A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physic...
A catenary anchor leg mooring system (CALM) is one of the main kinds of offshore transport terminals. Its buoyancy is connected by multiple anchor chains fixed on the seabed. Because a large deformation characterizes the anchor chain, it is prone to...
A centrifugal compressor is required to increase aerodynamic efficiency, ensure structural integrity, and reduce processing costs. This paper presents a dimension reduction technique based on proper orthogonal decomposition (POD) in combination with...
Experimental optimization with surrogate models has received much attention for its efficiency recently in predicting the responses of the experimental optimum. However, with the development of multi-fidelity experiments with surrogate models such as...
In this paper, the design and optimization method of rocket parameters based on the surrogate model and the trajectory simulation system of the 3-DOF air-launched rockets were established. The Gaussian kernel width determination method based on the r...
The increase in urban construction in China has been accompanied by increasing concrete output, which has reached 2250 million m3 in recent years, ranked as the highest in the world. Consequentially, its environmental burden is significant in terms o...
Recently, deep-learning methods have yielded rapid progress for object detection in synthetic aperture radar (SAR) imagery. It is still a great challenge to detect ships in SAR imagery due to ships’ small size and confusable detail feature. Thi...
Marine adaptive sampling is a technique that makes full use of limited observation resources by selecting the optimal positions. Recently, the design of an adaptive sampling scheme based on a mobile platform has become a research hotspot. However, ad...
Reliability-based design optimization (RBDO) has become a prevalent design for aeronautical and aerospace engineering. The main problem is that it is impractical in complex cases with multi-failure regions, especially in multi-objective optimization....
Traditional maritime target element resolution, relying on manual experience and uniform sampling, lacks accuracy and efficiency in non-uniform sampling, missing data, and noisy scenarios. While large language models (LLMs) offer a solution, their ge...
This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-chan...
Multi-target path planning for Automated Guided Vehicle (AGV) in complex logistics environments requires balancing planning efficiency, obstacle avoidance capability, and trajectory smoothness. To address these challenges, this paper proposes a bi-le...
Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF)...
This paper introduces an adaptive importance sampling scheme for the computation of group-based convolutions, a key step in the implementation of equivariant neural networks. By leveraging information geometry to define the parameters update rule for...
Surrogate-assisted evolutionary algorithms have become the mainstream approach for solving expensive constrained multi-objective optimization problems (ECMOPs). However, existing methods suffer from blind search issues, and their selection strategies...
Surrogate-based algorithms are indispensable in the aerospace engineering field for reducing the computational cost of optimization and uncertainty quantification analyses, particularly those involving computationally intensive solvers. This paper pr...
The cooperative localization of Unmanned Aerial Vehicles (UAVs) has emerged as a pivotal application in Internet of Things (IoT) tasks. However, the frequent exchange of localization data among UAVs leads to significant energy consumption and escalat...
Ensemble learning techniques are widely applied to classification tasks such as credit-risk evaluation. As for most credit-risk evaluation scenarios in the real world, only imbalanced data are available for model construction, and the performance of...
To address the imbalance between global exploration and local exploitation in the secretary bird optimization algorithm (SBOA), this paper presents a multi-strategy improved version termed MSISBOA. The proposed approach incorporates optimal Latin hyp...
The efficient design optimization of electric machines for electric power steering (EPS) applications poses challenges in meeting demanding performance criteria, including high power density, efficiency, and low vibration. Traditional optimization ap...
Solving global optimization problems is a significant challenge, particularly in high-dimensional spaces. This paper proposes a selective multistart optimization framework that employs a modified Latin Hypercube Sampling (LHS) technique to maintain a...
The research on the optimization of a low-carbon multimodal transportation path under uncertainty can have an important theoretical and practical significance in the high-quality development situation. This paper investigates the low-carbon path opti...
Background: Electroencephalography (EEG) has been widely used in Attention Deficit Hyperactivity Disorder (ADHD) recognition, but existing methods still suffer from limitations in dynamic modeling, small-sample adaptability, and training stability. T...
This paper applies the reliability-based robust optimization (RBRO) technique to investigate the probabilistic structural design characteristics of the Fairlead Chain Stopper (FCS), a newly developed detachable mooring apparatus for installation on a...
