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检索条件"任意字段=19th Data Compression Conference"

共 11664 条记录，以下是61-70 订阅

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Leveraging Deep Learning Technique with Meta-data-Featured Images Captured by Drone for Real-Time Victim Positioning in Flood-Affected Areas 19

Leveraging Deep Learning Technique with Meta-Data-Featured I...

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19th International conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Do, Trong Tuan Nguyen, Dinh Duy Ho, Xuan Hai Nguyen, Van Duc School of Electrical and Electronic Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9798331507817

Flooding has long been a major concern for nations frequently affected by natural disasters. the detection and localization of isolated victims for immediate temporary rescue have been a focal point for many researchers aiming to demonstrate the practical value of their solutions. However, accessing flood-affected areas poses significant challenges, as floods severely disrupt local transportation and damage infrastructure, rendering access nearly impossible. In such scenarios, the most feasible rescue approach is aerial intervention using Unmanned Aerial Vehicles (UAVs). Nevertheless, accurately detecting and localizing targets from a UAV’s perspective is a significant challenge. UAVs operate at various altitudes and speeds, resulting in motion blur of targets against densely populated backgrounds. Additionally, target localization is made difficult due to the limited contextual understanding of the surroundings. To address these challenges, we propose training the YOLOv10 model to enhance the detection of small objects, integrated with a GPS-based target localization system. Furthermore, to enable the practical, real-world application of this solution, we introduce a communication infrastructure that facilitates the timely and effective deployment of both the detection and localization models. Field experiments have yielded promising results, with the artificial intelligence model achieving an Average Precision (AP) between 85-95%, and the localization model processing targets at an impressive speed of under 1 ms. the combined detection and localization approach demonstrated a positioning error of only 7-9 meters. Moreover, integrating the AI model and the localization algorithm into a low-latency communication system has shown promising results, providing near-instantaneous outputs with delays ranging from 0.6 to 0.9 seconds from the moment a target enters the frame. © 2025 IEEE.

关键词： Drones

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Identification of the Key Parameters of the Photovoltaic Power Station Fault Crossing Based on the Measured data 19th

Identification of the Key Parameters of the Photovoltaic Pow...

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19th Annual conference of China Electrotechnical Society, ACCES 2024

作者： Wei, Wei Zang, YanTing Cao, XueFeng Guangxi National Energy Development Co. Ltd Guangxi Nanning China

ISBN: (纸本)9789819608966

To verify the high and low voltage ride-through capability of photovoltaic power stations and obtain their accurate model control parameters, this paper proposes a multi-strategy improved model identification method for photovoltaic power plants based on the measured data of high and low voltage ride-through tests and considering the characteristics of the whole process of fault ride-through. this method is applied to the grid-connected performance test of a photovoltaic power station in Guizhou to verify the high and low voltage ride-through ability of the power station under different working conditions and obtain its accurate control parameters. the results show that the proposed parameter identification method can accurately identify the key parameters of high and low voltage ride-through of the system under different working conditions, and the errors of simulation and measured data meet the technical requirements of relevant standards, which can be used for practical engineering calculations. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Photovoltaics

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Towards Real-Time Condition Monitoring of Electroplating Plants 19th

Towards Real-Time Condition Monitoring of Electroplating Pla...

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19th Global conference on Sustainable Manufacturing, GCSM 2023

作者： Lindner, M. Duckstein, R. Mennenga, M. Herrmann, C. Chair of Sustainable Manufacturing and Life Cycle Engineering Institute of Machine Tools and Production Technology Technische Universität Braunschweig Langer Kamp 19B Braunschweig38106 Germany Fraunhofer Institute for Surface Engineering and Thin Films IST Riedenkamp 2 Braunschweig38108 Germany

ISBN: (纸本)9783031774287

For securing a high-quality plating process, one of the main challenges in electroplating is the dosing of electrolytes. the optimal dosing and the related demand for resources do not only influence the coating quality but can also have a high environmental and economic relevance. Currently, the condition monitoring approaches related to dosing are mostly model-based and are seldom real-time capable. therefore, a concept for real-time data-based condition monitoring of electrolytes is proposed. the paper discusses the challenges of pre-processing and modeling time series data with machine learning algorithms and quality requirements and availability of data within the electroplating process. Moreover, the usage of neural networks for condition monitoring of time series data is presented and discussed in a case study with a focus on anomaly detection. With this example, the applicability of a data-based approach for dynamic prediction of electrolyte chemicals is presented and evaluated. © the Author(s) 2025.

关键词： Electroplating shops

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Sustainability data Map: Framework for data-Based Product Carbon Footprinting of Technical Products 19th

Sustainability Data Map: Framework for Data-Based Product Ca...

