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Optimal Guidance Law Based on Virtual Sliding Target

JOURNAL OF AEROSPACE ENGINEERING 2017年第3期30卷 1-11页

作者： Mozaffari, Mohiyeddin Safarinejadian, Behrouz Binazadeh, Tahereh Shiraz Univ Tech Dept Elect Engn Shiraz 71555313 Fars Iran

In this paper, a new guidance law, which is called virtual sliding target (VST) guidance law, is designed based on the concept of the virtual target (VT). The presented law is applicable for short- and medium-range missiles. It is shown that by using proportional navigation (PN) and considering the aerodynamic characteristics of the missiles, this law leads to a better performance than the PN law. In this approach, motion of the VT is started from a position higher than the real target. By controlling the speed of VT, which slides toward a predicted intercept point (PIP), speed, position, and trajectory of the missile can be controlled. Since arrival times of the missile to the VT and the VT to the real target are equal, the collision will happen. Furthermore, a new optimal guidance law is presented for long-range missiles based on the concept of the waypoint (WP) and VT. In the mentioned law, there are two important points. The first one is a constant point that is called a waypoint and the second one is called a virtual target, which slides toward the PIP. Then a teaching-learning-based optimization (TLBO) algorithm is used to find the optimal initial position of the WP and the VT to maximize the intercept speed. Simulation results illustrate better performance of the VST law over the PN law in terms of the intercept speed. (C) 2016 American Society of Civil Engineers.

关键词： Proportional navigation (PN) guidance law Virtual target Slide speed optimization algorithm

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Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8364页

作者： Lyu, Chao Zhou, Xiaoqiang Liu, Shuang Shanghai Ocean Univ Coll Econ & Management Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai 201306 Peoples R China

This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction data. To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict the weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature correlations. The random forest method is then used to determine the weights of factors, followed by the construction of an optimized backpropagation (BP) neural network using the ISOA-BP hybrid optimization algorithm. The model's performance is compared with other machine learning algorithms, including support vector regression, decision tree, gradient decision tree, and the BP network before optimization. The results show that the random forest method effectively quantifies the impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors. The constructed ISOA-BP model achieves higher prediction accuracy than traditional algorithms, demonstrating its potential for quality control in cable laying construction and data-driven prediction of cable burial depth. This research provides valuable theoretical and practical implications in the field.

关键词： submarine power cable burial depth optimization algorithm machine learning

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Robust transfer subspace learning based on low-rank and sparse representation for bearing fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第6期35卷 066204-066204页

作者： Yu, Fuchao Xiu, Xianchao Li, Xinrong Liu, Jingjing Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang 110819 Peoples R China Shanghai Univ Sch Microelect Shanghai Key Lab Chips & Syst Intelligent Connect Shanghai 200444 Peoples R China

With the development of industrial intelligence, data-driven fault diagnosis plays an important role in prognostics and health management. However, there is usually a large amount of unlabeled data from different working conditions, making cross-domain fault diagnosis unstable and inflexible. To deal with this issue, we propose two novel transfer subspace learning methods based on the low-rank sparse representation (LRSR), called LRSR-G and LRSR-R. Specifically, LRSR-G integrates an additional matrix with LRSR to characterize the Gaussian noise for robustness, as well as capture global and local structures. Furthermore, LRSR-R adaptively learns the label matrix from samples instead of using the binary labeling matrix in LRSR-G, thus providing the possibility to improve the flexibility. In addition, we develop two efficient algorithms using the alternating direction method of multipliers to solve the proposed LRSR-G and LRSR-R. Extensive experiments are conducted on the Case Western Reserve University dataset and Jiangnan University (JNU) dataset. The results show that the proposed LRSR-G and LRSR-R perform better than the existing methods, while LRSR-R has more potential in cross-domain fault diagnosis tasks.

