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An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms

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Building Simulation 2024年第11期17卷 2017-2042页

作者： Ke Liu Xiaodong Xu Ran Zhang Lingyu Kong Xi Wang Deqing Lin School of Architecture Southeast UniversitySipailou 2Xuanwu DistrictNanjing210096Jiangsu ProvinceChina

Urban block form significantly impacts energy and environmental ***,optimizing urban block design in the early stages contributes to enhancing urban energy efficiency and environmental ***,widely used multi-objective optimization methods based on performance simulation face the challenges of high computational loads and low *** study introduces a framework using machine learning,especially the xgboost model,to accelerate multi-objective optimization of energy-efficient urban block forms.A residential block in Nanjing serves as the case *** framework commences with a parametric block form model driven by design variables,focusing on minimizing building energy consumption(EUI),maximizing photovoltaic energy generation(PVE)and outdoor sunlight hours(SH).Data generated through Latin Hypercube Sampling and performance simulations inform the model *** training and hyperparameter tuning,xgboost’s predictive accuracy was validated against artificial neural network(ANN),support vector machine(SVM),and random forest(RF)***,xgboost replaced traditional performance simulations,conducting multi-objective optimization via the NSGA-II *** showcase the framework’s significant acceleration of the optimization process,improving computational efficiency by over 420 times and producing 185 Pareto optimal solutions with improved performance *** analysis highlighted shape factor(SF),building density(BD),and building orientation(BO)as key morphological parameters influencing EUI,PVE,and *** study presents an efficient approach to energy-efficient urban block design,contributing valuable insights for sustainable urban development.

关键词： urban form machine learning xgboost algorithm multi-objective optimization performance-driven urban design urban building energy modeling

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New method for damage detection in steel beam using time-frequency functions and machine learning

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STRUCTURES 2025年 78卷

作者： Hooshyar, Hesam Ahmadi, Hamid Reza Bayat, Mahmoud Hosseinzadeh, Erfan Mahdavi, Navideh Najafi, Mohammad Hossein Univ Maragheh Fac Engn Dept Civil Engn POB 55136-553 Maragheh Iran Univ Texas Arlington Sch Architecture Arlington TX 76019 USA Islamic Azad Univ Fac Engn Dept Civil Engn Marand Branch Marand Iran Iran Insurance Tehran Iran

The science of structural health monitoring (SHM) has received great attention in recent years and has reached a major milestone in its evolution. In this research, a new methodology for detecting damage in steel beams using machine learning will be introduced. Ease of use, high accuracy and reduction of monitoring costs have been among the presuppositions considered for presenting the new method. In this research, both the laboratory model and the analytical model have been used. At first, a 14 IPE steel beam was subjected to a dynamic load called an impact load, and the response signals were recorded in the undamaged state and under damage scenarios by acceleration mapping sensors. Then, the response signals were processed with quadratic timefrequency function and Smoothed Pseudo-Wigner-Ville function and the dynamic characteristics of the beams were extracted and prepared for machine learning training in order to detect damage. By using these data as machine learning input and determining the proportional outputs relative to the percentages of damage of each scenario, xgboost and multi task elastic net (MTEN) machine learning algorithms were trained. The K-fold index was used to evaluate the performance of machine learning models and algorithms, and the calculation results showed that the xgboost algorithm has a higher detection accuracy than the Multi-Task Elastic Net (MTEN) algorithm. In order to evaluate, validate and ensure the performance of the proposed method, an apple scenario was used to use both machine learning algorithms to predict damage. While the results showed the high detection accuracy of both methods and the lowest error rate in determining the location and severity of the damage, the high accuracy of the xgboost algorithm in identifying the severity and location of the damage was confirmed.

关键词： Damage detection Machine learning xgboost algorithm MTEN algorithm Time-frequency functions

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Screening of Functional Small Molecules via Modified Machine Learning Strategy toward Efficient All-Inorganic Perovskite Solar Cells

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ACS APPLIED MATERIALS & INTERFACES 2024年第24期16卷 31833-31842页

作者： Jiang, Kunlun Ma, Zhipeng Lan, Jing Chen, Dehao Li, Wenzhe Fan, Jiandong Jinan Univ Coll Informat Sci Technol Dept Elect Engn Guangzhou 510632 Peoples R China Jinan Univ Inst New Energy Technol Coll Phys & Optoelect Engn Guangzhou 510632 Peoples R China Jinan Univ Guangdong Higher Educ Inst Key Lab New Semicond & Devices Guangzhou 510632 Peoples R China Jinan Univ Guangdong Prov Key Lab Nanophoton Manipulat Guangzhou 511443 Peoples R China