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19th Global conference on Sustainable Manufacturing, GCSM 2023

作者： Schreiner, Nick Kürpick, Christian Kühn, Arno Dumitrescu, Roman Fraunhofer Institute for Mechatronic Systems Design IEM Paderborn33102 Germany Heinz Nixdorf Institute University of Paderborn Paderborn33102 Germany

ISBN: (纸本)9783031774287

Growing awareness and political regulations increase the pressure on companies to become more sustainable. Manufacturers of technical products in particular face the challenge of decarbonization, as machinery and plants, for instance, have an impact on the emissions of almost all industries. However, information on the Product Carbon Footprint (PCF) is often not transparent for manufacturing companies. Digitalization has proven to be a key enabler, as up to 90% of manufacturers’ emissions occur along the value chain, making it impossible to calculate the PCF without data. However, identifying the necessary data and the corresponding IT system is a major challenge, especially for technical products. the Sustainability data Map supports the identification and structuring of relevant data and its sources for product carbon footprinting across all product lifecycle phases. In addition, the Sustainability data Map serves as a workshop-based medium to communicate and integrate all relevant stakeholders in the value chain. It is based on an existing data map that has been extended to include the criteria required for PCF determination. As a result, the Sustainability data Map enables manufacturing companies to create more transparency about the data needed to assess the PCF of technical products. © the Author(s) 2025.

关键词： data map Product carbon footprint Technical products

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Safeguarding Privacy and data Protection Rights in AIEnhanced Education and Learning Analytics: an Interdisciplinary Approach in Secondary High School Educational Settings 19

Safeguarding Privacy and Data Protection Rights in AIEnhance...

引用

Doctoral Consortium of the 19th European conference on Technology Enhanced Learning, ECTELDC 2024

作者： Paludi, Mario Università Degli Studi d'Annunzio Chieti-Pescara Italy Università Degli Studi di Foggia Foggia Italy

the centrality of the right to privacy and personal data protection for high school students is fundamental in light of the increasing use of digital technologies for educational purposes and the effort to introduce learning analytics at the school level. the use of digital technologies, particularly those enhanced by artificial intelligence tools, necessitates heightened attention to data and privacy law and to the fundamental right to privacy and personal data protection for high school students, who are inherently vulnerable. All students will be compelled to interact with school-provided technology, with disabled or socially, culturally, and economically disadvantaged students being even more vulnerable. the definition of the legal framework in this domain is a prerequisite for the effective protection of privacy and data and the development of secure, data-driven technologies. A parallel understanding of the human factors that influence data handling and privacy is similarly of great consequence. the research project is structured as follows: (1) outline the legal and ethical rules and principles regarding privacy and personal data applicable to high school educational settings;(2) assess schools' preparedness in managing students' data in compliance with legal and ethical standards and evaluate teachers' and students' knowledge, attitude and awareness of privacy and personal data protection, and their behavior during educational activities in digital environments;(3) outline educational actions and improvement proposals for managing students' privacy and personal data, especially when AIED will be employed extensively, to help optimize learning and improve the environment in which it takes place. © 2024 Copyright for this paper by its authors.

关键词： Differential privacy

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Optimized data Privacy Framework Using Asynchronous Blockchain and Adaptive Homomorphic Encryption in Dynamic EV Charging Networks 19th

Optimized Data Privacy Framework Using Asynchronous Blockcha...

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19th Annual conference of China Electrotechnical Society, ACCES 2024

作者： Liu, Yiqi Song, Zehao Li, Zhiyi School of Electrical Engineering Zhejiang University Hangzhou310058 China

ISBN: (纸本)9789819613823

the rapid proliferation of electric vehicles (EVs) necessitates enhanced data privacy and efficiency in EV charging systems, which are increasingly vulnerable to cyber threats and inefficiencies due to centralized data processing models. We introduce a novel data privacy framework integrating asynchronous blockchain and adaptive homomorphic encryption technologies to address these challenges within dynamic EV charging networks. Our framework employs an asynchronous blockchain architecture, allowing individual charging stations to process transactions independently without awaiting network-wide consensus, significantly reducing latency and enhancing scalability. Concurrently, an adaptive homomorphic scheme is developed to dynamically adjust encryption parameters based on real-time network load and data sensitivity, optimizing computational resources while ensuring robust privacy preservation. We evaluate the effectiveness of our proposed framework through a series of simulations that demonstrate substantial improvements in transaction processing speed and data security compared to traditional synchronous blockchain and static encryption methods. this study provides a scalable and secure approach to managing data privacy in EV charging networks. It offers a blueprint for integrating these technologies in other real-time data-intensive applications. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Differential privacy

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Enhancing Security in Federated Learning: Detection of Synchronized data Poisoning Attacks 19th

Enhancing Security in Federated Learning: Detection of Syn...

