Search

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, there has been significant research directed toward the maximization of k-submodular functions, owing to their wide-ranging applications in fields such as social networks, sensor placement, and various other domains. The utilization of the greedy method and randomized techniques is prevalent for the design of algorithms that offer favorable performance guarantees for such problems. However, these approaches adopt a single-item selection strategy at each iteration, leading to high query complexity. The Threshold-Decreasing Algorithm is conceptualized to address this issue by choosing multiple items using a predefined threshold value, and in case no items remain, the threshold is decremented by a specified factor. This approach effectively reduces complexity. In this paper, we merge the principles of the threshold-decreasing and greedy methods to enhance the complexity of constrained k-submodular maximization problems, thereby achieving near-optimal approximation ratios. [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]

In radial distribution networks, the unreasonable use of distributed generators (DGs) will enhance the effects of voltage quality reduction and network loss. Consequently, the optimal allocation of distribution groups (OADG) has far-reaching implications for the economical, secure, and stable operation of radial distribution networks. The purpose of the study on the optimal allocation of distributed generators is to allocate the locations and capacities of DGs scientifically and rationally, enhance voltage quality, and reduce power loss. This paper proposes an enhanced ISC-SSA algorithm with constraint strategy for the OADG problems, which addresses the disadvantages of traditional salp swarm algorithm (SSA), such as low solution accuracy and slow convergence speed, and introduces a sine cosine algorithm (SCA) to optimise the salp swarm followers. To reduce the limitation of only the leader guiding the followers for position updates, a symbiotic strategy with multiple subpopulations is introduced to simulate the sharing of information of the salp swarm population, relying on the decision-maker and the leader to jointly guide the population movement, thereby increasing the global optimization-seeking capability of the algorithm, and the method is implemented in IEEE-33, IEEE-69, and IEEE-119 systems. In addition, the paper extends the ISC-SSA algorithm to the MOISC-SSA algorithm to solve in IEEE-33, IEEE-69 test systems the multi-objective OADG problems. Multiple sets of simulation results demonstrate that the proposed method solves radial distribution system problems of varying sizes with greater search precision and greater significance. [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]

In this paper, we propose a constraint preserving algorithm for the smallest Z-eigenpair of the compact Laplacian tensor of an even-uniform hypergraph, where Householder transform is employed and a family of modified conjugate directions with sufficient descent is determined. Besides, we prove that there exists a positive step size in the new constraint preserving update scheme such that the Wolfe conditions hold. Based on these properties, we prove the convergence of the new algorithm. Furthermore, we apply our algorithm to the hypergraph partitioning and image segmentation, and numerical results are reported to illustrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In an active headrest system virtual sensing tends to transfer the spatial zone of quiet from the residual error microphone to the ear canal. An attempt has been made in this paper to develop an auxiliary filter based virtual sensing scheme integrated with the filtered-x least mean square/fourth algorithm for an active headrest. The performance of the proposed method has been evaluated experimentally using periodic and band limited white noise. Improved noise control performance has been observed for both periodic and broadband noise. The effect of causality constraint of the performance of the algorithm has also been tested. [ABSTRACT FROM AUTHOR]

With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds. [ABSTRACT FROM AUTHOR]

Functions such as Internet browsing and online shopping have a great impact on people's lives. The footprints of people browsing various information and news on the web page are also increasing year by year. More and more people begin to pay attention to the visual communication in web design. Network information is widely loved by people because of its convenience, quickness, and simplicity. In order to study the visual problem in network information, this paper proposes to use a feature matching algorithm to study the visual information transmission in web design. Using stereo vision and feature matching algorithm, the target recognition function in the visual communication of web design is realized. The content of web design is defined from the perspective of visual beauty and overall harmony. By extracting the feature points in the data, the two-dimensional vision is transformed into a three-dimensional vision. It not only can accurately extract feature points from data but also can convert 2D vision to 3D vision. Finally, in order to optimize the feature matching speed of web design images, the epipolar constraint algorithm is used to optimize the feature matching function. The experimental results show that the content of this paper can truly show the virtual effect in web design and can intuitively upload and transmit simple and rich information content. This paper not only meets the aesthetic needs of the public for web design but also improves the problem of fuzzy information in the process of visual information transmission. [ABSTRACT FROM AUTHOR]

Deception jamming of synthetic aperture radar (SAR) has attracted extensive attention due to its low power consumption and high fidelity advantages. However, existing SAR deception jamming algorithms assume that SAR operates on a linear trajectory. In practice, SAR trajectories often become nonlinear due to factors such as atmospheric turbulence, which results in the jamming signals lacking the two-dimensional spatial variability of nonlinear-trajectory SAR echo signal and affects the imaging quality of deception jamming. This paper proposes a new algorithm for nonlinear-trajectory airborne SAR deception jamming based on hybrid domain efficient (HDE) modulation. This algorithm derives the jamming frequency response (JFR) with SAR trajectory deviation in the azimuth time–frequency hybrid domain. Based on the hybrid domain modulation, the jammer calculates the JFR of the linear trajectory in the azimuth frequency domain and constructs for the real-time trajectory deviation pulse by pulse at each azimuth moment. The real-time modulation process of the algorithm only involves range domain Fourier transform and complex multiplication, combining computational efficiency and modulation flexibility. The validity constraints of the algorithm have been analyzed to ensure the focusing ability of the jamming signal. Simulation and computational complexity analysis validate the excellent performance of the algorithm in imaging quality and efficiency. [ABSTRACT FROM AUTHOR]

