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Smart Sports Video Tracking and Safety Solutions Integrating GPS and Satellite Communications 4

Smart Sports Video Tracking and Safety Solutions Integrating...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ying, Sui Tianjin Vocational College of Communications Tianjin300110 China

ISBN: (纸本)9798331523923

As the system of intelligent sensing and video surveillance continues to improve, its application in different fields has also emerged. In this study, the smart sports video tracking and safety solutions integrating the GPS and satellite communications is discussed. The core goal of this research is to address the needs of athlete tracking and safety in complex environments. The designed system consists of three core modules, namely, efficient video tracking module, high-precision GPS tracking module and stable satellite communication module. First, the proposed video tracking module uses a Laplace operator-based deblurring algorithm for video image processing. Subsequently, the human target in the moving video is accurately tracked through the two-dimensional flow field distribution. As a global optimization, the edge similarity feature segmentation is used to achieve dynamic marker tracking. The designed GPS tracking module uses the single difference observation method to improve positioning accuracy. This module combines the direct rounding fuzzy solution to effectively solve the coordinate conversion and measurement error problems. Finally, the satellite communication module used works through the coordination of the main control machine, main controller and signal switching module. This architecture realizes efficient data transmission and processing. Through experimental verification, the effectiveness of this tracking and assurance framework can be explored. © 2025 IEEE.

关键词： Satellite communication systems

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Visual Decoding using a Learnable Wavelet-based Spatial-Spectral-Temporal EEG Embedding 13

Visual Decoding using a Learnable Wavelet-based Spatial-Spec...

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13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Choi, Yebin Kim, Jun-Mo Choi, WooHyeok Ji, Chang-Hoon Oh, Ji-Hye Kam, Tae-Eui Dept. Human-Centered Artificial Intelligence Sangmyung University Seoul Korea Republic of Dept. Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Visual decoding seeks to identify or reconstruct visual stimuli perceived by individuals based on neural activity. Although functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) have achieved remarkable success in visual decoding, their high costs, limited portability, and real-time processing challenges necessitate alternative approaches. Electroencephalography (EEG) provides a promising solution due to its cost-effectiveness, high temporal resolution, and suitability for real-time applications. However, conventional EEG-based encoders often rely on simplistic architectures, which limits their ability to fully capture the spatial, spectral, and temporal (SST) features of EEG signals, leading to suboptimal performance. In this study, we propose a novel brain decoding framework utilizing a state-of-the-art EEG encoder specifically designed to capture the SST characteristics of EEG signals. The proposed framework was evaluated on the THINGS-EEG dataset. It achieved a mean top-1 accuracy of 19.7% and a top-5 accuracy of 50.7% in zero-shot retrieval tasks, outperforming conventional EEG encoders. These results demonstrate the potential of our method in advancing EEG-based visual decoding task. © 2025 IEEE.

关键词： Electroencephalography

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

3rd International Conference on Advances in Information and ...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

ISBN: (纸本)9783031809422

The proceedings contain 113 papers. The special focus in this conference is on Advances in Information and Communication Technology. The topics include: Using Opals Program System and Sparse CNN Model in Processing and Classifying Airborne Laser Scanning Data;polyp Segmentation Based on Transformer;an Effective Approach for Object Detection in Adverse Weather Conditions;Enhancing DGA Botnet Classification Based on Large Language Models and Transfer Learning;deep Learning Application for Images Augmentation in Electrical Component Classification System;Enhancing the Performance of Vietnamese Online Public Service Chatbots with RAG;A Hybrid Approach: Transformer and LSTM Combination for Text Summarization in Vietnamese;vizAgent: Towards an intelligent and Versatile Data Visualization Framework Powered by Large Language Models;enhancing Telecom Churn Prediction Using an Advanced Stacking Model;an Efficient Methodology to Assess Ki-67 and Tumor-Infiltrating Lymphocytes in Heterogeneous Tumors Detection;transformating trigonometric function to apply in software reliability modeling;application of Large Language Models in Geographic Map Analysis and Visualization;FMN-Voting: A Semi-supervised Clustering Technique Based on Voting Methods Using FMM Neural Networks;An Automatic machine Learning Based Customer Segmentation Model with RFM Analysis;enhanced Literature Review Visualization: A Novel Sorted Stream Graphs with Integrated Word Elements;transfer Learning for Cervical Cancer Multi-class Classification;a Study on Ensemble Learning for Cervical Cytology Classification;EFL-Net: An Enhanced Feature-Based Learning Network for Object Detection in Rainy Weather Conditions;A Trace-Selection Preprocessing Approach of Discovering Structurally Complete ICN-Process Models from Process Logs;improve the Quality of machine Translation in Low-Resource Language Pairs;Faster-RCNN in human Detecting on Thermal Images;deep Learning for Speech Separation: A Comprehensive Review;predicting

