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arXiv 2025年

作者： Kriuk, Boris Gill, Simranjit Kaur Aslam, Shoaib Fakhrutdinov, Amir Hong Kong University of Science and Technology Dept. of Computer Science & Engineering Hong Kong University of Westminster Dept. of Computer Science & Engineering United Kingdom University of Engineering & Technology Lahore Dept. of Mechanical Mechatronics & Control Eng Pakistan Shanghai Jiao Tong University Dept. of Mechanical Engineering China

Fine-Grained Image Classification (FGIC) remains a complex task in computer vision, as it requires models to distinguish between categories with subtle localized visual differences. Well-studied CNN-based models, while strong in local feature extraction, often fail to capture the global context required for fine-grained recognition, while more recent ViT-backboned models address FGIC with attention-driven mechanisms but lack the ability to adaptively focus on truly discriminative regions. TransFG and other ViT-based extensions introduced part-aware token selection to enhance attention localization, yet they still struggle with computational efficiency, attention region selection flexibility, and detail-focus narrative in complex environments. This paper introduces GFT (Gradient Focal Transformer), a new ViT-derived framework created for FGIC tasks. GFT integrates the Gradient Attention Learning Alignment (GALA) mechanism to dynamically prioritize class-discriminative features by analyzing attention gradient flow. Coupled with a Progressive Patch Selection (PPS) strategy, the model progressively filters out less informative regions, reducing computational overhead while enhancing sensitivity to fine details. GFT achieves SOTA accuracy on FGVC Aircraft, Food-101, and COCO datasets with 93M parameters, outperforming ViT-based advanced FGIC models in efficiency. By bridging global context and localized detail extraction, GFT sets a new benchmark in fine-grained recognition, offering interpretable solutions for real-world deployment scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Ontological Analysis of the Digital Crime Conceptual Model 18

Ontological Analysis of the Digital Crime Conceptual Model

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Vlasenko, Lidiia Lutska, Nataliia Zaiets, Nataliia Lysenko, Vitaliy State University of Trade and Economics dept. of Software Engineering and Cyber Security Kyiv Ukraine National University of Food Technologies dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine dept. of Automation and Robotic Systems Kyiv Ukraine

ISBN: (纸本)9798350360462

A conceptual ontological model of Digital Crime has been developed, consisting of five non-empty classes. Identification and classification were carried out according to the experience of domestic and foreign experts. The central place of the model is occupied by objects of the Digital Crime class, which have their own attributes and are connected by various links with other objects. Objects of some classes are filled in by users, others are formed by knowledge engineers. Based on the ontological analysis, SWRL-rules are constructed that automatically form links between objects of the custom classes Digital crime and Digital evidence with objects of the Type of crime and Criminal Liability classes. The last class contains articles of the Criminal Code of Ukraine on criminal liability for digital crimes. The ontological model is developed taking into account possible requests to it. Such queries can be formed on the basis of direct relationships between human and automatic concepts. And also contain logical chains to concepts that do not contain an explicit link in the ontology. It is assumed that the model can be expanded and supplemented. The ontological model of Digital Crime can be used to share information, develop new systems and applications, improve information retrieval and ensure its accordance. © 2023 IEEE.

关键词： Ontology

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Paths Planning for Agricultural Robots: Recent Development, Taxonomy, Challenges, and Opportunities for Future Research 20

Paths Planning for Agricultural Robots: Recent Development, ...

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20th IEEE Student Conference on Research and Development, SCOReD 2022

作者： Yahaya, Zimit Aminu Buyamin, Salinda Chiroma, Haruna Azimi Mahmud, Mohd Saiful Hassan, Fazilah H Badi, Abubaker Ahmed Universiti Teknologi Dept. of Control and Mechatronics Engineering Malaysia University of Hafr Albatin Dept. of Computer Science Technology Saudi Arabia Universiti Teknologi Malaysia Malaysia

ISBN: (数字)9781665474887

ISBN: (纸本)9781665474887

The world is currently experiencing population explosion, and it has been forecast that the population would reach nine billion by the year 2050. This rapid population growth is accompanied with new challenges, including high demand for food supply, which may lead to food crises. Contemporary agricultural methods and resources cannot meet the forecast food demand. Precision agriculture is one of the best ways to meet this demand, and using agricultural robots is an important aspect of it. Many researchers have been conducting studies on the application of robots in agricultural tasks such as farm clearance, seeding, seedling, spraying, fertilization, and harvesting. This paper presents a comprehensive review on path planning techniques for agricultural robots. it focuses on different types of agricultural robots and path plans in use. It also provides taxonomy of agricultural robot path plans, metadata analysis, and deep insights into the recent developments in the research area, while pointing out the challenges being faced and possible future solutions. This article may serve as a guide to early career researchers with interest in agricultural robots to easily understand the current developments and the future research focus in this field. © 2022 IEEE.

关键词： Agricultural robots

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Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

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Forecasting Residential EV Charging Behavior: A Transfer Learning-Enabled Approach to Address Data Scarcity in the Residential Sector

Forecasting Residential EV Charging Behavior: A Transfer Lea...

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Smart Grid Conference (SGC)

作者： Mohammad Rastegar Mehrdad Ebrahimi Ali Forootani Dept. of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Dept. of Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

Charging a significant number of electric vehicles (EVs) can pose challenges for power systems, and a viable solution is smart charging. Reliable smart charging can be achieved by forecasting the required energy and plug-in duration of a charging event. However, despite the availability of data from public charging stations, the lack of sufficient real data for residential EV charging has consistently been a barrier, particularly when using deep learning methods. Moreover, enhancing the accuracy of forecasting residential EV charging behavior is crucial due to its significant impact on power systems. To address these challenges, this paper proposes an intelligent deep learning-based framework to forecast the required energy and plug-in duration of residential EV charging events by leveraging transfer learning. Additionally, the proposed framework utilizes an auxiliary deep learning model that forecasts charging duration to improve the accuracy of required energy forecasting. Various case studies have been conducted, and the results show that the proposed framework significantly improves the accuracy of residential charging behavior forecasting.