In the field of AGV, a path planning algorithm is always a heated area. However, traditional path planning algorithms have many disadvantages. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constraint A* algorithm and the following dynamic window approach algorithm. The kinematical constraint A* algorithm can plan the global path. Firstly, the node optimization can reduce the number of child nodes. Secondly, improving the heuristic function can increase efficiency of path planning. Thirdly, the secondary redundancy can reduce the number of redundant nodes. Finally, the B spline curve can make the global path conform to the dynamic characteristics of AGV. The following DWA algorithm can be dynamic path planning and allow the AGV to avoidance moving obstacle. The optimization heuristic function of the local path is closer to the global optimal path. The simulation results show that, compared with the fusion algorithm of traditional A* algorithm and traditional DWA algorithm, the fusion algorithm reduces the length of path by 3.6%, time of path by 6.7% and the number of turns of final path by 25%. [ABSTRACT FROM AUTHOR]

This paper proposes an autonomous obstacle avoidance method combining improved A-star (A*) and improved artificial potential field (APF) to solve the planning and tracking problems of autonomous vehicles in a road environment. The A*APF algorithm performs path planning tasks, and based on the longitudinal braking distance model, a dynamically changing obstacle influence range is designed. When there is no obstacle affecting the controlled vehicle, the improved A* algorithm with angle constraint combined with steering cost can quickly generate the optimal route and reduce turning points. If the controlled vehicle enters the influence domain of the obstacle, the improved artificial potential field algorithm will generate lane-changing paths and optimize the local optimal locations based on simulated annealing. Pondering the influence of surrounding participants, the four-mode obstacle avoidance process is established, and the corresponding safe distance condition is analyzed. A particular index is introduced to comprehensively evaluate speed, risk warning, and safe distance factors, so the proposed method is designed based on the fuzzy control theory. In the tracking task, a model predictive controller in the light of the kinematics model is devised to make the longitudinal and lateral process of lane-changing meet comfort requirements, generating a feasible autonomous lane-change path. Finally, the simulation was performed in the Matlab/Simulink and Carsim combined environment. The proposed fusion path generation algorithm can overcome the shortcomings of the traditional single method and better adapt to the dynamic environment. The feasibility of the obstacle avoidance algorithm is verified in the three-lane simulation scenario to meet safety and comfort requirements. [ABSTRACT FROM AUTHOR]

Constraint programming has roots in logic programming, where a model has both a declarative and a procedural interpretation. A model is declarative because its statements can be read as logical propositions that describe the problem, and it is procedural because the statements can be processed as instructions for how to find a solution. To make constraint programming material to practical problems one needs propagation algorithms that are both viable and proficient. The most incredible propagation algorithm for the alldifferent constraint, i.e. the one getting hyper-circular segment consistency, is to be sure extremely productive. The reason is that we can apply matching hypothesis from operations research. Likewise for the symmetric alldifferent constraint and the weighted alldifferent constraint powerful and effective propagation algorithms exist, again dependent on techniques from operations research. From this paper we show that some time alldifferent constraint is more easy way to solve the MIP. For this we will choose an auction strategy by which a company get more revenue. Despite this considerable progress, there remains great potential for further integration, with the concomitant improvement in both modeling and solution method. [ABSTRACT FROM AUTHOR]

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences. [ABSTRACT FROM AUTHOR]

To solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a model-free adaptive control algorithm with constraints (cMFAC) is proposed in this paper. In the cMFAC algorithm, a dynamic linearization technique with a time-varying parameter pseudo-gradient (PG) is used to linearize the multivehicle collaborative system. Then, a cMFAC controller is designed. The algorithm sets the input and output constraints at the same time to prevent the vehicle speed and other parameters from exceeding the specified range. The main advantage of the cMFAC algorithm is that the entire control process only needs the input and output data of each vehicle and can effectively handle the input and output constraints. In addition, the stability of the cMFAC method is verified through strict mathematical analysis, and its effectiveness is verified with semi-physical experiments based on a MATLAB/Simulink module and CarSim platform connection environment. It is worth noting that the proposed cMFAC controller is symmetric because the input cost function and PG cost function have symmetric and similar structures, and the forms of the two cost functions are the same. [ABSTRACT FROM AUTHOR]