关键词： bearing fault diagnosis transfer subspace learning low-rank and sparse representation optimization algorithm

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On the task assignment with group fairness for spatial crowdsourcing

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INFORMATION PROCESSING & MANAGEMENT 2023年第2期60卷

作者： Wu, Benwei Han, Kai Zhang, Enpei Univ Sci & Technol China Sch Comp Sci & Technol HuangShan Rd 443 Hefei 230022 Peoples R China Soochow Univ Sch Comp Sci & Technol Shizi Rd 1 Suzhou 215000 Peoples R China

Task assignment, the core problem of Spatial Crowdsourcing (SC), is often modeled as an optimization problem with multiple constraints, and the quality and efficiency of its solution determines how well the SC system works. Fairness is a critical aspect of task assignment that affects worker participation and satisfaction. Although the existing studies on SC have noticed the fairness problem, they mainly focus on fairness at the individual level rather than at the group level. However, differences among groups in certain attributes (e.g. race, gender, age) can easily lead to discrimination in task assignment, which triggers ethical issues and even deteriorates the quality of the SC system. Therefore, we study the problem of task assignment with group fairness for SC. According to the principle of fair budget allocation, we define a well-designed constraint that can be considered in the task assignment problem of SC systems, resulting in assignment with group fairness. We mainly consider the task assignment problem in a common One-to-One SC system (O2-SC), and our goal is to maximize the quality of the task assignment while satisfying group fairness and other constraints such as budget and spatial constraints. Specifically, we first give the formal definition of task assignment with group fairness constraint for O2-SC. Then, we prove that it is essentially an NP-hard combinatorial optimization problem. Next, we provide a novel fast algorithm with theoretical guarantees to solve it. Finally, we conduct extensive experiments using both synthetic and real datasets. The experimental results show that the proposed constraint can significantly improve the group fairness level of algorithms, even for a completely random algorithm. The results also show that our algorithm can efficiently and effectively complete the task assignment of SC systems while ensuring group fairness.

关键词： Spatial crowdsourcing Group fairness Human-centered algorithm optimization algorithm

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Nonlinear model updating algorithm for biaxial reinforced concrete constitutive models of shear walls

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JOURNAL OF BUILDING ENGINEERING 2021年 44卷

作者： Wang, Jia-Ji Liu, Cheng Nie, Xin Fan, Jian-Sheng Zhu, Ying-Jie Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China China Rd Transportat Verificat & Inspect Hitech C Beijing 100084 Peoples R China Univ Houston Dept Civil & Environm Engn Houston TX 77204 USA North China Univ Technol Sch Civil Engn Beijing 100144 Peoples R China

A novel biaxial constitutive model and a nonlinear model updating algorithm are proposed and implemented in ABAQUS software for simulating the reinforced concrete (RC) shear walls. First, the proposed model is established using the User Material (UMAT) subroutine to be compatible with the multi-layer shell elements and membrane elements with ABAQUS implicit solver. The modeling scheme, the biaxial and uniaxial constitutive models of concrete, and the uniaxial models of rebars are elaborately illustrated. Second, this research modifies five critical parameters in the developed model to consider the influence of initial cracking and softening of concrete and slip of longitudinal rebars. The modified parameters include the concrete stiffness reduction factor alpha(c) , the concrete compressive softening factor eta(c), the concrete shear softening strain gamma(u) , the steel stiffness reduction factor at initial tensile loading alpha(s) , and the steel stiffness reduction factor at peak load alpha(u) . Third, an optimization algorithm based on the discrete Frechet distance is proposed to quantify and minimize the difference between test results and finite element (FE) simulation results of load-displacement curves. Subsequently, a total of 24 RC shear walls are obtained from the literature, and the optimization method is adopted to obtain the most favorable material constitutive parameters based on the force versus lateral displacement curves. The comparison shows the proposed model with the optimization method predicts high accuracy simulation results for RC shear walls. Finally, five neural networks are trained to predict the material constitutive parameters using the 24 RC shear wall tests, and the accuracy of the neural networks is deemed satisfactory.

