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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Choi, Jeong-Hwan Kim, Dong-Han Yoo, Ji-Sang Kim, Beom-Joon Hwang, Jun-Tae DongSeoul University Department of Electronic Engineering Seongnam Korea Republic of Kyung Hee University Department of Computer Engineering Yongin Korea Republic of Kyung Hee University Department of Artificial Intelligence Yongin Korea Republic of Kyung Hee University Department of Software Convergence Yongin Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a method that combines imitation learning and reinforcement learning to improve the learning performance of autonomous vehicles. The actor model was pre-trained using expert demonstration data from the CARLA simulator, and then integrated into PPO-based reinforcement learning. The experimental results showed that the model combining imitation learning outperformed the model using only reinforcement learning, particularly in the early stages of training. However, this study is limited by using only image data, and future research will incorporate vehicle position and speed information for enhanced results. © 2025 IEEE.

关键词： Adversarial machine learning

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How Digital Transformation Affects Enterprise Performance: Evidence from machine Algorithms 24

How Digital Transformation Affects Enterprise Performance: E...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Zhang, Ying Fu, Rui Chen, Feng Xihua University Sichuan Yibin China

ISBN: (纸本)9798400709999

Digital transformation of enterprises, as one of the core issues in modern enterprise management, has become an important scenario for computer applications. This article empirically analyzes how digital transformation affects enterprise performance based on data from Chinese A-share listed companies from 2011 to 2022, using machine learning methods from artificial intelligence technology to explore the predictive capability of digital transformation on enterprise performance. The research results indicate a significant positive correlation between digital transformation and enterprise performance in Chinese companies, and that machine learning methods can effectively handle the relationships between variables, improving the accuracy of enterprise performance predictions. This study provides theoretical and practical support for how enterprises can improve their performance through the application of computer technology during the process of digital transformation. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Model-free tool path modification in ultra-precision diamond turning of freeform surfaces using iterative learning control

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PRECISION engineering-JOURNAL OF THE international SOCIETIES FOR PRECISION engineering AND NANOTECHNOLOGY 2025年 94卷 736-748页

作者： Zhang, Xinquan Wu, Hao Yu, Yangqin Jia, Zelong Wang, Xiangyuan Ren, Mingjun Zhu, Limin Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

With the aid of the commercial ultra-precision machine tool, the tool servo diamond turning process provides stable and deterministic material removal, meeting the demands for mass production of high-end freeform surface optics. However, in relatively high-speed applications, the machining accuracy is limited by the heavy servo axes, even within the working bandwidth of-3 dB. Therefore, to facilitate the industrial adoption of diamond turning, a cost-effective and user-friendly programming strategy is essential for enhanced motion accuracy in commercial machine tools. This work proposes a model-free tool path modification strategy using iterative learning control (ILC), which adjusts tool path amplitude iteratively based on the error data of servo axes. By aligning the geometry-based tool path with the dynamic properties of the servo axes, such adjustments reduce tracking errors caused by frequency-based phase lag and amplitude variation effects in high-speed applications. Additionally, this strategy eliminates the need for additional complex equipment or model identification, making it well-suited for industrial applications. The fundamental principle of the proposed method is first presented, followed by a demonstration of its convergence. A series of validation experiments are conducted through trajectory tracking and diamond turning. Experimental results indicate that trajectory tracking achieves a reduction of approximately 60 % in peak-to-valley error and about 80 % in root-mean-square error with the proposed strategy. For diamond turning experiments on sinusoidal grid surfaces, the form error is significantly reduced from 903 nm to 527 nm. Further experiments confirm the long-term effectiveness of the ILC-based tool path modification strategy in high-speed applications, offering valuable insights for industrial use.

关键词： Ultra-precision diamond turning Iterative learning control Tool path modification Freeform surface

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Application of machine learning in Prediction of Load Settlement Behavior of Piles Based on CPT data

Application of Machine Learning in Prediction of Load Settle...

