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1
Shang, Kun, and Kai Zhang. "Three-Dimensional Sonar Imaging Software on the DSP." Advanced Materials Research 219-220 (March 2011): 49–52. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.49.
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This paper has explored the design of three-dimensional imaging sonar software and its application to the hardware platform. In the process of designing, the software has been optimized in view of characteristics of the hardware structures and calculation of the TMS320C6416, and the specific optimization methods and effects have also been presented. It turns out that the effect of software optimization is obvious and the working system stable and reliable.
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Brune, Alexander, and Michal Kočvara. "On Barrier and Modified Barrier Multigrid Methods for Three-Dimensional Topology Optimization." SIAM Journal on Scientific Computing 42, no. 1 (2020): A28—A53. http://dx.doi.org/10.1137/19m1254490.
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Rebai, Maher, Matthieu le Berre, Faicel Hnaien, and Hichem Snoussi. "Exact Biobjective Optimization Methods for Camera Coverage Problem in Three-Dimensional Areas." IEEE Sensors Journal 16, no. 9 (2016): 3323–31. http://dx.doi.org/10.1109/jsen.2016.2519451.
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Simonetti, Hélio Luiz, Valério Silva Almeida, Francisco de Assis das Neves, Vírgil Del Duca Almeida, and Luttgardes de Oliveira Neto. "Reliability-Based Topology Optimization: An Extension of the SESO and SERA Methods for Three-Dimensional Structures." Applied Sciences 12, no. 9 (2022): 4220. http://dx.doi.org/10.3390/app12094220.
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This study takes an approach to reliability-based topology optimization (RBTO) for 3D structures by applying an expansion of smoothing evolutionary structural optimization (SESO) and sequential element rejection and admission (SERA) for three-dimensional optimization. In the search for the stable optimal solution, and a more reliable structure, we present a performance index with the ability to monitor the evolutionary optimization procedure and adopt the filtering scheme usually applied in solid isotropic material with penalization (SIMP). The limit state functions are the maximum displacement constraints imposed in the topology optimization procedure and a structure’s performance control; a comparative analysis of the deterministic topology optimization (DTO) with the RBTO models is also explored. The obtained results suggest the importance of using the RBTO concept in 3D structures as part of the design analysis process.
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Vozniak, Oleh, and Valery Korobov. "Time-optimal controlonasubspaceforthetwoand three-dimensional system." V. N. Karazin Kharkiv National University. Ser. Mathematics, Applied Mathematics and Mechanics, no. 100 (December 23, 2024): 48–60. https://doi.org/10.26565/2221-5646-2024-100-03.
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This article is devoted to the problem of the optimal synthesis on a subspace for the 2 and 3-dimensional case for the linear control system $\dot{x_1} = u, \dot{x}_i=x_{i-1}, i=\overline{2,n}$ with $|u|\le1$. This problem is related to the problem of optimal synthesis into the point, which solution was presented by V. I. Korobov and G. M. Sklyar and is based on the moment min-problem, but its difference with respect to the original problem is that the number of unknown functions is greater than the number of variables, which requires using the methods for the parametric optimization. As in the problem of optimal synthesis into the point, we search for the optimal solution in the form of piecewise function with $u=\pm 1$ and $n-1$ switching points, which is optimal according to Pontryagyn's maximum principle and the theorem on $n$ intervals. In this article we consider approaches and problems related to the finding of the general solution of the optimization problem and solve the cases of $n=2$ and $n=3$. The interest of the obtain results is the fact that unlike the solution for the single endpoint $x_T$ the general solution for the subspace may have less than $n-1$ switching points, or not have switching points at all, depending on the parameters of the subspace. In our work, we give the solution for the problem of synthesis of the two-dimensional system onto a subspace $G: {x_2 = k x_1}$ for all values of $k$ using the moment min-problem and the optimization methods. For the three-dimensional system we consider the problem of synthesis on a plane $x_3 = k_1 x_1 + k_2 x_2$ and obtain the number of the switching points depending on the values of $k_1$ and $k_2$, construct trajectories and present the equations the for optimal time $\Theta$ for different cases.
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von den Hoff, Bieke, Roberto Merino-Martínez, Dick G. Simons, and Mirjam Snellen. "Using global optimization methods for three-dimensional localization and quantification of incoherent acoustic sources." JASA Express Letters 2, no. 5 (2022): 054802. http://dx.doi.org/10.1121/10.0010456.
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Complex acoustic systems typically present three-dimensional distributions of noise sources. Conventional acoustic imaging methods with planar microphone arrays are unsuitable for three-dimensional acoustic imaging, given the computational demands and the incapability to explicitly account for the presence of multiple sources. This paper proposes the use of global optimization methods to solve these shortcomings. An experiment with three incoherent speakers proved that this method can accurately determine the three-dimensional location and the respective sound level of each individual source. In addition, super-resolution is achieved beyond half the Rayleigh resolution limit.
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Kureychik, Vladimir, Vladislav Danilchenko, and Evgeniya Danilchenko. "Routing of Autonomous Devices in Three-Dimensional Space." Informatics and Automation 24, no. 2 (2025): 492–525. https://doi.org/10.15622/ia.24.2.5.
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The article addresses the problem of routing autonomous devices in three-dimensional space, which is a relevant task for intelligent control. The three-dimensional space is characterized by a high degree of freedom, complex topology, and dynamic environmental changes, which significantly complicate the task of effective trajectory planning. The development of routing methods that ensure safety, energy efficiency, and computational efficiency is crucial for improving the performance of autonomous systems. The paper presents a comprehensive routing system based on a hybrid approach that combines high-level modeling of the working space with metaheuristic optimization methods. Hierarchical data structures, such as octrees, are used to represent the three-dimensional environment, providing compactness and flexibility for spatial models. These models are transformed into graph structures, allowing the routing problem to be described as an optimization problem on graphs. A modified metaheuristic ant colony optimization algorithm, belonging to the class of swarm optimization methods, is proposed. The algorithm is designed to build safe and energy-efficient routes, as well as to solve problems related to finding the shortest Hamiltonian cycles and dynamically reconfiguring routes in a changing external environment. The paper presents the results of computational experiments, including algorithm testing in three-dimensional space and a comparative analysis with other routing algorithms. The computational experiment confirmed the effectiveness of the developed routing algorithm, including reduced computation time and improved energy efficiency of autonomous devices. The prospects for further research include integrating the proposed system into a wide range of applications for autonomous devices aimed at optimizing control processes and enhancing performance in a dynamically changing external environment. It is worth noting that the developed algorithm can be adapted to solve complex tasks where routing and wind generator placement on a plane are interrelated. The placement problem is directly connected to route construction for servicing these objects, which requires a comprehensive approach for an efficient solution. This will be part of a decision support system designed for the planning and servicing of wind power complexes, ensuring their effective operation and resource management.
