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Visual scripting approach for structural safety assessment of masonry walls

CURVED AND LAYERED STRUCTURES 2025年第1期12卷

作者： Lasorella, Mariaceleste Percolla, Giulia Pisano, Aurora Angela Fuschi, Paolo Univ Mediterranea Reggio Calabria Dept dAeD Via Univ 25 I-89124 Reggio Di Calabria Italy

A visual scripting approach for limit analysis of masonry walls subjected to out-of-plane loads is proposed. To this aim, within a visual scripting framework, an interactive CAD representation of the structure and of the acting loads, boundary conditions, and restraints is coupled with an optimization algorithm to calculate the collapse load multiplier and visualize the related predicted collapse mechanism. The proposed approach can be useful for practical purposes, indeed it allows us to quickly identify the key factors that influence the structural response and, through the tuning of some input data, can furnish some hints for design purposes. The effectiveness of the promoted tool is verified by application to eight well-known benchmark cases.

关键词： visual scripting optimization algorithm Masonry macro-blocks limit analysis

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Toward Efficient Process Monitoring Using Spatiotemporal PCA

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第2期70卷 551-555页

作者： Li, Yunhui Xiu, Xianchao Liu, Wanquan Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

Principal component analysis (PCA) has shown its high efficiency in process monitoring (PM). However, most of the existing PCA-based PM approaches only consider the spatial prior and ignore the temporal prior. Therefore, in this brief, we propose a novel PM framework using spatiotemporal PCA (STPCA), which incorporates both spatial and temporal priors. Technically, the spatial prior is integrated to preserve the cause-effect relationship of process variables, and the temporal prior is embedded to maintain the geometric structure of process samples. Moreover, an efficient optimization algorithm is developed using the alternating direction method of multipliers (ADMM) in a symmetric Gauss-Seidel (sGS) manner. Finally, the improved monitoring performance is verified on the benchmark Tennessee Eastman (TE) process. In particular, compared with PCA, the fault detection rate of fault IDV(20) is increased by 9.88%. This suggests that the proposed framework is promising for PM.

关键词： Principal component analysis optimization Spatiotemporal phenomena Laplace equations Process monitoring Signal processing algorithms Convergence optimization algorithm principal component analysis (PCA) Index Terms process monitoring (PM) spatiotemporal prior

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Multi-layered coding-based study on optimization algorithms for automobile production logistics scheduling

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2021年 170卷 120889-120889页

作者： Yue, Guo Guo Tailai Dan, Wei Ningbo Inst Technol Ningbo 315211 Peoples R China SHU UTS SILC Business Sch Shanghai 200444 Peoples R China Changan Univ Xian 710064 Peoples R China

With the acceleration of economic globalization, competition among manufacturing industries has become increasingly fierce. Automobile manufacturing has always been a critical investment and development industry in different countries. For the automobile manufacturing industry, the logistics scheduling problem of automobile production is affects automobile manufacturing enterprises' ability to compete. This paper discusses disruptive technologies, such as AI, IoT, Big data, etc., to solve production problems. Therefore, production logistics systems research is essential to automobile manufacturing enterprises, to improve production efficiency, reduce production costs, and increase enterprises' economic benefits. We present three kinds of mathematical models designed and calculated by a genetic algorithm, aimed at the Pareto solution set to solve multi-objective optimization, as well as designs for a new contrast flow, which can quickly find the optimal solution and simulate the algorithm.

关键词： Automobile production Logistics scheduling Multi-layered coding Disruptive technologies optimization 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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An Industrial Data-Based Model to Reduce Octane Number Loss of Refined Gasoline for S Zorb Process

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PETROLEUM CHEMISTRY 2023年第3期63卷 299-309页

作者： Chen, Bo Wang, Jie Liu, Song Ouyang, Fusheng Xiong, Da Zhao, Mingyang East China Univ Sci & Technol Int Joint Res Ctr Green Energy Chem Engn Shanghai 200237 Peoples R China SINOPEC Shanghai Gaoqiao Petrochem Co Ltd Shanghai 200129 Peoples R China

S Zorb process is one of the main technologies for deep desulfurization of gasoline from fluid catalytic cracking (FCC) process, which by the process will also cause some research octane number (RON) loss of gasoline. Establishing a data-driven model with data mining technologies to optimize production is one of the development directions in petrochemical field. Based on the industrial data from a 1.20 Mt/a S Zorb unit in China in recent three years, 422 modeling samples and 22 modeling variables were screened out and then three data-driven models were established by back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) to predict RON of refined gasoline (r-RON). The results show that the BPNN model has the best prediction effect and generalization ability. Genetic algorithm (GA), particle swarm optimization algorithm (PSO) and simulated annealing algorithm (SA) in combination with the BPNN model respectively were used to optimize the operation variables to reduce the r-RON loss. The results indicate that the optimized performance of PSO-BPNN model is best because of its largest reduction in r-RON loss at 48.55%. The validity of the PSO-BPNN model was verified in the S Zorb unit and the research methods to establish a data-driven model for reducing r-RON loss are also worthy of reference for other S Zorb units.

