPPT) technique. For the validation of the proposed control technique and the functionality of the used topology, a set of simulations has been presented and investigated in this paper following different irradiance profile scenarios such as a constant irradiance profile and a variables irradiance profile where the main aim is to prove the effectiveness and flexibility of the proposed approach under variable irradiance conditions. The obtained results based on the simulations carried out in this study demonstrate that the proposed control approach with the used topology under different loads such as linear, non-linear, and unbalanced can effectively reduce the harmonics, eliminating the unbalance in the currents and compensating for the reactive component contained in the grid side. The obtained results prove also that the proposed control ensures a consistent flow of power based on the sharing principle between the grid and the PV system as well as enabling the efficient satisfaction of the load demand. It can be said that the proposal presented in this paper has been proven to have many dominant features such as the ability to accurately estimate the power sharing between the grid and the PV system for ensuring the harmonics elimination, the reactive power compensation, and the elimination of the neutral current based on the zero-sequence component compensation, even under variable irradiance conditions. This feature makes the used topology and the developed control a valuable tool for power quality improvement and grid stability enhancement with low cost and under clean energy. [ABSTRACT FROM AUTHOR]

Installing multi-camera systems and inertial measurement units (IMUs) in self-driving cars, micro aerial vehicles, and robots is becoming increasingly common. An IMU provides the vertical direction, allowing coordinate frames to be aligned in a common direction. The degrees of freedom (DOFs) of the rotation matrix are reduced from 3 to 1. In this paper, we propose a globally optimal solver to calculate the relative poses and scale of generalized cameras with a known vertical direction. First, the cost function is established to minimize algebraic error in the least-squares sense. Then, the cost function is transformed into two polynomials with only two unknowns. Finally, the eigenvalue method is used to solve the relative rotation angle. The performance of the proposed method is verified on both simulated and KITTI datasets. Experiments show that our method is more accurate than the existing state-of-the-art solver in estimating the relative pose and scale. Compared to the best method among the comparison methods, the method proposed in this paper reduces the rotation matrix error, translation vector error, and scale error by 53%, 67%, and 90%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a solution for the cooperative obstacle avoidance path planning problem in dual manipulator arms using an improved Rapidly Exploring Random Tree (RRT) algorithm. The dual manipulator arms are categorized into a main arm and a secondary arm. Initially, the obstacle avoidance path for the master arm is planned in the presence of static obstacles. Subsequently, the poses of the master arm during its movement are treated as dynamic obstacles for planning the obstacle avoidance path for the slave arm. A cost function incorporating a fast convergence policy is introduced. Additionally, adaptive weights between distance cost and variation cost are innovatively integrated into the cost function, along with increased weights for each joint, enhancing the algorithm's effectiveness and feasibility in practical scenarios. The smoothness of the planned paths is improved through the introduction of interpolation functions. The improved algorithm is numerically modeled and simulated in MATLAB. The verification results demonstrate that the improved RRT algorithm proposed in this paper is both feasible and more efficient. [ABSTRACT FROM AUTHOR]

Food reserve is the cornerstone of national strategic security. China strives to build a complementary reserve system between government and enterprises, and how to improve the efficiency of food reserves is a matter of great concern to the government and society. Based on the analysis of the connotation of food reserve efficiency, this paper constructs the revenue function and the cost function of food reserve in an emergency and uses the cost-benefit method to explore the best ratio of government reserve, corporate social responsibility reserve, and commercial inventory when maximizing the efficiency of food reserve. This ratio is closely related to the total food reserve, the scale of supply interruption, the reserve cost of food stored by enterprises, and the government's subsidy cost for the social responsibility reserve. The unit subsidy cost of government to social responsibility reserve is inversely proportional to the proportion of corporate social responsibility reserve to corporate reserve. Doing a good job in the social responsibility management of food enterprises is an effective way to improve the efficiency of food reserves. To expand the scale of social responsibility reserves and realize "storing food in enterprises," the subsidies given by the government for corporate social responsibility reserves should not be too high. Taking food social responsibility reserve as an important part of corporate social responsibility, it's the key to promoting enterprises to actively undertake food social responsibility reserves. The conclusion of this paper provides a theoretical basis and decision-making reference for how to improve the efficiency of food reserves. [ABSTRACT FROM AUTHOR]

Researchers usually expect to reduce costs while improving product robustness in product quality design. The concurrent optimization of parameter and tolerance design assumes that the output response follows a normal distribution. However, non-normal responses are also common in product quality design. As for the concurrent optimization of parameter and tolerance design with a non-normal response, a novel total cost function based on the two-stage Bayesian sampling method is proposed in this paper. First, the hierarchical Bayesian model constructs the functional relationship between output response, input factors, and tolerance variables. Secondly, a two-stage Bayesian sampling method is used to obtain the simulated values of the output responses. The simulated response values are used to build the rejection cost and quality loss functions. Then, the genetic algorithm is used to optimize the constructed total cost model, including the tolerance cost, rejection cost, and quality loss. Finally, the effectiveness of the proposed method is demonstrated by two examples. The research results show that the proposed method in this paper can effectively improve product quality and reduce manufacturing costs when considering the uncertainty of model parameters and the variation of the output response. [ABSTRACT FROM AUTHOR]

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account. [ABSTRACT FROM AUTHOR]

