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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

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MFE-Net: A remote sensing image dehazing model for the visual system of a flight simulator 6

MFE-Net: A remote sensing image dehazing model for the visua...

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6th international conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Ge, Wenyi Wang, Bo Liu, Qi Qin, Xiaolin Tan, Shihan Wang, Shengjie Yuan, Xia College of Computer Science Chengdu University of Information Technology Chengdu610255 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610213 China Jiuzhou Air Trafic Control Sichuan Jiuzhou Investment Holding GroupCo. Ltd Mianyang621000 China The Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan618307 China

ISBN: (纸本)9781510688018

Obtaining high-quality remote sensing images is crucial in generating a three-dimensional terrain for flight simulators. However, due to the presence of haze and other impact factors, collected remote sensing images usually suffer from blurred details, which limits its applicability real-world applications. To address this issue a deep learning-based dehazing model is proposed to advance hazy remote sensing images to build flight simulator visual systems. The encoder-decoder architecture is proposed to perform the image dehazing, in which an improved feature extraction module is designed to extract both global and local semantic features across multi-resolutions. In addition, the pixel-pixel and region-region mechanism are innovatively designed to achieve deep mapping relationships and further enhance image dehazing. Experimental results demonstrate that the proposed model outperforms other comparative baselines, achieving high-confidence dehazing in multi-scene remote sensing images, and with good generality and applicability in multi-resolution flight simulators and multi-terrain remote sensing. © 2025 SPIE.

关键词： Flight simulators

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Applied Mathematical Modelling in the Physics Problem-Solving Classroom 4th

Applied Mathematical Modelling in the Physics Problem-Solvin...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Serpe, Annarosa Department of Mathematics and Computer Science University of Calabria CS Rende Italy

ISBN: (纸本)9783031812460

This paper aims to contribute to the literature on planning and implementing mathematical modelling tasks in the higher education curriculum. Specifically, we propose and discuss a lesson plan carried out within the course of Mathematics Education II - Master’s Degree in Mathematics, to train and qualify students on how to capture the interplay between Physics and Mathematics in the perspective of mathematical modelling and problem-solving. The goal is to make transparent what is meant by modelling by showing how it is possible to develop integrated learning environments beyond the traditional boundaries between subjects both at the subject level and at the level of required pedagogy, illustrating this characterisation with an example regarding a theme that ‘subsumes’ the two disciplines. It is important in the educational training of future teachers to explore ways based on influential theoretical expositions of the concept of interdisciplinarity. This is especially true when examples of didactic transposition are provided where Mathematics and Physics are understood as forms of knowledge that have relationships and differences, which are deeply intertwined and which co-evolve, mutually generating new problems that lead to permeating the labile boundaries between them to generate new knowledge. These examples of didactic transposition, therefore, are rich in interdisciplinarity connections, which show students the relevant aspects of the two disciplines from a historical and epistemological point of view and their connections. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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A High-Gain DC-DC Boost Converter for Solar Microgrid systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prasanth, S. Priyadarsini, S. Lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS. © 2025 IEEE.

关键词： Microgrids

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Modified Higher-Order Dependencies in Networks for Anomaly Detection

Modified Higher-Order Dependencies in Networks for Anomaly D...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Kamala Challa Alladi Suresh Babu Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Anantapur Ananthapuramu India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Background: Anomaly detection is essential for detecting unusual behaviors in dynamic networks that may represent emerging security threats. Traditional models focus on First-Order Network representations that overlook the integration of higher-order dependencies. Methods: This work proposes a novel Modified Higher-Order Dependencies Network (HON) for anomaly detection. Initially, the input data is preprocessed by min-max normalization, followed by Random Oversampling to address the class imbalance. Then, this preprocessed data is sent to the novel Modified HON that finds the unusual behaviors of anomalies by modeling sequences with n-grams. An Improved Distance Measure is introduced as the loss function combines Improved Euclidean Distance and Interquartile Mean that improves the detection accuracy by prioritizing structural characteristics over node or edge attributes. Result: The proposed approach achieves 94.1% accuracy surpassing existing techniques by up to 12.5%. Conclusion: This technique offers better performance for anomaly detection that paves the way for advanced cybersecurity applications.

关键词： Accuracy Image edge detection Scalability Data preprocessing Euclidean distance Network security Loss measurement Time measurement Anomaly detection Tuning

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Detection of Multiple Small Biased GPS Spoofing Attacks on Autonomous Vehicles 4

Detection of Multiple Small Biased GPS Spoofing Attacks on A...