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Zangeneh, M., A. Goto, and H. Harada. "On the role of three-dimensional inverse design methods in turbomachinery shape optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 213, no. 1 (1999): 27–42. http://dx.doi.org/10.1243/0954406991522167.
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The application of a three-dimensional (3D) inverse design method in which the blade geometry is computed for a specified distribution of circulation to the design of turbomachinery blades is explored by using two examples. In the first instance the method is applied to the design of radial and mixed flow impellers to suppress secondary flows. Based on our understanding of the fluid dynamics of the flow in the impeller, simple guidelines are developed for input specification of the inverse method in order to systematically design impellers with suppressed secondary flows and a more uniform exit flow field. In the second example the method is applied to the design of a vaned diffuser. Again based on the understanding of the detailed flow field in the diffuser obtained by using 3D viscous calculations and oil flow visualizations, simple design guidelines are developed for input specification to the inverse method in order to suppress corner separation. In both cases the guidelines are verified numerically and in the case of the diffuser further experimental validation is presented.
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Chen, L. L., H. Lian, Z. Liu, H. B. Chen, E. Atroshchenko, and S. P. A. Bordas. "Structural shape optimization of three dimensional acoustic problems with isogeometric boundary element methods." Computer Methods in Applied Mechanics and Engineering 355 (October 2019): 926–51. http://dx.doi.org/10.1016/j.cma.2019.06.012.
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Xiaoyan, Song, Rao Yufei, Wei Shichao, Xia Xijie, and Li Haifeng. "Three-dimensional electric field decoupling based on PSO-GA fusion under inter-dimensional coupling interference." Journal of Computational Methods in Sciences and Engineering 25, no. 2 (2024): 1714–25. https://doi.org/10.1177/14727978241302985.
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In contrast to existing methods like solving sensitivity matrices or sensor structure optimization decoupling, this paper proposes a three-dimensional electric field separation technique centered on the PSO-GA fusion algorithm, to fully utilize the rapid integration property of the particle swarm optimization algorithm and the global search capability of the genetic algorithm to improve the decoupling accuracy and optimization efficiency. The interdimensional coupling interference problem arises during the course of three-dimensional electric field measurement in space. Furthermore, a correction coefficient-based anti-accumulated charge interference technique is suggested to lessen the impact of the accumulated charge on the sensor surface on the accuracy of 3D electric field measurements. The fitness function and the correction coefficients are modified, and several experimental measurements are made to confirm the viability of the suggested approach under complex inter-dimensional coupling. With a mean square error of 0.002 and an absolute error of 0.003 in the decoupled measurements, the experimental results demonstrate the effectiveness of the decoupling method in challenging measurement environments. This greatly enhances the 3D electric field sensors’ decoupling accuracy and optimization efficiency and opens up new application and technical support opportunities for monitoring and early warning systems in related fields.
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Kang, Yaw-Hong, Da-Chen Pang, and Yi-Ching Zeng. "Optimal Dimensional Synthesis of Ackermann Steering Mechanisms for Three-Axle, Six-Wheeled Vehicles." Applied Sciences 15, no. 2 (2025): 800. https://doi.org/10.3390/app15020800.
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This study employs four metaheuristic optimization methods to optimize the dimensional synthesis of Ackermann steering mechanisms for three-axle, six-wheeled vehicles with front-axle steering mode and reverse-phase steering mode. The employed optimization methods include Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO), Differential Evolution with golden ratio (DE-gr), and Linearly Ensemble of Parameters and Mutation Strategies in Differential Evolution (L-EPSDE). With a front-wheel steering angle range of 70 degrees, two hundred optimization experiments were conducted for each method, and statistical analyses revealed that DE-gr and L-EPSDE methods outperformed PSO and HPSO methods in terms of standard deviation, mean value, and minimum error. These two methods exhibited superior convergence stability, faster convergence, and higher accuracy compared to PSO and HPSO. Reverse-phase (K = 1) steering mode outperformed front-axle steering mode, delivering reduced steering errors and turning radii. Considering the transmission ratio of front to rear axle (K) as a design variable in reverse-phase steering mode increased design flexibility and significantly lowered steering errors for the front and rear axle steering mechanisms. However, this comes with a slight increase in the turning radius of the vehicle’s front part compared to when K = 1. The optimized mechanism, designed using the DE-gr method, was validated through kinematic simulations and steering analyses using MSC-ADAMS v2015 software, further confirming the effectiveness and reliability of the proposed design.
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Li, Linfeng, and Xiao-Jing Jia. "Three-Dimensional Reconstruction of Cerebrovascular and Algorithm Realization." Journal of Healthcare Engineering 2021 (November 24, 2021): 1–7. http://dx.doi.org/10.1155/2021/7422884.
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Objective. In the three-dimensional reconstruction of CT cerebrovascular medical image registration, a new optimization algorithm based on the relative position information between the contours of various blood vessels in the image is proposed. Methods. Using the rule that the center of gravity of the vascular tissue structure on the series of slices has continuity, find the registration relationship between the contours of the vessels in the two adjacent slices. Because the shape of cerebrovascular contour is relatively symmetrical, its center of gravity is slightly away from its geometric center. Therefore, the geometric center is used to replace the center of gravity, and the “mass” of each contour is calculated according to the area of each contour to achieve the registration of the blood vessel contour. Results. The method has the characteristics of global optimization and stronger robustness. Conclusion. The cerebrovascular image obtained by this method is more realistic and can be used for the import of various software, simulation training, and later research, which provides an effective method for preoperative simulation of cerebrovascular intervention surgery.
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Lou, Xiao Ping, Nai Guang Lv, Peng Sun, and Yi Min Lin. "Data Registration Method Based on Three Dimensional Target." Applied Mechanics and Materials 103 (September 2011): 87–91. http://dx.doi.org/10.4028/www.scientific.net/amm.103.87.
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Data registration method using special three dimensional target to track the structured light measurement system is discussed. Optical scanning device, tracking target and stereo vision system are integrated together to fulfill profile inspection of large-scale free-form surface objects without extra mark points. System architecture and processing steps are introduced and layout optimization methods of three dimensional target are illustrated. Experimental results are showed to evaluate the validity of the registration method and suggests are given to improve the accuracy of the system.