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Indian Geotechnical conference, IGC 2022

作者： Aggarwal, Mansi Gupta, Ashok K. Department of Civil Engineering Delhi Technological University Delhi110042 India

ISBN: (纸本)9789819761715

machine learning can be successfully utilized in geotechnical designing applications, where vulnerability is a portion of nature, to create a vigorous predictive model foundation for designing parameters/behaviors. Formerly, geotechnical plan parameters were not continuously straightforwardly measured from a research facility and in-situ tests or maybe frequently assessed from observational or numerical relationships that are created from regression fitting to a dataset. ML models were created to train a nearby dataset. The developing volume of data databases presents openings for progressed information examination methods from machine learning inquiries. Applied applications of ML are exceptionally distinctive from hypothetical or observational studies. The feasible applications of ML were examined and created a proposition for a seven-step preparation that can direct viable applications of ML in design. In this work, an ML model was developed to predict pile behaviors based on a cone penetration test (CPT). The ML model was used to develop approximately 500 data sets from the available literature. The ML model beats the conventional techniques when the predictions made by ML are compared to those provided by several conventional approaches in the study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Green machine learning for the IoT (GEMINI): A Position Paper

Green Machine Learning for the IoT (GEMINI): A Position Pape...

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international conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Lazzaroni, Luca Dabbous, Ali Nardello, Matteo Lebdeh, Muath Abu Berta, Riccardo Yildirim, Kasim Sinan Bellotti, Francesco Brunelli, Davide Department of Naval Electrical Electronics and Telecommunications Engineering University of Genoa Genoa16145 Italy Department of Industrial Engineering University of Trento Trento38122 Italy

ISBN: (纸本)9783031840999

The proliferation of IoT devices has significantly increased global energy consumption and carbon footprint due to the reliance on computationally intensive machine learning (ML) techniques. Traditionally implemented on high-end embedded devices or exploiting cloud computing, these applications further exacerbate environmental impacts. As IoT technology advances, there’s a shift towards smaller, low-power devices, necessitating decentralized computation. This shift has given rise to Tiny machine learning (TinyML), enabling ML tasks on edge devices. However, to fully exploit TinyML, integrated frameworks are essential for efficiently developing and deploying smart applications, especially on devices operating intermittently on harvested energy. The Green machine learning for the IoT (GEMINI) project aims to address these challenges by creating a framework for sustainable TinyML applications on battery-powered and batteryless energy-harvesting edge devices. GEMINI targets shifting computational workloads from the cloud to the edge, optimizing latency, bandwidth, energy use, and privacy. By integrating energy harvesting methods, zero-power communication protocols, and intermittent computing strategies, GEMINI seeks to ensure robust ML inference under intermittent power conditions. The project aims to include efficient data collection, ML model generation, and online learning directly on edge devices, reducing reliance on constant cloud connectivity. The open-source framework intends to offer a comprehensive toolkit for developing sustainable IoT solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Research on Fostering Intercultural Communication Competence of Chinese EFL Learners in Online learning Using machine learning Method 24

Research on Fostering Intercultural Communication Competence...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Yu, Juan Huang, Xinwei School of Foreign Languages Wuhan Business University Hubei Wuhan China Center for Chinese and Western Language and Culture Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9798400710094

In the context of Chinese culture going global and the increasing important role of China on the global stage, intercultural communication competence plays an increasingly important role in terms of personal, national and international communication. This paper provides an investigation on ICC, online learning and the integration of ICC development into online learning. This study proposes three ways on the integration of ICC cultivation into online curriculum, including creating online community, constructing real-life situations, and combining technology with online learning. This study also addresses the problem of assessing and improving ICC in online learning contexts by leveraging machine learning (ML)-based sentiment analysis. The key idea of our research study is to integrate ML-based sentiment analysis into the evaluation of students’ intercultural communication skills during online courses. The proposed model’s performance is assessed using various metrics, including accuracy, precision, recall, F1-score, and AUC. The results of the experiments designates that the Random Forest algorithm outperforms other ML models. This research has a capacity enhance the understanding of students’ intercultural communication experiences and provide actionable insights for instructors. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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machine learning Methods for Phenotype Prediction from High-Dimensional, Low Population Aquaculture data

Machine Learning Methods for Phenotype Prediction from High-...

