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1921
Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton
Published 2025-04-01“…This study introduces a method for identifying the moisture state of cotton using an enhanced MobileVit deep learning algorithm. This approach incorporates the Efficient Channel Attention (ECA) mechanism into the Fusion component of the MobileVit model, optimizes the first convolution in the Fusion component by replacing it with Depthwise Separable Convolution (DsConv), and substitutes the Local representation with the MobileOne block. …”
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1922
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1923
Applying Canny edge detection and Hough transform algorithms to identify irrigation channel boundaries in irrigation districts
Published 2025-05-01“…【Result】Resizing the images to 800 × 400 pixels yielded optimal results, reducing detection time to less than 1.3 seconds while maintaining accurate representation of the channel boundaries. …”
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1924
A Fine-Grained Aircraft Target Recognition Algorithm for Remote Sensing Images Based on YOLOV8
Published 2025-01-01“…This article addresses the issues of missed and false detections in existing aircraft target fine-grained recognition algorithms for remote sensing images by proposing an improved algorithm based on YOLOv8, called FD-YOLOv8 (Focus Detail-YOLOv8). …”
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1925
Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position
Published 2025-07-01“…Additionally, the investigation analyzed the potential role of Artificial Intelligence Dystocia Algorithm (AIDA) as an innovative decision support system in standardizing diagnostic approaches and optimizing clinical decision-making in cases of fetal malposition. …”
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1926
Operational response to contamination in water distribution systems: a multi-objective Bayesian optimization approach
Published 2025-05-01“…The optimization framework aims to balance the conflicting objectives of minimizing response time while maximizing water quality metrics after contamination events. …”
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1927
Intelligent recognition of “geological-engineering” sweet spots in tight sandstone reservoirs - an application to a tight gas reservoir in Ordos Basin, China
Published 2025-03-01“…This study, based on an integrated geological-engineering perspective and utilizing data analysis and multiple machine learning methods, innovatively proposes a regression prediction model that integrates the Triangulation Topology Aggregation Optimizer (TTAO) algorithm, Random Forest (RF), and Multi-Head Self-Attention Mechanism (MSA), aiming to enhance the accuracy of oil and gas sweet spot identification. …”
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1928
Designing Multi-Objective Optimization Model of Electricity Market Portfolio for Industrial Consumptions under Uncertainty
Published 2021-12-01“…Due to the small number of industrial subscribers, the whole population was studied. A genetic algorithm has been used to solve this optimization problem. …”
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1929
Evolved Motor Design Process to Reduce the Design Expense of Axial Flux Permanent Magnet Motor
Published 2025-01-01“…This paper proposes a design process that combines an initial electromagnetic design using analytical techniques with an optimal design based on a multi-objective optimization algorithm, aiming to reduce the design cost of an axial flux permanent magnet motor for urban air mobility traction. …”
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1930
Trajectory and communication scheduling optimization for the rechargeable UAV aided data collection system
Published 2022-09-01“…A rechargeable unmanned aerial vehicle (UAV) aided wireless sensor network was considered, which consists of multiple ground terminals with a large amount of time-sensitive data to be collected.Due to the limited battery capacity, the UAV cannot collect the data from all terminals through a single flight mission, and it needs to return to the charging pile to replenish its flight energy several times during the whole mission.The optimization of the terminal scheduling, trajectory, flight speed and transmission rate for the UAV was studied to maximize the number of terminals whose data had been collected within the data lifetime limit.Due to the variable coupling and the existence of discrete binary scheduling variables, the considered optimization problem is difficult to solve.To tackle such a difficulty, an efficient algorithm was proposed based on the stochastic optimization and the feature engineering.Specifically, the flight hover communication protocol was introduced to simplify the UAV flight process.And then a terminal scheduling algorithm was innovatively proposed with the influence factor and the stochastic preference, which extracted the features that affect the service time of the UAV, optimized the weights of the features, and further simplified the optimization problem into multiple subproblems.The subproblems were then solved by using the block coordinate descent and successive convex approximation techniques.Simulation results show that the proposed optimization algorithm achieves significant performance gains over several benchmark schemes in the scenarios with different data lifetime requirements and different numbers of ground terminals.…”
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1931
A Multiswarm Optimizer for Distributed Decision Making in Virtual Enterprise Risk Management
Published 2012-01-01“…We develop an optimization model for risk management in a virtual enterprise environment based on a novel multiswarm particle swarm optimizer called PS2O. …”
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1932
An Improved Constrained Multiobjective Optimization for Energy Multimodal Transport Among Clustering Islands
Published 2024-12-01“…To this end, this study proposes a novel energy optimization framework that aims to optimize the use of their different types of energy among clustering islands and improve the stability of the whole energy internet via a multilayer transportation network. …”
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1933
Integrated Optimization of Pipe Routing and Clamp Layout for Aeroengine Using Improved MOALO
Published 2021-01-01“…The integrated optimization method takes pipe and clamp as a whole system and then solves the Pareto solution set of pipe-clamp layouts by using improved MOALO, where the pipe path, clamp position, and rotation angle are selected as decision variables and are further optimized. …”
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1934
Stochastic Planning of Synergetic Conventional Vehicle and UAV Delivery Operations
Published 2025-05-01“…A nested genetic algorithm is initially used to solve the problem under fixed conditions. …”
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1935
Optimization of multi-structural parameters in metamaterials based on the DGN co-simulation method.
Published 2025-01-01“…Then the global algorithm is combined with the local algorithm to solve the problem of poor convergence of the global optimization algorithm while ensuring the optimization quality of the local optimization algorithm. …”
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1936
A multi-faceted review of wind turbine optimization techniques: Metaheuristics and related issues
Published 2025-03-01“…This article comprehensively reviews the current optimization state for wind energy systems and examines the use of metaheuristic optimization algorithms. …”
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1937
Optimized Integral Super-Twisting Sliding Mode Control for Acute Leukemia Therapy
Published 2025-03-01“…This algorithm is utilized to fine-tune the controller parameters, ensuring optimal achievement of control objectives. …”
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1938
Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing
Published 2019-02-01“…Reinforcement-learning-based routing protocol takes advantage of the intelligent algorithm of reinforcement learning to search for the optimal routing path for data transmission. …”
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1939
Research and Experimental Verification of an Efficient Subframe Lightweighting Method Integrating SIMP Topology and Size Optimization
Published 2025-07-01“…A topology optimization model was established using the Solid Isotropic Material with Penalization (SIMP) method and solved using the Method of Moving Asymptotes (MMA) algorithm. …”
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1940
Multidimensional Resource Task Scheduling Based on Particle Swarm Optimization in Edge Computing
Published 2025-01-01“…The proposed approach enables the simultaneous scheduling of multiple tasks while iteratively adjusting the inertia weight and learning factors in the discrete particle swarm optimization (DPSO) algorithm. …”
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