This paper delves into the problem of direct position determination (DPD) for non-Gaussian sources. Existing DPD algorithms are hindered by their high computational complexity from exhaustive grid searches and a disregard for the received signal characteristics by multiple nested arrays (MNAs). To address these issues, the paper proposes a novel DPD algorithm for non-Gaussian sources with MNAs: the Discrete Fourier Transform (DFT) and Taylor compensation algorithm. Initially, the fourth-order cumulant matrix of the received signal is computed, and the vectorizing method is applied. Subsequently, a computationally efficient DPD cost function is proposed by leveraging a normalized DFT matrix to reduce complexity. Finally, first-order Taylor compensation is utilized to enhance the accuracy of the localization results. The superiority of the proposed algorithm is demonstrated through numerical simulation results. [ABSTRACT FROM AUTHOR]

When scanning a document printed on both sides by using an electronic scanner, the printed material on the back (front) side may be transmitted to the front (back) side. This phenomenon is called show-through. The problem to remove the show-through from scanned images is called the show-through removal in the literature. In this paper, we propose a new method of show-through removal based on the following principle. The proposed method uses two scanned images with the front side and with the back side as input images. The proposed method is based on Ahmed's Blind Image Deconvolution method discovered in 2013, which succeeded in formulating Blind Image Deconvolution as a nuclear norm minimization. Since the structure of show-through removal resembles that of Blind Image Deconvolution, we discovered that the show-through removal can be reformulated into a nuclear norm minimization in the space of outer product matrix constructed from an image vector and a point spread function vector of blurring. Using this key idea, we constructed the proposed method as follows. First, our cost function consists of the following three terms. The first term is the data term and the second term is the nuclear norm derived from the above reformulation. The third term is a regularization term to overcome the underdetermined nature of show-through removal problem and the existence of noise in the measured images. The regularization term consists of Total Variation imposed on the images. The resulting nuclear norm minimization problem is solved by using Accelerated Proximal Gradient method and Singular Value Projection with some problem-specific modifications, which converges fast and requires a simple implementation. We show results of simulation studies as well as results of real image experiments to demonstrate the performances of the proposed method. [ABSTRACT FROM AUTHOR]

In the realm of smart communication systems, where the ubiquity of 5G/6G networks and IoT applications demands robust data confidentiality, the cryptographic integrity of block and stream cipher mechanisms plays a pivotal role. This paper focuses on the enhancement of cryptographic strength in these systems through an innovative approach to generating substitution boxes (S-boxes), which are integral in achieving confusion and diffusion properties in substitution–permutation networks. These properties are critical in thwarting statistical, differential, linear, and other forms of cryptanalysis, and are equally vital in pseudorandom number generation and cryptographic hashing algorithms. The paper addresses the challenge of rapidly producing random S-boxes with desired cryptographic attributes, a task notably arduous given the complexity of existing generation algorithms. We delve into the hill climbing algorithm, exploring various cost functions and their impact on computational complexity for generating S-boxes with a target nonlinearity of 104. Our contribution lies in proposing a new cost function that markedly reduces the generation complexity, bringing down the iteration count to under 50,000 for achieving the desired S-box. This advancement is particularly significant in the context of smart communication environments, where the balance between security and performance is paramount. [ABSTRACT FROM AUTHOR]

In this paper, the open-loop and closed-loop local and remote stochastic nonzero-sum game (LRSNG) problem is investigated. Different from previous works, the stochastic nonzero-sum game problem under consideration is essentially a special class of two-person nonzero-sum game problem, in which the information sets accessed by the two players are inconsistent. More specifically, both the local player and the remote player are involved in the system dynamics, and the information sets obtained by the two players are different, and each player is designed to minimise its own cost function. For the considered LRSNG problem, both the open-loop and closed-loop Nash equilibrium are derived. The contributions of this paper are given as follows. Firstly, the open-loop optimal Nash equilibrium is derived, which is determined in terms of the solution to the forward and backward stochastic difference equations (FBSDEs). Furthermore, by using the orthogonal decomposition method and the completing square method, the feedback representation of the optimal Nash equilibrium is derived for the first time. Finally, the effectiveness of our results is verified by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a dynamic Cournot game in the market with a nonlinear (isoelastic) demand function. In our model, there are N competing firms featured by nonlinear cost functions, which enhances our model's resemblance to real-world scenarios. Firstly, we focus on the homogeneous case where firms' marginal costs change at equal rates. We establish analytical expressions of the market supply at equilibrium and perform comparative static analysis. In addition, we investigate the local stability under different economies of scale and show that there could be multiple stable equilibria if firms face economies of scale. The heterogeneous case where firms' marginal costs change at distinct rates is much more complex, thus we investigate the duopoly game with only two firms involved. Methods of symbolic computations such as triangular decomposition and partial cylindrical algebraic decomposition are employed in the analytical investigations of the equilibrium, which is nearly impossible by using the pencil-and-paper approach since the closed-form equilibrium is quite complicated. According to the computational results, we derive that two stable positive equilibria may coexist if both firms face economies of scale. Additionally, we conduct preliminary numerical simulations and find two different types of complex dynamics of the model considered in this paper: complex trajectories such as periodic and chaotic orbits may appear; the topological structure of the basins of attraction may be complex. [ABSTRACT FROM AUTHOR]