Pairwise constraint-based semi-supervised clustering algorithms are a hot research topic in semi-supervised learning. To address the problem that the performance of semi-supervised clustering algorithms is limited by the number of pairwise constraints, most of the existing research relies on the number of original pairwise constraints. Therefore, this paper firstly proposes the Initialization Algorithm of Pair Constraints Based on Grey Relational Analysis（PCIG）. The algorithm calculates the similarity between data objects by degree of balance and approach, and determines the confidence interval based on the value of similarity, then expands the pairwise relationships between data objects by drawing on the initialization method of network structure. Finally, applying it to the label propagation clustering algorithm. Through the experiments on 5 benchmark datasets, the label propagation clustering algorithm based on improved pairwise constraint expansion has improved NMI and ARI values compared with other methods. Experimental results show that improving pairwise constraint expansion can effectively improve the clustering effect of label propagation algorithm. [ABSTRACT FROM AUTHOR]

In the context of channel estimation amid non-Gaussian impulse noise, traditional non-kernel-space methods face challenges of divergence, while many kernel-space methods fail to fully exploit the a priori information embedded in the channel. To address this, we introduce a robust sparse recursive adaptive filtering algorithm named convex regularized recursive kernel risk-sensitive loss (CR-RKRSL) in this paper. By combining the KRSL with a convex function constraint term, our proposed algorithm maximizes the utilization of channel a priori information. Furthermore, we delve into the theoretical aspects of the proposed algorithm, presenting expressions for the convergence and steady-state error. Through extensive simulation results, we demonstrate that CR-RKRSL outperforms the APSA, LHCAF, PRMCC, CR-RMC, RZAMCC algorithms. In comparison to existing algorithms, CR-RKRSL exhibits superior robustness and faster convergence, particularly in scenarios involving highly sparse systems. • We introduce a recursive variant of the KRSL method, denoted as RKRSL. The recursive approach achieves faster convergence and robustness against non-Gaussian noise. We incorporate convex regularization constraints into the recursive algorithm, enabling it to leverage the structural characteristics of the system to enhance its gains. • To assess the performance of our proposed method, we derive both the first-order and second-order steady state conditions of the algorithm. Furthermore, we establish the expression for mean square deviation for the RKRSL. [ABSTRACT FROM AUTHOR]

This paper presents a comparison of contemporary algorithms aimed at mitigating the saturation-induced challenges in active noise control (ANC) systems. The saturation effect introduces nonlinear elements into the adaptive algorithm, consequently impacting the ANC system's performance and degrading the system's stability. The detailed theoretical analysis indicates that the cause of the output saturation issue lies in the exceeding output power of the control signal. Recently, two categories of adaptive algorithms have been developed to address this issue. The first category focuses on effectively constraining the output signal to manage the saturation effect, exhibiting notable practical efficacy. The second category employs nonlinear ANC algorithms (NANC) to model the inherent signal nonlinearity, controlling harmonic distortion caused by saturation effects. This work summarizes the key results in the literature and demonstrates that output constraint algorithms outperform the NANC algorithms in computational efficiency and robustness. Hence, they should be a more practical choice than others in coping with the output-saturation issue in ANC systems. • Output saturation deforms the control signal and degrades the stability of adaptive algorithms. • Output-constraint algorithms constrain output saturation with low computational complexity. • Nonlinear algorithms use nonlinear models to counteract output saturation. [ABSTRACT FROM AUTHOR]

In this paper, we extend existing population growth models and propose a model based on a nonlinear cubic differential equation that reveals itself as a special subclass of Abel differential equations of first kind. We first summarize properties of the time-continuous problem formulation. We state the boundedness, global existence, and uniqueness of solutions for all times. Proofs of these properties are thoroughly given in the Appendix to this paper. Subsequently, we develop an explicit–implicit time-discrete numerical solution algorithm for our time-continuous population growth model and show that many properties of the time-continuous case transfer to our numerical explicit–implicit time-discrete solution scheme. We provide numerical examples to illustrate different behaviors of our proposed model. Furthermore, we compare our explicit–implicit discretization scheme to the classical Eulerian discretization. The latter violates the nonnegativity constraints on population sizes, whereas we prove and illustrate that our explicit–implicit discretization algorithm preserves this constraint. Finally, we describe a parameter estimation approach to apply our algorithm to two different real-world data sets. [ABSTRACT FROM AUTHOR]

Sensor deployment is an important issue in wireless sensor network (WSN), which is a typical nonlinear system. Conditions of both area coverage and point coverage should be considered in research studies on sensor coverage. It is generally necessary to ensure high coverage ratio of area when controlling sensor locations, and covering specific point targets to ensure long lifetime is also important sometimes. In current studies, swarm intelligence algorithms such as particle swarm optimization (PSO) are widely used to solve the sensor deployment problem in WSN. In this paper, coverage rate and network life indicators are analyzed comprehensively with establishment of a more general K-coverage model. In related calculation examples with different coverage requirements including target coverage, area coverage, and boundary coverage, several improved algorithms based on PSO are applied to solve the problem in the paper. Simulation results show that the improved algorithms can achieve a good performance and deployment effect. [ABSTRACT FROM AUTHOR]

