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Continuous accurate positioning in global navigation satellite system (GNSS)-denied environments is essential for robot navigation. Significant advances have been made with light detection and ranging (LiDAR)-inertial measurement unit (IMU) techniques, especially in challenging environments with varying lighting and other complexities. However, the LiDAR/IMU method relies on a recursive positioning principle, resulting in the gradual accumulation and dispersion of errors over time. To address these challenges, this study proposes a tightly coupled LiDAR/IMU/UWB fusion approach that integrates an ultra-wideband (UWB) positioning technique. First, a lightweight point cloud segmentation and constraint algorithm is designed to minimize elevation errors and reduce computational demands. Second, a multi-decision non-line-of-sight (NLOS) recognition module using information entropy is employed to mitigate NLOS errors. Finally, a tightly coupled framework via a resilient mechanism is proposed to achieve reliable position estimation for quadruped robots. Experimental results demonstrate that our system provides robust positioning results even in LiDAR-limited and NLOS conditions, maintaining low time costs. [ABSTRACT FROM AUTHOR]

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28 s, and when the number of iterations is 175–200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling. [ABSTRACT FROM AUTHOR]

• We present a multi-sensors fusion navigation algorithm for smartphones on shipboard. • The magnetometer-based turning detection method effectively detects turning motions. • We construct a NHC model with adaptive noise based on the turning detection results. • The algorithm effectively improves the attitude accuracy of smartphones. The rapid development of built-in sensors in smartphones has inspired a variety of ubiquitous public navigation applications. Nevertheless, previous works have focused on vehicle and pedestrian navigation by smartphones, with limited research on shipboard navigation. In this contribution, we designed a multi-sensors fusion navigation algorithm that integrates the global navigation satellite systems (GNSS), inertial measurement unit (IMU), and magnetometer (MAG) to achieve high-precision horizontal positioning on shipboard for smartphones. Considering the changeable motion state of a ship and the setting of non-holonomic constraint (NHC) noise, a magnetometer-based turning detection method is proposed, and a NHC model with adaptive noise is constructed based on the turning detection results. We conducted a lake experiment in Taku Forts Lake, Tianjin, using sensor data collected from a Xiaomi MI8 through our self-developed 'Sensor Logger' app. The experimental results demonstrated a high success rate of 94.22 % for magnetometer-based turning detection which can effectively detect turning motions. Compared to the traditional algorithm without constraint information, the roll, pitch and yaw accuracy improve by approximately 34.7 %, 17.9 %, and 57.9 %, respectively. Additionally, the position and velocity accuracy in the east and north directions improve to 0.0926 m, 0.1779 m, 0.0548 m/s, and 0.0589 m/s, respectively, representing enhancements of about 10.7 %, 7.4 %, 10.5 %, and 6.4 %. Overall, the proposed algorithm efficiently improves the shipboard navigation accuracy of smartphones, especially in terms of attitude accuracy. [ABSTRACT FROM AUTHOR]

Upper limb rehabilitation robots have important practical significance in helping patients recover their motor function, but traditional training methods often lack real-time and accurate evaluation of patient movement status and intention and have low interactive participation. Therefore, the study proposes a training system for upper limb rehabilitation robots based on multi-sensor trajectory data and human-computer interaction. The system is designed from three aspects: inertial sensor trajectory tracking, Kinect sensor image restoration, and interaction system design. A polynomial joint zero value constraint algorithm is introduced to correct errors, and variable parameter pixel filtering combined with a weighted average moving algorithm is used to improve image quality. The training results showed that the robot trajectory tracking accuracy significantly improved, and the improvement in motion trajectory error was greater than 20%. Compared with traditional training methods, interactive training had a recognition accuracy of over 85% in rehabilitation actions, and the system had better stability. This rehabilitation robot can effectively meet the feasibility and effectiveness of improving the interaction system, providing new technological means for intelligent medical services. [ABSTRACT FROM AUTHOR]

Risk budgeting is one of the most recent and successful approaches for the portfolio selection problem. Considering mean-standard-deviation as a risk measure, this paper addresses the risk budgeting problem under the uncertainty of the covariance matrix and the mean vector, assuming that a finite set of scenarios is possible. The problem is formulated as a scenario-based stochastic programming model, and its stability is examined over real-world instances. Then, since investing in all available assets in the market is practically impossible, the stochastic model is extended by incorporating the cardinality constraint so that all selected assets have the same risk contribution while maximizing the expected portfolio return. The extended problem is formulated as a bi-level programming model, and an efficient hybrid algorithm based on the cross-entropy is adopted to solve it. To calibrate the algorithm's parameters, an effective mechanism is introduced. Numerical experiments on real-world datasets confirm the efficiency of the proposed models and algorithm. [ABSTRACT FROM AUTHOR]