The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution of this paper is the presentation of a refined approach to sampling-based path planning algorithms that aligns with the specified mission objectives. This enhancement is achieved through a multi-layered framework approach, enabling a simplified discrete abstraction without relying on mesh decomposition. This abstraction is especially beneficial in complex or high-dimensional environments where mesh decomposition is challenging. The discrete abstraction effectively guides the sampling process, influencing the selection of vertices for extension and target points for steering in each iteration. To further improve efficiency, the algorithm incorporates a deep learning-based extension, utilizing training data to accurately model the optimal trajectory distribution between two points. The effectiveness of the proposed method is demonstrated through simulated tests, which highlight its ability to identify low-cost trajectories that meet specific mission criteria. Comparative analyses also confirm the superiority of the proposed method compared to existing methods. [ABSTRACT FROM AUTHOR]

An Improved Speed Loop (ISL) and Extended State Observer (ESO) strategy based on Model Predictive Control (MPC) of the Permanent Magnet Synchronous Motor (PMSM) is proposed in this paper. Firstly, considering the impact of load torque sudden changes on speed tracking performance, a reduced-order Luenberger observer is utilized to observe the load torque and combine with model prediction to form ISL. Secondly, the ESO is utilized to estimate the lumped disturbance and feedforward compensated to the improved speed loop, which improves the system's anti-interference performance. Then, a cost function that introduces the current tracking error at the switching point is constructed, reducing the current ripple. Finally, the experiments show that compared with the traditional PI speed control, the proposed strategy reduces the speed overshoot over a wide range of speeds, improves the speed tracking performance, and has superior dynamic performance and anti-disturbance performance under different operating conditions. [ABSTRACT FROM AUTHOR]

For future large-scale CubeSat applications in orbit, the deployer must accommodate a greater number of CubeSats and facilitate cluster releases. This paper introduces an improved A* algorithm tailored for CubeSat in-orbit transfer path planning. Unlike the traditional A* algorithm, this enhanced version incorporates a path coordination strategy to manage congestion caused by the simultaneous transfer of many CubeSats, ensuring they reach their designated release positions smoothly and thus significantly boosting the efficiency of CubeSat transfers. Additionally, the algorithm develops a cost model for attitude disturbances on the electromagnetic conveying platform and crafts an improved cost function. It strategically balances the reduction in attitude disturbances caused by CubeSat transfers with the efficiency of these transfers. The primary goal is to minimize platform disturbances while optimizing the number of steps CubeSats need to reach their intended positions. The effectiveness of this algorithm is demonstrated through detailed case studies, which confirm that during the CubeSat transfer process, the platform's attitude remains stable, and the transfer efficiency is well-managed, achieving efficient path planning for the in-orbit transfer of numerous CubeSats. [ABSTRACT FROM AUTHOR]

This paper investigates ESG from the perspective of changes in input elasticities of substitution and complementarity. Rather than compute these elasticities from the cost function, we compute them from the Input Distance Function (IDF). Our data are from Refinitiv Eikon Datastream database. We focus on the US economy due to her global role in the world economy and hence spillover effects of uncertainties on the rest of the world. The data consist of 5,798 companies comprising 38 US industries that span for 12 years from 2009 to 2020 and include: (i) financial data on sales, capital and employees; (ii) two financial ratios and (iii) three main ESG indicators. We compute Antonelli Elasticity of Complementarity (AEC) and Allen-Uzawa Elasticity of Substitution (AES) from the translog of IDF function. We find that the standard inputs have positive AEC elasticities; however, ESG cross-elasticities exhibit negative signs, classifying them as q-substitutes. Therefore, an increase in one of the ESG values leads to a decrease in the marginal value of the other. On the other hand, AES elasticities have a negative sign only for the Governance-Environment "doublet'; the rest of the pairs are positive implying that they are p-complements. [ABSTRACT FROM AUTHOR]

In this paper, a novel methodology is introduced for the inverse optimal control of non-affine, nonlinear and discrete-time systems. Although inverse optimal control of affine systems is studied in detail in technical literature, there is no adequate research about its implementation on non-affine systems. here are two main contributions of this work. Firstly using the input–output data of the system to be controlled its NARMA-L2 model is obtained using a multi-layer feedforward neural network, this step provides a conversion from a non-affine to affine system model. After the affine system model is obtained, the inverse optimal control law is applied. The second contribution of this paper is the computation of the inverse optimal control signal. The selection of the P matrix in the control law is crucial since its value directly affects the control performance. Here a novel method is proposed where an adaptive and optimal P matrix is computed online using a recurrent neural network to minimize a predefined cost function. The performance of the proposed control method is evaluated by simulations performed on benchmark problems. The robustness of the method is also tested by additional simulations where noise and disturbance is imposed on the system. The obtained results justify the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

In the world of human–robot coexistence, ensuring safe interactions is crucial. Traditional logic-based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all possible scenarios. Reinforcement learning has shown promise in robotics due to its superior adaptability over traditional logic. However, the exploratory nature of reinforcement learning can jeopardize safety. This paper addresses the challenges in planning trajectories for robotic arm manipulators in dynamic environments. In addition, this paper highlights the pitfalls of multiple reward compositions that are susceptible to reward hacking. A novel method with a simplified reward and constraint formulation is proposed. This enables the robot arm to avoid a nonstationary obstacle that never resets, enhancing operational safety. The proposed approach combines scalarized expected returns with a constrained Markov decision process through a Lagrange multiplier, resulting in better performance. The scalarization component uses the indicator cost function value, directly sampled from the replay buffer, as an additional scaling factor. This method is particularly effective in dynamic environments where conditions change continually, as opposed to approaches relying solely on the expected cost scaled by a Lagrange multiplier. [ABSTRACT FROM AUTHOR]