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Utilizing Transfer Learning Models to Classify Absence Defects on Aluminum Plates Using Feature-Based Approaches 2nd

Utilizing Transfer Learning Models to Classify Absence Defec...

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2nd international conference on intelligent Manufacturing and Robotics, ICIMR 2024

作者： Pandian, Kiran Zhen, Lim Weng Majeed, Anwar P. P. Abdul Teh, Sze-Hong Goon, Koon Yin Razman, Mohd Azraai Mohd Faculty of Manufacturing and Mechatronics Engineering Technology University Malaysia Pahang Al-Sultan Abdullah Pahang26600 Malaysia Department of Computing and Information Systems Sunway University Bandar Sunway Selangor Darul Ehsan47500 Malaysia Xi’an Jiaotong-Liverpool University Suzhou215123 China TT Vision Technologies Sdn. Bhd. Plot 106 Hilir Sungai Keluang 5 Bayan Lepas Phase 4 Penang11900 Malaysia

ISBN: (纸本)9789819639489

Screw – the small but important elements used in various industry. Its presence plays a significant role as it securely holds the product in place, preventing loosening or collision with the case. Such occurrences could lead to the displacement of small components or compartments, resulting in product failure. The advent of Industry 4.0 has contributed to reducing labor costs and human errors. This research aims to develop a robust classification model for machine vision detection, specifically for identifying the absence or presence of a screw. The model can be integrated into relevant robotics applications. To collect the customized dataset, a 6-degree-of-freedom (DOF) robot. The collected dataset was then categorized into two groups: absent and present. For the training process, a pretrained dataset called ImageNet was employed to facilitate the training process. Transfer learning techniques were used to extract the features required for different machine learning models. Each machine learning model underwent hyperparameter tuning to achieve the highest classification accuracy. The data was divided into training, validation, and testing sets using a sampling ratio of 60:20:20, respectively, before being fed into the various machine learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Screws

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Models of intelligent Tutoring systems Based on Autonomous Agents for Virtual Learning Environments: A Systematic Literature Review 22nd

Models of Intelligent Tutoring Systems Based on Autonomous A...

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22nd international conference on Practical Applications of Agents and Multi-Agent systems, PAAMS 2024

作者： López-Goyez, Juan P. González-Briones, Alfonso Chamorro, Andrea F. Grupo de Investigación BISITE Departamento de Informática y Automática Facultad de Ciencias University of Salamanca Instituto de Investigación Biomédica de Salamanca Calle Espejo 2 24.2 Salamanca37007 Spain Universidad Politécnica Estatal del Carchi Antisana S/N Tulcán040101 Ecuador