Laser point clouds are commonly affected by Gaussian and Laplace noise, resulting in decreased accuracy in subsequent surface reconstruction and visualization processes. However, existing point cloud denoising algorithms often overlook the local consistency and density of the point cloud normal vector. A feature map learning algorithm which integrates point normal constraints, Dirichlet energy, and coupled orthogonality bias terms is proposed. Specifically, the Dirichlet energy is employed to penalize the difference between neighboring normal vectors and combined with a coupled orthogonality bias term to enhance the orthogonality between the normal vectors and the subsurface, thereby enhancing the accuracy and robustness of the learned denoising of the feature maps. Additionally, to mitigate the effect of mixing noise, a point cloud density function is introduced to rapidly capture local feature correlations. In experimental findings on the anchor public dataset, the proposed method reduces the average mean square error (MSE) by 0.005 and 0.054 compared to the MRPCA and NLD algorithms, respectively. Moreover, it improves the average signal-to-noise ratio (SNR) by 0.13 DB and 2.14 DB compared to MRPCA and AWLOP, respectively. The proposed algorithm enhances computational efficiency by 27% compared to the RSLDM method. It not only removes mixed noise but also preserves the local geometric features of the point cloud, further improving computational efficiency. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed model predictive control (DMPC) for heterogeneous connected vehicle platoon (CVP) under denial-of-service (DoS) attacks. Firstly, a dynamic event-triggering mechanism (DETM) based on the information interaction between vehicles is proposed to reduce the communication and computational burdens. Due to the fact that the triggering moment for each vehicle cannot be synchronized and DoS attacks can break the communication between vehicles, a packet replenishment mechanism is designed to ensure the integrity and effectiveness of information interaction. Then, the effect of external disturbance is handled by adding robustness constraints to the DMPC algorithm. In addition, the recursive feasibility of the DMPC algorithm and input-to-state practical stability (ISPS) of the CVP control system are demonstrated. Finally, the effectiveness of the algorithm is verified by simulation and comparison results. • A general model for the longitudinal dynamics of heterogeneous vehicles with external disturbances is considered. • A secure platooning control protocol is designed for automated vehicles under DoS attacks. • A distributed model predictive control strategy is designed for each follower vehicle. • A dynamic event-triggering mechanism is designed to reduce the communication and computational burdens. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in the fields of ocean exploration, resource exploitation, and environmental monitoring. Path planning and obstacle avoidance are crucial to improve the operational capabilities of AUVs. However, most algorithms focus only on macro-global or micro-local path planning and rarely address both problems simultaneously. This study extends the classical A* algorithm by integrating field theory principles. The resulting Field Theory Augmented A* (FT-A*) algorithm combines the constraints in the AUV's dynamics and the threats posed by obstacles to ensure a safe navigation distance. The paths planned by the FT-A* algorithm were subsequently re-optimised in conjunction with Dubins curves, taking into account path smoothness and redundant node problems. Simulation experiments confirm that the improved algorithm can effectively help AUVs navigate safely around obstacles, which greatly improves navigation safety and increases the arithmetic power and navigation efficiency. The proposed FT-A* algorithm provides a robust solution for underwater path planning and demonstrates great practical value for AUV operation in complex marine environments. [ABSTRACT FROM AUTHOR]

Although the data-driven static voltage stability problems have been widely studied, most of the classical algorithms focus more on improving the accuracy of the system prediction, ignoring the error classification errors generated during the prediction process. Furthermore, current research ignores the utilization of data-driven voltage stability assessment of energy storage systems. Therefore, this paper proposes a static voltage stability assessment method for photovoltaic energy storage systems based on considering the error classification constraint algorithm using Neyman-Pearson umbrella algorithms. Firstly, the Spearman Correlation Coefficient is employed in the feature selection phase. Secondly, an updated voltage stability assessment (VSA) model is proposed. Compared with the existing data-driven prediction of system static voltage stability in the literature, it can realize voltage stability assessment more quickly. Furthermore, on the basis of rapid voltage stability assessment, the umbrella NP classifier can also effectively limit the first-class error and attenuate the effect of error classification by mirroring the control of the number of cycle splits and the type I classification error threshold. Finally, the simulation and experimental results show that the effectiveness and robustness of the scheme proposed in this paper in grid-connected photovoltaic energy farms. [ABSTRACT FROM AUTHOR]

In this paper, we consider a novel vector-valued rational interpolation algorithm and its application. Compared to the classic vector-valued rational interpolation algorithm, the proposed algorithm relaxes the constraint that the denominators of components of the interpolation function must be identical. Furthermore, this algorithm can be applied to construct the vector-valued interpolation function component-wise, with the help of the common divisors among the denominators of components. Through experimental comparisons with the classic vector-valued rational interpolation algorithm, it is found that the proposed algorithm exhibits low construction cost, low degree of the interpolation function, and high approximation accuracy. [ABSTRACT FROM AUTHOR]