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Xiao, Yanyang, Zhonggui Chen, Zhengtao Lin, et al. "Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds." Remote Sensing 12, no. 3 (2020): 473. http://dx.doi.org/10.3390/rs12030473.
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Surpervoxels are becoming increasingly popular in many point cloud processing applications. However, few methods have been devised specifically for generating compact supervoxels from unstructured three-dimensional (3D) point clouds. In this study, we aimed to generate high quality over-segmentation of point clouds. We propose a merge-swap optimization framework that solves any supervoxel generation problem formulated in energy minimization. In particular, we tailored an energy function that explicitly encourages regular and compact supervoxels with adaptive size control considering local geometric information of point clouds. We also provide two acceleration techniques to reduce the computational overhead. The performance of the proposed merge-swap optimization approach is superior to that of previous work in terms of thorough optimization, computational efficiency, and practical applicability to incorporating control of other properties of supervoxels. The experiments show that our approach produces supervoxels with better segmentation quality than two state-of-the-art methods on three public datasets.
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TAKAHASHI, Toko, Takahiro FUJIKAWA, and Koichi YONEMOTO. "Investigations of Shape Definition and Aerodynamics Analysis Methods for Three-Dimensional Aerodynamic Shape Optimization." Proceedings of Conference of Kyushu Branch 2019.72 (2019): C21. http://dx.doi.org/10.1299/jsmekyushu.2019.72.c21.
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Hu, Z., R. Yin, C. Sun, Q. Li, and Y. Chen. "A Comparison of Two Plan Optimization Methods in Three-Dimensional Brachytherapy for Cervical Cancer." International Journal of Radiation Oncology*Biology*Physics 117, no. 2 (2023): e653. http://dx.doi.org/10.1016/j.ijrobp.2023.06.2079.
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Tatar, Ahmet Burak. "Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods." Firat University Journal of Experimental and Computational Engineering 4, no. 1 (2025): 206–25. https://doi.org/10.62520/fujece.1604379.
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This study examines how printing parameters affect the roughness, tensile strength, and elongation of 3D-printed parts used in various applications. Machine learning-based regression models were employed to optimize product quality. The open-source "3D Printer Material Requirement" dataset obtained from the Kaggle platform was utilized to predict product quality. This dataset includes input parameters such as layer height, wall thickness, infill density, infill pattern, nozzle temperature, bed temperature, print speed, printing material (PLA and ABS), and fan speed. These parameters were analyzed for their impact on the product's roughness, load resistance, and elongation under tensile force. Based on these evaluations, product quality was estimated according to its intended use. Parameters such as layer height, wall thickness, infill density, infill pattern, nozzle temperature, bed temperature, print speed, printing material, and fan speed were identified as key factors influencing output performance. Within this framework, prediction models including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process Regression (GPR), and Multi-Layer Perceptron (MLP) were developed, and their performances were assessed using metrics such as accuracy (R²), error rates (RMSE, MSE, MAE), and computational time. Among these methods, GPR demonstrated the highest prediction accuracy for elongation, tensile strength, and roughness, with respective values of 0.98, 0.9, and 1. The findings indicate that machine learning applications are effective tools for quality prediction and optimization in the production processes of 3D printers. Furthermore, this study provides a novel perspective on quality control and design optimization in 3D printing processes.
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Wolosz, Krzysztof J., and Jacek Wernik. "Three-Dimensional Flow Optimization of a Nozzle with a Continuous Adjoint." International Journal of Nonlinear Sciences and Numerical Simulation 16, no. 3-4 (2015): 151–56. http://dx.doi.org/10.1515/ijnsns-2014-0011.
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AbstractThe article presents results of multi-criteria optimization of air nozzle topology. The optimization in the CFD has been recently developed since equations of flow in porous media were applied among others governing equations. Optimization is a seeking for extremum of an objective function with respect to the function constraints. With this definition in mind, the optimization by using a continuous adjoint for the current cases is a finding such channel topology which minimizes for example pressure or energy loss when the constraints of objective function are in the form of flow governing equations of momentum and continuity. This methodology of optimization makes a design process faster comparing to the methods related to Design of Experiments (DoE). However, for the sake of flow governing equations nonlinearity, the continuous adjoint method can be successfully applied only in relatively simply and steady-state cases. The reason is of possibility of finding the global extremum of the objective function only for that kind of cases. The results of optimization of two selected cases are presented in the article and show advantages and limitations of the method applied. The continuous adjoint simulation results indicate the nozzles design directions and can be applied in industry with limited reliability. The object of research reported in the article is the nozzle which is augmented equipment used with a pneumatic pulsator. The pulsators are devices that utilize an air stream to destruct vaults created in loose material structure. The pulsator productivity equipped with a nozzle depends on outlet pressure. Therefore, the optimization problem was stated so that pressure loss is to be as low as possible.
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Schoen, Fabio. "Global optimization methods for high-dimensional problems." European Journal of Operational Research 119, no. 2 (1999): 345–52. http://dx.doi.org/10.1016/s0377-2217(99)00136-8.
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Wang, Tongke. "Alternating Direction Finite Volume Element Methods for Three-Dimensional Parabolic Equations." Numerical Mathematics: Theory, Methods and Applications 3, no. 4 (2010): 499–522. http://dx.doi.org/10.4208/nmtma.2010.m99027.
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Gigante, Giacomo, and Christian Vergara. "Optimized Schwarz methods for the coupling of cylindrical geometries along the axial direction." ESAIM: Mathematical Modelling and Numerical Analysis 52, no. 4 (2018): 1597–615. http://dx.doi.org/10.1051/m2an/2018039.
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In this work, we focus on the Optimized Schwarz Method for circular flat interfaces and geometric heterogeneous coupling arising when cylindrical geometries are coupled along the axial direction. In the first case, we provide a convergence analysis for the diffusion-reaction problem and jumping coefficients and we apply the general optimization procedure developed in Gigante and Vergara (Numer. Math. 131 (2015) 369–404). In the numerical simulations, we discuss how to choose the range of frequencies in the optimization and the influence of the Finite Element and projection errors on the convergence. In the second case, we consider the coupling between a three-dimensional and a one-dimensional diffusion-reaction problem and we develop a new optimization procedure. The numerical results highlight the suitability of the theoretical findings.