Organic small molecules are proven to be capable of passivating the bulk/interfacial defects in inorganic perovskite solar cells. Considering the burdensome situation to screen the functional small molecules, we employ a modified machine learning (ML) strategy to guide screening suitable small molecules toward efficient solar cells through three modified ML algorithms to construct the prediction model: (i) random forest algorithm (RF), (ii) support vector machine algorithm (SVR), and (iii) xgboost. Among them, the xgboost algorithm displays a better overall predictive performance, whereby the R-2 index reaches 0.939. Accordingly, eight small molecules are selected to modify the interface of perovskite films, and both the theoretical and experimental results certify that the difluorobenzylamine with additional fluorine atoms has a better interface modification effect among the small molecules containing functional groups, e.g., the benzene ring and amino group. The high accuracy of the modified machine learning model enables us to simplify the small-molecule screening process and form an important step for ongoing developments in perovskite solar cells and other optoelectronic devices.

关键词： inorganic perovskite solar cell machine learning xgboost algorithm interface modification small molecules passivation photovoltaic performances

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Nonlinear vibration analysis of graphene platelets reinforced nanocomposite doubly curved concrete panels subjected to thermal shock: Development of an artificial intelligence technique

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第25期31卷 7079-7100页

作者： Yan, Gongxing Zhou, Xia El-Meligy, Mohammed A. Sharaf, Mohamed Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou Sichuan Peoples R China Luzhou Key Lab Intelligent Construct & Low Carbon Luzhou Sichuan Peoples R China King Saud Univ Coll Engn Dept Ind Engn Riyadh Saudi Arabia Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou 646000 Sichuan Peoples R China

When the temperature gradient across a component quickly changes, a phenomenon known as thermal shock occurs that exposes the component to fast variable thermal stresses and large-scale strains. One of the goals of this abrupt loading is to increase the capacity of different systems to withstand thermal shock. The nonlinear thermally induced vibration of doubly curved graphene nanoplatelet reinforced nanocomposite panels subjected to abrupt thermal shock is being studied right now. The bottom portion of the curved panel system is held at a reference temperature while the top portion is thermally shocked. The Crank-Nicholson technique and generalized differential quadrature (GDQ) are used to build up and solve the one-dimensional transient heat equation. The double-curved panel's thermomechanical characteristics are temperature dependent;hence this equation is nonlinear. Based on the law of decoupled thermoelasticity, Kant's theory, and von Karman's assumption of some kind of geometric nonlinearity, the overall function of the doubly curved panel is established. After modeling the current system mathematically and comparing the current approach's findings to those of earlier research, we revalidated the findings in a Python environment by comparing them to those of the Extreme Gradient Boosting (xgboost) machine learning algorithm. This approach is founded on the "boosting" idea, which combines additive training techniques with the identification of weak learners to produce strong learners. The findings demonstrate that certain structural geometrical and physical factors are significant contributors to the double-curved GPLRC panel's nonlinear frequency under thermal shock.

关键词： Thermally-induced vibrations xgboost algorithm coupled GDQ and Crank-Nicolson methods Von Karman non-linearity nonlinear vibration analysis

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Optimization of robotic polishing process parameters for mold steel based on artificial intelligence method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年第14期238卷 2180-2191页

作者： Pan, Ri Cheng, Xiaofang Xie, Yinhui Li, Jun Huang, Weilong Beijing Univ Technol Beijing Key Lab Adv Mfg Technol Beijing Peoples R China Chinese Acad Sci Quanzhou Inst Equipment Mfg Haixi Inst Quanzhou Peoples R China Chinese Acad Sci Quanzhou Inst Equipment Mfg Haixi Inst Beijing 362200 Peoples R China

Aimed to achieve quantitative control of workpiece surface after robotic polishing and improve polishing efficiency, a two-step processing optimization method involves artificial intelligence algorithms is investigated. Firstly, based on xgboost algorithm, a prediction model for polished workpiece surface depending on key parameters is proposed, and the accuracy of the model is verified by experiments. After that, by using the above model, the influence of each parameter on the roughness was evaluated quantitatively. Subsequently, target roughness-driven optimization of processing parameters was presented by combining the roughness prediction model with NSGA II-TOPSIS algorithm based on the influence of each parameter on the roughness. To verify the proposed processing optimization method, polishing experiments of mold steel samples were conducted. The experimental results show that the maximum absolute error between the predicted and experimental roughness is 0.035 mu m, and the maximum relative error is <9%. At the same time, when the minimum is set as the optimization objective. With the same length of polishing path, the feed rate is increased from 0.25 mm/s to 0.37 mm/s, and the efficiency is improved to 48%. The NSGA II-TOPSIS algorithm can achieve quantitative control of mold steel surface roughness after robotic polishing to improve polishing efficiency, and provide a basis for reasonable selection of processing parameters, which have certain practical value.