Quantum optimization is a significant area of quantum computing research with anticipated near-term quantum advantages. Current quantum optimization algorithms, most of which are hybrid variational-Hamiltonian-based algorithms, struggle to present quantum devices due to noise and decoherence. Existing techniques attempt to mitigate these issues through employing different Hamiltonian encodings or Hamiltonian clause pruning, but they often rely on optimistic assumptions rather than a deep analysis of the problem structure. We demonstrate how to formulate the problem Hamiltonian for a quantum approximate optimization algorithm that satisfies all the requirements to correctly describe the considered tactical aircraft deconfliction problem, achieving higher probabilities for finding solutions compared to previous works. Our results indicate that constructing Hamiltonians from an unconventional, quantum-specific perspective with a high degree of entanglement results in a linear instead of exponential number of entanglement gates instead and superior performance compared to standard formulations. Specifically, we achieve a higher probability of finding feasible solutions: finding solutions in nine out of nine instances compared to standard Hamiltonian formulations and quadratic programming formulations known from quantum annealers, which only found solutions in seven out of nine instances. These findings suggest that there is substantial potential for further research in quantum Hamiltonian design and that gate-based approaches may offer superior optimization performance over quantum annealers in the future. [ABSTRACT FROM AUTHOR]

In the field of power systems, the optimization challenge of combined heat and power units economic dispatch (CHPUED) holds immense importance. This study presents an improved Aquila optimization technique (IAQT) that effectively tackles the CHPUED. The primary objective of the enhanced IAQT model is to minimize the overall cost of power generation in CHP systems while satisfying demand and operational constraints. However, to achieve more accurate cost estimations and avoid suboptimal solutions, it is crucial to consider transmission losses in the optimization model. By incorporating transmission losses, the IAQT algorithm can allocate power generation resources more effectively, leading to improved system efficiency and reduced operational costs. The proposed IAQT algorithm addresses the limitations of the standard AQT and introduces novel features to enhance its search capabilities. One key limitation of the standard AQT is its heavy reliance on the best solution found during optimization. To overcome this drawback, the enhanced IAQT model eliminates the dependency on the best solution and enables a more thorough exploration of the search space. Moreover, the algorithm incorporates specific limitations and constraints for each dimension of the newly generated solutions, ensuring their feasibility and validity. The standard AQT and proposed IAQT are tested on CEC 20 benchmark functions. Moreover, the proposed approach is extensively evaluated through experimentation and testing on various scenarios, including 7–48-unit and large 96-unit systems with/without losses. Furthermore, the overall costs for the 7 unit-system are considered including the reserve constraint. The results exhibit the remarkable performance and efficiency of the enhanced IAQT model, outperforming the standard version and several previously reported results. This validation underscores the significant contribution of the study in addressing the CHPUED and highlights its potential for real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, the quadratic p-median optimization problem is discussed, where the goal is to connect users to a selected group of facilities (emergency services, telecommunications servers, healthcare facilities) at the lowest possible cost. The problem is aimed at minimizing the cost of connecting these selected facilities. The costs are symmetric, meaning connecting two different points is the same in both directions. This problem extends the traditional p-median problem, a combinatorial problem used in various fields like facility location, network design, transportation, supply chain networks, emergency services, healthcare, and education planning. Surprisingly, the quadratic version has not been thoroughly considered in the literature. The paper highlights the formulation of two mixed-integer quadratic programming models to find optimal solutions to this problem. One model is a classic formulation, and the other is based on set cover theory. Linear versions and Bender's decomposition formulations for each model are also derived. A Bender's decomposition is solved using an algorithm that adds constraints during each iteration to improve the solution. Lazy constraints in the Gurobi solver's branch and cut algorithm are dynamically added whenever a mixed-integer programming solution is found. Additionally, an efficient local search meta-heuristic is proposed that usually finds optimal solutions for tested instances. Challenging instances with up to 60 facilities and 2000 users are successfully solved. Our results show that Bender's models with lazy constraints are the most effective for Euclidean and random test cases, achieving optimal solutions in less CPU time. The meta-heuristic also finds near-optimal solutions rapidly for these cases. [ABSTRACT FROM AUTHOR]

The high proportion of distributed photovoltaic (DPV) access has changed the traditional distribution network structure and operation mode, posing a huge threat to the stable operation and economy of the distribution network. Aiming at a reasonable access capacity of DPV in the distribution network, this paper proposes an economic access capacity evaluation method for DPV in the distribution network considering proper PV power curtailment. Firstly, a method for generating typical joint light intensity and load power operation scenarios based on an improved K-means clustering algorithm is proposed, which provides comprehensive scenario support for the evaluation. Secondly, based on active and reactive power regulation, this paper proposes a DPV access capacity enhancement method to improve the DPV access capacity. Thirdly, considering proper PV power curtailment, an evaluation model of DPV economic access capacity in the distribution network is established to solve the maximum DPV economic access capacity in the distribution network. And aiming at the nonlinear problem in the model, the second-order cone relaxation method is employed to transform the model into the second-order cone programming model, so as to solve the objective function conveniently and efficiently. Finally, based on the improved IEEE 33-node distribution network analysis, the results show that the proposed method can be more comprehensive and effective in evaluating the DPV economic access capacity in the distribution network, and proper PV power curtailment can significantly increase the DPV economic access capacity in the distribution network. [ABSTRACT FROM AUTHOR]