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Gigante, Giacomo, and Christian Vergara. "Optimized Schwarz methods for the coupling of cylindrical geometries along the axial direction." ESAIM: Mathematical Modelling and Numerical Analysis 52 (October 12, 2018): 1597–615. https://doi.org/10.1051/m2an/2018039.
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In this work, we focus on the Optimized Schwarz Method for circular flat interfaces and geometric heterogeneous coupling arising when cylindrical geometries are coupled along the axial direction. In the first case, we provide a convergence analysis for the diffusion-reaction problem and jumping coefficients and we apply the general optimization procedure developed in Gigante and Vergara (<em>Numer. Math.</em> <strong>131</strong> (2015) 369–404). In the numerical simulations, we discuss how to choose the range of frequencies in the optimization and the influence of the Finite Element and projection errors on the convergence. In the second case, we consider the coupling between a three-dimensional and a one-dimensional diffusion-reaction problem and we develop a new optimization procedure. The numerical results highlight the suitability of the theoretical findings.
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Lv, Jia Liang, Ying Long Wang, Huan Qing Cui, and Nuo Wei. "Weight Selection and PSO Based Three-Dimensional Localization for Wireless Sensor Networks." Applied Mechanics and Materials 427-429 (September 2013): 2540–44. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2540.
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Localization is one of the key technologies of wireless sensor networks, and the problem of localization is always formulated as an optimization problem. Particle swarm optimization (PSO) is easy to implement and requires moderate computing resources, which is feasible for localization of sensor networks. To improve the efficiency and precision of PSO-based localization methods, this paper proposes a novel three-dimensional PSO method based on weight selection (WSPSO). Simulation results show that the proposed method outperforms standard PSO and existing localization algorithms.
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Xu, Liang, Shenglong Jin, Weiqi Ye, Yunlong Li, and Jianmin Gao. "A Review of Machine Learning Methods in Turbine Cooling Optimization." Energies 17, no. 13 (2024): 3177. http://dx.doi.org/10.3390/en17133177.
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In the current design work, turbine performance requirements are getting higher and higher, and turbine blade design needs multiple rounds of iterative optimization. Three-dimensional turbine optimization involves multiple parameters, and 3D simulation takes a long time. Machine learning methods can make full use of historically accumulated data to train high-precision data models, which can greatly reduce turbine blade performance evaluation time and improve optimization efficiency. Based on the data model, the advanced intelligent combinatorial optimization technology can effectively reduce the number of iterations, find the better model faster, and improve the optimization calculation efficiency. Based on the different cooling parts of turbine blades and machine learning, this research explores the potential of implementing different machine learning algorithms in the field of turbine cooling design.
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Li, Kene, Liuying Li, Chunyi Tang, Wanning Lu, and Xiangsuo Fan. "Three-Dimensional Path Planning Based on Six-Direction Search Scheme." Sensors 24, no. 4 (2024): 1193. http://dx.doi.org/10.3390/s24041193.
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In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.
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Gulayeva, Nataliya, Volodymyr Shylo, and Mykola Glybovets. "Genetic Algorithms as Computational Methods for Finite-Dimensional Optimization." Cybernetics and Computer Technologies, no. 3 (September 30, 2021): 5–14. http://dx.doi.org/10.34229/2707-451x.21.3.1.
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Introduction. As early as 1744, the great Leonhard Euler noted that nothing at all took place in the universe in which some rule of maximum or minimum did not appear [12]. Great many today’s scientific and engineering problems faced by humankind are of optimization nature. There exist many different methods developed to solve optimization problems, the number of these methods is estimated to be in the hundreds and continues to grow. A number of approaches to classify optimization methods based on various criteria (e.g. the type of optimization strategy or the type of solution obtained) are proposed, narrower classifications of methods solving specific types of optimization problems (e.g. combinatorial optimization problems or nonlinear programming problems) are also in use. Total number of known optimization method classes amounts to several hundreds. At the same time, methods falling into classes far from each other may often have many common properties and can be reduced to each other by rethinking certain characteristics. In view of the above, the pressing task of the modern science is to develop a general approach to classify optimization methods based on the disclosure of the involved search strategy basic principles, and to systematize existing optimization methods. The purpose is to show that genetic algorithms, usually classified as metaheuristic, population-based, simulation, etc., are inherently the stochastic numerical methods of direct search. Results. Alternative statements of optimization problem are given. An overview of existing classifications of optimization problems and basic methods to solve them is provided. The heart of optimization method classification into symbolic (analytical) and numerical ones is described. It is shown that a genetic algorithm scheme can be represented as a scheme of numerical method of direct search. A method to reduce a given optimization problem to a problem solvable by a genetic algorithm is described, and the class of problems that can be solved by genetic algorithms is outlined. Conclusions. Taking into account the existence of a great number of methods solving optimization problems and approaches to classify them it is necessary to work out a unified approach for optimization method classification and systematization. Reducing the class of genetic algorithms to numerical methods of direct search is the first step in this direction. Keywords: mathematical programming problem, unconstrained optimization problem, constrained optimization problem, multimodal optimization problem, numerical methods, genetic algorithms, metaheuristic algorithms.
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Sikiru, Yinka, Jitendra Paliwal, and Chyngyz Erkinbaev. "Three-Dimensional Characterization of Potatoes Under Different Drying Methods: Quality Optimization for Hybrid Drying Approach." Foods 13, no. 22 (2024): 3633. http://dx.doi.org/10.3390/foods13223633.
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The quality evaluation of processed potatoes is vital in the food industry. In this study, the effect of three different drying methods on the post-processing quality of potatoes utilizing 4, 8, 12, and 16 h of freeze drying (FD), infrared drying (ID), and oven drying (OD) was investigated. The impact of the drying methods on the potato’s microstructure was analyzed and quantified using 3D X-ray micro-computed tomography images. A new Hybrid Quality Score Evaluator (HQSE) was introduced and used to assess the Quality Index (QI) and Specific Energy Consumption Index (SECI) across various drying methods and durations. Mathematical models were developed to predict the optimal drying method. FD showed significantly higher (p < 0.05) colour retention, rehydration ratio, and total porosity, with minimal shrinkage, although it had higher energy consumption. ID had the shortest drying time, followed by OD and FD. The optimization showed that for FD, the optimal time of 5.78 h increased QI by 9.7% and SECI by 30.6%. The mathematical models could accurately predict the QI and SECI under different drying methods, balancing quality preservation with energy efficiency. The findings suggest that a hybrid drying system could optimize potato quality and energy consumption.