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4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Mohammadi, Ahmad Hemmati, Vahid Ahmari, Reza Owusu-Ambrose, Frederick Mahmoud, Mahmoud Nabil Homaifar, Abdollah North Carolina A&T State University Electrical and Computer Engineering Greensboro United States North Carolina A&T State University Computer Science Greensboro United States

ISBN: (纸本)9798331518882

This paper introduces an algorithm designed to detect GPS spoofing attacks in Autonomous Driving systems (ADS). The algorithm combines data from in-vehicle sensors, including the speedometer and gyroscope. This data is then fed into a Deep Neural Network (DNN) to predict the vehicle's displacement between timestamps. These predictions are continuously compared with GPS-provided displacement and velocity to detect the turn-by-turn, stop, and overshoot GPS spoofing attacks. Also, the same data is processed through an analytical model that uses the vehicle's dynamic movement equations to compute its position and velocity and to juxtapose them with GPS-provided position and velocity to detect the aforementioned spoofing attacks. A threshold is predetermined by using clean datasets to calculate the maximum possible deviation between in-vehicle sensor and GPS provided position/displacement and speed. Subsequently, spoofing attack detection uses this threshold for its continuous, real time comparison. Additionally, the proposed algorithm is able to detect a sequence of multiple biased attacks that fall below the mentioned threshold but can create a large deviation between the position/displacement and speed provided by GPS and Inertial measurement Unit (IMU). To validate the effectiveness of the work, datasets simulating four distinct spoofing scenarios such as turn-by-turn, overshoot, stop and multiple biased attacks were generated using real-world data from [1]. The analyses shows that the Data-driven model successfully identifies the turn-by-turn, stop, overshoot and multiple biased spoofing attacks with the accuracies of 100%, 99.95%, 99.91%, 97.83%, respectively. © 2025 IEEE.

关键词： Global positioning system

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TOWARDS CLINICALLY FEASIBLE NONINTRUSIVE QUALITY AND INTELLIGIBILITY INDICES FOR HEARING AIDS

TOWARDS CLINICALLY FEASIBLE NONINTRUSIVE QUALITY AND INTELLI...

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

作者： Ashkanichenarlogh, Vahid Folkeard, Paula Parsa, Vijay National Centre for Audiology Western University London Canada Department of Electrical and Computer Engineering Western University London Canada School of Communication Sciences and Disorders Faculty of Health Sciences Western University London Canada

ISBN: (纸本)9798350368741

Speech quality and intelligibility are of significant importance during clinical hearing aid (HA) fitting and verification. Validated intrusive objective predictors of intelligibility and quality such as the Hearing Aid Speech Perception Index (HASPI) and the Hearing Aid Speech Quality Index (HASQI) have not been widely adopted for implementation within clinically available HA test systems. Recent advances in nonintrusive measures, such as those from Clarity Prediction Challenges (CPCs) and HASA-Net, are also not yet accessible to clinicians. Moreover, most of these advancements rely on datasets from simulated HAs, not the commercial devices used by audiologists. This work aims to develop non-intrusive quality and intelligibility indices using custom databases of noisy speech recorded in a HA test box. Similar to the successful models from previous CPCs, the indices were derived using a novel non-intrusive model leveraging automatic speech recognition and self-supervised learning techniques. The proposed non-intrusive model was trained to predict the intrusive HASPI/HASQI values and later validated against subjective intelligibility data obtained from a group of listeners with hearing loss. The proposed model resulted in strong correlations with HASQI (95.96%) and HASPI (97.59%), and a moderate correlation with the subjective intelligibility scores (76.63%). © 2025 IEEE.

关键词： Hearing Aids Nonintrusive Self-Supervised Learning Speech Quality and Intelligibility measurement Transformers

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Enhancing Human Resource Management Practices in Marketing Companies Using Dual Graph Attention Networks 3

Enhancing Human Resource Management Practices in Marketing C...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Roopalatha, N. Dhanalakshmi, K. Babu, G.N.P.V. Krishna, P. Vamsi Perada, Anvesh Mulagapati, Harun Kumar School of Business Gitam University Hyderabad India School of Business and Management Christ University Bangalore India School of Management and Commerce Malla Reddy University Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Srkr Engineering College Department of Engineering Mathematics and Humanities Chinna Amiram India

ISBN: (纸本)9798331508456

Marketing organization features and strategy implementation have been studied for over 30 years. These include organizational structure, culture, leadership, and processes. HR regulations can motivate marketing professionals to support group and individual goals when correctly implemented, but this part of HR has gotten little attention. Model preparation, feature extraction, and training comprise the suggestive technique. It reviewed data quality, evaluated dataset structure, and described data types during pre-processing. Principal component analysis (PCA) ranked and evaluated decision-making units to reduce dimension. Model training used MGGAN. In comparison to GAN and CNN, the proposed model performed well. With an average accuracy rate of 94.36%, it surpassed earlier approaches and captured all dataset peculiarities. MGGAN modeling can increase predictive performance, and marketing organizations should integrate HR regulations, according to this study. This study opens up new organizational analysis and strategy execution methods. © 2025 IEEE.

