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Application Designing Additive Structures

Procedia Engineering 2017年 185卷 131-138页

作者： Valeriy A. Komarov Alexander A. Pavlov Svetlana A. Pavlova Aircraft Design Department Samara University Samara Russian Federation

The objective of this paper is to demonstrate the opportunities of topology optimization applied to additive technology which will permit to design ultra-light structures practically without regard to the technological limits. The article gives a brief historical overview of the mutual influence of structures, materials and manufacturing technologies. The additive technology seems to have the broadest opportunities for producing existing structures using conventional materials without design changes. A hypothetical variable density material provides the means to solve an auxiliary problem of optimal material distribution considering stress or stiffness constraints. The special optimization algorithm allows the optimal topology layout to be found which will have a minimum value of the integral characteristic, called “load-carrying factor” (LCF). The LCF is a powerful tool for estimating the perfection limit of any structural and technological solution. Along with the optimal structure, such solutions are difficult for manufacturing using conventional materials and technologies. A creation of real material with the characteristics of hypothetical material is considered as one of the nearest areas of additive technology's development for finding optimal structures.

关键词： structure additive technology hypothetic material optimization algorithm load-carrying factor optimal structure

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Dual trace gas detection using a compact two-channel multipass cell with dense and line spot patterns

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HELIYON 2023年第2期9卷

作者： Kong, Rong Ma, Ningyi Liu, Peng Zhou, Xin Beijing Normal Univ Ctr Adv Quantum Studies Dept Phys Appl Opt Beijing Area Major Lab Beijing 100875 Peoples R China

A highly sensitive dual-gas sensor based on a two-channel multipass cell (MPC) was designed and developed for simultaneous detection of atmospheric methane (CH4) and carbon dioxide (CO2) by using two distributed feedback lasers emitting at 1653 nm and 2004 nm. The nondominated sorting genetic algorithm was applied to intelligently optimize the MPC configuration and accelerate the dual-gas sensor design process. A compact and novel two-channel MPC was used to achieve two optical path lengths of 27.6 m and 2.1 m in a small volume of 233 cm3. Simultaneous measurements of CH4 and CO2 in the atmosphere were performed to demonstrate the stability and robustness of the gas sensor. According to the Allan deviation analysis, the optimal detection precision for CH4 and CO2 was 4.4 ppb at an integration time of 76 s and 437.8 ppb at an integration time of 271 s, respectively. The newly developed dual-gas sensor exhibits superior characteristics of high sensitivity and stability, cost-effectiveness and simple structure, which make it well-suited for multiple trace gas sensing in various applications, including environmental monitoring, safety inspections and clinical diagnosis.

关键词： Tunable diode laser absorption spectroscopy Dual -gas sensor Multipass cell optimization algorithm

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UAV formation path planning for mountainous forest terrain utilizing an artificial rabbit optimizer incorporating reinforcement learning and thermal conduction search strategies

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Wang, Wentao Li, Xiaoli Tian, Jun Nankai Univ Coll Software Tianjin 300350 Peoples R China ASTAR Inst Infocomm Res Singapore Singapore

The path planning of Unmanned Aerial Vehicle (UAV) formations plays a crucial role in mountainous forest monitoring missions. However, path planning is particularly challenging due to steep terrain and dense vegetation, making it difficult to generate optimal flight paths. The goal of UAV formation path planning in forest monitoring is to create safe, feasible flight paths for each UAV, avoiding terrain obstacles and ensuring coordination and safety, ultimately improving the quality of mission accomplishment. This study establishes a mathematical model that incorporates multiple constraints, such as flight distance, collision threats, and path stability, effectively transforming the complex problem of UAV formation path planning into an optimization problem. To address this multi-constraint path planning optimization problem, an Artificial Rabbit optimization algorithm incorporating Reinforcement Learning and Thermal conduction search strategy (RLTARO) is proposed. The incorporation of multiple strategies aims to improve the balance of exploration and exploitation of the algorithms as well as algorithmic convergence in the face of complex path planning problems. The comprehensive comparison of the RLTARO algorithm with nine advanced algorithms of similar type in the CEC2017 suite demonstrates its outstanding convergence and robustness across various types of optimization problems. The results of path planning experiments conducted on six mountainous forest terrains with varying complexities demonstrate that RLTARO can efficiently and reliably plan flight paths for UAV formations. Furthermore, the Friedman test results from multiple experiments consistently indicate that RLTARO holds significant performance advantages over the comparison algorithms.