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Liu, Junbing, Xiaoqiang Fan, and Xiao Tang. "Development of Automated Processes for Three-Dimensional Numerical Simulation of Compressor Performance Characteristics." Applied Sciences 14, no. 2 (2024): 623. http://dx.doi.org/10.3390/app14020623.
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Compressor characteristic evaluation is a critical step in design and optimization. Corrected characteristic curves are typically derived via experimental testing or CFD computation which is typically executed through manual manipulation. For compressors necessitating extensive characteristic computation across multiple speeds and operational conditions, the involved process is inherently complex. This paper introduces an automation approach, employing dichotomy and optimization algorithms aligned with a 3D numerical solver, to streamline the derivation of compressor characteristic curves. Initially, the paper delineated the procedural frameworks for two methodologies and validated them using a single-stage transonic compressor. Both methods achieved the automated resolution of the characteristic lines. The result indicates that the volume of the iterative computations hinges on the granularity of the space searching step and the precision in identifying the stall boundary point. Pertaining to the subject investigated herein, the dichotomy method entailed fewer iterations relative to the optimization algorithm. Subsequent adjustments to the optimization algorithmic process revealed that direct optimization mitigated computational demands substantially by reducing a cycle, thereby attesting to its superior efficacy. In summary, when simulating compressor characteristic curves, the dichotomy method proves more efficient when the interval value of the stall boundary range can be approximated. Conversely, direct optimization is preferable when the estimation of the compressor’s stall boundary is imprecise. The proposed methodologies contribute to compressor research and expedite the progression of compressor development and performance advancement.
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BROADIE, MARK, and WEIWEI SHEN. "HIGH-DIMENSIONAL PORTFOLIO OPTIMIZATION WITH TRANSACTION COSTS." International Journal of Theoretical and Applied Finance 19, no. 04 (2016): 1650025. http://dx.doi.org/10.1142/s0219024916500254.
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This paper studies Merton’s portfolio optimization problem with proportional transaction costs in a discrete-time finite horizon. Facing short-sale and borrowing constraints, investors have access to a risk-free asset and multiple risky assets whose returns follow a multivariate geometric Brownian motion. Lower and upper bounds for optimal solutions up to the problem with 20 risky assets and 40 investment periods are computed. Three lower bounds are proposed: the value function optimization (VF), the hyper-sphere and the hyper-cube policy parameterizations (HS and HC). VF attacks the conundrums in traditional value function iteration for high-dimensional dynamic programs with continuous decision and state spaces. HS and HC respectively approximate the geometry of the trading policy in the high-dimensional state space by two surfaces. To evaluate lower bounds, two new upper bounds are provided via a duality method based on a new auxiliary problem (OMG and OMG2). Compared with existing methods across various suites of parameters, new methods lucidly show superiority. The three lower bound methods always achieve higher utilities, HS and HC cut run times by a factor of 100, and OMG and OMG2 mostly provide tighter upper bounds. In addition, how the no-trading region characterizing the optimal policy deforms when short-sale and borrowing constraints bind is investigated.
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Wang, Zhan Zhong, Jing Fu, Lan Fang Liu, and Rui Rui Liu. "Three Dimensional Offline Packing Optimization Problem Based on Genetic Simulated Annealing Algorithm." Applied Mechanics and Materials 744-746 (March 2015): 1919–23. http://dx.doi.org/10.4028/www.scientific.net/amm.744-746.1919.
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In this paper, we try to solve 3D offline packing optimization problem by combining two methods-genetic algorithm’ global performance and simulated annealing algorithm’ local performance. Given Heuristic rules in loading conditions, we use the optimal preservation strategy and the roulette wheel method to choose selection operator, integrating simulated annealing algorithm into genetic algorithm , and achieving code programming and algorithms by Matlab.This paper carries out an actual loading in a vehicle company in Changchun City, then makes a contrast between the final optimization results and each suppliers’ current packing data.The experimental results show that the algorithm has a certain validity and practicability in multiple container packing problem.
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Zhao, Qinghai, Xiaokai Chen, Zheng-Dong Ma, and Yi Lin. "Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/580980.
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A robust topology optimization (RTO) approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach.
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Mao, Yilin. "Using simulated annealing algorithm to optimize three-dimensional spatial structure in environmental art design." Journal of Computational Methods in Sciences and Engineering 25, no. 4 (2025): 3697–712. https://doi.org/10.1177/14727978251324146.
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Existing methods for optimizing three-dimensional spatial structures in environmental art design often rely on subjective experience and intuition, lacking scientific rigor and systematic quantitative analysis. This limitation frequently results in suboptimal solutions, particularly in multi-objective optimization scenarios where local optima are prevalent, thereby restricting innovation and practicality. To address these challenges, this paper proposes the use of the simulated annealing (SA) algorithm to optimize spatial structures. A multi-objective optimization model is constructed, integrating functionality, aesthetics, and space utilization, with mathematical modeling employed to quantify these objectives. To enhance solution quality, a hybrid strategy combining domain knowledge with Latin hypercube sampling ensures uniformity and representativeness of initial solutions. The SA algorithm is configured with carefully selected parameters, including initial temperature and cooling rate, while a comprehensive penalty function handles multi-dimensional constraints. The transition probability mechanism further improves global search capability, preventing premature convergence. Experimental results demonstrate the algorithm’s superior performance: an average generation speed of 3.28 seconds/scheme, optimization time of 21.85 minutes, functional realization rate of 90.83%, space utilization rate of 94.79%, and significantly higher aesthetic scores compared to other methods. This study bridges the gap between artistic creativity and computational optimization, offering a transformative approach to spatial design. By enabling innovative, efficient, and balanced solutions, it advances intelligent methodologies in environmental art design, fostering sustainable and aesthetically pleasing environments.
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Lu, Guozhi, Rui Wang, Qianshun Li, and Fei Hu. "Three-Dimensional Dynamic Visualization Simulation of Underground Mining Engineering considering Chaos Optimization of Symmetric Varieties." Mobile Information Systems 2022 (July 31, 2022): 1–7. http://dx.doi.org/10.1155/2022/2916971.
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Aiming at the complex situation of mine underground engineering, this paper is chaos optimization to design 3D visualization simulation system of mine underground engineering, especially to satisfy the basic functions of 3D dynamic system and chaos optimization algorithm. A symmetric manifold algorithm is adopted. Explanations, transformation methods, and projective transformation methods and optical processing and conventional vector calculation techniques are used for analyzing underground mining engineering cases; it can be seen as follows: chaos-optimized symmetric variety algorithms in real life as an example. It can offer new ideas for optimizing the composition of underground mining engineering.