关键词： Robotic polishing Mold steel Roughness prediction xgboost algorithm NSGA II-TOPSIS algorithm

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A Novel Approach Using Learning algorithm for Parkinson's Disease Detection with Handwritten Sketches'

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CYBERNETICS AND SYSTEMS 2024年第8期55卷 2388-2404页

作者： Shrivastava, Anurag Chakkaravarthy, Midhun Shah, Mohd Asif Lincoln Univ Coll LUC Dept Elect & Commun Engn Kota Baharu Kelantan Malaysia Lincoln Univ Coll Fac Comp Sci & Multimedia Kota Baharu Kelantan Malaysia Kebri Dehar Univ ECE Somali Ethiopia

The degenerative neurological condition known as Parkinson's disease is distinguished by tremors, stiffness, and difficulties with walking, balance, and motor coordination. Parkinson's disease is a progressive condition. Identifying whether this guy suffers from Parkinson's disease is the purpose of this puzzle. Early detection and effective treatment of Parkinson's disease can significantly improve the quality of life for those affected. This highlights the significance of early disease detection. The objective of this initiative is to facilitate the identification of Parkinson's disease through the use of hand-drawn graphics. Three unique Machine Learning algorithms are used in this article. The adaptability of the model has been demonstrated through the usage of a variety of designs, including but not limited to spirals, waves, cubes, and triangles. Experiment results showed that datasets 89.7% correct, and proposed xgboost algorithm is 98.75% accurate.

关键词： K-NN NB Parkinson's Disease (PD) Machine Learning xgboost algorithm

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Design of a Novel PID Controller Based on Machine Learning algorithm for a Micro-Thermoelectric Cooler of the Polymerase Chain Reaction Device

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IEEE ACCESS 2024年 12卷 61959-61977页

作者： Arikusu, Yilmaz Seryar Bayhan, Nevra Manisa Celal Bayar Univ Dept Elect Engn TR-45400 Manisa Turkiye Istanbul Univ Cerrahpasa Dept Elect & Elect Engn TR-34320 Istanbul Turkiye

The combined use of Extreme Gradient Boosting (xgboost) algorithm, one of the machine learning (ML) methods, and a generalization of Hermite-Biehler theorem to obtain a novel PID controller that will ensure robustly stable and optimized operation of a micro thermoelectric cooler (Micro-TEC), which is the main part of a Polymerase chain reaction (PCR) device, is a unique approach of our study compared to previous studies. Therefore, we first established a mathematical model of the micro-TEC by making real-time measurements and then, a new data set was created to find the optimum parameter values of PID controller, and finally, xgboost Hyperparameters with GridSearchCV was used for the first time to predict PID controller parameters. The xgboost algorithm achieved 97% training success and 91% test success in estimating the parameters of the PID controller. Moreover, the novel controller developed using the xgboost algorithm in this study has an impressive speed of 3 seconds. Additionally, our proposed method was compared with various metaheuristic optimization algorithms in terms of error percentage. The error percentages of xgboost, the equilibrium optimization, the particle swarm optimization and the artificial bee colony optimization algorithms were found to be 0.4%, 1.1%, 3.7% and 11.1%, respectively. It is observed the settling times of micro-TEC with ML-PID controller for all five PCR cycles are 4.86, 44, 83.4, 123 and 162.5 seconds, respectively, and the overshoot values are below 5%. The proposed method gave the smallest settling time, error and overshoot percentages compared to these metaheuristic optimization algorithms.

关键词： Temperature control Machine learning algorithms Polymers Stability analysis Prediction algorithms Power system stability Thermoelectric materials xgboost algorithm GridSearchCV thermoelectric cooler machine learning

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Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3551-3551页

作者： Hong, Xiaobin Chen, Guanqiao Chen, Yuanming Cai, Ruimou South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510641 Peoples R China Guangzhou Shipyard Int Co Ltd Guangzhou 511462 Peoples R China

Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference between the background and targets. To address these issues, this paper proposes an infrared image ship segmentation algorithm called the Infrared Image Edge-Enhanced Segmentation Network (IERNet) to extract ship temperature information. By using pseudo-color infrared images, the sensitivity to edges is enhanced, improving the edge features of ships in infrared images. The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. Finally, a specialized loss function with edge weights supervises the fusion features. An eXtreme Gradient Boosting (xgboost) machine learning model is then established to predict the ship image brightness temperature threshold, using engine brightness threshold, water area brightness threshold, boundary brightness threshold, and temperature gradient as predictive elements. In terms of image segmentation, our algorithm achieves a segmentation performance of 89.17% mIoU. Regarding the xgboost model's performance, it achieves high goodness of fit and small error values on both the training and testing sets, demonstrating its good performance in predicting ship temperature. The model achieves over 70% goodness of fit, and

