Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…[Objective] The attitude of a shield machine is a critical parameter that significantly affects tunnel construction, directly determining construction safety and project quality. …”
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A novel fault diagnosis method for gearbox based on RVMD and TELM with composite chaotic grey wolf optimizer by Xuebin Huang, Anfeng Xu, Hongbing Liu, Bingcheng Ye

“…Abstract Fault diagnosis for gearbox by robust variational mode decomposition (RVMD) and twin extreme learning machine (TELM) with composite chaotic grey wolf optimizer (CCGWO) is proposed in this study. …”
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Research Studies on the Thrust of Special-Shaped Full-Sectional Cutterheads of Quasirectangular Shield by Yaohong Zhu, Jiawei Wang, Bayang Zhang, Xuqing Zhang, Jue Zhu

“…Thrust of shield cutters is the major parameter of tunnel construction and an important index for shield machine design. …”
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Application of Fault Diagnosis of Seawater Hydraulic Pump Based on Transfer Learning by Yang Miao, Yuncheng Jiang, Jinfeng Huang, Xiaojun Zhang, Lei Han

“…Finally, the results of diagnosis and classification are compared with traditional machine learning. When the number of training data is 5 groups, the accuracy of transfer learning is 30.5% higher than that of traditional machine learning. …”
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Credit Rating Model Based on Improved TabNet by Shijie Wang, Xueyong Zhang

“…Bayesian optimization is then employed to accelerate hyperparameter selection and automate a parameter search for TabNet. To further enhance classification and predictive performance, a stacked ensemble learning approach is implemented: the improved TabNet serves as the feature extractor, while XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Categorical Boosting), KNN (K-Nearest Neighbors), and SVM (Support Vector Machine) are selected as base learners in the first layer, with XGBoost acting as the meta-learner in the second layer. …”
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Survey of Quantum Generative Adversarial Networks (QGAN) to Generate Images by Mohammadsaleh Pajuhanfard, Rasoul Kiani, Victor S. Sheng

“…The most advanced method is Parameterized Quantum Wasserstein GAN (PQWGAN), which uses a hybrid quantum-classical structure to obtain high-resolution image processing for 28 × 28 grayscale datasets while trying to maintain parameter efficiency. …”
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Modern Methods for Diagnosing Faults in Rotor Systems: A Comprehensive Review and Prospects for AI-Based Expert Systems by Oleksandr Roshchupkin, Ivan Pavlenko

“…Several modern diagnosis methods are widely utilized to monitor the condition in real-time mode, such as vibration parameter analysis, temperature deviation analysis, acoustic emission analysis, and other operational parameter analyses, to avoid the possibility of rotor failure. …”
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Near-field sound source localization using principal component analysis–multi-output support vector regression by Lanmei Wang, Yao Wang, Guibao Wang, Jianke Jia

“…Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. …”
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Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers by Yi Lyu, Quan-Cheng Jiang, Shuai Yuan, Jing Hong, Chun-Feng Chen, Hai-Mei Wu, Yi-Qin Wang, Yu-Jing Shi, Hai-Xia Yan, Jin Xu

“…This study extracted features using a modified extended Geneva Minimalistic Acoustic Parameter Set and compared seven machine learning models. …”
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Coordinated Control Strategies for Enhancing Frequency Stability of Photovoltaic and Storage Networking Systems by JIANG Shouqi, ZHANG Haifeng, FU Gui, XIN Yechun, WANG Lixin

“…It designs the control parameters and adaptive recovery strategy based on the power regulation margin of the synchronous machine. …”
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Pengukuran Performa Apache Spark dengan Library H2O Menggunakan Benchmark Hibench Berbasis Cloud Computing by Aminudin Aminudin, Eko Budi Cahyono

“…Agar di dalam Apache Spark mampu melakukan proses machine learning, maka di dalam paper ini akan dilakukan eksperimen yaitu dengan mengintegrasikan Apache Spark yang bertindak sebagai lingkungan pemrosesan data yang besar dan konsep parallel komputing akan dikombinasikan dengan library H2O yang khusus untuk menangani pemrosesan data menggunakan algoritme machine learning. …”
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IMPROVEMENT OF BUS OPERATIONAL CHARACTERISTICS WHILE USING INTEGRATED CONTROL OF SUSPENSION AND TRANSMISSION by V. V. Mikhailau, A. G. Snitkov, S. V. Liahov

“…Semi-natural tests for active pneumatic suspension of a 18-tonne tourist bus with the system of automatic body stabilization of the mobile machine and feed-back coupling according to kinematics parameter. …”
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Multi-Response Optimization in AA6063/SS304 Bimetalic Friction Welding using Taguchi Grey Relational Analysis by S. Senthil Murugan, M. Vishnoi, S. Kattimani, T. G. Mamatha

“…This study aimed to create a robust joint between dissimilar materials, specifically AA6063-T6 aluminium alloy and SS304 austenitic stainless steel (ASS), and optimize the parameters. The experiments were conducted by employing the rotary friction welding (RFW) process, with an experimental setup devised on a conventional lathe machine utilizing friction-generated heat and plastic deformation. …”
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Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…A relevancy factor analysis quantified the influence of each input parameter on model outputs, whereas the Leverage technique identified outliers and defined the parameter ranges for optimal algorithm performance. …”
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Beautiful Majorana Higgses at colliders by Benjamin Fuks, Jonathan Kriewald, Miha Nemevšek, Fabrizio Nesti

“…Our findings demonstrate that this channel probes a region of parameter space where the neutral Higgs triplet and heavy neutrino masses are relatively light (m ∆ ≲ 250 GeV, m N ≲ 80 GeV), indirectly constraining the W R boson to the deep multi-TeV domain, with sensitivity extending up to 70–80 TeV, effectively turning the LHC into a precision machine.…”
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DETECTING URBAN SLUMS IN DKI JAKARTA: A KOTAKU DATA APPROACH WITH ENSEMBLE METHODS by Muhammad Muawwad MS, Rani Nooraeni, Ananda Galuh Intan Prasetya

“…The slum indicator model without additions has good performance after going through the parameter tuning process with parameters ntree = 500 and mtry = 6. …”
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Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR by Zongyu Zhang, Huan Wang, Meng Tang, Jie Zhang, Xinhan Hu

“…To address this challenge, this paper introduces a hybrid mechanism and data-driven modeling approach. Initially, the parameter perturbation method was employed to elucidate the interdependencies between system parameters and the VFDR's dynamic and steady-state responses. …”
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Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold by Li Yang, Qiong Yao, Xiang Xu

“…Ultimately, the spiking features with the earliest emission times are retained and utilized for classifier training via a support vector machine (SVM). Extensive experiments conducted across three benchmark finger vein datasets reveal that our ATSNN model not only achieves remarkable recognition accuracy but also excels in terms of reduced parameter count and model complexity, surpassing several existing FVR methods. …”
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Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand by Ni Putu Nita Nathalia, Gede Andra Rizqy Wijaya, Kadek Yota Ernanda Aryanto, Ni Putu Novita Puspa Dewi, Putu Hendra Saputra, Ni Putu Karisma Dewi, Mellisa Damayanti, Kadek Losinanda Prawira

“…Each model has its own parameters, making parameter selection crucial in the training process. …”
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Effects of film thickness on structural color of DLC films deposited by plasma CVD method by Katsuki Akiniwa, Daigo Hasegawa, Himari Kitamura, Morimasa Nakamura, Takashi Matsuoka

“…And the variations of these parameters were confirmed to be caused by the thickness of the films. …”
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