In this study, a method for assimilating FY4A advanced geostationary radiance imager (AGRI) cirrus‐effected radiances (CER) is investigated, and the impact of this method on water vapor analysis and rainstorm forecasting is examined through observing system simulation experiments and actual case experiments. The high proportion of inverted humidity profiles in the cirrus‐effected pixels is the main reason for the negative effect of assimilation in the mid‐to‐lower troposphere. To address this, relevant constraint conditions are incorporated into the cost function. The statistical results reveal that the addition of a CER assimilation improves the analysis increment of water vapor, with pattern correlation coefficients of 0.33, 0.35, and 0.20 at 200, 300, and 400 hPa, respectively, which are greater than those of a clear‐sky radiance assimilation (0.28, 0.33, and 0.17, respectively). Moreover, the inclusion of a CER assimilation greatly improves data utilization, and has a neutral to positive effect on precipitation forecasting. Plain Language Summary: Infrared all‐sky radiance assimilation is an attractive but challenging problem in satellite data assimilation. Is there an alternative approach to achieve the assimilation of infrared radiance for a certain type of cloud? It is found that cirrus clouds, which are loose ice clouds composed of ice crystals, can partially transmit infrared radiance and are large in number. Considering these advantages, a new direct assimilation method for FY4A advanced geostationary radiance imager (AGRI) cirrus‐effected radiances is proposed in this paper. The method addresses the uncertainty in the assimilation of cirrus‐effected radiances by adding weak constraints of inverse humidity. This research highlights the substantial increase in AGRI data usage when incorporating cirrus‐effected radiance data, as well as the neutral‐to‐positive impact on water vapor analysis and precipitation forecasting. This study also suggests that future endeavors could combine infrared channels with lower‐level microwave channels, which may have a more significant contribution to infrared radiance assimilation. Key Points: A high percentage of inverse humidity exists in the atmospheric profile corresponding to cirrus cloud fields of view over landThe assimilation of cirrus cloud pixels greatly improves the utilization rateAssimilating advanced geostationary radiance imager cirrus‐effected radiances has a neutral to positive effect on water vapor analysis and precipitation forecasting [ABSTRACT FROM AUTHOR]

It is well known that the recursive least squares (RLS) algorithm is renowned for its rapid convergence and excellent tracking capability. However, its performance is significantly compromised when the system is sparse or when the input signals are contaminated by impulse noise. Therefore, in this paper, the minimum error entropy (MEE) criterion is introduced into the cost function of the RLS algorithm in this paper, with the aim of counteracting the interference from impulse noise. To address the sparse characteristics of the system, we employ a universally applicable convex function to regularize the cost function. The resulting new algorithm is named the convex regularization recursive minimum error entropy (CR-RMEE) algorithm. Simulation results indicate that the performance of the CR-RMEE algorithm surpasses that of other similar algorithms, and the new algorithm excels not only in scenarios with sparse systems but also demonstrates strong robustness against pulse noise. [ABSTRACT FROM AUTHOR]

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions. [ABSTRACT FROM AUTHOR]

A planetary exploration rover has been employed for scientific endeavors or as a precursor for upcoming manned missions. Predicting rover traversability from its wheel slip ensures safe and efficient autonomous operations of rovers on deformable planetary surfaces; path planning algorithms that reduce slips by considering wheel-soil interaction or terrain data can minimize the risk of the rover becoming immobilized. Understanding wheel-soil interaction in transient states is vital for developing a more precise slip ratio prediction model, while path planning in the past assumes that slips generated at the path is a series of slip ratio in steady state. In this paper, we focus on the transient slip, or slip rate the time derivative of slip ratio, to explicitly address it into the cost function of path planning algorithm. We elaborated a regression model that takes slip rate and traction force as inputs and outputs slip ratio, which is employed in the cost function to minimize the rover slip in path planning phase. Experiments using a single wheel testbed revealed that even with the same wheel traction force, the slip ratio varies with different slip rates; we confirmed that the smaller the absolute value of the slip rate, the larger the slip ratio for the same traction force. The statistical analysis of the regression model confirms that the model can estimate the slip ratio within an accuracy of 85% in average. The path planning simulation with the regression model confirmed a reduction of 58% slip experienced by the rover when driving through rough terrain environments. The dynamics simulation results insisted that the proposed method can reduce the slip rate in rough terrain environments. [ABSTRACT FROM AUTHOR]

Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents. [ABSTRACT FROM AUTHOR]

A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases where demand forecasting should be considered (e.g., the case where a large number of orders are carried by a small number of agents). In this paper, we consider an online pickup and delivery problem considering fuel and demand forecasting. First, the pickup and delivery problem with fuel constraints is formulated. The information on demand forecasting is included in the cost function. Based on the orders, the agents' paths (e.g., the paths from stores to customers) are calculated. We suppose that the target area is given by an undirected graph. Using a given graph, several constraints such as the moves and fuels of the agents are introduced. This problem is reduced to a mixed integer linear programming (MILP) problem. Next, in online optimization, the MILP problem is solved depending on the acceptance of orders. Owing to new orders, the calculated future paths may be changed. Finally, by using a numerical example, we present the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We consider in this paper the budgeted minimum s - t cut problem. Suppose that we are given a directed graph G = (V , A) with two distinguished nodes s and t, k non-negative cost functions c 1 , ... , c k : A → Z + , and k - 1 budget bounds b 1 , ... , b k - 1 . The goal is to find a s - t cut C satisfying the budget constraints c h (C) ⩽ b h , for h = 1 , ... , k - 1 , and whose cost c k (C) is minimum. In this paper we discuss the linear relaxation of the problem and introduce a strict partial ordering on its solutions. We give a necessary and sufficient condition for which it has an integral optimal minimal (with respect to this ordering) basic solution. We also show that recognizing whether this is the case is NP-hard. [ABSTRACT FROM AUTHOR]