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity in the reentry trajectory planning problem for reusable launch vehicles (RLVs), the scale of the problem after high-precision discretization can become significantly large, and the non-convex path constraints are prone to exceed limits. Meanwhile, the objective function oscillation phenomenon may occur due to successive convexification, which results in poor convergence. To address these issues, a novel sequential convex programming (SCP) method utilizing modified hp-adaptive mesh refinement and variable quadratic penalty is proposed in this paper. Firstly, a local mesh refinement algorithm based on constraint violation is proposed. Additional mesh intervals and mesh points are added in the vicinity of the constraint violation points, which improves the satisfaction of non-convex path constraints. Secondly, a sliding window-based mesh reduction algorithm is designed and introduced into the hp-adaptive pseudospectral (PS) method. Unnecessary mesh intervals are merged to reduce the scale of the problem. Thirdly, a variable quadratic penalty-based SCP method is proposed. The quadratic penalty term related to the iteration direction and the weight coefficient updating strategy is designed to eliminate the oscillation. Numerical simulation results show that the proposed method can strictly satisfy path constraints while the computational efficiency and convergence of SCP are improved. [ABSTRACT FROM AUTHOR]

Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent years, researchers have shifted towards enhancing continuous optimization algorithms and incorporating constraints to address WFLO challenges. This approach has shown remarkable promise, outperforming traditional genetic algorithms and gaining traction among researchers. To further elevate the performance of continuous optimization algorithms in the context of WFLO, we introduce a multi-local search-based SHADE, termed MS-SHADE. MS-SHADE is designed to fine-tune the trade-off between convergence speed and algorithmic diversity, reducing the likelihood of convergence stagnation in WFLO scenarios. To assess the effectiveness of MS-SHADE, we employed a more extensive and intricate wind condition model in our experiments. In a set of 16 problems, MS-SHADE's average utilization efficiency improved by 0.14% compared to the best algorithm, while the optimal utilization efficiency increased by 0.3%. The results unequivocally demonstrate that MS-SHADE surpasses state-of-the-art WFLO algorithms by a significant margin. [ABSTRACT FROM AUTHOR]

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application. [ABSTRACT FROM AUTHOR]

The meshing and limit equations of worm drive usually have strong nonlinearities such as multiple solutions, solution nonexistence and equation singularity. Meanwhile, the tooth surfaces of worm drive are in nonconforming line contact, which often requires mesh refinement of contact region for the loaded contact analysis. These two challenges cause that the modeling of worm drive heavily relies on manual adjustment and the loaded contact analysis of worm drive is still rare especially when edge contact and assembly error are concerned. Focusing on the double enveloping hourglass worm (DEHW) drive with planar generatrix, this work presents procedures to solve meshing and limit equations with global convergence. The instantaneous contact line, meshing limit line, curvature interference limit line and tooth surface grid discretization are adaptively generated, without manual trial or adjustment. On the basis of adaptive mesh refinement of tooth surface, the mortar virtual element method is adopted for loaded contact analysis of DEHW drive with edge contact and center distance error. Under sliding friction, the discrete system of governing equalities and inequalities is solved by semi-smooth Newton algorithm after constraint condensation. Numerical results for meshing theory and loaded contact analysis of DEHW drive with planar generatrix are discussed. [ABSTRACT FROM AUTHOR]

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment. [ABSTRACT FROM AUTHOR]

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. For situations where the radiation field of unknown materials is reconstructed or prior knowledge cannot be obtained, this paper proposes a multi-spectral temperature field reconstruction technology under a sparse projection. This technology utilizes the principles of multi-spectral temperature measurement technology, takes the correlation of radiation information between sub-regions of the temperature field as the optimization objective, and establishes statistical rules between the missing information by combining the equation constraint optimization algorithm and multi-spectral temperature measurement technology. Finally, the temperature field to be measured is reconstructed. The simulation and experimental tests show that, without any prior knowledge, the proposed method can reconstruct the temperature field under two projection angles, with an accuracy of 1.64~12.25%. Moreover, the projection angle is lower, and the robustness is stronger than that of the other methods. [ABSTRACT FROM AUTHOR]

Motivation Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs). Results We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time. Availability and implementation https://github.com/shawn-peng/xlms [ABSTRACT FROM AUTHOR]

This research introduces a novel approach for object tracking, capitalizing on the Bayes filter algorithm within the constraints of a single-camera setup. Object tracking is a pivotal aspect of computer vision, significantly influencing system performance in diverse applications. The integration of the Bayes filter algorithm provides a probabilistic framework, effectively addressing challenges posed by occlusions, lighting variations, and unpredictable object movements in real-world scenarios. Our methodology not only streamlines the tracking setup by utilizing a single camera but also enhances practicality, making it particularly relevant for applications with resource constraints. The paper offers a comprehensive exploration of this approach, delving into the theoretical foundations and technical intricacies that underlie the fusion of advanced object tracking techniques with the Bayes filter algorithm. Through empirical evaluations across varied tracking scenarios, our approach demonstrates superior effectiveness compared to traditional methods, showcasing the algorithm's ingenuity in improving tracking accuracy and adaptability. It achieved a dynamic simulation efficiency of 97.025%, a sensitivity of 96.2616%, and an overall system quality (F-score) of 97.0493%. This research contributes valuable insights to the evolving landscape of object tracking methodologies, presenting a practical and efficient solution that combines the Bayes filter algorithm's power with the simplicity of a single camera setup. The findings presented herein offer a nuanced perspective for researchers and practitioners seeking to elevate the precision and real-time adaptability of object tracking systems in diverse applications. [ABSTRACT FROM AUTHOR]

