Experimental study on the piezoelectric active vibration reduction system of wind tunnel aerodynamic models by LI Weiguang, YANG Zhichun, ZHU Ximing

“…To effectively suppress the flow-induced vibration response of wind tunnel models, a hybrid control algorithm combining the filtered-x least mean square (FxLMS) algorithm and the linear active disturbance rejection control (LADRC) controller is proposed. …”
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Accurate Rotor Temperature Prediction of Permanent Magnet Synchronous Motor in Electric Vehicles Using a Hybrid RIME-XGBoost Model by Jianzhao Shan, Zhongyuan Che, Fengbin Liu

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Passive Drag Reduction Optimization for Complex Commercial Vehicle Models by G. Liao, M. Xue, L. Yue, H. Yang, P. Guo, X. Hu, Z. Wang, T. Ma

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Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 by Morteza Abbasi, Amir Hossein Namadchi, Mehdi Abbasi, Mohsen Abbasi

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EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines by Di Wu, Yong Hong, Jie Wang, Shaojun Wu, Zhihao Zhang, Yizhang Liu

“…This multi-task learning model utilizes a shared feature extraction layer and numerous branching networks to create individual predictions for each task: length, width, and depth. …”
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Research on Wind Power Grid Integration Power Fluctuation Smoothing Control Strategy Based on Energy Storage Battery Health Prediction by Bin Cheng, Jiahui Wu, Guancheng Lv, Zhongbo Li

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Robust prediction of tool-tissue interaction force using ISSA-optimized BP neural networks in robotic surgery by Yong-Li Yan, Teng Ren, Li Ding, Tiansheng Sun, Shandeng Huang

“…Methods The current proposal concerns a deep learning-based solution utilizing a backpropagation neural network (BPNN) optimized by improved sparrow search algorithm (ISSA) to predict clamp force on soft tissue. …”
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Post-extrasystolic variation of ST segment and T wave as a mortality risk predictor after myocardial infarction by Ralf J. Dirschinger, Ralf J. Dirschinger, Alexander Müller, Petra Barthel, Alexander Steger, Michael Dommasch, Axel Bauer, Karl-Ludwig Laugwitz, Georg Schmidt, Daniel Sinnecker, Daniel Sinnecker

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Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks by K. Lakshmi Prabha, Hanan Abdullah Mengash, Hamed Alqahtani, Randa Allafi

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Comparative study on RF-BP model prediction of mining water-conducting fracture zone height in Binchang Coal Mine by Yadong JI, Xuan LIU, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…The research results can provide some references for predicting the development height of the water-conducting fracture zone in Binchang Coal Mine.…”
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Application of improved clustering algorithm in mixed teaching of modern educational technology by Lei Shu, Guirong Li

Subjects: “…Clustering algorithm…”
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Algorithm of Development and Methodology of Using Software for Information Conversion in the Educational Process by V. A. Gerasimov, O. V. Boyprav

“…The article is devoted to the results of substantiation and creation of the algorithm for the development of a software tool for information transformation, which is advisable to be used in the process of digital transformation of education in higher educational institutions. …”
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Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education by Wanting Zheng

“…The research and application of the integration algorithm plays an important role in the analysis of the mental health education system. …”
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Model predictive control for energy efficient AC motor drives: An overview by Muhammad Bilal Shahid, Weidong Jin, Muhammad Abbas Abbasi, Abdul Rashid Bin Husain, Hafiz Mudassir Munir, Mannan Hassan, Aymen Flah, Ahmed Saad Eddine Souissi, Thamer A. H. Alghamdi

“…Then, the important challenges in MPC implementation, such as computational complexity reduction and delay compensation, weighting factor selection for multi‐objective cost functions, steady state performance and ripple reduction, parameter variations/model mismatching and achieving extended prediction horizons, are surveyed and most relevant solutions are reviewed. …”
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Prediction Approaches for Smart Cultivation: A Comparative Study by Amitabha Chakrabarty, Nafees Mansoor, Muhammad Irfan Uddin, Mosleh Hmoud Al-adaileh, Nizar Alsharif, Fawaz Waselallah Alsaade

“…Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively.…”
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Predictive Modeling of Surface Subsidence Considering Different Environmental Risk Zones by Yunsong Li, Yongjun Qin, Liangfu Xie, Yangchun Yuan, Jie Ran

“…Adopt four different noise reduction algorithms for data noise reduction on the raw data of the monitoring points at the intervals of different risk zones, and combine the time series prediction as well as the deep learning prediction method to get the prediction model for environmental risk zoning based on the environmental risk zoning. …”
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Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. This experimental research focuses on evaluating existing robust cross-validation variants to address the issues of validating credit default models. …”
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Efficacy of two radiographic algorithms for detection of peri-implant bone defects on cone-beam computed tomography scans by Faezeh Yousefi, Ali Heidari, Azita Ehsani, Maryam Farhadian, Marzieh Ehsani

“…This study aimed to assess the efficacy of metal artifact reduction (MAR) and advanced noise reduction (ANR) algorithms for detection of peri-implant bone defects. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…However, machine learning models offer a time-efficient solution for predicting pavement performance. This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. …”
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The role of artificial intelligence in vascular care by Nehaar Nimmagadda, BS, Edouard Aboian, MD, Sharon Kiang, MD, Uwe Fischer, MD

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