关键词： Deep learning Accuracy Transfer learning Predictive models Power system reliability Reliability State of charge Forecasting Energy management Smart charging

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Play Everywhere: A Temporal Logic based Game Environment Independent Approach for Playing Soccer with Robots

arXiv

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arXiv 2024年

作者： Suriani, Vincenzo Musumeci, E. Nardi, D. Bloisi, D.D. Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Dept. of Mathematics Computer Science and Economics University of Basilicata Potenza Italy

Robots playing soccer often rely on hard-coded behaviors that struggle to generalize when the game environment change. In this paper, we propose a temporal logic based approach that allows robots’ behaviors and goals to adapt to the semantics of the environment. In particular, we present a hierarchical representation of soccer in which the robot selects the level of operation based on the perceived semantic characteristics of the environment, thus modifying dynamically the set of rules and goals to apply. The proposed approach enables the robot to operate in unstructured environments, just as it happens when humans go from soccer played on an official field to soccer played on a street. Three different use cases set in different scenarios are presented to demonstrate the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Temporal logic

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The Implementation of Fuzzy logic controller for the obstacle avoidance in 3 Mecanum-wheeled Robot 4

The Implementation of Fuzzy logic controller for the obstacl...

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4th Novel Intelligent and Leading Emerging Sciences Conference, NILES 2022

作者： Elrehem, Mahmoud Abd Hassan, Amgad Ojo, Damilare Hassan, Hossam Hosny, Mona Nile University Giza Egypt Menofia University EAS School Nile University Faculty of Electronic Engineering SESC Research Center Automatic Control Dept. Giza Egypt

ISBN: (数字)9781665452410

ISBN: (纸本)9781665452410

mobile robots play an enormous role in different fields of daily life applications including military, safety, and logistic multi-tasking capabilities. A new approach is introduced to the market which is the 3 Mecanum wheeled mobile robot (3-MWMR) is being tested and validated. The main goal of the proposed design is to achieve all the desired directions of motion and to improve the performance of the mobile robot in avoiding obstacles using the fuzzy logic controller. © 2022 IEEE.

关键词： Mobile robots

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Reference Tracking for Constrained Uncertain Linear Systems by Stochastic MPC

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IFAC-PapersOnLine 2023年第2期56卷 10421-10427页

作者： Jannik Hahn Olaf Stursberg Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel 34121 Kassel Germany

This paper addresses model predictive control of a class of linear systems subject to additive stochastic disturbances and constraints. The underlying stochastic optimal control problem combines inverse cumulative distribution functions with ellipsoid-in-polyhedron formulations to reduce the conservatism induced by constraint satisfaction. By use of terminal constraints and time-varying weights within the cost functional, the presented control scheme satisfies criteria for mean-square stability and can be adapted to reference-tracking problems for arbitrary reference signals.

关键词： Probabilistic Constraints Mean-Square Stability Predictive control Stochastic control Uncertain Systems

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A Receding Horizon Scheme for EV Charging Stations in Demand Response Programs

A Receding Horizon Scheme for EV Charging Stations in Demand...

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IEEE Conference on Decision and control

作者： Giovanni Gino Zanvettor Marta Fochesato Marco Casini Antonio Vicino Dept. of Information Engineering and Mathematics University of Siena Italy Automatic Control Laboratory ETH Zurich Switzerland

Demand response is expected to play a fundamental role in providing flexibility for balancing operations to the grid. On the other hand, the fast electrification of the transportation sector calls for new solutions to enforce safe and reliable grid operation. Here we consider an electric vehicle charging station that participates in demand response programs. The demand response program asks for a change of the charging station load profile in exchange for a monetary reward. A stochastic receding horizon scheme that exploits the charging flexibility is then designed to optimally coordinate vehicle charging. Numerical simulations show that the proposed approach ensures substantial cost reduction compared to simpler benchmarks while maintaining the computation time feasible for real-world applications.

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The Transient Predictor

The Transient Predictor

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IEEE Conference on Decision and control

作者： Keith Moffat Florian Dörfler Alessandro Chiuso Automatic Control Laboratory Swiss Federal Institute of Technology (ETH) Zürich Switzerland Dept. of Information Engineering University of Padova Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper introduces the Transient Predictor and describes how it can be used to estimate the Multistep Predictor, which can be applied to applications such as Data-Driven Predictive control (DDPC). The Transient Predictor has two desirable traits that differentiate it from other methods for estimating the Multistep Predictor, such as the standard Subspace Predictor method: 1) Causality-the Transient Predictor asserts a causal relationship between future inputs and future outputs; and 2) Bias-the Transient Predictor is a consistent predictor of future outputs. This paper provides an easy-toimplement algorithm for estimating the Transient Predictor and in turn the Multistep Predictor, and demonstrates its efficacy for DDPC. In experiments, we find that the Transient Predictorbased DDPC performs remarkably well with small lead-in data lengths, indicating that it is well-suited for tasks in which large amounts of data are not available. In addition, the Transient Predictor is not afflicted by the same bias as subspace-based methods when data is gathered in closed loop.

关键词： Lead Prediction algorithms Transient analysis Standards Predictive control

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