引用

19th International conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： Anastasiadis, Dimitrios Refanidis, Ioannis Department of Applied Informatics University of Macedonia Thessaloniki Greece

ISBN: (纸本)9783031815416

Federated learning systems face critical security risks from data poisoning attacks, where malicious clients manipulate training data to compromise model integrity. Traditional detection methods focus on isolating clients that frequently deviate from the average weight update across training rounds. Building upon this concept, this paper introduces an advanced detection strategy that identifies malicious clients through the analysis of similarities in their updates rather than deviations from the average. Our method computes the Euclidean distance between clients’ weight updates vectors over the training rounds. If some clients consistently appear in close proximity to each other, beyond a predefined threshold, they are flagged as potentially malicious. this approach not only refines detection by focusing on synchronization patterns among attackers but also enhances the robustness of the federated model against coordinated data poisoning attacks. We demonstrate the efficacy of our detection method through systematic experiments and discuss optimal hyperparameter tuning strategies, offering a significant step forward in securing federated learning environments. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Batch-Balancing Improvement with data Augmentation Techniques for Clinical Electroencephalographic data 19th

Batch-Balancing Improvement with Data Augmentation Technique...

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19th International conference on Hybrid Artificial Intelligence Systems

作者： Fernandez-Madera Gonzalez, David Moncada Martins, Fernando Gonzalez, Victor M. Villar, Jose R. Garcia Lopez, Beatriz Isabel Gomez-Menendez, Ana Univ Oviedo Elect Engn Dept Gijon Spain Univ Oviedo Comp Sci Dept Oviedo Spain Univ Oviedo Biomed Engn Ctr Oviedo Spain Burgos Univ Hosp Clin Neurophysiol Dept Burgos Spain

ISBN: (纸本)9783031741821;9783031741838

Photosensitivity is a neurophysiological condition in which the brain produces epileptic discharges, known as Photoparoxysmal Responses, as a reaction to light flashes which may lead to seizures. the standardized diagnosis procedure consists of stimulating the patient with a flashing light while Electroencephalography records their brain activity until one of these epileptic reactions occurs, which causes a scarcity of such phenomena in the recordings that, added to the low prevalence of this condition, generates an extremely imbalanced dataset which decreases the performance of automatic detection using Deep Learning techniques. this research proposes applying two different data Augmentation techniques in the data Generator phase of a Convolutional Neural Network to create synthetic paroxysmal instances and balance each input batch independently. Various network architectures are also tested. Results obtained from these experiments concluded that both DA techniques are equivalent in performance, but could not outperform previous experiments. this project is being developed with patients from Burgos University Hospital, Spain.

关键词： Photosensitivity Epilepsy Electroencephalography EEG Photoparoxysmal Response PPR Deep Learning data Augmentation

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Concentrate Grade Prediction for Flotation Process Based on Multi-condition Ensemble Learning 16th

Concentrate Grade Prediction for Flotation Process Based on...

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16th International conference on Modelling, Identification and Control, ICMIC 2024

作者： Zheng, Shi-Qi China Railway 19th Bureau Group Mining Investment Company Co. Ltd. Beijing100161 China

ISBN: (纸本)9789819617760

the grade of concentrate is a crucial indicator reflecting the effectiveness of the flotation process, which differentiates minerals based on their physicochemical properties. the complexity of operational conditions poses significant challenges to the precise prediction of concentrate grade. this paper proposes a method for predicting concentrate grade in the flotation process based on multi-condition integrated learning. Firstly, to address the complexity of operational conditions, an adaptive density peak clustering algorithm based on information entropy index is proposed to intelligently segment the operational conditions of the flotation process, and establish sub-models for concentrate grade prediction under different conditions. Secondly, to address the difficulty of effectively modeling the complex flotation process with a single model, a multi-model ensemble strategy using BP neural network Stacking is proposed, which integrates different condition sub-models to enhance prediction accuracy. Finally, the effectiveness and accuracy of the proposed method are validated using industrial site data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Flotation

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Clinical and Acquisition data for Optimizing MGMT Methylation Status Prediction: A Comprehensive Ensemble Strategy Emphasizing Non-invasive Approaches 19th

Clinical and Acquisition Data for Optimizing MGMT Methylatio...

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19th International conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： Miteva, Mariya Nisheva-Pavlova, Maria Faculty of Mathematics and Informatics Sofia University St. Kliment Ohridski 5 James Bourchier Blvd. Sofia1164 Bulgaria

ISBN: (纸本)9783031815416

Motivated by recent research findings on glioblastoma multiforme (GBM) highlighting the insufficiency of relying solely on imaging data for predicting the O6-methylguanine-DNA methyltransferase (MGMT) methylation status, this study takes an innovative approach by integrating both preoperative clinical and non-invasive acquisition data. A stacked dataset, combining predictions from models developed on these two distinct data sources, is employed for the analysis. Leveraging the Hard Voting Classifier ensemble (Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM)) on a resampled dataset with replacement, the models demonstrated an average accuracy of 85.9%, emphasizing the robustness of the predictive performance. the non-invasive nature of the approach, relying solely on structured data from diverse datasets, provides a comprehensive view of GBM dynamics. Results, consolidating clinical and acquisition data insights, are presented, underscored by their reliance solely on non-invasive structured data, providing valuable implications for non-invasive diagnostic and therapeutic advancements in GBM research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ensemble Algorithms Glioblastoma Intelligent Decision Support Machine Learning Medical Imaging MGMT Promoter MRI Scans Radiogenomics

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