Applying the pairwise constraint algorithm to spectral clustering has become a hot topic in data mining research in recent years. In this paper, a clustering algorithm is proposed, called an active constraint spectral clustering based on Hessian matrix (ACSCHM); this algorithm not only use Hessian matrix instead of Laplacian matrix to free the parameter but also use an active query function to dynamically select constraint pairs and use these constraints to tune and optimize data points. In this paper, we used active query strategy to replace the previous random query strategy, which overcame the instability of the clustering results brought by the random query and enhanced the robustness of the algorithm. The unique parameter in the Hessian matrix was obtained by the spectral radius of the matrix, and the parameter selection problem in the original spectral clustering algorithm was also solved. Experiments on multiple UCI data sets can prove the effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

In reality, the range of sensor response is limited in many sensor systems due to the saturation characteristics of the sensor. That is, the value exceeding the sensor response range is not observed. Using traditional adaptive algorithms to identify the system of this type may lead to the performance degradation. To address this problem, the censored regression algorithms have been proposed. However, when the mixed sub-Gaussian and super-Gaussian/impulsive noises occur, these algorithms may fail to work. To overcome these drawbacks, a family of robust M-shaped (FRMS) functions for censored regression (CR-FRMS) is proposed in this paper. When the system to be identified exhibits a certain degree of sparsity, the CR-FRMS algorithm cannot fully utilize the characteristics of the sparse system. Therefore, in this paper, proportionate FRMS (PFRMS) algorithm based on l 0 -norm constraint for censored regression ( l 0 -CRPFRMS) is also proposed accordingly. The simulations using Gaussian white noise as the input signal and the non-Gaussian mixed noise as the background noise show that the proposed algorithm performs better than other algorithms. [ABSTRACT FROM AUTHOR]

Several constrained optimization problems have been adequately solved over the years thanks to the advances in the area of metaheuristics. Nevertheless, the question as to which search logic performs better on constrained optimization often arises. In this paper, we present Dual Search Optimization (DSO), a co-evolutionary algorithm that includes an adaptive penalty function to handle constrained problems. Compared to other self-adaptive metaheuristics, one of the main advantages of DSO is that it is able auto-construct its own perturbation logics, i.e., the ways solutions are modified to create new ones during the optimization process. This is accomplished by co-evolving the solutions (encoded as vectors of integer/real values) and perturbation strategies (encoded as Genetic Programming trees), in order to adapt the search to the problem. In addition to that, the adaptive penalty function allows the algorithm to handle constraints very effectively, yet with a minor additional algorithmic overhead. We compare DSO with several algorithms from the state-of-the-art on two sets of problems, namely: (1) seven well-known constrained engineering design problems and (2) the CEC 2017 benchmark for constrained optimization. Our results show that DSO can achieve state-of-the-art performances, being capable to automatically adjust its behavior to the problem at hand. [ABSTRACT FROM AUTHOR]

• A constrained path planning algorithm for Ackerman mobile robot is proposed. • An improved ant colony algorithm with angle constraint is introduced. • An improved B-spline curve smoothing method with curvature constraint is proposed to ensure that the smooth path follows the maximum curvature limit of the mobile robot. In this paper, a new approach is proposed for the smooth path planning of Ackermann mobile robots based on an improved ant colony algorithm and B-spline curves. Firstly, by incorporating path length constraints and path smoothing constraints into the objective function, the smooth path planning problem for Ackermann mobile robots is transformed into a multi-objective optimization problem. Secondly, to address the limitations of the traditional ant colony algorithm, an improved ant colony algorithm based on the turning angle constraint (IACO-TAC) is proposed. IACO-TAC incorporates the distance factor and steering angle penalty factor in the heuristic function to reduce the path search's blindness. Moreover, the pheromone update method is improved, consisting of local pheromone update and global pheromone update, which uses a reward penalty mechanism to improve the convergence speed of the algorithm and increases the pheromone concentration of the global optimal path, respectively. Thirdly, an improved B-spline curve smoothing algorithm that considers the minimum turning radius constraint is proposed to generate a path that satisfies the kinematic constraints of the Ackermann mobile robot. Finally, the proposed method is evaluated by conducting gradient comparison experiments and ant colony algorithm comparison experiments on maps of different sizes. The experimental results demonstrate that our method exhibits a fast convergence rate and plans a path that balances path length and turn frequency while satisfying the kinematic constraints of the mobile robot. Thus, the proposed method offers an efficient and smooth path planning solution for Ackermann mobile robots in complex environments. [ABSTRACT FROM AUTHOR]

Local search algorithms are widely applied in solving large-scale distributed constraint optimization problem (DCOP). Distributed stochastic algorithm (DSA) is a typical local search algorithm to solve DCOP. However, DSA has some drawbacks including easily falling into local optima and the unfairness of assignment choice. This paper presents a novel local search algorithm named VLSs to solve the issues. In VLSs, sampling according to the probability corresponding to assignment is introduced to enable each agent to choose other promising values. Besides, each agent alternately performs a greedy choice among multiple parallel solutions to reduce the chance of falling into local optima and a variance adjustment mechanism to guide the search into a relatively good initial solution in a periodic manner. We give the proof of variance adjustment mechanism rationality and theoretical explanation of impact of greed among multiple parallel solutions. The experimental results show the superiority of VLSs over state-of-the-art DCOP algorithms. [ABSTRACT FROM AUTHOR]