关键词： UAV formation path planning Mountainous forest monitoring Reinforcement learning Thermal conduction search strategies strategy optimization algorithm

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Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Ustun, Deniz Toktas, Abdurrahim Erkan, Ugur Akdagli, Ali Engn Fac Dept Comp Engn TR-33400 Tarsus Mersin Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Elect Elect Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Comp Engn TR-70200 Karaman Turkey Mersin Univ Engn Fac Dept Elect Elect Engn TR-33343 Mersin Turkey

Artificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee;in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted "modified ABC by DE" (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergence.

关键词： Artificial bee colony (ABC) Differential evolution (DE) optimization algorithm Modified ABC Modified algorithm Hybrid algorithm

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Structured Joint Sparse Orthogonal Nonnegative Matrix Factorization for Fault Detection

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhang, Xi Xiu, Xianchao Zhang, Chao Beijing Jiaotong Univ Sch Math & Stat Beijing Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

As modern industrial processes become complicated, and some faults are difficult to be detected due to noises and nonlinearity of data, data-driven fault detection (FD) has been extensively used to detect abnormal events in functional units. To obtain better FD performance of nonnegative matrix factorization (NMF), this article first proposes an FD method using the structured joint sparse orthogonal NMF (SJSONMF). The core idea is to incorporate the graph regularization, sparsity, and orthogonality constraints into the classical NMF, which enjoys stronger discriminative ability, removes redundancy of different basis vectors, and improves fault interpretability. More importantly, an optimization algorithm based on the proximal alternating nonnegative least squares (PANLS) is developed, which can guarantee and speed up the convergence. Finally, the effectiveness of the proposed method is demonstrated by the experiments on the benchmark Tennessee Eastman Process (TEP) and two practical bearing datasets. Particularly, compared with the classical NMF, the T-2 statistic has a gain of 33.13% for the fault IDV(16) on the TEP. The results show that the proposed model and algorithms are promising for FD.

关键词： Sparse matrices Convergence optimization Data models Principal component analysis Laplace equations Fault detection Fault detection (FD) non-Gaussian process nonnegative matrix factorization (NMF) optimization algorithm orthogonality constraint

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A hybrid day-ahead electricity price forecasting framework based on time series

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ENERGY 2023年 264卷

作者： Xiong, Xiaoping Qing, Guohua Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Zhuang Peoples R China

Electricity price forecasting (EPF) plays an indispensable role in the decision-making processes of electricity market participants. However, the complexity of electricity markets has made EPF increasingly difficult. Currently, popular methods for EPF are based on signal decomposition and suffer from computational redundancy and hyperparameter optimization challenges. In this paper, we propose a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies. First, an adaptive copula-based feature selection (ACBFS) algorithm based on the maximum correlation minimum redundancy criterion is proposed for selecting model input features. Second, a new method of signal decomposition technique for EPF field is proposed based on decomposition denoising strategy. Third, a Bayesian optimization and hyperband (BOHB) optimized long short-term memory (LSTM) model is used to improve the effect of hyperparameter settings on the prediction results. The effectiveness of the different techniques was broadly cross-validated using five datasets set up for the PJM electricity market, and the results indicated that the proposed hybrid algorithm is more effective and practical for day-ahead EPF.

关键词： Electricity price forecasting Copula entropy Feature selection Variational mode decomposition optimization algorithm Long short-term memory

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An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning

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SENSORS 2021年第5期21卷 1814-1814页

作者： Tang, An-Di Han, Tong Zhou, Huan Xie, Lei Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China

The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Levy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.

