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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Kashanian, Kiarash Kim, Il Yong Queens Univ Dept Mech & Mat Engn Kingston ON K7L 2N8 Canada

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Existing optimization algorithms do not allow for simultaneous thickness optimization and material selection of non-laminate shell structures. The proposed concurrent thickness and material optimization (CTMO) scheme fills this niche, with a focus on practical, industry-style problems. A complex aircraft wing model is developed and optimized through CTMO. An extensive parameter sweep is conducted for this model, generating 384 results with different designable thickness limits and masses. These results are analyzed to show overall trends in compliance based on problem constraints. Selected results are presented qualitatively to study material selection and optimized thickness. Finally, the relevance of this work to real-world engineering studies is discussed, along with some areas for future improvement of CTMO.

关键词： Aircraft Wing Design optimization algorithm Finite Element Modeling Light Sport Aircraft Topology optimization Numerical Stability Composite Laminates Material Properties Structural Load Poisson's Ratio

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An Adaptive and Efficient Network Traffic Measurement Method Based on SDN in IoT 13th

An Adaptive and Efficient Network Traffic Measurement Method...

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13th European-Alliance-for-Innovation (EAI) International Conference on Simulation Tools and Techniques (EAI SIMUtools)

作者： Cai, Wansheng Song, Xi Liu, Chuan Jiang, Dingde Huo, Liuwei State Grid Elect Power Res Inst Nanjing 210003 Peoples R China State Grid Gansu Elect Power CORP Lanzhou 730000 Peoples R China Global Energy Interconnect Res Inst Co Ltd Nanjing 210003 Peoples R China Univ Elect Sci & Technol China Sch Astronaut & Aeronaut Chengdu 611731 Peoples R China

ISBN: (纸本)9783030971243;9783030971236

The Internet of Things (IoT) is a worldwide information network that connects thousands of technological gadgets. We incorporate the SDN network architecture into IoT networks and investigate the characteristics of SDN-based IoT networks in order to make the IoT more flexible and extendable. SDN (Software Defined Networking) is a logical control center with a centralized control plane that makes network management more flexible and efficient. For IoT network management, fine-grained and reliable traffic information is critical. Then, in SDN-based IoT networks, we construct a network traffic model by analyzing the self-similarity of network traffic in IoT network. Then, we collect some traffic statistics in OpenFlow-based switches as the source data and use it to train the proposed network traffic estimation model. Using the measured network traffic in the IoT network, we use the Kalman Filtering to measure and estimate each flow, this scheme just increases a little overhead. Then, we propose to an algorithm to search the more accuracy of traffic. Finally, we run additional simulations to ensure that the suggested measuring system is accurate. Simulation findings suggest that using intelligent optimization approaches, we can improve the granularity and accuracy of traffic data.

关键词： Internet of Things Software Defined Network optimization algorithm Network measurement

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Trajectory optimization of Unmanned Aerial Vehicles for Wireless Coverage under Time Constraint

Trajectory Optimization of Unmanned Aerial Vehicles for Wire...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Daniel, Adam T. Wu, Yufei Wang, Zhenbo Gan, Hao Univ Tennessee Tickle Coll Engn Knoxville TN 37996 USA Univ Tennessee Inst Agr Dept Biosyst Engn & Soil Sci Knoxville TN 37996 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

In this paper, we consider the problem of planning the path of an unmanned aerial vehicle (UAV) flying in an area with set boundary and a given number of ground users. The purpose of the UAV is to provide a wireless connection to the ground users. The UAV has set beginning and end locations, as well as a time constraint to perform its task. From these specifications, we formulate an optimal control problem that seeks to maximize the connectivity between the UAV and the ground users. Two cases are considered for this problem: one is a two-dimensional case where the UAV must fly at a fixed height, and the other one is a three-dimensional case where the UAV is free to move in all three spatial dimensions. A pseudospectral optimal control approach is implemented to solve these problems. The results have shown that the pseudospectral optimal control method can generate the trajectories for the UAV to provide the optimal wireless coverage during its mission.