• The AICKF for dual foot-mounted sensors pedestrian positioning is proposed. • Estimating the magnetic disturbance in real time during the pedestrian walking. • We illustrate the theoretical findings using experiments based on the MTI-G710. The advantages of foot-mounted inertial pedestrian position system are passive, full-autonomous and not affected by environment. However, the disadvantage of the accumulating pedestrian position error, which is introduced by Micro Inertial Measurement Unit (MIMU) noise, limits its application. Zero Velocity Update (ZUPT) algorithm is proposed to correct the accumulating error. But ZUPT has poor performance due to the unobserved position error and azimuth misalignment angle, which results in a continuous increase in azimuth error and an increase in positioning error. To solve the problem, the improved method for dual foot-mounted Inertial/Magnetometer pedestrian positioning based on adaptive inequality constraints Kalman Filter is proposed in this paper. Our first contribution in this paper is studying the adaptive inequality constraints Kalman Filter algorithm for dual-foot inertial position algorithm. In this method, the adaptive inequality constraint is introduced in the Kalman Filter of ZUPT, and the constraints parameter can be online adaptive adjusted based on the character of pedestrian step length. Therefore, the accumulating position error can be decreased and limited in a range. As our second contribution, we propose an approach for estimating the magnetic disturbance in real time during the pedestrian walking period. After studying the observability properties of magnetic disturbance parameters, the magnetic disturbance can be estimated when the foot satisfied the conditions of static and angular motion. Then, the azimuth misalignment angle can be corrected by magnetometer during the ZUPT period. We illustrate the findings of our theoretical analysis using experiments based on the MTI-G710 MIMU. The achieved performance indicates that our proposed method can conveniently be used in consumer products. [ABSTRACT FROM AUTHOR]

Optimization for all disciplines is very important and applicable. Optimization has played a key role in practical engineering problems. A novel hybrid meta-heuristic optimization algorithm that is based on Differential Evolution (DE), Gradient Evolution (GE) and Jumping Technique named Differential Gradient Evolution Plus (DGE+) are presented in this paper. The proposed algorithm hybridizes the above-mentioned algorithms with the help of an improvised dynamic probability distribution, additionally provides a new shake off method to avoid premature convergence towards local minima. To evaluate the efficiency, robustness, and reliability of DGE+ it has been applied on seven benchmark constraint problems, the results of comparison revealed that the proposed algorithm can provide very compact, competitive and promising performance. [ABSTRACT FROM AUTHOR]

Submodular maximization is one of the central topics in combinatorial optimization. It has found numerous applications in the real world. In the past decades, a series of algorithms have been proposed for this problem. However, most of the state-of-the-art algorithms are randomized. There remain non-negligible gaps with respect to approximation ratios between deterministic and randomized algorithms in submodular maximization. In this paper, we propose deterministic algorithms with improved approximation ratios for non-monotone submodular maximization. Specifically, for the matroid constraint, we provide a deterministic 0.283 − o (1) approximation algorithm, while the previous best deterministic algorithm only achieves a 1/4 approximation ratio. For the knapsack constraint, we provide a deterministic 1/4 approximation algorithm, while the previous best deterministic algorithm only achieves a 1/6 approximation ratio. For the linear packing constraints with large widths, we provide a deterministic 1 / 6 − ϵ approximation algorithm. To the best of our knowledge, there is currently no deterministic approximation algorithm for the constraints. [ABSTRACT FROM AUTHOR]

The CoMirror algorithm, by Beck et al. (Oper Res Lett 38(6):493–498, 2010), is designed to solve convex optimization problems with one functional constraint. At each iteration, it performs a mirror-descent update using either the subgradient of the objective function or the subgradient of the constraint function, depending on whether or not the constraint violation is below some tolerance. In this paper, we combine the CoMirror algorithm with inexact cut generation to create the SIP-CoM algorithm for solving semi-infinite programming (SIP) problems. First, we provide general error bounds for SIP-CoM. Then, we propose two specific random constraint sampling schemes to approximately solve the cut generation problem for generic SIP. When the objective and constraint functions are generally convex, randomized SIP-CoM achieves an O (1 / N) convergence rate in expectation (in terms of the optimality gap and SIP constraint violation). When the objective and constraint functions are all strongly convex, this rate can be improved to O (1 / N) . [ABSTRACT FROM AUTHOR]