ISBN: (纸本)9783031704147

Virtual education combines didactic strategies with information and communication technologies to facilitate student access, interaction, and active participation. Academic monitoring and tutoring should be conducted within virtual learning environments (VLE) with the support of a virtual tutor to provide personalized advice and feedback. The review integrates theories, didactic strategies, learning styles, and computational models. This research presents a systematic review of literature on tutoring models in virtual education. The objective is to identify intelligent tutoring systems (ITS) models that use autonomous agents and expert systems in virtual learning environments. Additionally, strategies for implementing ITS in VLEs are presented. The identified models focus on adaptive learning, learning styles, and student skills for developing a specific course. It is proposed to design an architecture in the Moodle platform for academic monitoring of students in the online modality using autonomous agents. A holistic approach is considered, combining the efficiency of AI with didactic strategies that personalize learning through friendly human-machine interaction. The research on ITS models focuses on adaptive learning, considering new technological trends, while also considering the interaction between the student and the virtual tutor. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Virtual environments

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Study on intelligent Decision Aided Technology of Power Grid Operation Based on Procedure Knowledge

Study on Intelligent Decision Aided Technology of Power Grid...

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international conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Su, Yun Li, Yan Tu, Qi Jin, Minjie Cao, Jiaxin Han, Zheng Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company Shanghai200040 China Artificial Intelligence Application Center China Electric Power Research Institute Beijing100192 China Shibei Power Supply Bureau State Grid Shanghai Municipal Electric Power Company Shanghai200232 China

ISBN: (纸本)9789819620456

Based on the regulation electronic rule knowledge base, the model data such as real-time measurement, operation mode, section limit, equipment output upper and lower limits, and rotation reserve are integrated to match the operation conditions of the power grid, and the regulation knowledge, mechanism model and power grid operation data are derived based on the dispatching business knowledge. Based on different scenarios such as power grid faults and abnormal power flow changes, this paper proposes that it is necessary to study an intelligent decision-making method based on professional knowledge of scheduling procedures, and combine electronic control knowledge and expert experience to accurately match various operation modes of power grids, so as to solve the problem that auxiliary decision-making based on traditional mechanism models is highly dependent on expert experience. It is urgent to study the misjudgment/missing judgment identification technology of artificial intelligence technology, fully integrate the advantages of artificial intelligence technology in computing speed and expert database in decision-making reliability, and ensure the safe, stable and economic operation of the new power system. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： intelligent decision aided procedure knowledge scheduling operation

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Orbit: A Framework for Designing and Evaluating Multi-objective Rankers 25

Orbit: A Framework for Designing and Evaluating Multi-object...

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30th international conference on intelligent User Interfaces, IUI 2025

作者： Yang, Chenyang Xiao, Tesi Shavlovsky, Michael Kästner, Christian Wu, Tongshuang Carnegie Mellon University PittsburghPA United States Amazon Palo AltoCA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713064

machine learning in production needs to balance multiple objectives: This is particularly evident in ranking or recommendation models, where conflicting objectives such as user engagement, satisfaction, diversity, and novelty must be considered at the same time. However, designing multi-objective rankers is inherently a dynamic wicked problem - there is no single optimal solution, and the needs evolve over time. Effective design requires collaboration between cross-functional teams and careful analysis of a wide range of information. In this work, we introduce Orbit, a conceptual framework for Objective-centric Ranker Building and Iteration. The framework places objectives at the center of the design process, to serve as boundary objects for communication and guide practitioners for design and evaluation. We implement Orbit as an interactive system, which enables stakeholders to interact with objective space directly and supports real-time exploration and evaluation of design trade-offs. We evaluate Orbit through a user study involving twelve industry practitioners, showing that it supports efficient design space exploration, leads to more informed decision-making, and enhances awareness of the inherent trade-offs of multiple objectives. Orbit (1) opens up new opportunities of an objective-centric design process for any multi-objective ML models, as well as (2) sheds light on future designs that push practitioners to go beyond a narrow metric-centric or example-centric mindset. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

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Sorting Objects Using the Vision System of the Robot

Sorting Objects Using the Vision System of the Robot

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international conference on Computational cybernetics (ICCC)

作者： Martin Pollák Adrián Vodilka Marek Kočiško Petr Baron Faculty of Manufacturing Technologies Technical University of Kosice Prešov Slovakia

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

Currently, robots and robotics as a field of science have expanded to be used in almost all industrial areas as well as in everyday life. The current direction of the automation of industrial production is oriented towards the introduction of machines and devices enabling the automatic execution and management of work previously performed by a conventional machine and worker. These automatically controlled devices relieve workers not only from monotonous physical work, but also mental activity, and at the same time improve productivity, quality of processes and products. The article describes the design of a robotic application for automatic sorting of components using a vision camera system. It points to the creation of the control program and the definition of the necessary settings in the software interface necessary for the correct operation of the used robotic arm. The result of the design is a functional robotic workplace developed for the automated sorting of post tensioning wedges as a detected object according to their hole diameter, pointing out the possibility of using machine vision in robotics.