This paper primarily investigates a neural-network-based safe control scheme for solving the optimal control problem of continuous-time (CT) nonlinear systems with asymmetric input constraints under non-zero-sum (NZS) differential game scenarios. Initially, by constructing a novel non-quadratic function, the issue of asymmetric input constraints in the non-zero-sum differential game controllers is addressed. Subsequently, the safe Hamilton-Jacobi-Bellman (HJB) equation is derived from the direct integration of the control barrier function (CBF) into the traditional cost function, ensuring that the system states remain within a safe region. Then, the safe learning control scheme based on single critic neural network (NN) and adaptive dynamic programming (ADP) is proposed to approximate the optimal control strategy, differing from the dual-network update method commonly used in traditional ADP. Based on the constructed neural network weight adjustment rules, the optimal solution to the HJB equation can be derived within the safe learning control framework. Following this, Lyapunov's stability theory demonstrates that the errors in neural network weights and all signals within the closed-loop system are uniformly ultimately bounded (UUB). Finally, the effectiveness of the developed neural-network-based safe learning control method is validated through two simulation results. [ABSTRACT FROM AUTHOR]

A new multi gain SC inverter topology with fewer switching components and lower voltage stress is presented in this paper. It comprises eight switches and two capacitors, which raise the output voltage by three times the supply voltage. The capacitors voltages are automatically balanced to ensure proper operation. The circuit constructional, operation, and parameter optimisation techniques are discussed in details. A simple logic-based multicarrier Level Shift Pulse Width Modulation (LS-PWM) is employed to control the switching operation that reduces the complexity of the control circuit. Furthermore, a comparative analysis with other recent topologies has been presented to demonstrate the virtues of this approach. Finally, the proposed topology (PT) verification, feasibility, and effectiveness have been demonstrated both in simulation and experimentation. [ABSTRACT FROM AUTHOR]

Usually inactive or also known as thinned elements are used to simplify the array design complexity by turning off some of the active elements in uniformly filled arrays. Consequently, the far-field radiation characteristics such as sidelobe level, beamwidth, and directivity may be negatively changed if no optimizer is used. Further, these radiation characteristics may be unavoidably deteriorated when the main beam is scanned to new directions other than the referenced broadside direction. In this paper, an efficient optimization method based on the genetic algorithm and a dynamic deactivation method is proposed to randomly deactivate a number of array elements to minimize the peak sidelobe level and at the same time maintain the array directivity undistorted, while scanning the main beam. The deactivation method chooses optimally the suitable number of elements and their locations that need to be deactivated such that the resulting radiation characteristics positively change according to the specified cost function. Also, the proposed scanned array uses binary coefficients to activate and deactivate the array elements, thus, the feeding network of the proposed array is very simple, and it can be easily implemented in practice. Through extensive simulation results, we show that the proposed optimization method has good performance under wide range of scanned main beam directions. It is also found that the number of deactivation elements (i.e., the optimization variables) increases with larger scan angle. [ABSTRACT FROM AUTHOR]

Tensor networks are emerging architectures for implementing quantum classification models. The branching multi-scale entanglement renormalization ansatz (BMERA) is a tensor network known for its enhanced entanglement properties. This paper introduces a hybrid quantum-classical classification model based on BMERA and explores the correlation between circuit layout, expressiveness, and classification accuracy. Additionally, we present an autodifferentiation method for computing the cost function gradient, which serves as a viable option for other hybrid quantum-classical models. Through numerical experiments, we demonstrate the accuracy and robustness of our classification model in tasks such as image recognition and cluster excitation discrimination, offering a novel approach for designing quantum classification models. [ABSTRACT FROM AUTHOR]

Borehole pulsed eddy-current (PEC) systems based on uniform linear multicoil arrays (ULMAs) perform efficient nondestructive evaluations (NDEs) of metal casings. However, the limited physical space of the borehole restricts the degrees of freedom (DoFs) of ULMAs to be less than the number of constraints, which leads to the difficulty of compensating for the differences in signals acquired by different receivers with different transmitting-to-receiving distances (TRDs), and thus limits the effectiveness of the ULMA system. To solve this problem, this paper proposes sparse linear constraint minimum variance (S-LCMV) for NDEs of downhole casings employing ULMAs. By transforming and characterizing the original PEC signal, it was observed that the signal power dramatically decreased with increasing Legendre polynomial stage, confirming that the signal was sparsely distributed over the Gauss–Legendre stages. Using this property, the S-LCMV cost function with reduced constraints was constructed to provide enough DoFs to accurately calculate the weight coefficients, thus improving the detection performance. The effectiveness of the proposed method was verified through field experiments on an 8-element ULMA installed in a borehole PEC system for NDEs of oil-well casings. The results demonstrate that the proposed method could improve the weighting effect by reducing the number of constraints by 70% while ensuring the approximation accuracy, which effectively improved the signal-to-noise ratio of the measured signals and reduced the computational cost by about 87.9%. [ABSTRACT FROM AUTHOR]

