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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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Assessing district heating potential at large scale: Presentation and application of a spatially-detailed model to optimally match heat sources and demands.

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APPLIED ENERGY 2024年 372卷

作者： Spirito, G. Denarie, A. Fattori, F. Muliere, G. Motta, M. Persson, U. Politecn Milan Dept Energy I-20156 Milan Italy Univ Insubria Dipartimento Sci Teor & Applicate I-21100 Varese Italy Halmstad Univ Sch Business Engn & Sci POB 823 S-30118 Halmstad Sweden

This paper presents a newly developed methodology aimed at assessing at national level the techno-economic potential of district heating (DH) based on renewables and excess heat sources. The novelty of the model lies in the use of an optimization approach to match heat demand and heat sources at large scale level, while keeping a high degree of spatial detail. Areas suitable for DH adoption are identified by minimizing heat delivery costs, and therefore by choosing the most economical technology between district heating and the alternative individual solution. The optimization approach, usually applicable at limited analytical scope because of the computational burden, is here adapted to large scale analysis through the introduction of novel methodological elements with which the network topology is simulated nationwide. The methodology applies to preliminarily identified maps of available heat sources and eligible heat demand, with the quantification of the latter including retrofitting and low connection rate scenarios. It then consists in two steps: connecting elements in a graph through triangulation and routing algorithms and optimizing connections to minimize the overall heat delivery costs, either by adopting district heating or individual heating systems. The whole methodology is based on open-source data and tools for broad applicability. The paper presents the elaborated methodology together with the application of the entire model to Italy. The outcome is a map of the potential district heating systems identified with significant spatial detail nationwide. A four-fold

关键词： District heating potential Matching heat demand and sources optimization algorithm Routing algorithm GIS Energy graph

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Fuzzy deep learning for modeling uncertainty in character recognition using EEG signals

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Latifi, Farzaneh Hosseini, Rahil Sharifi, Arash Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Shahr E Qods Branch Tehran Iran

Classification of brain signals for character recognition is a challenging task associated with uncertainties. This study aims to present an Improved Type-2 Fuzzy Deep Learning (IT2FDL) framework for uncertainty management in character recognition using EEG signals by learning rules from each other in the learning layer. The Evolutionary algorithm (EA) was employed for this study's optimum design of the proposed IT2FDL model. This algorithm was used to determine the T2F set parameters. The model parameters were tuned by Parallel Swarm optimization (PSO) algorithm. The present framework optimized the fusion layer of the model. This layer is a multi-layered Recurrent Type2 Fuzzy Neural Network (RT2FNN) with a learning layer composed of RNN cells. For performance evaluation, the models were examined using three public databases: BCI Competition IIIDataset II, BCI Competition II-Dataset IIb, and EEG Dataset. The optimized IT2FDL model outperformed the counterpart models with a significant improvement of test score of 2.5% for Competition III-Dataset II at epoch 15, 2.5% for Competition II-Dataset IIb at epoch 3, and 1% for EEG Dataset at epoch 15. The proposed IT2FDLPSO (GRU) model showed a mean test score of 96.2% at epoch 15 for all 58 subjects, which outperformed other models with many subjects for character recognition.

关键词： Brain-compute interfaces (BCI) Electroencephalogram (EEG) Spelling Classification Type2 fuzzy system Recurrent neural network (RNN) optimization algorithm PSO

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Digital twin method for the stress field of a deep-diving spherical shell based on a simulation database

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OCEAN ENGINEERING 2024年 300卷

作者： Cao, Yu Li, Jie Wu, Yu Wang, Fang Wang, Xueliang Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai Engn Res Ctr Marine Renewable Energy 999 Huchenghuan Rd Shanghai 201306 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116081 Liaoning Peoples R China China Ship Sci Res Ctr Wuxi 21408 Peoples R China

