Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line by Sahatsawat Wainiphithapong, Chana Phutthananon, Sompote Youwai, Pitthaya Jamsawang, Phattarawan Malaisree, Ochok Duangsano, Pornkasem Jongpradist

“…This paper presents a study on multi-objective optimization (MOO) of shield operational parameters (SOPs) for soft ground tunneling using a tunnel boring machine (TBM) in an urban environment, focusing on the case study of the MRT Blue Line in Bangkok. …”
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ANALYSIS METHOD AND PROCESS OPTIMIZATION OF VIBRATION SIGNAL IN PRECISION MILLING BASED ON BLIND SOURCE SEPARATION by GUO MiaoXian, HUANG Chao, GUO WeiCheng, WU ChongJun

“…Finally, the optimization method of machining parameters is proposed.…”
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Peculiarities of Implementing Multifractal Analysis of the Milled Surfaces Microrelief by Nataliia Balytska, Oleksandr Prylypko, Larysa Hlembotska, Valentina Shadura, Pavel Moskvin

“…It was established that the analysis of multifractal parameters of the machined surface should consider the cutting process’s physical characteristics. …”
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Optimizing droplet coalescence dynamics in microchannels: A comprehensive study using response surface methodology and machine learning algorithms by Seyed Morteza Javadpour, Erfan Kadivar, Zienab Heidary Zarneh, Ebrahim Kadivar, Mohammad Gheibi

“…Droplet coalescence in microchannels is a complex phenomenon influenced by various parameters such as droplet size, velocity, liquid surface tension, and droplet-droplet spacing. …”
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Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models by Kennedy C. Onyelowe, Viroon Kamchoom, Tammineni Gnananandarao, Krishna P. Arunachalam

“…For developing the SVM_Poly, SVM_RBK models, an extensive number of trials were conducted using various combinations of C and d for polynomial kernels and C and ∂ for radial basis function kernel-based support vector machines (SVMs) utilizing user-defined parameters. …”
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System for assessment and prediction of the technical condition of power oil-filled transformer equipment of distribution networks using machine learning by A. R. Galyautdinova, I. V. Ivshin, S. A. Solovev

“…As an example, we consider the oil-filled power transformer TMN-6300, its diagnostic parameters, external and operating parameters. The technical condition of the TMN-6300 transformer is assessed and a predictive model is created based on the existing monitoring system and machine learning methods, which make it possible to formalize expert knowledge and automate the process of data processing and analysis.RESULTS. …”
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Development of Automatic Inspection and Optimization Platform for Computer Numerical Control Machining Using Automatic Optical Inspection and Artificial Intelligence by Qi-Ren Lin, Bo-Cing Hu, Liang-Yin Kuo, Ting-Yi Shen

“…We developed an automatic optical inspection (AOI) system for detecting defects in finished workpieces and determining the parameters for CNC machining. The system addresses quality control issues in CNC machining using image processing, machine learning, and G-code analysis techniques. …”
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Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete by Xiaoyan Wang, Yantao Zhong, Fei Zhu, Jiandong Huang

“…In addition, hybrid models GOA–MLP and GWO–MLP are developed, with parameters fine-tuned to enhance predictive accuracy. …”
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Can Saccade and Vergence Properties Discriminate Stroke Survivors from Individuals with Other Pathologies? A Machine Learning Approach by Alae Eddine El Hmimdi, Zoï Kapoula

“…This software computes multiple parameters for each type of eye movement, including the latency, accuracy, velocity, duration, and disconjugacy. …”
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An optimal neural network to design generators and stabilizers for multi-machine power systems based on a promoted firefly algorithm by Xiujun Nie, Nan Sun, Buqin Wang, Ganbar Akbari

“…The network has been optimized by a new promoted version of the firefly algorithm for PSS design and the parameters of this controller in a number of specific working conditions in a multi-machine power system. …”
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Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms by Sujitra Arwatchananukul, Saowapa Chaiwong, Nattapol Aunsri, Sila Kittiwachana, Kietsuda Luengwilai, Tatiya Trongsatitkul, Pramod Mahajan, Jose Blasco, Rattapon Saengrayap

“…All the physical, chemical, and acoustic properties were used to classify for maturity and defects using the factor analysis (FA) technique and machine learning (ML). Results showed that maturity was correctly classified at 84.0 % by all parameters, while elected non-destructive parameters (color, specific gravity, and stiffness coefficients) showed lower results for distinguishing pineapples. …”
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THEORETICAL RESEARCHES OF HYDROMECHANICAL SYSTEM WITH A SOURCE OF THE EXPENSE OF CONSTANT PRESSURE ON THE BASIS OF THE AUTOMATIC MACHINE OF UNLOADING WITH THE DIFFERENTIAL VALVE by S. A. Zatolokin, A. T. RYBAK

“…In the work influence of constructive and functional parameters of the unloading automatic machine with the differential valve on work quality of hydromechanical system of constant pressure is investigated.…”
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Finite Element Analysis of Orthogonal Cutting Forces in Machining AISI 1020 Steel Using a Carbide Tip Tool by Bashistakumar M., Pushkal B.

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Machine Learning-Based Multi-Objective Optimization for Enhancing the Performance of Block Support Structures for Electron Beam Additive Manufacturing by Mustafa M. Nasr, Wadea Ameen, Abdulmajeed Dabwan, Abdulrahman Al-Ahmari

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Design, Optimization and Prototype of a Multi-Phase Fractional Slot Concentrated Windings Surface Mounted on Permanent Magnet Machine by Amir Nekoubin, Jafar Soltani, Milad Dowlatshahi

“…The results of the prototyped machine have validated the results of the theatrical analyses of the machine, and accurate consideration of the parameters improved the performance of the machine.…”
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Fractional-Order Control of a Nonlinear Time-Delay System: Case Study in Oxygen Regulation in the Heart-Lung Machine by S. J. Sadati, A. Ranjbar Noei, R. Ghaderi

“…A fractional-order controller will be proposed to regulate the inlet oxygen into the heart-lung machine. An analytical approach will be explained to satisfy some requirements together with practical implementation of some restrictions for the first time. …”
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Machine learning based optimization of titanium electropolishing using artificial neural networks and Taguchi design in eco-friendly electrolytes by Hyun-Kyu Hwang, Seong-Jong Kim

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Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…However, these machine-dependent parameters alone are insufficient for accurately predicting part density. …”
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Adaptive Generation Method for Small Volume Easy Fabrication Freeform Unobscured Three-Mirror Systems Based on Machine Learning by Yiwei Sun, Yangjie Wei, Ji Zhao

“…First, an error function based on volume, fabrication, and imaging quality functions is constructed, and a dataset is generated using this error function. Then, a machine learning model is trained using this dataset, enabling efficient prediction of the parameters for small-volume, easy-to-fabricate freeform unobscured three-mirror systems. …”
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Drying of Nettle Using Concentrated Air Collector and Concentrated Photovoltaic Thermal Supported Drying System and Modeling with Machine Learning by Mehmet Onur Karaagac

“…The experimental were carried out in October 2022, and the room temperature, total efficiency and moisture content parameters were investigated. The data obtained from the drying system were modelled using machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and gradient boosting decision trees (GBDT). …”
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