This paper presents a model for a "hub-and-spoke network design considering congestion and flow-based cost function". The number of hubs and spokes is unknown, and the objective is to minimize the cost (including the transportation cost, lost demand, and facility setup cost). In the post-pandemic era, it is expected to have government-imposed restrictions on the congestion of airports, as a measure of health and safety. Unlike the current literature which considers a monetary penalty for congestion, we consider congestion as an externally imposed factor, which should be modeled as a constraint. We take a gravity-based modeling approach to obtain the desirability of a facility and calculate the demand matrix of the network. To solve the model, a Benders decomposition approach is proposed. Without the Benders decomposition approach, only instances with up to ten nodes were solved within a reasonable time, but with the Benders decomposition approach, instances with up to forty nodes were solved. A heuristic algorithm is developed to have a mechanism for dealing with larger instances. A set of experiments are conducted using data from the Turkish Network dataset to study various aspects of the proposed formulation and different parameters' effects on the performance of the model. [ABSTRACT FROM AUTHOR]

Considering that the existing path planning algorithms for mobile robots under rugged terrain do not consider the ground flatness and the lack of optimality, which leads to the instability of the center of mass of the mobile robot, this paper proposes an improved A* algorithm for mobile robots under rugged terrain based on the ground accessibility model and the ground ruggedness model. Firstly, the ground accessibility and ruggedness models are established based on the elevation map, expressing the ground flatness. Secondly, the elevation cost function that can obtain the optimal path is designed based on the two types of models combined with the characteristics of the A* algorithm, and the continuous cost function is established by connecting with the original distance cost function, which avoids the center-of-mass instability caused by the non-optimal path. Finally, the effectiveness of the improved algorithm is verified by simulation and experiment. The results show that compared with the existing commonly used path planning algorithms under rugged terrain, the enhanced algorithm improves the smoothness of paths and the optimization degree of paths in the path planning process under rough terrain. [ABSTRACT FROM AUTHOR]

Distributed drive electric vehicles improve steering response and enhance overall vehicle stability by independently controlling each motor. This paper introduces a control framework based on Adaptive Model Predictive Control (AMPC) for coordinating handling stability, consisting of three layers: the dynamic supervision layer, online optimization layer, and low-level control layer. The dynamic supervision layer considers the yaw rate and maneuverability limits when establishing the β − β ˙ phase plane stability boundary and designs variable weight factors based on this stability boundary. The online optimization layer constructs the target weight-adaptive AMPC strategy, which can adjust the control weights for maneuverability and lateral stability in real time based on the variable weight factors provided by the dynamic supervision layer. The low-level control layer precisely allocates the driver's requested driving force and additional yaw moment by using torque distribution error and tire utilization as the cost function. Finally, experiments are conducted on a Simulink-CarSim co-simulation platform to assess the performance of AMPC. Simulation results show that, compared to the traditional MPC strategy, this control strategy not only enhances maneuverability under normal conditions but also improves lateral stability control under extreme conditions. [ABSTRACT FROM AUTHOR]

The focus of this paper is the design of open-loop control profiles for linear differentially flat systems whose model parameters are uncertain and are represented probabilistically. To account for the time-invariant model parameter uncertainties, polynomial chaos is used to derive a surrogate model which permits easy evaluation of the mean and variance of the uncertain states. A chance constrained optimisation problem is posed to minimise the terminal error of the stochastic states for a prescribed risk of not satisfying the terminal state bounds. To permit posing a convex optimisation problem, the cost function which is the residual energy is approximated by a hypercube circumscribing the hypersphere which bounds the terminal residual error. This relaxation permits posing a convex optimisation problem to arrive at the robust control profile. The proposed technique is illustrated on two examples including the benchmark spring-mass-dashpot system undergoing a rest-to-rest maneuver and a UAV undergoing a translation in a prescribed time. Parametric studies are conducted where the impact of varying the order of the polynomial chaos expansion and the degree of the parameterised control profiles are used to conclude that a 2–3 degree polynomial chaos expansion is sufficient to capture the time evolution of the mean and variance of the states which are required for the chance constrained optimisation problems. As expected, the design results in improved performance as the number of free variables used to parameterise the control profiles increases. [ABSTRACT FROM AUTHOR]

Solving large-scale systems of nonlinear equations (SoNE) is a central task in mathematics that traverses different areas of applications. There are several derivative-free methods for finding SoNE solutions. However, most of the methods contributed to find SoNE solutions involve a monotone cost function. Methods dealing with pseudomonotone cost function remain rare. In this paper, we introduce two classes of derivative-free spectral three-term methods to solve large-scale continuous pseudomonotone SoNE. We combine the projection method of Solodov and Svaiter with the structure of the recently developed spectral three-term conjugate gradient method for unconstrained optimization by Amini and Faramarzi. We prove that the proposed methods possess sufficient descent property, trust region property, and global convergence without relying on Lipschitz continuity. Numerical experiments show that the proposed methods are efficient and competitive with existing methods. Finally, the proposed methods have been successfully applied to recover a sparse signal from incomplete and contaminated sampling measurements, yielding promising results. [ABSTRACT FROM AUTHOR]