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Pintér, J. "Globally convergent methods for n-dimensional multiextremal optimization." Optimization 17, no. 2 (1986): 187–202. http://dx.doi.org/10.1080/02331938608843118.
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Deng, Lixia, Huanyu Chen, Xiaoyiqun Zhang, and Haiying Liu. "Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization." Mathematics 11, no. 9 (2023): 1987. http://dx.doi.org/10.3390/math11091987.
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The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.
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Lu, Changyu, Jianmin Yang, Bernt Johan Leira, Qihang Chen, and Shulin Wang. "Three-Dimensional Path Planning of Deep-Sea Mining Vehicle Based on Improved Particle Swarm Optimization." Journal of Marine Science and Engineering 11, no. 9 (2023): 1797. http://dx.doi.org/10.3390/jmse11091797.
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Three-dimensional path planning is instrumental in path decision making and obstacle avoidance for deep-sea mining vehicles (DSMV). However, conventional particle swarm algorithms have been prone to trapping in local optima and have slow convergence rates when applied to underwater robot path planning. In order to secure a safe and economical three-dimensional path for the DSMV from the mining area to the storage base in connection with innovative mining system, this paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization (IPSO) path planning. Firstly, we construct an unstructured seabed mining area terrain model with hazardous obstacles. Consequently, by considering optimization objectives such as the path length, terrain undulation, comprehensive energy consumption, and crawler slippage rate, we convert the path planning problem into a multi-objective optimization problem, constructing a multi-objective optimization mathematical model. Following that, we propose an IPSO algorithm to tackle the multi-objective non-linear optimization problem, which enables global optimization for DSMV path planning. Finally, we conduct a comprehensive set of experiments using the MATLAB simulation platform and compare the proposed method with existing advanced methods. Experimental results indicate that the path planned by the IPSO exhibits superior performance in terms of path length, terrain undulation, energy consumption, and safety.
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Li, Xuyang, Zhiqiang Wan, Xiaozhe Wang, and Chao Yang. "Aeroelastic Optimization Design of the Global Stiffness for a Joined Wing Aircraft." Applied Sciences 11, no. 24 (2021): 11800. http://dx.doi.org/10.3390/app112411800.
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Due to the complexity and particularity of the joined wing layout, traditional design methods for the global stiffness of a high-aspect wing are not applicable for a joined wing. Herein, a beam-frame model and a three-dimensional wing-box model are built to solve the global stiffness aeroelastic optimization design problem for a joined wing. The goal is to minimize the weight, and the constraints are the overall aeroelastic requirements. Based on a genetic algorithm, two methods for the beam-frame model and one method for the three-dimensional model are used for comparative analysis. The results show that the optimization method for a diagonal beam section and the optimization method for an exponential/linear combination function fit are adequate for optimizing and designating the joined wing global stiffness. The distributions obtained using the two methods have good consistency and are similar to the distribution of the three-dimensional model. The stiffness distribution data and the beam section parameters can be converted from each other, which is convenient for redesigning the structure parameters using the stiffness distribution data, and is valuable for engineering applications.
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Ma, Y. Z. "Three-Dimensional Modeling of Mineral/Elemental Compositions for Shale Reservoirs." SPE Journal 25, no. 04 (2020): 2067–78. http://dx.doi.org/10.2118/201118-pa.
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Summary Mineral compositional analysis of rocks is important for developing shale resources because the relationships between mineral compositions and petrophysical properties are critical for resource evaluation and completion optimization. Elementary properties are now routinely analyzed at wells in evaluating shale reservoirs. However, these properties have not been modeled in the three-dimensional (3D) reservoir. This is because an elemental composition has a physical constraint that is relatively easily adhered to in data analysis for wells but not in 3D modeling of reservoirs. A critical condition of elemental composition is that the sum of its components is equal to 100% to honor the mass-preservation principle. Traditional modeling methods do not satisfy this physical condition, sometimes producing nonphysical values, such as negative porosity values and fluid-saturation values greater than 100%. To date, only the compositional-modeling methods using a log-ratio transform can consistently satisfy this physical constraint. This paper presents modeling methods using additive log-ratio transform for modeling mineral compositions.
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Wang, Xueying, Jie Zheng, Jianmin Wang, Yibing Yu, Xi Wang, and Feifei Zhang. "Efficient Design of Three-Dimensional Well Trajectories with Formation Constraints and Optimization." Processes 13, no. 4 (2025): 1215. https://doi.org/10.3390/pr13041215.
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Current methods for designing three-dimensional trajectories rarely account for complex formation constraints, focusing primarily on geometric relationships. However, trajectory adjustments are often necessary during drilling operations. These field adjustments typically lack systematic optimization, resulting in suboptimal trajectories. This study introduces a novel trajectory optimization framework that integrates formation fitness for curve construction and proactive anti-collision trajectory adjustment (PACTA). The framework begins by incorporating PACTA and optimizing the initial trajectory to minimize total measured depth (TMD) using a genetic algorithm. Subsequently, a second optimization phase identifies curve sections passing through formations with low build-up fitness, automatically splitting them into combinations of curves and straight lines. Dynamic trajectory equations are then constructed based on these adjustments, and the final trajectory is optimized accordingly. Case studies demonstrate that the proposed method effectively adjusts curve positions in the presence of multiple formations with low build-up fitness while avoiding wellbore collisions. The approach achieves an average 10% reduction in total drilling time when minimizing TMD and an average 19.7% reduction in drillstring torque when torque minimization is prioritized. This new trajectory design method is expected to significantly reduce well construction costs.
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Outinen, K., H. Vuorela, and R. Hiltunen. "Optimization of HPLC separation by computer-assisted optimization methods. Comparison of three methods." European Journal of Pharmaceutical Sciences 4, no. 4 (1996): 199–210. http://dx.doi.org/10.1016/0928-0987(95)00040-2.
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Krasniuk, A. V., O. F. Tatarinov, and T. V. Ulchenko. "OPTIMIZATION OF THREE-DIMENSIONAL MODELING IN ENGINEERING AND BUILDING DESIGN IN THE EDUCATIONAL PROCESS." Science and Transport Progress, no. 29 (October 25, 2009): 159–63. http://dx.doi.org/10.15802/stp2009/14088.