关键词： unmanned aerial vehicle optimization algorithm equilibrium optimizer constrained optimization path planning

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Optimal design of facility allocation and maintenance strategy for a cellular network

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 205卷 107253-107253页

作者： Wu, Shengna Yang, Jun Peng, Rui Zhai, Qingqing China Inst Atom Energy Dept Isotope Beijing Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Beijing Univ Technol Sch Econ & Management Beijing Peoples R China Shanghai Univ Sch Management Shanghai Peoples R China

A cellular network is widely used for transmitting text (i.e., alphanumeric) messages between subscribers from different geographical locations. The geographic area of a cellular network is divided into a large number of cells, which are served by at least one facilities from base stations among predetermined positions. Both reliability and cost of a cellular network depend on the number, location and failure rate of facilities in the base stations. Thus, the joint optimization of facility allocation and maintenance strategy in a cellular network is relevant and significant for guiding optimal decisions on cellular network planning. This paper models a cellular network with repairable facilities characterized by a two-parameter Weibull *** objective is to jointly optimize the facility allocation among predetermined positions and the facilities maintenance strategy in order to minimize the expected life cycle cost of the cellular network. An optimization algorithm for the proposed model is given based on heuristic algorithm and simulation. A case study is presented to illustrate the application of the proposed model, and sensitivity analysis shows the relationship between the optimal preventive replacement interval and the ratio of the preventive replacement cost to the minimal repair cost.

关键词： Cellular network Performance reliability Life cycle cost optimization algorithm Sensitivity analysis

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Short-term industrial load forecasting based on error correction and hybrid ensemble learning

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ENERGY AND BUILDINGS 2024年 313卷

作者： Fan, Chaodong Nie, Shanghao Xiao, Leyi Yi, Lingzhi Li, Gongrong Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the power system. Due to the strong fluctuation and complex characteristics of industrial loads, it is difficult to accurately predict shortterm power demand. To address this issue, this paper proposes a deep learning prediction model based on hybrid ensemble and error correction. The proposed model is divided into two phases: in the first phase, deep power features are extracted from multivariate data through a hybrid ensemble strategy consisting of Random Subspace, Boosting, Ensemble Pruning, and Multi-Objective Molecular Dynamics Theory optimization algorithm (MMDTOA). First, the strategy splits high-dimensional industrial data into multiple sub-datasets. Subsequently, for the features of each sub-dataset, the proposed MMDTOA is applied to perturb the parameters of GRU to generate base learning machines that balance accuracy and diversity. Finally, these base learning machines are integrated by kernel ridge regression stacking. Among them, the two-stage selection strategy and co-evolutionary strategy are embedded into the MMDTOA to enhance the optimization searching effect;in the second stage, a combined error correction strategy is proposed by utilizing the residual information in the prediction results. By combining the dynamic Gaussian error correction function and GRU error correction model, the prediction accuracy of the ensemble model is further improved;Experimental results on real Korean datasets show that the proposed method achieves a minimum value of 3.684% and 7.266% in NMAE and NRMSE, which outperforms eight comparative models, such as SVM, ELM, and CNN, with higher accuracy and robustness.

关键词： Industrial Load Multi -objective Molecular Dynamics Theory optimization algorithm Ensemble Learning Error Correction

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Updating assembly parameters for spacecraft assembly process state changes: based on data fusion method

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FRONTIERS IN PHYSICS 2024年 12卷

作者： Liu, Yue Li, Lijuan Lin, Xuezhu Guo, Lili Sun, Jing Wang, Hao Changchun Univ Sci & Technol Sch Optoelect Engn Changchun Peoples R China Changchun Univ Sci & Technol Zhongshan Inst Zhongshan Peoples R China

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.

关键词： thermal protection system fiber optic sensor data 3D point cloud data data fusion state change assembly parameters model establishment optimization algorithm

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