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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Markus F. Wieghaus Warda Saeed Muhammad Yasir Department of Computing Science Division Microrobotics and Control Engineering University of Oldenburg Oldenburg Germany

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

Scanning probe microscopy techniques changed the understanding of micro and nanometer scaled objects. Since the invention of the scanning tunneling microscope and the atomic force microscope, these techniques were further developed and combined with different measurement methodologies such as scanning microwave microscopy. By introducing an electromagnetic signal in the microwave frequency range via the scanning probe microscope tip to the sample, electromagnetic characteristics of the specimen can be acquired. Using a series of frequencies in one scan, even more information like dopant concentration or different layers of material can be captured. The drawback of this method is the long data acquisition time, which cannot be neglected on nanometer scale. To address this issue, we are using reduced raster scan techniques with broadband scanning microwave microscopy and calculate the missing data information by using image inpainting algorithms afterwards. In this work we will present the development of a deep learning algorithm and compare the output of it with different inpainting algorithms, which can even be used in other scanning probe microscope setups.

关键词： Atomic measurements Atomic force microscopy Scanning probe microscopy Neural networks Training data Tunneling Broadband communication

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Improving graph collaborative filtering with network motifs

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Neural computing and Applications 2025年 1-20页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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PID-fuzzy switching-based strategy to heading control for remote operated vehicle

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Neural computing and Applications 2024年 1-17页

作者： Xie, Baolong He, Shuping Cao, Xiang Wang, Honghai Stojanovic, Vladimir Shi, Kaibo School of Electrical Engineering and Automation Anhui University Hefei230039 China School of Artificial Intelligence Anhui University Hefei230039 China School of Computing and Artificial Intelligence Chaohu University Chaohu238024 China Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil Engineering University of Kragujevac Kraljevo36000 Serbia School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China

Accurate heading control is the premise for underwater vehicles to complete underwater operations. In order to improve the performance of heading control, this paper proposes a heading control strategy based on PID-fuzzy switching. First, the force analysis of the steering motion was carried out to derive the dynamics model of the steering motion. Then, the whole heading control process is divided into two phases, steering phase and holding phase. Fuzzy control is used in the steering phase, and PID control is used in the holding phase. By setting the automatic switching switch, the control system can switch freely between the two to meet the needs of system adjustment under different errors. Finally, the performance of the established heading control system is verified through motion control simulations and pool experiments, and the proposed control strategy is compared with other methods. The results show that the control strategy can effectively achieve ROV heading control with good steady-state accuracy, and the overall performance has been greatly improved. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fuzzy control

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Weakly supervised target detection based on spatial attention

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Visual Intelligence 2024年第1期2卷 1-11页

作者： Zhao, Wenqing Xu, Lijiao School of Control and Computer Engineering North China Electric Power University Hebei Baoding 071000 China Department of Hebei Key Laboratory of Knowledge Computing for Energy & Power North China Electric Power University Hebei Baoding 071000 China

Due to the lack of annotations in target bounding boxes, most methods for weakly supervised target detection transform the problem of object detection into a classification problem of candidate regions, making it easy for weakly supervised target detectors to locate significant and highly discriminative local areas of objects. We propose a weak monitoring method that combines attention and erasure mechanisms. The supervised target detection method uses attention maps to search for areas with higher discrimination within candidate regions, and then uses an erasure mechanism to erase the region, forcing the model to enhance its learning of features in areas with weaker discrimination. To improve the positioning ability of the detector, we cascade the weakly supervised target detection network and the fully supervised target detection network, and jointly train the weakly supervised target detection network and the fully supervised target detection network through multi-task learning. Based on the validation trials, the category mean average precision (mAP) and the correct localization (CorLoc) on the two datasets, i.e., VOC2007 and VOC2012, are 55.2% and 53.8%, respectively. In regard to the mAP and CorLoc, this approach significantly outperforms previous approaches, which creates opportunities for additional investigations into weakly supervised target identification algorithms. © The Author(s) 2024.

关键词： Erasure concept Multi instance learning Object detection Spatial attention Weakly supervised learning

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs

User Input Search - Custom Motion Estimation Algorithm Optim...

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Proceedings of the International Convention MIPRO

作者： J. Benjak D. Hofman Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data – the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested.

