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10th IEEE Smart World Congress, SWC 2024

作者： Guo, Yixin Chen, Zhenxue Rong, Xuewen Liu, Chengyun Li, Yidi Shandong University School of Control Science and Engineering Jinan China Taiyuan University of Technology Computer Science and Technology Taiyuan China

ISBN: (纸本)9798331520861

Traditional semantic segmentation approaches primarily utilize RGB images, which struggle in complex scenes. To address this challenge, the multi-modal solution that fuses RGB and thermal (RGB-T) images can exploit the advantages of these modalities to improve accuracy and stability of segmentation. However, how to effectively fuse multi-modal and multi-level features remains a significant challenge. To this end, a Redundancy-Reduced and Frequency-Aware Feature Fusion Network (RFNet) is proposed. The RFNet employs a dual-stream encoder-decoder architecture to separately handle the extracted features from two modalities. This network reduces redundancy in shallow feature fusion and integrates frequency-aware attention mechanisms in deep feature fusion, efficiently capturing complementary information from multi-modal and multi-level features. Experiments on the MFNet dataset indicate that RFNet outperforms the majority of current networks in terms of segmentation performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Semi-mind controlled robots based on Reinforcement Learning for Indoor Application 8

Semi-mind controlled robots based on Reinforcement Learning ...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Naidji, Ilyes Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This paper presents a solution to this challenge by introducing interactive feedback derived from brain signals to train robots using deep reinforcement learning, particularly in the context of indoor maze navigation. Our objective is to enhance the learning process in a human-robot interaction scenario by incorporating human emotion or attention feedback. To accomplish this, we empowered the robot to learn new tasks through a dynamic policy network based on human feedback, and we augmented this input with other sensor data, including LIDAR. Various experiments are conducted to compare the efficacy of manual feedback, brain signal feedback, and no brain signal feedback, employing diverse Reinforcement Learning models. Additionally, we explore different models for emotion classification, employing Graph Neural Network models and traditional deep learning models, and subsequently compare the outcomes. © 2023 Copyright for this paper by its authors.

关键词： Feedback

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Biomass Fuel Classification from Agricultural Residues Using Machine Learning 12

Biomass Fuel Classification from Agricultural Residues Using...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Pachauri, Nikhil Gupta, Himanshu Verma, Om Prakash Kumar Singh, Sushil Verma, Deepak Rn, Ravikumar Manipal Academy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Karnataka Manipal India Dr B R Ambedkar National Institute of Technology Jalandhar Department of Instrumentation and Control Engineering Jalandhar India Marwadi University Department of Computer Engineering of Computer Engineering Gujarat Rajkot India Marwadi University Dept. of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350340204

This work incorporates an adaptive learning-based boosting (ADboost) ML classifier to classify four types of fuel: agricultural residue, coals, wood, and produced biomass. Further, the ADboost's hyperparameters, such as learning rate, maximum number of splits, and minimum leaf size are adjusted using teaching learning-based optimization (TLBO), resulting in TADboost. The performance of TADboost is compared against various popular ML (NN, BAG, NB, and SVM) models. Simulated result reveals that the suggested classifiers outperform other compared ML classifiers for fuel classification with the classification accuracy, precision, recall, F1-score, and kappa as 0.9659, 0.9671, 0.9449, 0.9558, and 0.9482, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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Communication Countermeasure Strategy Decision-Making Based on Incomplete Information Game 27

Communication Countermeasure Strategy Decision-Making Based ...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Liu, Jinyue Gao, Xiang Yang, Haowei Zhang, Jihao Liu, Yuhan Gong, Peng National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Group Beijing China

ISBN: (纸本)9791188428137

In modern battlefields, the stability of wireless communications is crucial for intelligence transmission, command coordination, and maintaining strategic advantage. However, with the rapid advancement of communication technologies, the electromagnetic environment has become increasingly complex, making intelligent decision-making in communication countermeasures a prominent research focus. However, most existing studies adopt deep learning and reinforcement learning methods, which require extensive data samples and computational resources. To address these limitations, this paper proposes a game-theoretic model for intelligent radio communication countermeasures under incomplete information. It quantitatively evaluates common attack and defense tactcis used in communication countermeasures and explores the mixed-strategy equilibrium of the game when both the attackers and defenders consist of multiple types. Furthermore, simulation-based comparative analyses are conducted to assess the factors influencing the utility of both the attacker and defender. The findings offer theoretical insights and decision-making support for the deployment of communication countermeasure strategies in practical military operations, contributing to significant strategic value. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep reinforcement learning

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Crowdfunding Readiness Level Assessment for Innovative Projects 18th

Crowdfunding Readiness Level Assessment for Innovative Proje...

