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Study on thermal error modeling of high-speed motorized spindle inner

CASE STUDIES IN THERMAL ENGINEERING 2024年 58卷

作者： Dai, Ye Pang, Jian Rui, Xukun Li, Weiwei Harbin Univ Sci & Technol Key Lab Adv Mfg Intelligent Technol Minist Educ Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Inst Digital Design & Automatic Machinery Prod Dev Harbin 150080 Peoples R China

In addressing the challenge of unattainable temperature data at critical heat source points in bearings, this study solves for the temperatures of the bearing inner ring and rollers by merging thermal resistance network calculations with experimental data, referencing finite element simulation outcomes. The impact of temperature data from various positions within bearings on model accuracy is assessed using a BP prediction model. Subsequently, the training dataset of the thermal error model is optimized to enhance its predictive performance. To further boost the predictive capabilities of the model, a whale optimization algorithm is employed to optimize the BP neural network, which is then compared against both the conventional BP model and the GPR model. The findings reveal that a model optimized with the input of inner ring temperatures and the WOA algorithm achieves an accuracy rate of 97.748 %, outperforming both the BP model and the GPR model. This study provides a new idea for the field of thermal error modeling of motorized spindle.

关键词： High-speed motorized spindle Thermal characteristic simulation analysis Thermal resistance network Thermal error prediction model optimization algorithm

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Determination of tracking errors with respect to the geometrical errors based on optimization algorithm

Determination of tracking errors with respect to the geometr...

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International Conference of the SolarPACES

作者： Sun, Feihu Wang, Zhifeng Guo, Minghuan Bai, Fengwu Xu, Zijian Chinese Acad Sci Inst Elect Engn Key Lab Solar Thermal Energy & Photovolta Syst Zhongguancun Haidian Dis 100190 Peoples R China

Accurate dual-axis sun tracking is the key feature of a heliostat and is critical for the performance of a solar tower power plant. The primary tracking errors with respect to the geometrical errors could be theoretically determined from the measurements of the BCS based on optimization algorithm. Tests are performed on two heliostats in DAHAN solar tower plant and analyses are performed to evaluate the comprehensive effect of the six angular geometrical errors on the heliostat tracking accuracy. The test results show that the altitude-azimuth tracking angle formulas for several fixed geometrical errors work well and have a effectiveness for a given period of time. (C) 2013 Zhifeng Wang. Published by Elsevier Ltd.

关键词： BCS tracking angle geometrical errors optimization algorithm

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Traffic Management System With Symbolic Discrete Controller Synthesis Technique

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IEEE ACCESS 2024年 12卷 105393-105402页

作者： Kurucan, Mehmet Ardahan Univ Dept Comp Engn TR-75002 Ardahan Turkiye

In contemporary urban environments, the growing utilization of vehicles has emerged sophisticated challenges. Notably, degradation in air quality and amplified fuel consumption are the most issues that are being faced in urban cities due to traffic-related disruptions. Addressing these challenges is necessary to concern the adverse impacts on public health and economic aspects. This paper presents a comprehensive Traffic Management System employing the Symbolic Discrete Controller Synthesis Technique, offering a paradigm shift in urban traffic control. Our approach synthesizes effective controllers to reduce congestion and enhance system reliability by benefitting formal control frameworks and advanced modeling techniques. The key features of the approach include a symbolic safety algorithm to ensure compliance with regulations, an optimization algorithm to minimize congestion costs. Simulations across various scenarios validate the efficacy and robustness of our framework and also it suggests its potential to significantly improve urban mobility. The research directions may explore scalability and real-time data integration for broader applicability, however, our work lays a foundation for integrated traffic management systems combining formal control techniques, safety algorithms, and optimization strategies.