In this paper, a Digital Twin (DT) method is proposed for quickly predicting the stress fields of deep-diving spherical shells based on a simulation database. The DT method can completely cover the stress field distribution of the entire pressure-resistant shell and provide stress predictions for different sizes of deep-diving protection shells. The DT model has a three-level structure. The Level-1 DT maps the Finite Element Model to the digital model, which is verified by experiments. The difference between the experimental and numerical results is less than 9.4%. Then, the Level-2 DT deduces the digital model and expands the simulation database samples. The uncalculated stress field results in the simulation database could be obtained by the local Lagrangian interpolation method. Furthermore, the Level-3 DT can rapidly predict the stress field distribution in deep-diving spherical shell digital models. By comparison with different optimization algorithms, the Particle Swarm optimization algorithm demonstrates strong optimization capabilities and fast convergence, and the error compared with the simulation results is less than 1%. Finally, combined with actual sensor measurements and historical experience data, this method can be used to carry out real-time mapping and synchronous deduction of the service state of the dynamic stress digital twin.

关键词： Digital twin Deep-diving spherical shell Stress field distribution Simulation database optimization algorithm

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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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Iterative feature mode decomposition: a novel adaptive denoising method for mechanical fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096101-096101页

作者： Ruan, Xiaolong Yuan, Rui Dang, Zhang Lv, Yong Jing, Xiaolong Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

Remaining useful life prediction of rolling bearings highly relies on feature extraction of signals. The use of denoising algorithms helps to better eliminate noise and extract features, thereby constructing health indicators to predict remaining useful life. This paper proposes a novel adaptive denoising method based on iterative feature mode decomposition (IFMD) to accurately and efficiently extract fault features. The feature mode decomposition (FMD) employs correlation kurtosis (CK) as the objective function for iterative filter bank updates, enabling rapid identification of fault features. To achieve IFMD, the sparrow search algorithm combines sine-cosine algorithm and cauchy variation (SCSSA) to optimize two key parameters in FMD. During the continuous iteration process of the SCSSA algorithm, filter length and number of modes were determined. IFMD does not require empirical setting of initial parameters. During iterative process, the signal is accurately decomposed and the noise is eliminated. Compared with other optimization algorithms, SCSSA has obvious advantages in iterative rate and global optimization. The envelope spectrum feature energy ratio (ES-FER) is used to select decomposed modes, and the mode with the largest ES-FER is chosen as the optimal mode. Bearing fault diagnosis is realized by envelope spectrum analysis of the optimal mode. The numerical simulations and experimental verifications both validate the effectiveness and superiority of the proposed IFMD in mechanical fault diagnosis.

关键词： fault diagnosis iterative feature mode decomposition optimization algorithm denoising algorithm

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Modeling and Control of Irrational Fractional-Order System and Its Block Diagram Simulation 34

Modeling and Control of Irrational Fractional-Order System a...

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34th Chinese Control and Decision Conference (CCDC)

作者： Chen, Lanfeng Xue, Dingyu Cui, Xinshu Shenyang Normal Univ Coll Phys Sci & Technol Shenyang 110034 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781665478960

The use of fractional-order to describe an actual system can better reveal its essential characteristics and its behavior. The description and analysis of an irrational fractional-order system are more complicated than that of a rational fractional-order system. To solve this difficulty, this paper studies its system identification and controller design for the characteristics of such systems. Firstly, based on the frequency domain response data of these systems, combined with the optimization algorithm to realize the rationalization of them, the curves fitting are used to illustrate the correctness and effectiveness of the identification algorithm. Then, based on the identified rational models, the optimum controllers are designed. Step response comparison shows that the performance of fractional-order controllers is superior to that of traditional integer-order controllers. Finally, through the block diagram implementation, the effectiveness and correctness of the fractional-order controller design are explained.

关键词： irrational fractional-order system frequency domain response data optimization algorithm optimum controllers block diagram

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A quantum inspired differential evolution algorithm with multiple mutation strategies 21

A quantum inspired differential evolution algorithm with mul...