关键词： Pseudospectral Optimal Control Unmanned Aerial Vehicle Nonlinear Programming Lagrange Multipliers Pseudospectral Methods optimization algorithm Energy Efficiency Heading Angle

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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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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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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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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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Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle algorithm

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REMOTE SENSING 2024年第24期16卷 4665-4665页

作者： Akbari, Elahe Boloorani, Ali Darvishi Verrelst, Jochem Pignatti, Stefano Hakim Sabzevari Univ Fac Geog & Environm Sci Dept Remote Sensing & Geog Informat Syst Sabzevar *** Iran Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran *** Iran Univ Valencia Image Proc Lab IPL C Catedrat Jose Beltran 2 Valencia 46980 Spain CNR Inst Methodol Environm Anal IMAA C da S Loja Snc I-85050 Tito Italy

Accurate crop yield estimation is critical to successful agricultural operations. Current crop growth models often overlook the spatial and geographic components of the lands, leading to suboptimal yield estimates. To address this issue, assimilation of satellite vegetation products into these models can account for spatial variations in the land and improve estimation accuracy. In this paper, the AquaCrop model, a water-driven crop growth model, was selected for recalibration and assimilation of satellite-derived biophysical products due to its simplicity and lack of computational complexity. To this end, field samples of soil (sampled before cultivation) and crop features were collected during the growing season of silage maize. Digital hemisphere photography (DHP) and destructive sampling methods were used for measuring fraction vegetation cover (fCover) and biomass in Qaleh-Now County, southern Tehran, in 2019. Based on our proposed workflow in previous studies, a Gaussian process regression-particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. Here, we propose the use of an optimization water cycle algorithm (WCA) instead of a PSO algorithm as an assimilation method for the parameter calibration of AquaCrop. This study also focused on using both fCover and biomass state variables simultaneously in the model, as opposed to only the fCover, and found that using both variables led to significantly higher calibration accuracy. The WCA method outperformed the PSO method in AquaCrop's calibration, leading to more accurate results on maize yield estimates. It has enhanced results, decreasing RMSE values by 3.8 and 4.7 ton/ha, RRMSE by 6.4% and 10%, and increasing R2 by 0.17 and 0.35 for model calibration and validation, respectively. These results suggest that assimilating satellite-

关键词： remote sensing data assimilation optimization algorithm water cycle algorithm yield estimation crop model

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A Local Area Segmentation algorithm for the Underwater Transponder Calibration 7

A Local Area Segmentation Algorithm for the Underwater Trans...

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7th International Conference on Robotics and Automation Engineering (ICRAE)

作者： Zhang, Haixu Xu, Xiaosu Southeast Univ Sch Instrument Sci & Engn Minist Educ Key Lab Microinertial Instrument & Adv Nav Techno Nanjing Peoples R China

ISBN: (纸本)9781665489188

The accuracy in depth estimation of the transponder calibration algorithms based on Long Baseline (LBL) technology needs to be improved, especially in the case of no Depth Gauge (DG), poor depth estimation will directly affect the spatial positioning accuracy. In order to comprehensively improve the solution accuracy of transponder calibration, this paper proposes a calibration algorithm based on Local Area Segmentation (LAS) method. According to the simulation results, LAS performs the best in depth estimation, horizontal positioning solution and spatial positioning accuracy.

关键词： transponder calibration TOA optimization algorithm underwater positioning LBL

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Robust Pole Assignment via State Feedback for Uncertain High-order System 4

Robust Pole Assignment via State Feedback for Uncertain High...

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4th International Conference on Control and Robotics (ICCR)

作者： Gao, Yajun Duan, Guang-Ren Harbin Inst Technol Ctr Control Syst & Guidance Technol Harbin Peoples R China Southern Univ Sci & Technol Ctr Control Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9781665486415

The problem of robust pole assignment for the uncertain high-order system is formulated. On the strength of a previous eigenstructure assignment result for the high-order system, by introducing the sensitivity of uncertain parameters in the system model to the closed-loop poles, the parametric expression of the state feedback gain matrix to the problem of robust pole assignment is obtained. The robust optimal solution can be derived by a simple independent optimization algorithm. The closed-loop poles can be fixed or chosen within certain desired regions on the complex plane, which further improves the robustness. The example illustrates robust state feedback controller obtained by the proposed algorithm has better robustness.

关键词： Robust pole assignment Uncertain high-order system Complete parametric solution optimization algorithm

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