关键词： Traffic congestion Biological system modeling Traffic control Roads Transportation Symbols Solid modeling Discrete event systems discrete controller synthesis safety algorithm optimization algorithm traffic management systems

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Machine learning-based prediction method for drying shrinkage of recycled aggregate concrete

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JOURNAL OF BUILDING ENGINEERING 2024年 96卷

作者： Wang, Qinghe Dai, Ruihong Zhang, Huan Zheng, Huanhuan Liang, Xiuqing Shenyang Jianzhu Univ Sch Civil Engn Shenyang 110168 Peoples R China Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China China Construct Third Engn Bur Grp Co Ltd Wuhan 430070 Peoples R China China Construct Railway Investment Construct Grp C Wuhan 430070 Peoples R China

The objective of this study is to develop a broadly applicable, high-precision, and robust prediction model for the drying shrinkage of recycled aggregate concrete, a material that exhibits significantly greater shrinkage compared to natural aggregate concrete due to its complex characteristics. To achieve this, the study began by selecting relevant characteristic parameters based on international concrete codes, followed by the application of various machine learning algorithms including Backpropagation Neural Network, Support Vector Machine, Random Forest, eXtreme Gradient Boosting, Gaussian Process Regression, k-Nearest Neighbor, Linear Regression, and Long Short-Term Memory to model and forecast the drying shrinkage of recycled aggregate concrete. Subsequently, the SHapley Additive exPlanations (SHAP) method was employed to identify the crucial factors influencing RAC drying shrinkage, such as drying age, elastic modulus, and water-binder ratio, thereby optimizing the input parameters of the ML model. To further enhance the XGBoost algorithm, the sparrow search algorithm (SSA) and the whale optimization algorithm (WOA) were utilized. The optimized WOA-XGBoost model exhibits superior predictive performance, with a determination coefficient of 0.980 and a mean ratio of predicted to experimental values of 1.025, significantly outperforming traditional specification models. The model's applicability may be limited since the dataset is mainly derived from laboratory conditions, which may differ from actual engineering environments. Future research could consider different types of recycled aggregates and curing conditions and test the model on larger data sets to improve its robustness and applicability.

关键词： Recycled aggregate concrete Drying shrinkage Machine learning Model interpretation optimization algorithm

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Implementation of machine learning techniques with big data and IoT to create effective prediction models for health informatics

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 94卷

作者： Zamani, Abu Sarwar Hashim, Aisha Hassan Abdalla Shatat, Abdallah Saleh Ali Akhtar, Md. Mobin Rizwanullah, Mohammed Mohamed, Sara Saadeldeen Ibrahim Prince Sattam bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship AlKharj Saudi Arabia Int Islamic Univ Malaysia Dept Elect & Comp Engn Gombak Malaysia Appl Sci Univ Coll Adm Sci Dept Management Informat Syst Eker Bahrain Riyadh Elm Univ REU Coll Appl Med Sci Dept Basic Sci Riyadh Saudi Arabia

As a result of the availability of healthcare data in sheer size, big data analytics has to grow regularly in this industry to ensure new and effective opportunities. This is helpful in providing early prevention, prediction, and detection of disease, thus helping in the enhancement of the overall life quality of the individuals. Likewise, in this paper, a machine learning-based big data analytics model is developed for predicting multi-diseases to provide a better decision support system for various healthcare applications. This developed framework utilizes the MapReduce framework, where the map phase performs feature extraction and the reduce phase performs feature selection for the purpose of handling and processing big data. The required healthcare data is collected from external web sources. In the map phase, the statistical features and the Principal Component Analysis (PCA) features are extracted. In the reduction phase, the optimal features are selected with the aid of the developed Hybrid Flower Pollination Bumblebees optimization algorithm (HFPBOA). Then, the Ensemble Learning (EL) model is developed to predict the multi-diseases. Moreover, the parameters present in the EL classifiers are optimized by using the same HFPBOA. The final prediction output is obtained by averaging the weight function between the outputs of the NN, KNN, and fuzzy classifier. Thus, the offered model attains 40.1%, 28.7%, 23.6%, and 10.5% improved than SSA-EL, DOA -EL, BOA -EL, and FA -EL respectively in terms of best value. The effectiveness computed for the developed multi-disease prediction framework is guaranteed by comparing the results among the recently developed prediction approaches.