This paper discusses in detail a new 17-level inverter that employs a switched-capacitor (SC)-based configuration. The proposed SC-based inverters require just a single DC power supply, three capacitors, four diodes and 13 switches. The peak inverse voltage of the proposed 17-level SC-based inverter half-bridge is cut in half when compared to the prior arrangement, which was published not long ago. The blocking voltage is reduced by an order of magnitude as well. Many of the features of the proposed SC-based inverter, such as a high boosting gain factor (G) of eight times, self-balanced capacitor voltage and decreased voltage ripples in capacitors, have been carried over to other SC-based MLIs. The circuit description, working theory, PWM modulation techniques and capacitor voltage ripples are all examined. Finally, the results of the experiments show without a doubt that the proposed SC-based inverter can be made. [ABSTRACT FROM AUTHOR]

In many research studies, multiple objectives need to be considered simultaneously to ensure an effective and efficient investigation. A compound optimal design provides a viable solution to this problem, allowing for the maximization of overall benefits through the integration of several factors. The paper addresses the application of compound optimal designs in the context of progressive first‐failure censoring, with a particular focus on the Generalized Exponential distribution with two parameters. The paper provides an illustrative example of compound designs by considering the cost function along with trace, variance, and determinant of inverse Fisher information. The best design is determined using a graphical solution technique that is both comprehensible and precise. Using a simple example, we demonstrate the advantage of compound optimal designs over constraint optimal designs. Furthermore, the paper examines real‐world data collection to demonstrate the practical utility of compound optimal designs. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions. [ABSTRACT FROM AUTHOR]

The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC. [ABSTRACT FROM AUTHOR]

Energy efficiency in drives is an important issue. In converter-supplied variable-speed drives, switching losses can amount to a significant portion of all losses. This has been considered in Predictive Stator Current Control (PSCC), considering commutations at the power converter. However, in multi-phase drives, the computational burden limits the application of said techniques. Recent fast predictive algorithms have enabled shorter application times with enhanced tracking results. However, the switching frequency becomes larger with diminishing sampling periods. This paper presents a method that retains the fast computation of recent methods while reducing the switching frequency. The proposal revolves around a modification of the cost function to penalize commutations in a nonlinear way. For this task, a novel, gradual penalization is introduced. The method is experimentally applied to a five-phase induction motor. Experimental results show a significant reduction in switching frequency without compromising other control objectives. This results in an enhanced PSCC with a small sampling period and reduced switching losses. [ABSTRACT FROM AUTHOR]

Azimuth multi-channel synthetic aperture radar (SAR) has always been an important technical means to achieve high-resolution wide-swath (HRWS) SAR imaging. However, in the space-borne azimuth multi-channel SAR system, random phase noise will be produced during the operation of each channel receiver. The phase noise of each channel is superimposed on the SAR echo signal of the corresponding channel, which will cause the phase imbalance between the channels and lead to the generation of false targets. In view of the above problems, this paper proposes a random phase noise compensation method for space-borne azimuth multi-channel SAR. This method performs feature decomposition by calculating the covariance matrix of the echo signal and converts the random phase noise estimation into the optimal solution of the cost function. Considering that the phase noise in the receiver has frequency-dependent and time-varying characteristics, this method calculates the phase noise estimation value corresponding to each range-frequency point in the range direction and obtains the phase noise estimation value by expectation in the azimuth direction. The proposed random phase noise compensation method can suppress false targets well and make the radar present a well-focused SAR image. Finally, the usefulness of the suggested method is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimization problems. The QAOA utilizes a quantum-classical loop, consisting of a quantum ansatz and a classical optimizer, to minimize some cost function, computed on the quantum device. This paper presents an investigation into the impact of realistic noise on the classical optimizer and the determination of optimal circuit depth for the Quantum Approximate Optimization Algorithm (QAOA) in the presence of noise. We find that, while there is no significant difference in the performance of classical optimizers in a state vector simulation, the Adam and AMSGrad optimizers perform best in the presence of shot noise. Under the conditions of real noise, the SPSA optimizer, along with ADAM and AMSGrad, emerge as the top performers. The study also reveals that the quality of solutions to some 5 qubit minimum vertex cover problems increases for up to around six layers in the QAOA circuit, after which it begins to decline. This analysis shows that increasing the number of layers in the QAOA in an attempt to increase accuracy may not work well in a noisy device. [ABSTRACT FROM AUTHOR]

This paper studies the consensus problem of Multi-Agent Systems (MASs) with input-delay using the Model Predictive Control (MPC) approach. Due to challenges such as input delay and communication graph, the traditional MPC is not efficient for the considered systems. In this regard, a novel MPC is developed for MASs with input delay. The main advantage of this paper is to design distributed controllers based on minimizing given cost functions which result in the improvement of the transient responses. For this purpose, first, distributed observers are derived to estimate the leader's states, perfectly. Then, distributed controllers are achieved through the MPC approach. Finally, numerical and practical examples are simulated to affirm the efficiency and applicability of the presented scheme. [ABSTRACT FROM AUTHOR]