关键词： Biaxial constitutive model optimization algorithm Nonlinear model updating Fixed-angle crack model RC shear wall

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Upper Limit and Power Generation Loss of Water Supplement from Cascade Hydropower Stations to Downstream under Lancang-Mekong Cooperation

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WATER 2021年第20期13卷 2826-2826页

作者： Deng, Jiahui Li, Yu Ding, Wei Zhang, Bingyao Xu, Bo Zhou, Huicheng Dalian Univ Technol Sch Hydraul Engn Dalian 116024 Peoples R China

In cross-border water supplement cooperation, the supplement water discharged from upstream hydropower stations is the key to improving downstream benefits, but will lead to upstream power generation loss, so the upstream hydropower stations have to be aware of how much water they can offer and how much power they will lose to make the water supplement cooperation more reasonable. Therefore, this study puts forward a model to calculate the upper limit flow of water supplement of cascade hydropower stations under firm power constraints and water level constraints and proposes a new optimization method called the "collaborative-independent " joint optimization method to calculate the power generation loss under water supplement constraints. The results show that the upper limit flow will increase with the increase of annual inflow, and the uncertainty of the distribution of inflow in the year will also affect the upper limit flow: the larger the proportion of non-flood season inflow, the higher the upper limit flow. In normal and wet years, delaying water supplement time can significantly increase the upper limit flow by about 5% per month. Additionally, the "collaborative-independent " joint optimization method newly proposed in this paper can significantly improve the local optimization problem compared to the traditional optimization method. The power generation loss increases with the increase of water supplement flow, and delaying water supplement time can significantly reduce the power generation loss. The results of this paper can provide essential data support for future water resources cooperation negotiations in the Lancang-Mekong river basin to promote efficient and orderly water resources cooperation in the basin.

关键词： water supplement reservoir operation optimization algorithm water resource cooperation

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Design of quasi-axisymmetric stellarators with varying-thickness permanent magnets based on Fourier and surface magnetic charges method

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NUCLEAR FUSION 2021年第2期61卷

作者： Xu, G. S. Lu, Z. Y. Chen, D. H. Chen, L. Zhang, X. Y. Wu, X. Q. Ye, M. Y. Wan, B. N. Chinese Acad Sci Hefei Inst Phys Sci Inst Plasma Phys Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

To obtain engineering-feasible designs of stellarators with permanent magnets and simplified coils, a new algorithm has been developed based on Fourier decomposition and surface magnetic charges method. The strong toroidal fields in a quasi-axisymmetric stellarator are still generated by coils. The permanent magnets are designed to compensate the normal magnetic field B-n on the plasma surface partial derivative P created by the coils and plasma. The normal magnetic fields created by the permanent magnets B-pmn are calculated as the difference between the magnetic fields created by the surface magnetic charges on the inner surface partial derivative D and the outer surface partial derivative D-h of the magnets. The Fourier coefficients of the magnet thickness function h (theta, phi) are computed through matrix division operation based on the least square principle with dominant Fourier components selected through 2D Fourier transformation of B-pmn. The residual uncompensated B-n is minimized through iteration to progressively optimize the thickness function. This new algorithm has been successfully applied to design the permanent magnets of an l = 2 quasi-axisymmetric stellarator with background magnetic field created by 12 identical circular planar coils for demonstrations. High accuracy has been achieved, allowing for a flux-surface- averaged residual B-n relative to the total field similar to 1.3 x 10(-5) and a maximum residual B-n of less than 2 Gs for similar to 1 T total field. This new design has some advantages in engineering implementations: all permanent magnet pieces have the same remanence Br;only one single layer of magnets are mounted perpendicular to the winding surface;the magnets can be easily inserted into the cells of a gridded frame attached to the winding surface and fixed with springs from the back, which greatly simplifies the manufacture, assembly and maintenance of the magnets, and thus facilitates precisio

关键词： quasi-axisymmetric stellarator permanent magnet optimization algorithm Fourier decomposition coil design surface magnetic charges

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Dynamic thermal line rating model of conductor based on prediction of meteorological parameters

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期224卷

作者： Song, Tianhua Teh, Jiashen Univ Sains Malaysia USM Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Penang Malaysia

This paper developed a new algorithm to predict dynamic thermal line rating to increase the capacity of transmission lines, which can enhance the capacity of wind power integrated to the grid and reduce the curtailment. The proposed dynamic thermal line rating prediction model was trained by analyzing historical meteorological data and conductor physical parameters, and used deep learning with parameters optimized by an optimized algorithm. The prediction accuracy of the model is verified by Mean Absolute Error, R2 and comparison with other models. The simulation results show that the proposed prediction model has a good performance. The suggested dynamic thermal line rating algorithm, which bears resemblance to the actual value, boosts the static thermal line rating by varying degrees of 23% to 75% at different instances throughout the sample. At the same time, this paper designs an optimal power flow economic dispatch objective function. By comparing the economic dispatch of the power grid calculated by adding static thermal line rating and the prediction models, the method proposed in this paper can effectively increase the amount of wind power inte-gration and reduce power generation costs.