关键词： Prediction Model Health Informatics Big Data Machine Learning Techniques Hybrid Flower Pollination Bumblebees optimization algorithm Neural Networks Fuzzy Classifier K-Nearest Neighbour

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Optimizing Microgrid Management with Intelligent Planning: A Chaos Theory-Based Salp Swarm algorithm for Renewable Energy Integration and Demand Response

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Zhao, Fangyi Jilin Railway Technol Coll Jilin 132000 Peoples R China

This paper presents a novel intelligent planning approach to optimize microgrid management with multiple random renewable energy sources. The key contribution is a developed slap algorithm enhanced with chaos theory to prevent local optima and premature convergence. The system incorporates various components-photovoltaic units, wind turbines, fuel cells, microturbines, energy storage, electrolysis-and accounts for smart home participation in energy demand response. Using a scenario-based method, it models uncertainties like wind speed, solar radiation, electricity demand, and price. The paper compares batteries and hydrogen storage tanks as energy storage options and validates the algorithm's effectiveness through four cases evaluating hydrogen storage and demand response. Findings demonstrate significant economic benefits and performance improvements in microgrid management by integrating hydrogen storage and load response programs. The study evaluates four cases, comparing systems with and without demand response (DR) and hydrogen storage. The results show that integrating DR and hydrogen storage reduces costs by 12.4% and 23.4%, respectively, compared to the reference model. The paper also presents a comparative analysis of battery and hydrogen storage, highlighting the efficiency and economic benefits of hybrid storage systems. By incorporating stochastic modeling and multi-objective optimization, the proposed approach enhances energy efficiency, reliability, and cost-effectiveness.

关键词： Microgrid management Intelligent planning Renewable energy sources optimization algorithm Demand response program

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Preliminary Techno-Economic Study of Optimized Floating Offshore Wind Turbine Substructure

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ENERGIES 2024年第18期17卷 4722-4722页

作者： Ojo, Adebayo Collu, Maurizio Coraddu, Andrea Univ Strathclyde Dept Naval Architecture Ocean & Marine Engn Glasgow G4 0LZ Scotland Delft Univ Technol Dept Maritime & Transport Technol NL-2628 CD Delft Netherlands

Floating offshore wind turbines (FOWTs) are still in the pre-commercial stage and, although different concepts of FOWTs are being developed, cost is a main barrier to commercializing the FOWT system. This article aims to use a shape parameterization technique within a multidisciplinary design analysis and optimization framework to alter the shape of the FOWT platform with the objective of reducing cost. This cost reduction is then implemented in 30 MW and 60 MW floating offshore wind farms (FOWFs) designed based on the static pitch angle constraints (5 degrees, 7 degrees and 10 degrees) used within the optimization framework to estimate the reduction in the levelized cost of energy (LCOE) in comparison to a FOWT platform without any shape alteration-OC3 spar platform design. Key findings in this work show that an optimal shape alteration of the platform design that satisfies the design requirements, objectives and constraints set within the optimization framework contributes to significantly reducing the CAPEX cost and the LCOE in the floating wind farms considered. This is due to the reduction in the required platform mass for hydrostatic stability when the static pitch angle is increased. The FOWF designed with a 10 degree static pitch angle constraint provided the lowest LCOE value, while the FOWF designed with a 5 degree static pitch angle constraint provided the largest LCOE value, barring the FOWT designed with the OC3 dimension, which is considered to have no inclination.