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21st IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Liu, Jie Qin, XingSheng Jiang, F. Guangxi Normal Univ Coll Elect & Engn Guilin Peoples R China Univ Technol Sydney NSW Australia

ISBN: (纸本)9781665494250

The advent of the digital age and the internet has recently seen a corresponding increase in security concerns. Intrusion detection systems are one of the crucial factors to consider in today's digital world. This paper proposes a metaheuristic algorithm based on quantum differential evolution with multiple strategies. This algorithm proposes a new differential mutation strategy approach to improve the search capability and convergence speed. Then, a quantum rotation gate is used to perform the secondary evolution of the population. Finally, various benchmark functions are chosen to demonstrate the optimization ability of the algorithm. The experimental results show that the model outperforms differential evolution and quantum differential evolution. And has better optimization capability, efficiency and stability. This evolutionary technique plays an important role in identifying network intrusions and security attacks.

关键词： quantum inspired differential evolution algorithm mutation strategy network security rotation gate optimization algorithm

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A Novel Data-Driven Method to Estimate Invisible Solar Power Generation: A Case Study in Taiwan 58

A Novel Data-Driven Method to Estimate Invisible Solar Power...

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58th IEEE/IAS Industrial and Commercial Power Systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Thi Bich Phuong Nguyen Wu, Yuan-Kang Manh-Hai Pham Natl Chung Cheng Univ 168 Univ Rd Chiayi 62102 Taiwan Elect Power Univ 235 Hoang Quoc Viet Rd Hanoi Vietnam

ISBN: (纸本)9781665409186

As the penetration of photovoltaic (PV) solar generation increases, numerous residential and commercial solar PV systems without meters are being installed. The majority of these systems, however, are not monitored by power system operators. Therefore, the uncertainty of net load owing to these invisible solar power generation will raise additional challenges for power system operation. To reduce the above-mentioned impact, this work proposes a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that assists in the identification of representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating the aggregated power generation compared to other existing studies.

关键词： Photovoltaic (PV) Invisible Solar Power Behind-the-Meter data dimension reduction hybrid method optimization algorithm

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Deep Belief-MobileNet1D: A novel deep learning approach for anomaly detection in industrial big data

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INTERNET OF THINGS 2025年 31卷

作者： Chen, Tzu-Chia Tamkang Univ Dept Artificial Intelligence New Taipei 251301 Taiwan

Early fault or unusual behavior detection can reduce the risk of equipment failure improve performance and increase safety. Anomaly detection in industrial big data involves identifying deviations from normal patterns in large-scale datasets. This method assists in preventing equipment failures optimizing maintenance schedules and raising overall operational efficiency in industrial settings by identifying anomalous behaviors or outliers. Through the utilization of deep learning procedures, this investigation endeavours to apply are fined procedure for anomaly detection in industrial big data. Pre-processing, feature selection and Anomaly detection are three steps of a process that are used. The input data is first fed into MapReduce framework where it is divided and pre-processed. Imputation of missing data and Yeo-Jhonson transformation are then applied to eliminate noise from data. After pre-processed data is generated, it is put through a feature selection phase using Serial Exponential Lotus Effect optimization algorithm (SELOA). The algorithm is created newly by combining Lotus Effect optimization algorithm (LOA) with Exponential Weighted Moving Average (EWMA). Finally, anomaly detection is done using the features that are selected by means of Deep Belief-MobileNet1D, which combines MobileNet1D and Deep Belief Network (DBN). With a recall of 96.2 %, precision of 92.8 %, F1 score of 94.5 % and accuracy of 95.9 %, results show that the proposed strategy surpasses standard approaches. These findings demonstrate Deep Belief-MobileNet1D model's ability to detect anomalies in industrial big data.

关键词： Anomaly detection Deep learning Big data Feature selection optimization algorithm Industrial application

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