The Sine–Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm's capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications. [ABSTRACT FROM AUTHOR]

The Cardinality-Constrained Portfolio Selection Problem (CCPSP) consists of allocating resources to a limited number of assets. In its classical form, it is represented as a multi-objective problem, which considers the expected return and the assumed risk in the portfolio. This problem is one of the most relevant subjects in finance and economics nowadays. In recent years, the consideration of cardinality constraints, which limit the number of assets in the portfolio, has received increased attention from researchers, mainly due to its importance in real-world decisions. In this context, this paper proposes a new hybrid heuristic approach, based on a Non-dominated Sorting Genetic Algorithm with Local Search structures, to solve PSP with cardinality constraints, aiming to overcome the challenge of achieving efficient solutions to the problem. The results demonstrated that the proposed algorithm achieved good quality results, outperforming other methods in the literature in several classic instances. [ABSTRACT FROM AUTHOR]

Autonomous collision avoidance decision making for maritime autonomous surface ships (MASS), as one of the key technologies for MASS autonomous navigation, is a research hotspot focused on by relevant scholars in the field of navigation. In order to guarantee the rationality, efficacy, and credibility of the MASS autonomous collision avoidance scheme, it is essential to design the MASS autonomous collision avoidance algorithm under the stipulations of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). In order to enhance the autonomous collision avoidance decision-making capability of MASS in accordance with the relevant provisions of COLREGs, an improved NSGA-II autonomous collision avoidance decision-making algorithm based on the good point set method (GPS-NSGA-II) is proposed, which incorporates the collision hazard and the path cost of collision avoidance actions. The experimental results in the four simulation scenarios of head-on situation, overtaking situation, crossing situation, and multi-ship encounter situation demonstrate that the MASS autonomous collision avoidance decision making based on the GPS-NSGA-II algorithm under the constraints of COLREGs is capable of providing an effective collision avoidance scheme that meets the requirements of COLREGs in common encounter situations and multi-ship avoidance scenarios promptly, with a promising future application. [ABSTRACT FROM AUTHOR]

The problem of finding a globally optimal k-partition of a set A is a very intricate optimization problem for which in general, except in the case of one-dimensional data, i.e., for data with one feature ( A ⊂ R ), there is no method to solve. Only in the one-dimensional case, there are efficient methods based on the fact that the search for a globally optimal k-partition is equivalent to solving a global optimization problem for a symmetric Lipschitz-continuous function using the global optimization algorithm DIRECT. In the present paper, we propose a method for finding a globally optimal k-partition in the general case ( A ⊂ R n , n ≥ 1 ), generalizing an idea for solving the Lipschitz global optimization for symmetric functions. To do this, we propose a method that combines a global optimization algorithm with linear constraints and the k-means algorithm. The first of these two algorithms is used only to find a good initial approximation for the k-means algorithm. The method was tested on a number of artificial datasets and on several examples from the UCI Machine Learning Repository, and an application in spectral clustering for linearly non-separable datasets is also demonstrated. Our proposed method proved to be very efficient. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technology has been widely applied across a variety of fields, with head-up displays (HUDs) being one of its prominent uses, offering immersive three-dimensional (3D) experiences and interaction with digital content and the real world. AR-HUDs face challenges such as limited field of view (FOV), small eye-box, bulky form factor, and absence of accommodation cue, often compromising trade-offs between these factors. Recently, optical waveguide based on pupil replication process has attracted increasing attention as an optical element for its compact form factor and exit-pupil expansion. Despite these advantages, current waveguide displays struggle to integrate visual information with real scenes because they do not produce accommodation-capable virtual content. In this paper, we introduce a lensless accommodation-capable holographic system based on a waveguide. Our system aims to expand the eye-box at the optimal viewing distance that provides the maximum FOV. We devised a formalized CGH algorithm based on bold assumption and two constraints and successfully performed numerical observation simulation. In optical experiments, accommodation-capable images with a maximum horizontal FOV of 7.0 degrees were successfully observed within an expanded eye-box of 9.18 mm at an optimal observation distance of 112 mm. [ABSTRACT FROM AUTHOR]

This paper adopts the multi-criterion decision-making model of fuzzy-TODIM and genetic algorithm (GA) for optimal portfolio allocation. We applied Markowitz's portfolio parameters as inputs for the fuzzy TODIM model to rank stocks that are constituents of each index from three different markets. Portfolios are then generated dynamically using three weighting techniques and subject to multi-objective criteria and additional constraints. The results indicate a significant variation in performance metrics between the model-generated portfolios and the market indices. Replication of the procedure produces a similar outcome. Moreover, the out-of-sample tests conducted over 3 years validate the results' robustness, indicating that fuzzy TODIM, combined with GA, can achieve superior performance in dynamic portfolio allocation. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