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One solution of a Distributed System intended for communication between ADAS and IVI Systems

One solution of a Distributed System intended for communicat...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Cabrilo, Vuk Kenjic, Dusan Zlatkovic, Radomir Antic, Marija University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia RT-RK Institute of Computer Based Systems Novi Sad Serbia

ISBN: (纸本)9798350347722

Current trends of digitalization and automation in the automotive industry have increased the complexity of computer clusters in different domains of vehicular ecosystems, which are required to support new features. This has led to redundancy in resource usage in specific use cases, resulting in increased production costs and manufacturer suggested retail price (MSRP) markups for the finalized products. The average retail price of a new vehicle in the United States increased by 13.4% between 2015 and 2020. Considering the global chip shortage of 2020 and current global inflation, the average price of a new vehicle in the United States reached 49,500 in 2023 [1]. With the electrification of current automotive platforms underway, as well as the move to centralized system architectures, new approaches to inter-domain communication are expected. This paper proposes a solution of a distributed AUTOSAR Adaptive system that can establish communication with the In-Vehicle Infotainment (IVI) domain. The proof of concept implemented in a controlled environment proves viable for inter-domain communication, as it provides good scalability and adaptability to different scenarios. The use of a distributed approach facilitates the applicability of distributed system principles, allowing for the implementation of a stable system. © 2023 IEEE.

关键词： Vehicles

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Improving finite-difference time-domain memory bandwidth by using block floating-point arithmetic 31

Improving finite-difference time-domain memory bandwidth by ...

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31st Telecommunications Forum, TELFOR 2023

作者： Pijetlovic, Stefan Kastelan, Ivan Subotic, Milos Pjevalica, Nebojsa RT-RK Institute for Computer Based Systems Novi Sad Serbia University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798350303131

Finite-difference time-domain is a numerical method used for modelling of computational electrodynamics. The method is resource intensive, especially regarding memory usage since multiple memory accesses are required per single computation. In other words, memory, or more precisely its speed, is a limiting factor for the overall performance. Existing solutions focus either on structures with great parallelism such as graphical processing units or custom designs. The former have issues with underutilized resources, while the latter require significant time and effort to achieve similar or slightly better performances. The approach taken during this research was to target the memory itself and find a way to improve the efficiency of memory usage across multiple platforms. The main contribution of the paper is using block floating-point in a novel way regarding the finite-difference time-domain method. The presented solution is a verification of earlier research, proving that the performance of this algorithm can indeed be improved using the suggested methods. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Design of adaptive controllers by means of PPO algorithm using MATLAB

Design of adaptive controllers by means of PPO algorithm usi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Veljko Radojičić Milan R. Rapaić Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper investigates reinforcement learning (RL) as a practical framework for achieving optimal adaptive control across several simple dynamical system models. All experiments were conducted using the Proximal Policy Optimization (PPO) algorithm, implemented within the MATLAB Reinforcement Learning Toolbox. The primary focus of this study is to explore how the learning process can be empirically designed to sufficiently excite the system dynamics and obtain a sufficiently robust controller.

关键词： Adaptation models System dynamics Heuristic algorithms Process control Reinforcement learning MATLAB Dynamical systems Adaptive control Optimization

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Modification of the PSO Algorithm: Variable Population Size and Hybridization with the Nelder-Mead Method 24

Modification of the PSO Algorithm: Variable Population Size ...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Topalov, Stefan Radović, Mirna University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

This paper presents a modified Particle Swarm Optimization (PSO) algorithm designed to enhance computational efficiency without compromising solution quality. Two approaches are proposed: the first emphasizes the advantages of incorporating a variable population size to optimize both computational efficiency and solution quality. The second integrates this adaptive population strategy with the Nelder-Mead method to refine local search capabilities. By combining these techniques, the hybrid algorithm achieves a balance between global exploration and local exploitation, while also improving the algorithm's speed without sacrificing solution quality. The performance of both approaches is evaluated through numerical experiments, demonstrating their ability to deliver high-quality solutions with reduced computational resources. © 2025 IEEE.

关键词： Local search (optimization)

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Modification of the PSO Algorithm: Variable Population Size and Hybridization with the Nelder-Mead Method

Modification of the PSO Algorithm: Variable Population Size ...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Stefan Topalov Mirna Radović Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper presents a modified Particle Swarm Optimization (PSO) algorithm designed to enhance computational efficiency without compromising solution quality. Two approaches are proposed: the first emphasizes the advantages of incorporating a variable population size to optimize both computational efficiency and solution quality. The second integrates this adaptive population strategy with the Nelder-Mead method to refine local search capabilities. By combining these techniques, the hybrid algorithm achieves a balance between global exploration and local exploitation, while also improving the algorithm’s speed without sacrificing solution quality. The performance of both approaches is evaluated through numerical experiments, demonstrating their ability to deliver high-quality solutions with reduced computational resources.

关键词： Heuristic algorithms Stochastic processes Switches Linear programming Robustness Computational efficiency Optimization Standards Tuning Convergence

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