关键词： Dynamic thermal line rating Forecasting algorithm optimization algorithm Deep learning Optimal economic dispatch

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Novel IAPSO-LSTM neural network for risk analysis and early warning of food safety

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期230卷

作者： Geng, Zhiqiang Wang, Xintian Jiang, Yuangang Han, Yongming Ma, Bo Chu, Chong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Univ Int Business & Econ Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China Harvard Univ Harvard Med Sch Dept Biomed Informat Cambridge MA USA

Ensuring food quality and safety is essential after ensuring the quantity of food. Therefore, an improved adaptive particle swarm optimization algorithm (IAPSO) for optimizing the long short-term memory (LSTM) neural network (IAPSO-LSTM) is proposed to develop a food safety risk early warning model in this paper. The proposed IAPSO algorithm is verified by comparing with the traditional PSO (TPSO), the classic PSO (CPSO), the adaptive PSO (APSO) and the deterministic and adaptive PSO (DAPSO) based on five common benchmark functions in terms of the convergence speed and precision. Results show that the IAPSO algorithm has the best convergence speed and precision. Then, the analytic hierarchy process based on the sum product is used to obtain the risk value of food safety detection data, which is used as the desired output of the LSTM. And the root mean square error of the predicted risk value and the real food risk value is used as the objective function of the IAPSO algorithm to optimize the hyperparameters of the LSTM. Finally, the food safety risk early warning model based on the IAPSO-LSTM is evaluated using the composite seasoning detection data from a food testing agency in Guizhou Province, China. The proposed model effectively identifies high-risk factors affecting the risk value, with sodium cyclamate being the primary high-risk factor. The overall performance of the IAPSO-LSTM model is superior to that of the TPSO-based LSTM, the CPSO-based LSTM, the APSO-based LSTM, the DAPSO-based LSTM and the IAPSO-BP, with the prediction error of 0.474, R2 value of 0.998 and the absolute error less than 5. Moreover, the proposed model can help the relevant government departments effectively warn the potential food safety risks to ensure the food safety.

关键词： Food safety Risk early warning Risk factor Long short-term memory Improved adaptive particle swarm optimization algorithm

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A Novel algorithm for Optimal Discretization of Stress-Strain Material Curves for Application in Finite Element Analyses

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APPLIED SCIENCES-BASEL 2024年第16期14卷 7081页

作者： Markovic, Ela Basan, Robert Marohnic, Tea Univ Rijeka Fac Engn Vukovarska 58 Rijeka 51000 Croatia

Featured Application The maximal vertical distance (MVD) algorithm is intended to be used for the discretization of stress-strain metallic material curves for defining a multilinear material model when conducting a finite element analysis of engineering components with included material *** The maximal vertical distance (MVD) recursive algorithm, a novel approach for the optimal discretization of stress-strain material curves, is proposed. The algorithm simplifies the process of defining multilinear curves from material stress-strain curves when conducting a finite element analysis (FEA) of components. By directly selecting points on the material curve, the MVD algorithm eliminates the requirement for initial discretization, thereby minimizing information loss. As the measure of goodness of fit of the simplified polyline to the original curve, the percentage of stress deviation (SD) is proposed. The algorithm can generate multiple multilinear curves while keeping the stress deviation of each curve within a predefined limit. This feature is particularly beneficial during the finite element analysis of components exhibiting complex and position-dependent material properties, such as surface-hardened components, ensuring consistent modelling accuracy of material properties across the components' geometry. Consistent accuracy also proves advantageous when exploring multiple differing material states of quenched and tempered steel, ensuring fair and reliable comparisons. The MVD algorithm was compared with existing algorithms from the literature, consistently maintaining the accuracy of the multilinear curves within predetermined limits using the fewest possible points.

关键词： stress-strain curve finite element analysis multilinear material model optimization algorithm optimal discretization

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