Finite element analysis (FEA) is commonly used in orthopaedic research to estimate localised tissue stresses and strains. A variety of boundary conditions have been proposed for isolated femur analysis, but it remains unclear how these assumed constraints influence FEA predictions of bone biomechanics. This study compared the femoral head deflection (FHD), stresses, and strains elicited under four commonly used boundary conditions (fixed knee, mid-shaft constraint, springs, and isostatic methods) and benchmarked these mechanics against the gold standard inertia relief method for normal and pathological femurs (extreme anteversion and retroversion, coxa vara, and coxa valga). Simulations were performed for the stance phase of walking with the applied femoral loading determined from patient-specific neuromusculoskeletal models. Due to unrealistic biomechanics observed for the commonly used boundary conditions, we propose a novel biomechanical constraint method to generate physiological femur biomechanics. The biomechanical method yielded FHD (< 1 mm), strains (approaching 1000 µε), and stresses (< 60 MPa), which were consistent with physiological observations and similar to predictions from the inertia relief method (average coefficient of determination = 0.97, average normalized root mean square error = 0.17). Our results highlight the superior performance of the biomechanical method compared to current methods of constraint for both healthy and pathological femurs. [ABSTRACT FROM AUTHOR]

More accurate inversion of source fault geometry and slip parameters under the constraint of the Bayesian algorithm has become a research hotspot in the field of geodetic inversion in recent years. In nonlinear inversion, the determination of the weight ratio of the joint inversion of multisource data is more complicated. In this context, this paper proposes a simple and easily generalized weighting method for inversion of source fault parameters by joint geodetic multisource data under the Bayesian framework. This method determines the relative weight ratio of multisource data by root mean square error (RMSE) value and can be extended to other nonlinear search algorithms. To verify the validity of the method in this paper, this paper first sets up four sets of simulated seismic experiment schemes. The inversion results show that the joint inversion weighting method proposed in this paper has a significant decrease in the large residual value compared with the equal weight joint inversion and the single data source joint inversion method. The east–west deformation RMSE is 0.1458 mm, the north–south deformation RMSE is 0.2119 mm and the vertical deformation RMSE is 0.2756 mm. The RMSEs of the three directions are lower than those of other schemes, indicating that the proposed method is suitable for the joint inversion of source parameters under Bayesian algorithm. To further verify the applicability of the proposed method in complex earthquakes, the source parameters of the Maduo earthquake were inverted using the method of this paper. The focal depth of the inversion results in this paper is closer to the focal depth released by the GCMT agency. In terms of strike angle and dip angle, the joint inversion in this paper is also more inclined to the GCMT results. The joint inversion results generally conform to the characteristics of left-lateral strike-slip, which shows the adaptability of this method in complex earthquakes. [ABSTRACT FROM AUTHOR]

In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms. • For distributed optimization problem with convex constraints, we design an effective prescribed-time distributed algorithm which can guarantee the multi-agent system converge to the optimal solution. • The designed algorithm in this paper can achieve convergence within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. • In this paper, we not only consider the condition that the agents' states are constrained, but also ensure that the multi-agent system can reach the optimal solution within any prescribed-time, which is more practical. [ABSTRACT FROM AUTHOR]

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel longitudinal reference trajectory tracking guidance framework for Mars atmospheric entry. Unlike traditional methods that require strict adherence to pre-defined safe trajectories, this guidance framework incorporates path constraints into the closed-loop tracking process to ensure safety. The framework comprises a safety module and a guidance law module. The safety module employs an online path constraints management algorithm that converts real-time path constraints into equivalent control constraints and applies them to the guidance module. The guidance law module uses the anti-saturation generalized super-twisting based sliding mode (ASGSTSM) guidance law, which provides sufficient rapidity and robustness to achieve trajectory tracking while accounting for uncertainties and control saturation. Simulation results demonstrate that the proposed guidance framework effectively ensures compliance of the entry vehicle with path constraints. The ASGSTSM guidance law exhibits notable speed and robustness, making it adaptable within the guidance framework. Particularly, in Monte Carlo simulations introducing significant uncertainty, incorporating the ASGSTSM guidance law within the guidance framework resulted in convergence in all trajectory tracking processes, whereas guidance laws in the control group led to numerous failures. Additionally, the paper offers practical principles for designing guidance laws within this universal framework. This facilitates future research to easily replace the guidance law module based on specific mission requirements. [ABSTRACT FROM AUTHOR]

We demonstrate a symmetry condition for the mixed partial derivatives of measured data when performing a coherent diffraction imaging (CDI) experiment for differentiable samples under scientific investigation. The proposed condition can be used as a physical restriction to improve real data measurements and has been used within the most celebrated phase-retrieval inversion algorithms as an ad hoc constraint without proof. The symmetry relies on John's ultrahyperbolic equation for the X-ray transform, which is also demonstrated to be valid in the imaging regime for CDI. The obtained conditions are easy to implement and can be used as a constraint by computational imaging methods. [ABSTRACT FROM AUTHOR]