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18th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Faldani, Giovanni Rossignolo, Enrico Signor, Eleonora Longo, Alessio Faggion, Sara Bargelloni, Luca Comin, Matteo Pizzi, Cinzia Department of Information Engineering University of Padova Padova35131 Italy Department of Comparative Biomedicine and Food Science University of Padova PD Legnaro35020 Italy

Recent research has increasingly focused on classification rules within the big data framework, yet many bioin formatics applications still address prediction problems that involve small-sample, high-dimensional data. In phenotype prediction, especially with the rise of large-scale genomic data, a central challenge arises from han dling high-dimensional datasets where the number of genetic features (such as SNPs) far exceeds the sample size. A significant example of such high-dimensional, low-sample datasets is found in aquaculture, a rapidly growing sector within global food production and a crucial source of high-quality protein. This study uses data from an experiment performed on European seabass as a test case, focusing on predicting resistance to Viral Nervous Necrosis (VNN) as a specific phenotype of interest. We explore a range of machine learning tech niques to address the complexities of high-dimensional data, from established methods like gradient boosting, SVM, and deep learning to newer approaches. This paper evaluates various methods for associating SNPs with phenotypic traits, benchmarking their performance on challenging aquaculture genomic data to provide insight into the effectiveness of these techniques. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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machine learning-Enhanced Overcoupled Plasmonic Resonators for Biomolecular Detection 38

Machine Learning-Enhanced Overcoupled Plasmonic Resonators f...

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38th IEEE international conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Li, Dongxiao Zhou, Hong Ren, Zhihao Xu, Cheng Lee, Chengkuo National University of Singapore Department of Electrical & Computer Engineering Singapore

ISBN: (纸本)9798331508890

Plasmonic biosensors offer a unique opportunity to precisely control light-matter coupling in surface-enhanced infrared absorption (SEIRA) spectroscopy. However, the broadband nature of infrared spectra and the complex molecular information pose challenges in detecting and distinguishing biomolecules. Here, we propose the design of an overcoupled plasmonic resonator with ultrasensitive (7.25%/nm) and ultrabroadband (1000-3333 cm-1) sensing capabilities. These features allow the resonator to enhance multiple molecular fingerprint vibrations with high sensitivity. To further unlock the sensor's potential, machine learning is employed, achieving 100% accuracy in identifying five biomolecules based on rich vibrational data. With outstanding sensing performance and molecular recognition capabilities, the machine-learning-enhanced overcoupled resonator paves the way for broader SEIRA applications. © 2025 IEEE.

关键词： Infrared absorption

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Enhancing Convolutional Models for Indoor Radio Mapping via Ray Marching

Enhancing Convolutional Models for Indoor Radio Mapping via ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Mengfan Skocaj, Marco Boban, Mate Munich Research Center Huawei Technologies Germany School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (纸本)9798350368741

In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machine learning pipeline developed for the ICASSP 2025 Signal Processing Grand Challenge, specifically the First Indoor Pathloss Radio Map Prediction Challenge. Our method incorporates a ray-marching feature that, combined with a UNet architecture enhanced by dilated convolution layers, significantly improves indoor pathloss prediction accuracy. Our approach achieved a 3rd-place ranking in the challenge with a weighted RMSE of 10.33 on the test dataset. © 2025 IEEE.

关键词： feature engineering machine learning radio maps Wireless communications

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Enhancing elderly care services through integrated sentiment analysis and knowledge reasoning: A deep learning approach

International Journal of Cognitive Computing in Engineering

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international Journal of Cognitive Computing in engineering 2025年 6卷 477-494页

作者： Ai, Yongguan Chu, Shiwei Wang, Juan Xu, Nianfang School of Public Administration Anhui Vocational and Technical College Anhui Hefei230011 China School of Electronic Information Engineering Anhui University Hefei230039 China School of Computer and Information Technology Anhui Vocational and Technical College Anhui Hefei230011 China

This study proposes a pioneering integrated care model for elderly care service robots that integrates sentiment analysis and knowledge reasoning through a deep learning framework. The primary objective of this research is to address the limitations of current elderly care robots in providing emotionally intelligent and personalized care. The model utilizes advanced deep learning techniques, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to analyze multimodal data comprising speech, facial expressions and body language. This enables the model to provide a comprehensive understanding of an elderly individual's emotional and health status. The efficacy of the model is demonstrated by its ability to enhance the precision of care decisions, improve the quality of care, user satisfaction, and system reliability. The experimental results demonstrate substantial improvements in sentiment recognition accuracy (96.5 %), reasoning accuracy (93.7 %), decision execution time (3.2 s), user satisfaction (4.9 points), and system stability (98.4 %), highlighting the transformative potential of the model in revolutionizing elderly care services. © 2025

关键词： Adversarial machine learning

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