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AutoCAD possibilities of 3D solid modeling are described in the article. The optimum methods of designing for construction and machine drawing applying are analyzed. The choice of methods for 3D solids drawing is proved by samples.
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Bykovsky, N. V., R. V. Harutyunyan, and A. V. Nasedkin. "A combinatorial approach to optimizing the placement of irregularly shaped elements on two-dimensional and three-dimensional switching fields with complex topology." T-Comm 18, no. 10 (2024): 44–54. https://doi.org/10.36724/2072-8735-2024-18-10-44-54.
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This paper presents a novel optimization method for placing non-standard configuration elements on two-dimensional and three-dimensional interconnection fields with complex topologies. A combinatorial analog of the Gauss-Seidel method has been developed, adapted for efficient handling of components with irregular shapes and varying dimensions. The algorithm accounts for specific characteristics of the interconnection field, including the presence of forbidden zones and structural het erogeneity. To increase the probability of finding the global optimum, a multi-start procedure has been applied. A software implementation has been created with modules for placement optimization and result visualization. The approach's effectiveness has been verified on original test problems of the "multidimensional snake" type. Examples for two-dimensional and three-dimensional fields with elements of various sizes and geometric constraints are considered. The results demonstrate the method's adaptability to different placement conditions for nonstandard shaped elements. Quasi-optimal placement with an error of 0.055% was achieved in 1010 iterations, with computation times of 5 minutes for two-dimensional and 10 minutes for three-dimensional problems. The method is applicable in integrated circuit design, electrical equipment layout, and optimization of equipment placement in production systems. Quantitative assessments of the algorithm's efficiency for fields of varying complexity are presented. The implementation features for components with non-trivial geometry and interconnection fields of non-standard topology are analyzed. The advantages of the proposed approach compared to classical optimization methods for placing elements of different sizes are identified. Directions for further research are defined, including adaptation to specific requirements of various application areas and development of parallel computing strategies for large-scale problems with irregularly shaped elements on multidimensional interconnection fields with complex topologies.
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Wołosz, Krzysztof J., and Jacek Wernik. "ICEEM07: Three-Dimensional Flow Optimization of a Pneumatic Pulsator Nozzle with a Continuous Adjoint." International Journal of Nonlinear Sciences and Numerical Simulation 16, no. 1 (2015): 3–9. http://dx.doi.org/10.1515/ijnsns-2014-0043.
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AbstractThe article presents results of multi-criteria optimization of air nozzle topology. The optimization in the Computational Fluid Dynamics (CFD) has been recently developed since equations of flow in porous media were applied among others governing equations. Optimization is a seeking for extremum of an objective function with respect to the function constraints. With this definition in mind, the optimization by using a continuous adjoint for the current cases is a finding such channel topology which minimizes for example pressure or energy loss when the constraints of objective function are in the form of flow governing equations of momentum and continuity. This methodology of optimization makes a design process faster comparing to the methods related to Design of Experiments (DoE). However, for the sake of flow governing equations nonlinearity, the continuous adjoint method can be successfully applied only in relatively simply and steady-state cases. The method consists in seeking of a global extremum of objective function. This extremum can sometimes be find in only simple and stationary events. The results of optimization of two selected cases are presented in the article and show advantages and limitations of the method applied. The continuous adjoint simulation results indicate the nozzles design directions and can be applied in industry with limited reliability. The object of research reported in the article is the nozzle which is augmented equipment used with a pneumatic pulsator. The pulsators are devices that utilize an air stream to destruct vaults created in loose material structure. The pulsator productivity equipped with a nozzle depends on outlet pressure. Therefore, the optimization problem was stated so that pressure loss is to be as low as possible.
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Korolyov, Vyacheslav, Maksim Ogurtsov, and Oleksandr Khodzinskyi. "Research on Algorithms for Local Robot Positioning Using Methods of Discrete Optimization." Cybernetics and Computer Technologies, no. 2 (June 6, 2025): 5–16. https://doi.org/10.34229/2707-451x.25.2.1.
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Introduction. The use of robots is becoming increasingly prevalent each year. Simultaneously, there is a gradual shift from using individual robots to deploying collective robots, as this approach proves to be more efficient for various tasks such as agricultural surveys and irrigation. However, there are specific challenges, particularly the lack of access to global satellite navigation systems for collective robots, which can be jammed for security reasons. The purpose of the article study is to develop an algorithm for the local positioning system of a small collective of robots to maintain a stable structure during group movement while executing tasks in three-dimensional conditions. It is assumed that the collective is controlled by an artificial intelligence single operator, regardless of the number of robots in the group. Results. An algorithm for local positioning based on solving the problem of discrete optimization has been developed. By anchoring one object to the origin point and fixing the position of the second object on the horizontal axis, the problem of ambiguity in the solution, manifested in the symmetrical reflection of points and their displacement relative to real positions, has been resolved. Conclusions. Improvements to the existing local positioning system robot group based on an enhanced discreet optimization algorithm are discussed. The study formulated the task of forming and maintaining the structure of three robots in a two-dimensional space. For constructing the relative coordinate system, a method of forming a base triangle using a nonlinear discrete optimization method was proposed. An algorithm for building a local positioning system for a robot group was developed to ensure the stable configuration of the group in the absence of access to global or cellular navigation systems while performing tasks in two-dimensional spaces. The proposed algorithm serves as a foundation for developing software applications for controlling a small robot group by artificial intelligence or a single operator and avoiding collisions between UAVs. The optimization problem is solved using the SLSQP method, which is suitable for solving problems with nonlinear constraints. It allows for optimizing the coordinates of three objects, minimizing distance and angle errors considering physical constraints, such as fixing the coordinates of certain points and ensuring the minimum distance between objects is not exceeded. The algorithm from the scipy.optimize.minimize library numerically finds the solution to the discrete optimization problem with a combined objective function considering the specified constraints, ensuring coordinate recovery accuracy of approximately 10 % and acceptable program execution speed for fixed-wing UAVs. Keywords: UAV group, local positioning, nonlinear discrete optimization.
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Du, Juan, and Yuelin Long. "Landscape Image Layout Optimization Extraction Simulation of 3D Pastoral Complex under Big Data Analysis." Complexity 2020 (November 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/6620216.