关键词： Human resource management

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A Hybrid GRU-Transformer Model for Financial Forecasting and Risk Management 3

A Hybrid GRU-Transformer Model for Financial Forecasting and...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Pradhan, Rahul Alazzam, Malik Bader Keswani, Sarika Bhasin, Narinder Kumar Jaff, Nadhema Ahmed Muthuperumal, S. Gla University Department of Computer Engineering & Applications Mathura India Jadara University Faculty of Information Technology Irbid Jordan Pune India Indian Institute of Banking and Finance Mumbai India College of Administrative and Financial Sciences Knowledge University Department of Business Administration Erbil Iraq K.Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798331508456

Financial sector demands precise forecasting models to mitigate risk and optimize asset management. This study presents a novel Hybrid GRU-Transformer Model that integrates Gated Recurrent Units (GRUs) for capturing local temporal patterns and the Transformer T5 architecture for modeling global dependencies. Financial datasets sourced from Kaggle, including stock prices and trading volumes, were preprocessed through normalization and categorical encoding, followed by an 80:20 dataset split for training and testing. The proposed model outperformed existing methods such as ARFIMA-LSTM and EMD-CNN-LSTM, achieving superior metrics, including a Mean Squared Error of 0.08 and an R2 of 0.95. Results underline the model's accuracy and robustness in predicting financial risks and trends. By enhancing predictive performance and risk management strategies, this model addresses the dynamic complexities of financial markets. Future research will explore its adaptability across diverse instruments and markets to further its application in real-time forecasting scenarios. The system is implemented using Python integrated with MATLAB. © 2025 IEEE.

关键词： Risk management

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Generative Adversarial Network for Image Reconstruction from Human Brain Activity

Generative Adversarial Network for Image Reconstruction from...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Tanner, Tim Cutsuridis, Vassilis School of Computer Science University of Lincoln Lincoln United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom

Decoding of brain activity with machine learning has enabled the reconstruction of thoughts, memories and dreams. In this study, we designed a methodology for reconstructing visual stimuli (digits) from human brain activity recorded during passive visual viewing. Using the MindBigData EEG dataset, we preprocessed the signals and cleaned them from noise, muscular artifacts and eye blinks. Using the Common Average Reference (CAR) method and past studies’ results we reduced the available electrodes from 14 to 4 keeping only those containing discriminative features associated with the visual stimulus. A convolutional neural network (CNN) was then trained to encode the signals and classify the images. A 92% classification performance was achieved post-CAR. Three variations of an auxiliary conditional generative adversarial network (AC-GAN) were evaluated for decoding the latent feature vector with its class embedding and generating black-and-white images of digits. Our objective was to create an image similar to the presented stimulus through the previously trained GANs. An average 65% reconstruction score was achieved by the AC-GAN without a modulation layer, a 60% by the AC-GAN with modulation layer and multiplication, and a 63% by the AC-GAN with modulation and concatenation. Rapid advances in generative modeling promise further improvements in reconstruction performance. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Generative adversarial networks

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17th international conference on Intelligent Robotics and Applications, ICIRA 2024

17th International Conference on Intelligent Robotics and Ap...

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17th international conference on Intelligent Robotics and Applications, ICIRA 2024

ISBN: (纸本)9789819607730

The proceedings contain 197 papers. The special focus in this conference is on Intelligent Robotics and Applications. The topics include: Design and Kinematics Characteristic Analysis of a 3-PURU/S Photoelectric Stabilized Platform;vibration Analysis and Test of Working Arm of the Anchor Drilling Robot;design and Optimization of Vibration Sieve for Specific Sandy Soil and Its Screening Motion Parameters;Design of a Trajectory-Tracking Controller for OMRs Based on Minimizing Tire Wear;conceptual Design, Control System Development and Prototype Fabrication of a 5-Axis Hybrid Machining Robot for Handicraft Carving;twin Mushroom Picker Suspension Chassis Design Facing the Narrow Space;Kinematic Calibration Method Based on Point Cloud measurement for 3-RPS Parallel Robot;design and Analysis of a Multi-panel Solar Array with Struts;design and Analysis of a Consecutive Aquatic Jumping Robot Inspired by Water-Dwelling Frog Hind Legs;design of Multi-Mode Morphing Wings Based on Multi-Stable Beam-Type Metastructures;kinematic modeling and Analysis of Robotic Axial Tilt Errors;Minimum Jerk Trajectory Planning for a Redundant Actuated Three-DOF Cable-Suspended Parallel Robot with Parallelogram Architecture;Kinematic Analysis and Control Strategy Research of Hydraulic Robotic Arm Drive Module 2-UPS+U Parallel Mechanism;design and Envelope Grasping Analysis of Deployable Manipulator for Space Capture;design, Modelling and Experiment of a Spatial Multi-locomotion Tensegrity Mobile Robot;Inverse Dynamics Model Learning for Robotic Manipulator Based on Swarm Optimization AdaBoost-LSTM;design and Experiment of a Percussion Mechanism for a Self-Penetrating Drilling Robot;thermal-Structural Analysis of a Deployable Metamorphic Robotic Grasper with Finite Element Method;design and Analysis of an Origami-Inspired Gripper with Passive Bistable Grasping Capability;pneumatic Gripper Design Concurrent Shape Topology Optimization.

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