The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence of external factors. This paper proposes a coplanar hybrid calibration algorithm based on the error screening model by combining the error screening model, mathematical model and neural network model. It is mainly divided into two steps. The first step is to use the radial array constraint calibration algorithm based on the error screening model to solve the camera's internal and external parameters. The second step uses the camera internal and external parameters obtained in the first step to convert the pixel coordinates into real three-dimensional coordinates, and compares the calculated three-dimensional coordinates with the actual coordinates. Using machine learning to establish a compensation network, get a compensation function, and use the resulting 3D world coordinates to perform point cloud stitching. Experiments show that compared with the traditional calibration algorithm, the calibration algorithm has a small error and reduces the calibration error by about 6.5%. [ABSTRACT FROM AUTHOR]

Traffic management strategies grounded on reliable traffic demand data can reduce the costs associated with traffic congestions. While traffic video imaging can deliver real-time turning movement (TM) information, their cost can be prohibitive, especially to smaller agencies. The Network Flow (NETFLO) model uses the Minimum Cost Flow optimization by generating network flows along weighted arcs from a sparse set of detected link flows. The network is constructed so that weighted arcs represent both turning movements and links. When constrained, the NETFLO algorithm can infer reliable TM estimates from link flows detected in quasi-real-time. The research presented here establishes a consistently robust constraint regime that enables reliable estimation of urban intersection TMs. Turning movements are identified as through, left, and right. The optimal NETFLO constraint regime assigns a weight of 35 for lefts, 2 for throughs, 28 for right intersection turns. A weight of one applies to all other arcs. The optimal weighting scheme consistently outperforms a non-weighted regime, when tested estimating 5-minute TM flows on a real network in Orem, Utah, U.S.A. The optimal weights yield a mean coefficient of determination (R²) of 93% between observed and modeled TM flows, while the non-weighted regime produces an R² of 78%. With the optimal weight set, almost all through, 50% of right and 40% of left movements at intersections are reliably predicted. The lower reliability of left and right estimates can be attributed to their low observed flows, when compared to the dominant through flows. This paper shows the details of how NETFLO is constrained to give consistent urban intersection TM estimations. [ABSTRACT FROM AUTHOR]

Evolutionary algorithm (EA) is a generic term for optimization algorithms inspired by biological optimization processes in the natural world. Although EAs are widely applied to complex real‐world problems because they do not require mathematical expression of target problems, theoretical design methods of EAs have not been established. To solve this issue, this paper proposes an approach of designing EAs within an algorithm framework in which mathematical characteristics are derived, and it also presents a concrete framework for that purpose. The presented framework, Regulated Evolution Strategies (RES) provides a stability analysis result that contributes to designing algorithms with expected behavior. The RES framework has a high degree of freedom in designing algorithms, so that it is possible to incorporate various contrivances such as local improvement of samples and reduction of constraint violations in RES‐based algorithms while maintaining the stability analysis result. Numerical experiments prove that the RES framework has a capability for designing high‐performance EAs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In order to ensure the path continuity of redundant manipulators with constrained motion planning, this paper proposed a rapidly exploring random trees algorithm with progressive constraint extension ( PCE-RRT) for the constrained failure and path failure problems of direct interpolation and continuous interpolation. The algorithm introduced a progressive constrained extension method when performing tree expansion, which used the inverse kinematics method with the nearest point information, iteratively shortened the distance between the nearest point and the extended point while ensuring the task constraint, so that each extension could guarantee the constraint and continuity of the tree. PCE-RRT could make the final tree fit well into the constrained manifold subspace, and any two connected nodes in the search tree could satisfy the continuous condition. The simulation experiments verify the validity of the algorithm, and the results show that the proposed algorithm can provide a path solution that satisfied the constraints and continuity conditions for the manipulator constrained motion planning problem. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to integrating the cell formation, group layout of rectangle-shaped machines and routing selection problems. The problem is formulated as a mixed-integer program with the objective of minimizing the handling costs. Due to the computational complexity of the problem, a hybrid simulated annealing (SA) is employed to solve the problem. The sequence-pair representation, originally proposed for block placement, is utilized for solution encoding. Two placement algorithms are developed to evaluate the objective function value of an encoded solution in the SA. The first placement algorithm is based on solving a linear program embedded with a constraint reduction algorithm. The second one is a fast heuristic that can evaluate an encoded solution in much less computational time. Benchmarks selected from the literature are solved to verify the performance of the SA and to accomplish comparisons. The computational results demonstrated the high performance of both designed hybrid SA algorithms. The comparison against the literature also indicated that the flexibility incorporated into the model results in better layouts, even if the model is solved only by a solver such as CPLEX. [ABSTRACT FROM AUTHOR]