Many deterministic multi-objective optimization methods generate Pareto outcomes by repeatedly solving scalarized subproblems for different preference or reference vectors. When the number of objectives is
The Sparrow Search Algorithm (SSA), proposed by Jiankai Xue in 2020, is a swarm intelligence optimization algorithm that has received extensive attention due to its powerful optimization-seeking ability and rapid convergence. However, similar to othe...
In reality, estimations for the unknown parameters of truncated distribution with censored data have wide utilization. Truncated normal distribution is more suitable to fit lifetime data compared with normal distribution. This article makes statistic...
Planning renewable energy bases with chronological source-grid-storage simulation makes exhaustive capacity screening impractical. This study develops a phase-adaptive constrained active-sampling framework whose core mechanism is a simulator-verified...
The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could impr...
This paper deals with an enhanced robust design optimization (RDO) method and its application to the strength design problem of seat belt anchorage, related to the front crash safety of multi-purpose vehicles. In order to determine the rational desig...
In recent years, the research on source-free domain adaptation has received increasing attention and has achieved considerable progress. It can overcome the dependence on source domain data and obtain a target domain model with robust performance on...
This paper introduces a novel formulation of the clustering problem, namely, the minimum sum-of-squares clustering of infinitely tall data (MSSC-ITD), and presents HPClust, an innovative set of hybrid parallel approaches for its effective solution. B...
Unmanned Aerial Vehicles (UAVs) continue to gain popularity in applications such as military reconnaissance, environmental monitoring in remote locations, and package delivery. High-Altitude Long-Endurance (HALE) UAVs can remain airborne for extended...
Radiation mapping is a desirable task to automate because of the inherent risks involved and its tedious nature. A novel system was designed to address this by combining various existing technologies, utilizing behavior-based robotics and Bayesian op...
The Laser Light Screen System faces critical technical challenges in high-speed, long-range target detection: when a target passes through the light screen, weak light flux variations lead to significantly degraded signal-to-noise ratios (SNRs). Trad...
In this study, an optimization framework for turbomachinery blades using a hybrid surrogate model assisted by proper orthogonal decomposition (POD) is introduced and then applied to the aero-structural multidisciplinary design optimization of a trans...
This paper details the development of an autonomous robotic solution for the long-term surveillance of low-level radiation in nuclear facilities. Implementing such a system mitigates personnel health risks by minimizing radiation exposure and automat...
Sampling-based algorithms are pivotal for high-dimensional UAV path planning, especially in 3D urban environments. The Rapidly-Exploring Random Tree (RRT) suffers from inadequate sampling methods and a single, fixed sampling policy, which lead to elo...
In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix...
Data imbalance is a serious problem in machine learning that can be alleviated at the data level by balancing the class distribution with sampling. In the last decade, several sampling methods have been published to address the shortcomings of the in...
Empirical models based on sampled data can be useful for complex chemical engineering processes such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. In this case, the goal is to predict the monomer conversi...
Given the complexity of the operating conditions of rolling bearings in the actual rolling process of a hot mill and the difficulty in collecting data pertinent to fault bearings comprehensively, this paper proposes an approach that diagnoses the fau...
As critical equipment in power systems, the reliable operation of power transformers is directly linked to the overall safety of the power grid. Traditional fault diagnosis methods based on dissolved gas analysis generally rely on a single gas featur...
When using machine learning methods to make predictions, the problem of small sample sizes or highly noisy observation samples is common. Current mainstream sample expansion methods cannot handle the data noise problem well. We propose a multipath sa...
Landslide susceptibility algorithms demonstrate high reliability in quantifying the likelihood of landslide occurrence. However, traditional methods are often limited by computationally intensive sampling strategies and models with limited adaptabili...
This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfy...
Load forecasting techniques can be an essential method to save energy and shave peak loads in order to improve energy efficiency and maintain the stability of a power grid. To achieve this goal, machine learning-based approaches have been proposed re...
Path planning is a complex task in robotics, requiring an efficient and adaptive algorithm to find the shortest path in a dynamic environment. The traditional path planning methods, such as graph-based algorithms, sampling-based algorithms, reaction-...
The Centralized Teacher with Decentralized Student (CTDS) framework is a multi-agent reinforcement learning (MARL) approach that utilizes knowledge distillation within the Centralized Training with Decentralized Execution (CTDE) paradigm. In this fra...
The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems...
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