关键词： Automation Service robots Robot vision systems Cameras Manipulators Software Hardware Robots intelligent sensors Sorting

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Advanced Examination systems: Applying Fuzzy Logic and machine Learning Methods in Education

Advanced Examination Systems: Applying Fuzzy Logic and Machi...

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international conference on Computational cybernetics (ICCC)

作者： Éva Karl Enikő Nagy György Molnár Várkerti Primary School Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Györ Hungary Óbuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Budapest Hungary Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University Apáczai Csere János Faculty of Humanities Györ Hungary

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

In our paper, we present an innovative educational assessment system that attempts to overcome the limitations of traditional educational evaluation methods by applying machine learning, artificial intelligence, fuzzy logic, and advanced mathematical techniques. This system can provide a more objective and personalized assessment of students’ knowledge. Current examination systems in educational institutions are outdated and do not meet modern societal and technological requirements. A central element of the system is the application of fuzzy logic, which allows for handling uncertainties in knowledge assessment. Using the FP-growth algorithm and the fuzzy analytical hierarchy process (AHP) aids in optimizing the evaluation process and enables a more profound analysis of students’ performance. During the system's development, we aim to adaptively manage the difficulty level of questions, taking into account students’ prior performance and individual capabilities. The study highlights the security risks and efficiency issues of current “manual” question compilation methods. The new system aims to minimize these risks while improving the quality of education and reducing the workload of educators.

关键词： Fuzzy logic Uncertainty Computational cybernetics machine learning Manuals Educational technology Analytic hierarchy process Security

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Improving Speech Emotion Recognition: Novel Aggregation Strategies for Self-supervised Features 25th

Improving Speech Emotion Recognition: Novel Aggregation Stra...

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25th international conference on intelligent Data Engineering and Automated Learning

作者： Valls, Oscar Pastor-Naranjo, Fran del Amor, Rock Gomez-Zaragoza, Lucia Marin-Morales, Javier Alcaniz Raya, Mariano Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano HUMAN Valencia 46022 Spain Valencian Grad Sch & Res Network Artificial Intel Valencia 46022 Spain

ISBN: (纸本)9783031777301;9783031777318

Speech Emotion Recognition (SER) is crucial for enhancing human-machine voice interactions, allowing systems to better interpret the speaker's emotional state and improve user experience. Self-supervised learning (SSL) models have significantly advanced speech recognition by generating speech representations from large unlabeled corpus, which can then be fine-tuned with smaller labeled datasets for downstream tasks such as voice command detection, automatic transcription, speaker identification, and SER. However, SSL models are typically optimized for general tasks rather than emotion recognition. This poses a challenge, as the limited labeled data in SER can hinder the generalization capabilities of these models, making the choice of SSL architecture and training strategy vital. The most common procedure in Speech Emotion Recognition is performing an average time pooling of the SSL features to train classification models, which often leads to a loss of temporal relationships in the data. In this study, we introduce two novel SSL feature aggregation methods that leverage attention mechanisms to better capture temporal dependencies in speech data. These methods significantly enhance the extraction of relevant information from SSL features, leading to improvements in classification accuracy. Our proposed approach outperforms the standard average time pooling method, achieving up to a 6.3% increase in weighted accuracy (WA) on the IEMOCAP database.

关键词： Affective Computing Speech Emotion Recognition Transformer Models Self-Supervised Speech Features Attention-Based Temporal Aggregation

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