Wireless sensor networks involved every part of application in today's life. This can detect the various type of information from environment and communicated to users in real time. Information is stored by using cloud technology and that can be accessed by the users. The information is prone to attacks as they are part of cooperative communication. Building algorithms at node level is not secured as they transfer data in open traffic. This has provided scope for this study to concentrate on algorithms that are available for denial-of-service attacks as they put down the network performance to a less minimum. The paper henceforth proposes optimized energy-based constraint DoS (denial-of-service) detection algorithm, i.e., OBES algorithm for handling denial-of-service attacks that learns the network traffic and manages the intruders. This has been compared with denial-of-service attack detection with energy constraint in WSN. The implementation has been done in NS2. The performance has been presented in terms of nodes, interval and lifetime. From results, it can be observed that the proposed OBES algorithm is efficient as it achieves more available energy, less delay, less packet loss and more lifetime for the network. From results, it can be observed that OBES algorithm performs well compared to denial-of-service attack detection with energy constraint algorithm. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a fundamental problem in power system operating and planning. The need for distributed solutions is rapidly growing in distribution systems with radial networks, discrete equipments and many distributed generation units. Typically the model is difficult to solve due to the highly non-convexity and even non-continuity of the arising OPF problem. In this paper, we address these challenges by developing a penalty dual decomposition (PDD) based algorithm to obtain distributed solutions. The proposed PDD based algorithm mainly consists of two loops: in the outer loop we update the dual variables and the penalty parameter according to the constraint violation, while in the inner loop we divide the primal variables into several blocks and employ the block successive upper-bound minimization (BSUM) method to iteratively optimize each block variables with fixed penalty parameter and dual variables. Each subproblem in the proposed PDD based algorithm can be solved either in closed-form or by the bisection method and every limit point generated by the proposed algorithm is guaranteed to be a stationary point of the OPF problem. Simulation results, and comparison with centralized PDD and concave-convex procedure (CCCP) based algorithms are presented to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm based on collective neurodynamic system is investigated for distributed constrained convex optimization, whose objective function is a sum of smooth convex functions and non-smooth L 1 -norm functions. Inspired by recent advances in distributed convex optimization, the continuous-time and discrete-time distributed optimization algorithms described by collective neurodynamic systems are proposed. In the systems, each of the smooth objective functions is allocated to each node as well as each of the L 1 -norm functions. However, the L 1 -norm functions are realized by projection operators. Meanwhile, each node satisfies the local linear and bound constraints. Then a connected network is constituted from all the nodes with consensus to find the optimal solutions. [ABSTRACT FROM AUTHOR]

The traditional point cloud alignment method suffers from drawbacks such as a large extensive computation, low computing speed, and poor alignment accuracy. To overcome these problems, this paper proposes a fast and highly accurate algorithm based on fast point feature histograms (FPFH) algorithm and spatial constraints. The proposed algorithm first filters, denoises the point cloud dataset, and calculates the point cloud normal to obtain the FPFH eigenvalue. Then, the vertebral space is divided into three regions according to its location, and the feature points in each region are calculated. The Euclidean distance between a feature point and the boundary of the adjacent region, and the weight coefficient corresponding to the feature point are given according to the calculated distance. The method overcomes the defect that the query feature point has a large workload in the traditional ICP algorithm and improves the registration precision of the point cloud. The experimental results show that the proposed method effectively solves the problems of the traditional point cloud registration algorithm, can effectively reduce the mismatch rate of point cloud registration, and can improve the registration accuracy and stability without reducing the registration of the elements. [ABSTRACT FROM AUTHOR]

One of the most widely used methods for reconstructing images through a scattering medium is the Hybrid Input Output (HIO) phase retrieval algorithm. However, the stability and noise robustness of the HIO method are poor, as they are affected by initial values and noise during the imaging process. This paper presents an optimized HIO phase retrieval algorithm that utilizes the Huber penalty as a sparsity constraint to effectively enhance the quality and stability of the reconstruction algorithm. The introduced constraint is designed to guide the iteration process away from local optimal solutions, ensuring superior recovery images even with random initial values. Additionally, an adaptive step size parameter is incorporated to control the iterations and convergence steps, resulting in stronger robustness against noise effects. Moreover, the error criterion for image restoration is defined as the sum of the Fourier transform amplitude of the gradient. Experimental results demonstrate that the proposed phase retrieval algorithm achieves superior quality in the recovered images, while significantly improving noise immunity and stability for randomly selected initial values. • The Huber penalty is introduced into the HIO phase retrieval algorithm as the sparsity constraint so as to reduce the dependence on the selection of initial values. • The error criterion value to assess the quality of the restored solution is introduced into the integrated phase retrieval process framework to control the number of iterations. • An adaptive step size in the iteration process is designed to strengthen the noise robustness of the algorithm. [ABSTRACT FROM AUTHOR]

Local Fourier analysis is a commonly used tool for the analysis of multigrid and other multilevel algorithms, providing both insight into observ ed convergence rates and predictive analysis of the performance of many algorithms. In this paper, for the first time, we adapt local Fourier analysis to examine variants of two- and three-level balancing domain decomposition by constraints (BDDC) algorithms, to better understand the eigenvalue distributions and condition number bounds on these preconditioned operators. This adaptation is based on a modified choice of basis for the space of Fourier harmonics that greatly simplifies the application of local Fourier analysis in this setting. The local Fourier analysis is validated by considering the two-dimensional Laplacian and predicting the condition numbers of the preconditioned operators with different sizes of subdomains. Several variants are analyzed, showing that the two- and three-level performance of the "lumped" variant can be greatly improved when used in multiplicative combination with a weighted diagonal scaling preconditioner, with weight optimized through the use of local Fourier analysis. [ABSTRACT FROM AUTHOR]