In this paper, the properties of 10 different feature selection algorithms for generalized additive models (GAMs) are compared on one simulated and two real-world datasets under concurvity. Concurvity can be interpreted as a redundancy in the feature set of a GAM. Like multicollinearity in linear models, concurvity causes unstable parameter estimates in GAMs and makes the marginal effect of features harder interpret. Feature selection algorithms for GAMs can be separated into four clusters: stepwise, boosting, regularization and concurvity controlled methods. Our numerical results show that algorithms with no constraints on concurvity tend to select a large feature set, without significant improvements in predictive performance compared to a more parsimonious feature set. A large feature set is accompanied by harmful concurvity in the proposed models. To tackle the concurvity phenomenon, recent feature selection algorithms such as the mRMR and the HSIC-Lasso incorporated some constraints on concurvity in their objective function. However, these algorithms interpret concurvity as pairwise non-linear relationship between features, so they do not account for the case when a feature can be accurately estimated as a multivariate function of several other features. This is confirmed by our numerical results. Our own solution to the problem, a hybrid genetic–harmony search algorithm (HA) introduces constrains on multivariate concurvity directly. Due to this constraint, the HA proposes a small and not redundant feature set with predictive performance similar to that of models with far more features. [ABSTRACT FROM AUTHOR]

Collimated flat-top beam shapers primarily consisting of freeform lenses have a wide range of applications and pose challenges in terms of processing and integration when the diameter is less than millimeters. Metasurfaces represent a promising solution to planarize optics, can mimic any surface curvature without additional fabrication difficulty, and are suitable for flat-top optics. The conventional metasurface design approach relies on imparting the required phase using meta-atoms and encounters challenges in amplitude modulation due to near-field coupling and varying transmittances among meta-atoms with different phases, making the design of flat-top beam shapers difficult. In this paper, we propose a complex-amplitude constraint Gerchberg–Saxton algorithm for designing a collimated flat-top beam shaper metasurface doublet, which avoids the influence of strong near-field coupling on the amplitude distribution and simultaneously considers the amplitude-phase characteristics of the meta-atoms. A collimated flat-top beam with exceptional homogeneity Up of approximately 0.01, a wavefront error less than 0.1λ, and a transmittance higher than 86 % is experimentally obtained, comparable to commercial products based on freeform lenses. A collimated flat-top beam shaper metasurface doublet for generating flat-top beam is introduced, promoting efficient integration with laser systems. [ABSTRACT FROM AUTHOR]

In this paper, the reentry phase of the Aerospaceplane is taken as the research object, and the performance parameters of the reusable rocket of a private company are analyzed. Aiming at the guidance and control scheme of the spacecraft returning to the reentry trajectory in the real environment, the original natural algorithm is optimized by considering various reentry flight constraints, and the improved original natural algorithm is used to optimize the reentry trajectory of the Aerospaceplane. We obtained two types of reentry trajectories in the presence of large flight-restricted areas, the "S-type" trajectory and the "spiral-type" trajectory, and obtained data on various influencing factors. The results showed that the basic state parameters of the spiral trajectory optimized using the improved original natural algorithm after adding constraints met the constraint conditions. The aerodynamic heating rate and overload of the spiral reentry trajectory were to some extent greater than those of the S-type trajectory. Under the increasingly stringent requirements of the aerospace environment, new requirements were put forward for the thermal protection system to meet the wider environmental situation. This paper uses the improved original natural algorithm for the first time and applies it to the field of aerospace reentry and entry and adds more constraints to this algorithm for computation. Besides, for the first time, the macroscopic nature of trajectory types is used as a comparative element for parameter comparison, providing a reference basis for selecting trajectory optimization directions from the macroscopic perspective of trajectory types. [ABSTRACT FROM AUTHOR]

During the propagation of seismic waves underground, the high-frequency seismic response of thin reservoir is absorbed and attenuated, which poses a challenge in seismic thin reservoir prediction. The high-resolution processing techniques have the capability to significantly expand the frequency range of the seismic data, so it becomes a key technique for thin reservoir prediction. Most of these techniques necessitate the extraction of seismic wavelets. However, the spatial and temporal variations of seismic data result in multiple solutions for wavelet extraction. Simultaneously, the majority of techniques fail to consider the influence of spatial tectonic features on the high-resolution processing. In this paper, we propose a novel solution to address these two fundamental challenges by utilizing seismic spectral expansion, sparse reflection coefficients, and spatial continuity constraints. First, we propose an innovative spectral fitting method that aims to expand the frequency bandwidth while adhering to the desired wavelet constraints. This method allows us to fully utilize the effective frequency information. It not only obtains broadband seismic data but also captures precise wavelets. Then, sparse deconvolution is employed to further extend the frequency range by utilizing the accurately expected wavelet and obtaining a high-resolution reflection coefficient. Finally, the Hessian matrix regularization is employed to constrain the spatial continuity of the reflection coefficient. This method is validated in both the model and real seismic data. Compared to traditional sparse deconvolution and spectral modeling deconvolution with spatial constraints, this method not only expands the frequency bandwidth and enhances seismic resolution but also preserves operational frequency information and improves the spatial continuity of seismic data. It has been verified that this approach can be used to forecast thin reservoir and reconstruct spatial tectonic characteristics. [ABSTRACT FROM AUTHOR]

In this paper, we consider an infinite inequality system defined by a family of proper lower semicontinuous quasiconvex functions in real normed linear spaces. By using the interior-point condition and approximate continuity assumption of the functions, we establish some sufficient conditions for ensuring the basic constraint qualification for quasiconvex programming. [ABSTRACT FROM AUTHOR]

Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian's motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is the technological basis of environmental sensing, and it has been widely applied to autonomous navigation. In combination with deep learning methods, dynamic SLAM algorithms have emerged to provide a certain stability and accuracy in dynamic scenes. However, the robustness and accuracy of existing dynamic SLAM algorithms are relatively low in dynamic scenes, and their performance is affected by potential dynamic objects and fast-moving dynamic objects. To solve the positioning interference caused by these dynamic objects, this study proposes a geometric constraint algorithm that utilizes a bidirectional scoring strategy for the estimation of a transformation matrix. First, a geometric constraint function is defined according to the Euclidean distance between corresponding feature points and the average distance of the corresponding edges. This function serves as the basis for determining abnormal scores for feature points. By utilizing these abnormal score values, the system can identify and eliminate highly dynamic feature points. Then, a transformation matrix estimation based on the filtered feature points is adopted to remove more outliers, and a function for evaluating the similarity of key points in two images is optimized during this process. Experiments were performed based on the TUM dynamic target dataset and Bonn RGB-D dynamic dataset, and the results showed that the added dynamic detection method effectively improved the performance compared to state of the art in highly dynamic scenarios. [ABSTRACT FROM AUTHOR]

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics. [ABSTRACT FROM AUTHOR]

Pedestrian tracking in surveillance videos is crucial and challenging for precise personnel management. Due to the limited coverage of a single video, the integration of multiple surveillance videos is necessary in practical applications. In the realm of pedestrian management using multiple surveillance videos, continuous pedestrian tracking is quite important. However, prevailing cross-video pedestrian matching methods mainly rely on the appearance features of pedestrians, resulting in low matching accuracy and poor tracking robustness. To address these shortcomings, this paper presents a cross-video pedestrian tracking algorithm, which introduces spatial information. The proposed algorithm introduces the coordinate features of pedestrians in different videos and a linear weighting strategy focusing on the overlapping view of the tracking process. The experimental results show that, compared to traditional methods, the method in this paper improves the success rate of target pedestrian matching and enhances the robustness of continuous pedestrian tracking. This study provides a viable reference for pedestrian tracking and crowd management in video applications. [ABSTRACT FROM AUTHOR]

This research introduces a rule-based decision-making model to investigate corporate governance, which has garnered increasing attention within financial markets. However, the existing corporate governance model developed by the Security and Future Institute of Taiwan employs numerous indicators to assess listed stocks. The ultimate ranking hinges on the number of indicators a company meets, assuming independent relationships between these indicators, thereby failing to reveal contextual connections among them. This study proposes a hybrid rough set approach based on multiple rules induced from a decision table, aiming to overcome these constraints. Additionally, four sample companies from Taiwan undergo evaluation using this rule-based model, demonstrating consistent rankings with the official outcome. Moreover, the proposed approach offers a practical application for guiding improvement planning, providing a basis for determining improvement priorities. This research introduces a rule-based decision model comprising ten rules, revealing contextual relationships between indicators through if–then decision rules. This study, exemplified through a specific case, also provides insights into utilizing this model to strengthen corporate governance by identifying strategic improvement priorities. [ABSTRACT FROM AUTHOR]

The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes. [ABSTRACT FROM AUTHOR]

In order to investigate the equivalent complex refractive index of atmospheric aerosols near the Earth's surface, we conducted measurements in the Hefei region from March to April 2022. These measurements utilized a micro-pulse lidar, an Aethalometer, and a Portable Optical Particle Profiler. These measurements encompassed aerosol particle size distribution as well as standard meteorological parameters including temperature, humidity, atmospheric pressure, and wind speed. Subsequently, this dataset was employed to develop an optimization algorithm for retrieving the equivalent complex refractive indices of near-surface aerosols. The methodology relies on lookup tables containing data for extinction efficiency and absorption efficiency factors. It operates on the premise of aerosol property stability within a defined time frame, utilizing measured extinction and absorption coefficients as simultaneous constraints during this period to inversely derive both the real and imaginary parts of the aerosol complex refractive index. Results from the simulation analysis reveal that the newly optimized retrieval algorithm, which relies on lookup tables, exhibits reduced sensitivity to instrument errors when compared to single-point constraint algorithms. This enhancement results in a more efficient and dependable approach for retrieving the aerosol complex refractive index. Empirical inversion and simulation studies were carried out to determine the aerosol equivalent complex refractive index in the Hefei region, utilizing measured data. This inversion process yielded an average complex refractive index of 1.48-i0.017 for aerosols in the Hefei region throughout the experimental period. Correlation analysis unveiled a positive association between the real part of the aerosol complex refractive index and the single-scattering albedo (SSA), whereas the imaginary part displayed a linear negative correlation with the SSA. The mathematical relationship between the real part and the SSA is y = 0.19 x + 0.62 , and the corresponding relationship between the imaginary part and the SSA is y = − 5.3 x + 0.99 . This research offers a novel method for the retrieval of the aerosol equivalent complex refractive index. [ABSTRACT FROM AUTHOR]