关键词： FOWT optimization algorithm shape parameterization CAPEX fixed-bottom

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Adaptive Heterogeneous Client Sampling for Federated Learning Over Wireless Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第10期23卷 9663-9677页

作者： Luo, Bing Xiao, Wenli Wang, Shiqiang Huang, Jianwei Tassiulas, Leandros Duke Kunshan Univ Data Sci Res Ctr Kunshan 215316 Peoples R China Peng Cheng Lab PCL Shenzhen 518066 Peoples R China Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Sch Sci & Engn Shenzhen 518172 Peoples R China Yale Univ Dept Elect Engn New Haven CT 06520 USA Yale Univ Inst Network Sci New Haven CT 06520 USA

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity (e.g., diverse computation and communication capacities) and statistical heterogeneity (e.g., unbalanced and non-i.i.d. data). This article aims to design an adaptive client sampling algorithm for FL over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. We obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probability. Based on the bound, we analytically establish the relationship between the total learning time and sampling probability with an adaptive bandwidth allocation scheme, which results in a non-convex optimization problem. We design an efficient algorithm for learning the unknown parameters in the convergence bound and develop a low-complexity algorithm to approximately solve the non-convex problem. Our solution reveals the impact of system and statistical heterogeneity parameters on the optimal client sampling design. Moreover, our solution shows that as the number of sampled clients increases, the total convergence time first decreases and then increases because a larger sampling number reduces the number of rounds for convergence but results in a longer expected time per-round due to limited wireless bandwidth. Experimental results from both hardware prototype and simulation demonstrate that our proposed sampling scheme significantly reduces the convergence time compared to several baseline sampling schemes. Notably, for EMNIST dataset, our scheme in hardware prototype spends 71% less time than the baseline uniform sampling fo

关键词： Convergence Bandwidth Training Wireless networks Prototypes optimization Probability Client sampling convergence analysis federated learning optimization algorithm statistical heterogeneity system heterogeneity wireless networks

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Compact Ultra-Wideband Wilkinson Power Divider in Parallel Stripline with Modified Isolation Branches

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SENSORS 2024年第11期24卷 3437页

作者： Go, Dong-Jae Min, Byung-Cheol Kim, Mun-Ju Choi, Hyun-Chul Kim, Kang-Wook Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

An efficient design method for a compact and ultra-wideband multi-stage Wilkinson power divider in a parallel stripline (PSL) is proposed. To enhance the frequency bandwidth of the proposed power divider while reducing its size, the isolation branch is modified;that is, two capacitors are connected to both sides of a resistor at each isolation branch. For an efficient design process, the PSL power divider is equivalently represented by two microstrip power dividers, and the design equations are derived. Based on the design equations, an in-house algorithm is utilized to optimally determine the design parameters, including the line impedance, resistance, and capacitance of each stage. For example, a three-stage PSL power divider is designed with three lambda/4 transmission lines at a base frequency of 5 GHz. To verify the accuracy of the design procedure, 3D EM simulations and measurements are performed, and the results show good agreement. Compared with the conventional three-stage Wilkinson power divider, the proposed PSL power divider achieves a wider frequency bandwidth of 1.16 to 6.51 GHz (139.5%) and a 23% shorter transmission line length of 207 degrees, while exhibiting an insertion loss of 0.7 to 1.4 dB.

关键词： parallel stripline power divider/combiner ultra-wideband isolation branch optimization algorithm

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Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities

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APPLIED SCIENCES-BASEL 2024年第19期14卷 8861页

作者： Rosca, Cosmina-Mihaela Stancu, Adrian Neculaiu, Cosmin-Florinel Gortoescu, Ionu-Adrian Petr Gas Univ Ploiesti Fac Mech & Elect Engn Dept Automat Control Comp & Elect Ploiesti 100680 Romania Petr Gas Univ Ploiesti Fac Econ Sci Dept Business Adm Ploiesti 100680 Romania

Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product.

关键词： AI methods public urban transport optimization algorithm smart cities Microsoft Azure Computer Vision Azure service Google Maps Service C#

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