关键词： infrared image ship image segmentation blurred edges small targets xgboost algorithm

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Understanding the relationship between rural morphology and photovoltaic (PV) potential in traditional and non-traditional building clusters using shapley additive exPlanations (SHAP) values

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APPLIED ENERGY 2025年 380卷

作者： Liu, Jiang Peng, Changhai Zhang, Junxue Southeast Univ Sch Architecture Nanjing 210096 Peoples R China Southeast Univ Key Lab Urban & Architectural Heritage Conservat Minist Educ Nanjing Peoples R China Jiangsu Univ Sci & Technol Sch Civil Engn & Architecture Zhenjiang 212100 Peoples R China

Rural areas have a large quantity of rooftops and facades appropriate for installing PV panels. However, the unclear impact of rural morphology on PV potential hinders their effective utilization. To address this challenge, this study examined 300 clusters of traditional and non-traditional rural buildings in Nanjing. 17 morphological indicators were identified, representing plot shape, built density, building form, and terrain variation. The annual PV power generation and Levelized cost of electricity (LCOE) were simulated. Using an explainable machine learning framework (xgboost algorithm combined with SHAP values), we explored the relationship between rural building morphology and PV potential. The results revealed that mean building height (BH) and floor area ratio (FAR) are key factors for PV power generation, while only BH is crucial for LCOE. As BH and FAR increase, PV generation declines, while LCOE rises. Particularly, BH has a stronger influence on technical potential in traditional clusters, whereas FAR plays a comparable role in non-traditional ones. Using these indicators, rural clusters can be categorized into three typologies for technical potential: low BH-low FAR, high BHlow FAR, and high BH-high FAR, and two for economic potential: low BH and high BH, with mean values being 176.1, 134, 121.5 kWh/m2/y, and 0.5, 0.53 CHY/kWh, respectively. A demonstration conducted outside Nanjing showed that our findings can be applied to the broader Yangtze River Delta region with a maximum error of less than 15 %. This study provides insights to inform rural PV policy-making and system planning, which are essential for China's low-carbon energy transition.

关键词： Rural morphology PV potential xgboost algorithm SHAP value Impact analysis

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Machine learning-aided biochar design for the adsorptive removal of emerging inorganic pollutants in water

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 362卷

作者： Ullah, Habib Khan, Sangar Zhu, Xiaoying Chen, Baoliang Rao, Zepeng Wu, Naicheng Idris, Abubakr M. Zhejiang Univ Innovat Ctr Yangtze River Delta Haining 311400 Zhejiang Peoples R China Zhejiang Univ Dept Environm Sci Hangzhou 310058 Zhejiang Peoples R China Ningbo Univ Dept Geog & Spatial Informat Tech Ningbo 315211 Peoples R China King Khalid Univ Coll Sci Dept Chem Abha 62529 Saudi Arabia

The escalating presence of emerging inorganic pollutants (EIPs) including vanadium (V), antimony (Sb), thallium (Tl), mercury (Hg), fluoride (F- ), and rare earth elements (REEs) in aquatic environments poses a significant threat to water quality and human health. Therefore, remediation of EIPs contaminated water is of pressing concern. Biochar adsorption offers a promising, environmentally benign, and cost-effective approach for EIP removal. However, inconsistent experimental methodologies and varying research objectives in previous studies hinder the selection of optimal biochar for specific EIP. Developing biochar materials with high adsorption capacity is crucial for effectively removing EIPs from water. However, the optimization of biochar designing using advanced artificial intelligence (AI) methodologies has not been thoroughly reviewed. This study employed a dataset of 528 data points from 61 biochar samples, collected from adsorption experiments conducted between 2014 and 2024, encompassing 24 variables related to various EIPs. To predict adsorption capacity and elucidate adsorption mechanisms, Random Forest (RF), Support Vector Regression (SVR), xgboost, and CatBoost machine learning algorithms were applied. The xgboost model outperformed the others, achieving a coefficient of determination (R2) of 0.96 and a lower root mean squared error (RMSE) of 0.4. Feature importance and SHAP value analysis identified reaction pH, initial concentration and pyrolysis temperature as key predictors of adsorption efficiency. Future predictions from the xgboost model indicate that reaction pH, initial concentration pyrolysis temperature and biochar pH, are critical factors influencing EIP adsorption. This research offers novel insights into EIPs adsorption and establishes a framework for designing sustainable biochar-based adsorbents for wastewater treatment.

关键词： Machine learning (ML) Inorganic water pollution Adsorption Biochar xgboost algorithm

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