TOMFuN: A tensorized optical multimodal fusion network by Xian Xiao, Yequan Zhao, Yuan Yuan, Geza Kurczveil, Marco Fiorentino, Ray Beausoleil, Zheng Zhang

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Optimising the Grinding Wheel Design for Flute Grinding Processes Utilising Numerical Analysis of the Complex Contact Conditions by Eckart UHLMANN, Bernhard GÜLZOW, Arunan MUTHULINGAM

“…Therefore, it has a substantial influence on the machining behaviour of, for example, milling tools. …”
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Novel channel attention-based filter pruning methods for low-complexity semantic segmentation models by Md. Bipul Hossain, Na Gong, Mohamed Shaban

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Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

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Quantification of the Influence of Morphologies on Laser Cutting Quality by Maria Ramard, Romain Laniel, Mathieu Miroir, Olivier Kerbrat

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D3: A Small Language Model for Drug-Drug Interaction prediction and comparison with Large Language Models by Ahmed Ibrahim, Abdullah Hosseini, Salma Ibrahim, Aamenah Sattar, Ahmed Serag

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Hydro-Forming a Cross-Shaped Component from Tube Billet by Mai Thi TRINH, Dac TRUNG Nguyen, Tuan Quoc Pham, Anh Ngoc Pham, Van Duy Dinh

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Dynamic Model and PD Control with Forces Compensation of Dual-Stage Gough-Stewart Platform by Ha Huy Hung, Phan Bui Khoi, Hoang Quang Chinh, Quan Dinh Nguyen, Ha Thanh Hai

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Influence of Shock Absorber on Electric Axle Vibration in Semi-Trailer by Marek Stembalski, Tomasz Szydlowski, Arkadiusz Czarnuch

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Application of TOPSIS an PIV Methods for Multi - Criteria Decision Making in Hard Turning Process by Do Duc Trung

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A Novel Method for Additive Manufacturing of Complex Shape Curved Parts by Using Variable Height Layers by Matthieu Rauch, Jorge Piedra Dorado, Jean-Yves Hascoet, Guillaume Ruckert

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The Combination of Taguchi – Entropy – WASPAS - PIV Methods for Multi-Criteria Decision Making when External Cylindrical Grinding of 65G Steel by Do Duc Trung

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A New Design Approach for Milkrun-Based In-Plant Supply in Manufacturing Systems by Tamás Bányai, Ádám Francuz

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Applications of cluster-based transfer learning in image and localization tasks by Liuyi Yang, Patrick Finnerty, Chikara Ohta

“…Transfer learning can address the issue of insufficient labels in machine learning. Using knowledge in a labeled domain (source domain) can assist in acquiring and learning knowledge in a domain (target domain) that lacks some or all labels. …”
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Controlled Turning Process for the Production of Friction-Reduced Cylinder Liners with a Defined Free-Form Geometry by Berend Denkena, Benjamin Bergmann, Miriam Handrup, Christopher Schmidt

“…The tool wear and the behavior during free-form fine machining of cylinder lin-ers are investigated. A process control system is introduced that controls the cylinder liner geometry by adapting the process parameters during free-form turning.…”
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An Artificial Intelligence Based Autonomous Road Lane Detection and Navigation System for Vehicles by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Bhargav Sable, Chetan Taywade, Roshan Umate

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Scaling of hardware-compatible perturbative training algorithms by B. G. Oripov, A. Dienstfrey, A. N. McCaughan, S. M. Buckley

“…Previous research has suggested that perturbative training methods do not scale well to large problems since in these methods, the time to estimate the gradient scales linearly with the number of network parameters. However, in this work, we show that the time to reach a target accuracy—that is, actually solve the problem of interest—does not follow this undesirable linear scaling and in fact often decreases with network size. …”
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Precision glass thermoforming assisted by neural networks by Yuzhou Zhang, Mohan Hua, Jinan Liu, Haihui Ruan

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Influence of the Substrate Size on the Cooling Behavior and Properties of the DED-LB Process by Fabian Bieg, Clemens Maucher, Hans-Christian Möhring

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Toward intelligent control of MeV electrons and protons from kHz repetition rate ultra-intense laser interactions by Nathaniel Tamminga, Scott Feister, Kyle D. Frische, Ronak Desai, Joseph Snyder, John J. Felice, Joseph R. Smith, Chris Orban, Enam A. Chowdhury, Michael L. Dexter, Anil K. Patnaik

“…Ultra-intense laser–matter interactions are often difficult to predict from first principles because of the complexity of plasma processes and the many degrees of freedom relating to the laser and target parameters. An important approach to controlling and optimizing ultra-intense laser interactions involves gathering large datasets and using these data to train statistical and machine learning models. …”
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