关键词： FCC gasoline RON loss data-driven model neural network optimization algorithm

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Learning Rate Dropout

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第11期34卷 9029-9039页

作者： Lin, Huangxing Zeng, Weihong Zhuang, Yihong Ding, Xinghao Huang, Yue Paisley, John Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA Columbia Univ Data Sci Inst New York NY 10027 USA

optimization algorithms are of great importance to efficiently and effectively train a deep neural network. However, the existing optimization algorithms show unsatisfactory convergence behavior, either slowly converging or not seeking to avoid bad local optima. Learning rate dropout (LRD) is a new gradient descent technique to motivate faster convergence and better generalization. LRD aids the optimizer to actively explore in the parameter space by randomly dropping some learning rates (to 0);at each iteration, only parameters whose learning rate is not 0 are updated. Since LRD reduces the number of parameters to be updated for each iteration, the convergence becomes easier. For parameters that are not updated, their gradients are accumulated (e.g., momentum) by the optimizer for the next update. Accumulating multiple gradients at fixed parameter positions gives the optimizer more energy to escape from the saddle point and bad local optima. Experiments show that LRD is surprisingly effective in accelerating training while preventing overfitting.

关键词： Training Convergence optimization Neural networks Perturbation methods Informatics Adaptation models Neural network optimization algorithm regularization

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Weaponizing Favorite Test Functions for Testing Global optimization algorithms: An illustration with the Branin-Hoo Function

Weaponizing Favorite Test Functions for Testing Global Optim...

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AIAA Aviation Forum

作者： Jekel, Charles F. Haftka, Raphael T. Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Some popular functions used to test global optimization algorithms, such as the Branin-Hoo and Himmelblau functions, have multiple local optima, all with the same value of the objective function. That is all local optima are also global optima. This renders them easy to optimize, because it is impossible for the algorithm to get stuck in a local optimum that is not the global one. Such functions actually present an opportunity to create challenging problems for optimization algorithms, because, as illustrated here, it is easy to convert them to functions with competitive local optima by adding a localized bump at the location of one of the optima. This process is illustrated here for the Branin-Hoo function, which has three global optima. We use the popular Python SciPy differential evolution (DE) optimizer for the illustration, because its wide use is likely to imply a well written code. DE also allows the use of the gradient-based BFGS local optimizer for final convergence. By making a large number of replicate runs we establish the probability of reaching a global optimum with the original and weaponized Branin-Hoo. With the original function we find 100% probability of success with a moderate number of function evaluations. With the weaponized version, we found that the probability of getting trapped in a non-global optimum can be made small only with a much larger number of function evaluations.

关键词： optimization algorithm Python Particle Swarm optimization Adaptive Sampling algorithms Search algorithm Annealing

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Deep learning based optimal energy management framework for community energy storage system

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ICT EXPRESS 2023年第3期9卷 333-340页

作者： Alam, Md Morshed Jang, Yeong Min Kookmin Univ Dept Elect Engn Seoul 02707 South Korea

This paper proposes a deep learning-based integrated framework for multiple cooperative households to achieve optimal energy distribution. The corresponding energy generation and consumption problems are formulated by a long short-term memory algorithm is combined with an optimization algorithm to produce an optimal solution. In this study, a PV-community energy storage system (CESS) integrated is considered where the scheduling decision of the CESS and utility grid can be subsequently achieved through formulated constraints. The test results demonstrate the efficacy and robustness of the proposed system that achieves superior performance on effective renewable energy usages of maximum 31.74% in a home environment. & COPY;2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Energy storage system Long short term memory optimization algorithm Home energy management system

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Hybrid metaheuristic algorithm methods and econometric models in prediction of dogecoin price

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JOURNAL OF MODELLING IN MANAGEMENT 2025年第3期20卷 1030-1080页

作者： Farahani, Milad Shahvaroughi Babaei, Shiva Kharazan, Zahra Sadat Bai, Ali Rahmati, Zahra Ghasemi, Ghazal Alipour, Fardin Farrokhi-Asl, Hamed Khatam Univ Dept Finance Tehran Iran Univ Northern Iowa Coll Business Cedar Falls IA USA Univ Wisconsin Milwaukee Lubar Coll Business Milwaukee WI USA Islamic Azad Univ Dept Law Shahreza Iran Khatam Univ Dept Finance Tehran Iran Univ Wisconsin Milwaukee Cofrin Sch Business Green Bay WI USA

Purpose - This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach - An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between cryptoprice and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings - The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value - The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies' prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

关键词： Cryptocurrency Artificial intelligence optimization algorithm Econometric methods Curve-fitting

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Game-Theoretic Flexible-Final-Time Differential Dynamic Programming Using Gaussian Quadrature

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第4期46卷 742-751页

作者： Zheng, Xiaobo Guan, Haodong Lin, Defu He, Shaoming Shin, Hyo-Sang Beijing Inst Technol Sch Aerosp Engn 5th Zhongguancun South St Beijing 100081 Peoples R China Cranfield Univ Sch Aerosp Transport & Mfg Coll Rd Cranfield MK430AL England

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