The effective procedure for activating muscles and improving movements in individuals who have a central nervous system injury is Functional electrical stimulation (FES). To have accurate movements of the limbs, appropriate stimulation patterns should be generated and exerted on the muscles. The challenging problem is the redundancy in the musculoskeletal system that makes it necessary to use optimal control to derive muscle activations. For doing this, the redundant musculoskeletal model of the ankle joint is derived in this paper, then an optimal control method for estimating muscle activation during the gait cycle is designed. The aim of optimal control theory is to minimize the cost function, including muscle activation as the control input and joint angle tracking error as the output of the model. Two optimal control algorithms are implemented in this research. These approaches present direct and indirect control methods to solve the optimization problem of ankle movement achieved by experimental research on two healthy males during the gait cycle. Based on the results, the direct method is smoother for nonlinear systems and estimates desired muscle activation more precisely and the cost function coefficients are chosen with logical and less values in comparison to the indirect method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel finite speed-set model reference adaptive system (FSS-MRAS) based on the current predictive control (CPC) of a permanent magnet synchronous generator (PMSG) in wind energy turbine systems (WETSs). The mathematical models of wind energy systems (WESs) coupled with a permanent magnet synchronous generator (PMSG) are presented in addition to the implementation of the CPC of PMSGs. The proposed FSS-MRAS is based on eliminating the tuning burden of the conventional MRAS by using a limited set of speeds of the PMSG rotor that are employed to predict the rotor speed of the generator. Consequently, the optimal speed of the rotor is the one resulting from the optimization of a proposed new cost function. Accordingly, the conventional MRAS controller is eliminated and the main disadvantage represented in the tuning burden of the constant-gain proportional-integral (PI) controller has been overcome. The proposed FSS-MRAS observer is validated using MATLAB/Simulink (R2023b) at different operating conditions. The results of the proposed FSS-MRAS have been compared with those of the conventional MRAS, which proved the high robustness and reliability of the proposed observer. [ABSTRACT FROM AUTHOR]

Finite-set model predictive control (FS-MPC) is an easy and intuitive control technique. However, parametric uncertainties reduce the accuracy of the prediction. Classical MPC requires many calculations; therefore, the calculation time generates a considerable time delay in the actuation. This delay deteriorates the performance of the system and generates a significant current ripple. This paper proposes a finite-set integral sliding modes predictive control (FS-ISMPC) for a permanent magnet synchronous motor (PMSM). The conventional decision function is replaced by an integral sliding cost function, which has several advantages, such as robustness to parameter uncertainties, and convergence in finite time. The proposed decision function does not require the inductance and resistance parameters of the motor. In addition, the proposal includes compensation for the calculation delay of the control vector. The proposed control strategy was compared with traditional predictive control with delay compensation using a real-time hardware-in-the-loop (HIL) simulation. The results obtained from the comparison indicated that the proposed controller has a lower THD and computational burden. [ABSTRACT FROM AUTHOR]

Problem definition: This paper studies the single-warehouse assortment selection problem that aims to minimize the order fulfillment cost under the cardinality constraint. We propose two fulfillment-related cost functions corresponding to spillover fulfillment and order splitting. This problem includes the fill rate maximization problem as a special case. We show that although the objective function is submodular for a broad class of cost functions, the fill rate maximization problem with the largest order size being two is NP-hard. Methodology/results: To make the problem tractable to solve, we formulate the general warehouse assortment problem under the two types of cost functions as mixed integer linear programs (MILPs). We also provide a dynamic programming algorithm to solve the problem in polynomial time if orders are nonoverlapping. Furthermore, we propose a simple heuristic called the marginal choice indexing (MCI) policy that allows the warehouse to store the most popular products. This policy is easy to compute, and hence, it is scalable to large-size problems. Although the performance of MCI can be arbitrarily bad in some extreme scenarios, we find a general condition under which it is optimal. This condition is satisfied by many multi-purchase choice models. Managerial implications: Through extensive numerical experiments on a real-world data set from RiRiShun Logistics, we find that the MCI policy is surprisingly near optimal in all the settings we tested. Simply applying the MCI policy, the fill rate is estimated to improve by 9.18% on average compared with the current practice for the local transfer centers on the training data set. More surprisingly, the MCI policy outperforms the MILP optimal solution in 14 of 25 cases on the test data set, illustrating its robustness against demand fluctuations. History: This paper has been accepted as part of the 2021 M&SOM Data-Driven Research Challenge. Funding: This work was supported by the Singapore Ministry of Education (MoE) Tier 1 [Grant 23-0619-P0001]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0428. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) is a promising complementary technology to its radio frequency (RF) counterpart to satisfy the high quality-of-service (QoS) requirements of intelligent vehicular communications by reusing LED street lights. In this paper, a hybrid handover scheme for vehicular VLC/RF communication networks is proposed to balance QoS and handover costs by considering the vertical handover and horizontal handover together judging from the mobile state of the vehicle. A Markov decision process (MDP) is formulated to describe this hybrid handover problem, with a cost function balancing the handover consumption, delay, and reliability. A value iteration algorithm was applied to solve the optimal handover policy. The simulation results demonstrated the performance of the proposed hybrid handover scheme in comparison to other benchmark schemes. [ABSTRACT FROM AUTHOR]

It is worthwhile to calculate the execution cost of a manipulator for selecting a planning algorithm to generate trajectories, especially for an agricultural robot. Although there are various off-the-shelf trajectory planning methods, such as pursuing the shortest stroke or the smallest time cost, they often do not consider factors synthetically. This paper uses the state-of-the-art Python version of the Robotics Toolbox for manipulator trajectory planning instead of the traditional D–H method. We propose a cost function with mass, iteration, and residual to assess the effort of a manipulator. We realized three inverse kinematics methods (NR, GN, and LM with variants) and verified our cost function's feasibility and effectiveness. Furthermore, we compared it with state-of-the-art methods such as Double A* and MoveIt. Results show that our method is valid and stable. Moreover, we applied LM (Chan λ = 0.1) in mobile operation on our agricultural robot platform. [ABSTRACT FROM AUTHOR]