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Big data has brought about opportunities for landscape architecture, changing the design thinking of layout optimization simulation, expanding the platform for public participation in layout optimization simulation design, reflecting social and humanistic care, and promoting the integration of discipline cooperation and data. At the same time, it also brings about challenges. The proposal of data theory, the acquisition and analysis of data, and the protection of privacy are all issues that we need to face and solve. First, build a layout optimization simulation program under the background of big data. Follow the procedures of conventional layout optimization simulation, add big data analysis technology to the preliminary analysis, layout optimization simulation design, and later evaluation management, introduce new data processing methods, and build a theoretical framework of landscape optimization simulation methods that integrate new and traditional data. Second, studying the evolution of the landscape image layout of the three-dimensional pastoral complex is different: the overall landscape image layout of the three-dimensional pastoral complex shows a trend of fragmentation and heterogeneity. Among them, the arable land is gradually fragmented, the wetland and water area are simplified in form, the woodland and grassland patches are gradually distributed, the fragmentation is reduced by 76.19%, and the connectivity index is gradually increased. From a spatial perspective, the edge area outside the Fourth Ring Road becomes the most obvious area of fragmentation tendency. Finally, by introducing big data into the landscape layout optimization simulation, a layout optimization simulation method based on big data is constructed and used in the layout optimization simulation of the landscape image layout of the three-dimensional pastoral complex to guide the participatory layout optimization simulation.
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Lahanas, M., D. Baltas, S. Giannouli, N. Milickovic, and N. Zamboglou. "Generation of uniformly distributed dose points for anatomy-based three-dimensional dose optimization methods in brachytherapy." Medical Physics 27, no. 5 (2000): 1034–46. http://dx.doi.org/10.1118/1.598970.
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Ng, Seng-Beng, Kok-Why Ng, Rahmita Wirza O. K. Rahmat, and Yih-Jian Yoong. "Blending of three-dimensional geometric model shapes." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 102. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp102-109.
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Three-dimensional (3D) geometric model shapes blending method can create various in-between models from two inputs of models shapes. Though, many blended shapes are implausible due to different inputs of model type, inappropriate matching-parts, improper parts-segmentation, and non-tally number of segmentation parts. are crucial and should be taken into account. The objective of this paper is to study the strengths and weaknesses of some prominent shapes blending methods and the 3D reconstruction methods. An interpolated shape blending program using the Laplacian-based contraction and Slinky-based segmentation method is developed to illustrate the critical problems arise in the shape blending process. Output results are to be compared with some prominent existing methods and one will observe the potential research direction in the blending research work
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Yao, Guobiao, Ziheng Wang, Guozhong Wei, et al. "Multi-View Three-Dimensional Reconstruction Based on Feature Enhancement and Weight Optimization Network." ISPRS International Journal of Geo-Information 14, no. 2 (2025): 43. https://doi.org/10.3390/ijgi14020043.
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Aiming to address the issue that existing multi-view stereo reconstruction methods have insufficient adaptability to the repetitive and weak textures in multi-view images, this paper proposes a three-dimensional (3D) reconstruction algorithm based on Feature Enhancement and Weight Optimization MVSNet (Abbreviated as FEWO-MVSNet). To obtain accurate and detailed global and local features, we first develop an adaptive feature enhancement approach to obtain multi-scale information from the images. Second, we introduce an attention mechanism and a spatial feature capture module to enable high-sensitivity detection for weak texture features. Third, based on the 3D convolutional neural network, the fine depth map for multi-view images can be predicted and the complete 3D model is subsequently reconstructed. Last, we evaluated the proposed FEWO-MVSNet through training and testing on the DTU, BlendedMVS, and Tanks and Temples datasets. The results demonstrate significant superiorities of our method for 3D reconstruction from multi-view images, with our method ranking first in accuracy and second in completeness when compared to the existing representative methods.
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Li, Xingxing, Dangwei Wang, and Xiaoyan Ma. "Three-Dimensional Target Localization and Cramér-Rao Bound for Two-Dimensional OFDM-MIMO Radar." International Journal of Antennas and Propagation 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4171452.
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Target localization using a frequency diversity multiple-input multiple-output (MIMO) system is one of the hottest research directions in the radar society. In this paper, three-dimensional (3D) target localization is considered for two-dimensional MIMO radar with orthogonal frequency division multiplexing linear frequency modulated (OFDM-LFM) waveforms. To realize joint estimation for range and angle in azimuth and elevation, the range-angle-dependent beam pattern with high range resolution is produced by the OFDM-LFM waveform. Then, the 3D target localization proposal is presented and the corresponding closed-form expressions of Cramér-Rao bound (CRB) are derived. Furthermore, for mitigating the coupling of angle and range and further improving the estimation precision, a CRB optimization method is proposed. Different from the existing methods of FDA-based radar, the proposed method can provide higher range estimation because of multiple transmitted frequency bands. Numerical simulation results are provided to demonstrate the effectiveness of the proposed approach and its improved performance of target localization.
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Zhang, Li, Bo Wang, Minghao Hu, Xian Shi, Liu Yang, and Fujian Zhou. "Research Progress on Optimization Methods of Platform Well Fracturing in Unconventional Reservoirs." Processes 13, no. 6 (2025): 1887. https://doi.org/10.3390/pr13061887.
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Unconventional reservoirs are characterized by low porosity, low permeability, and limited hydrocarbon abundance, making them economically unviable for production under natural conditions. Large-scale hydraulic fracturing has emerged as a critical technology for enabling the effective development of these resources. The three-dimensional development of platform wells employs batch drilling and batch fracturing techniques. By implementing simultaneous fracturing or zipper fracturing approaches, the process achieves well placement, fracturing, and fracture placement in a single step, thereby reducing costs and improving operational efficiency. Platform well fracturing (PWF) involves numerous parameters that require optimization, and the underlying physical processes are highly complex, presenting significant challenges to the design and control of fracturing strategies. To address these challenges, this study focuses on the following aspects: (1) identifying key parameters in PWF and reviewing prior optimization efforts that use production capacity and net present value as objective functions; (2) systematically comparing numerical simulation methods for modeling fracture propagation and simulating production performance, highlighting their role in linking fracturing parameters to objective functions; (3) evaluating the strengths and limitations of single-factor analysis, orthogonal experimental design, and intelligent automatic optimization methods, and proposing a high-dimensional intelligent optimization workflow for fracturing design; (4) examining the technological challenges of PWF and suggesting future directions for its development. This study provides valuable insights into the selection of optimization methods for PWF schemes and offers guidance for advancing the technology’s development, contributing to more efficient and effective resource recovery from unconventional reservoirs.