Facial feature location is the key procedure in the process of face image analysis. To increase the localization accuracy and success rate of complex facial images due to lack of shape constraint in robust cascaded pose regression algorithm, the paper had proposed the novel part-based cascaded regression algorithm combining pose estimation. The facial landmarks are divided into several areas and pose estimation has used as shape constraint. To enhance the algorithm's performance, a priori knowledge has been exerted during sampling shape-indexed features to constrain the distance between pixel's pair, and the number of regression has reduced. The experimental results of proposed algorithm and others have demonstrated that the proposed algorithm has outperformed in localization accuracy and achieved better robustness for occlusion and complex facial images, the better localization success rate has been obtained, and the proposed method can realize real-time processing standard. [ABSTRACT FROM AUTHOR]

Highlights • Constraint handling using augmented Lagrangian method. • Water cycle algorithm (WCA) is inspired by water cycle process and explosion of landmines. • Incorporation of augmented Lagrangian method in WCA called WCA-ALM. • WCA-ALM offers significant convergence efficiency. Abstract In this paper, a study was carried out to investigate the application and impact of the Augmented Lagrange Method (ALM) on the recently proposed Water Cycle Algorithm (WCA). The WCA-ALM was compared with the original WCA for constrained optimization of some engineering benchmark problems. The performance of quadratic penalty method was also investigated in this research. The approach transforms a constrained problem into an unconstrained problem by imposing penalty on the objective function. The comparison results show significant performance in convergence as well as solution quality in some cases. Furthermore, a simulation was also carried out to examine how the method handles constraints regarding feasible and infeasible solutions. The results show ALM efficiently handles the constraints for the WCA algorithm resulting in improved convergence and/or solution quality. [ABSTRACT FROM AUTHOR]

Process mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high‐quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high‐quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high‐quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges. [ABSTRACT FROM AUTHOR]

To deal with low correct matching, high time-consuming and high-error in most stitching algorithms, this paper developed an improved image stitching algorithm based on merging match strategies. First, the algorithm extracted SIFT feature points only from the overlap area of pair-image, and computed the SURF descriptors. Second, it merged three match strategies included ratio of the nearest neighbor and the 2nd nearest neighbor distances, cross-check of two directions and pair-matches’ difference threshold method, combined coordinate constraint with RANSAC algorithm to complete the feature match. Finally, it used registration data to calculate the projection model which could transform any image in the sequences to reference image coordinate space, then used multithread technology to accelerate. After the brightness correction, the algorithm used the weighted fusion method to composite panorama images. The experimental results show’ that the proposed algorithm obtains a 10% ~20% correct rate increase in feature match with about 1/3 time-consuming of the traditional frame-to-frame expand stitching algorithm, greatly decreases in accumulated error, and little image distortion. [ABSTRACT FROM AUTHOR]

In many practical situations, we would like to compute the set of all possible values that satisfy given constraints. It is known that even for computable (constructive) constraints, computing such set is not always algorithmically possible. One reason for this algorithmic impossibility is that sometimes, the dependence of the desired set on the parameters of the problem is not continuous, while all computable functions of real variables are continuous. In this paper, we show that this discontinuity is the only case when the desired set cannot be computed. Specifically, we provide an algorithm that computes such a set for all the cases when the dependence is continuous. [ABSTRACT FROM AUTHOR]

The constrained rank minimization problem has various applications in many fields including machine learning, control, and signal processing. In this paper, we consider the convex constrained rank minimization problem. By introducing a new variable and penalizing an equality constraint to objective function, we reformulate the convex objective function with a rank constraint as a difference of convex functions based on the closed-form solutions, which can be reformulated as DC programming. A stepwise linear approximative algorithm is provided for solving the reformulated model. The performance of our method is tested by applying it to affine rank minimization problems and max-cut problems. Numerical results demonstrate that the method is effective and of high recoverability and results on max-cut show that the method is feasible, which provides better lower bounds and lower rank solutions compared with improved approximation algorithm using semidefinite programming, and they are close to the results of the latest researches. [ABSTRACT FROM AUTHOR]

In this paper, we develop an image pixel based histogram equalization model for image contrast enhancement. The approach is to propose a variational model containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform. This idea is different from existing histogram equalization algorithms where a histogram based on the input image is constructed, a mapping is determined to output a uniform histogram and then the pixel values of the input image are adjusted based on the mapping. In the variational model, a mean brightness term is incorporated to preserve the brightness of the input image, and a geometry constraint can also be added to keep the geometry structure of the input image. Theoretically, the existence of the minimizer of the proposed model, and the convergence of the proposed algorithm are given. Experimental results are reported to demonstrate that the performance of the proposed model are competitive with the other testing histogram equalization methods for several testing images. [ABSTRACT FROM AUTHOR]