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18th International Conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Cseminschi, Stanislav Ionica, Andreea Cristina Leba, Monica Engineering and Management Doctoral School University of Petrosani Petrosani Romania Management and Industrial Engineering Department University of Petrosani Petrosani Romania Computer and System Control Engineering Department University of Petrosani Petrosani Romania

ISBN: (纸本)9783031813771

The study examines the multifaceted determinants influencing a project's community utility, including technological refinement, team dynamics, market feasibility, and funding sources. Crowdfunding, a prominent path for finan- cial support in the digital landscape, provides non-repayable backing. However, ensuring its effective utilization is crucial for upholding stakeholders' confidence. We propose the development of metrics to evaluate the readiness of innovative projects for crowdfunding initiatives. These metrics empower backers to facilitate the advancement or scalability of manufacturing processes prior to market entry. Based on the findings, we explore the best approaches to advance creative projects in higher education, particularly those that are started by student groups. Through meticulous analysis, we pinpointed areas of vulnerability within each project and proposed strategic directions for future enhancements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Surveying

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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A Web Scraping Algorithm to Improve the Computation of the Maximum Common Subgraph

A Web Scraping Algorithm to Improve the Computation of the M...

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18th International Conference on Software Technologies, ICSOFT 2023

作者： Calabrese, Andrea Cardone, Lorenzo Licata, Salvatore Porro, Marco Quer, Stefano DAUIN Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (纸本)9789897586651

The Maximum Common Subgraph, a generalization of subgraph isomorphism, is a well-known problem in the computer science area. Albeit being NP-complete, finding Maximum Common Subgraphs has countless practical applications, and researchers are continuously exploring scalable heuristic approaches. One of the state-of-the-art algorithms to solve this problem is a recursive branch-and-bound procedure called McSplit. The algorithm exploits an intelligent invariant to pair vertices with the same label and adopts an effective bound prediction to prune the search space. However, McSplit original version uses a simple heuristic to pair vertices and to build larger subgraphs. As a consequence, a few researchers have already focused on improving the sorting heuristics to converge faster. This paper concentrate on these aspects and presents a collection of heuristics to improve McSplit and its state-of-the-art variants. We present a sorting strategy based on the famous PageRank algorithm, and then we mix it with other approaches. We compare all the heuristics with the original McSplit procedure, and against each other. In particular, we distinguish the heuristics based on the node degree and novel ones based on the PageRank algorithm. Our experimental section shows that PageRank can improve both McSplit and its variants significantly regarding convergence speed and solution size. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Graph algorithms

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Attention Based Graph Neural Networks 3

Attention Based Graph Neural Networks

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Khalil, Ibrahim Abbas, Muhammad Affan Nanjing University of Science and Technology Computer Science and Engineering Nanjing China Tianjin University Control Science and Engineering Tianjin China

ISBN: (纸本)9798350337952

The performance of graph neural networks (GNNs) in a variety of graph-related tasks, such as node categorization, has been remarkably good. Existing GNN models, especially when working with big and sparse graphs, are constrained in how well they can capture complicated graph topologies. In order to overcome this issue, we incorporate an attention mechanism into the GNN design in this study. During the message-passing phase, our proposed model, Attention-Based Graph Neural Networks (AB-GNN), uses a learned attention mechanism to differentially weight the significance of surrounding nodes. Using numerous benchmark datasets for node classification, we test the performance of the AB-GNN and demonstrate that it outperforms current state-of-the-art GNN models. Our tests specifically show that AB-GNN improves accuracy by up to 1% in comparison to the top baseline model. According to our findings, the attention mechanism enhances the model's capacity to detect critical aspects in the graph, resulting in more precise node classification on Cora and CiteSeer datasets in our case. Comprehensively, our work demonstrates the potential of attention mechanisms to enhance the functionality of GNN models and offers directions for further study in this field. © 2023 IEEE.

关键词： Graph neural networks

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Embedded Multi-Port Converters for Synthesis and Enhancement of Hybrid-Clamped Multilevel Converters

Embedded Multi-Port Converters for Synthesis and Enhancement...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Li, Yuzhuo Gunawardena, Pasan Tian, Hao Li, Yunwei University of Alberta Department of Electrical and Computer Engineering Edmonton Canada Shandong University School of Control Science and Engineering Jinan China

ISBN: (纸本)9798350316445

Leveraging a number of inner capacitors/inductors, hybrid-clamped multilevel converters (MLCs) normally face great challenges among good performance (proper charge/discharge of these devices), high efficiency (maintaining low losses) and high power density (compact profile). On the other hand, these multiple-device energy-processing requirements have been addressed well in some promising multi-port converters (MPCs), and, therefore, inspire us to implement well-developed compact MPCs to facilitate the voltage/current level generation process in hybrid-clamped MLCs. Though recently, some researchers started to integrate active cells into hybrid-clamped MLCs and improve capacitor voltage control and generate extra output levels, the systematic synthesis method is still unclear and rarely discussed in the literature. To address this gap, we propose a systematic synthesis method for those hybrid clamped MLCs that can benefit from embedding well-developed MPCs. The approach can be applied for both voltage-source and current-source hybrid-clamped MLCs, covering emerging MLCs. In particular, we also derived and verified a new mixed hybrid MLC family through an emerging current-fed dual-input isolated multi-port converter. This topology features both current-source and voltage-source benefits and is ideal for future renewable generation integration. © 2023 IEEE.

关键词： Converters Synthesis Hybrid-Clamped Multi-level Converters Multi-port Converters

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Object Tracking Algorithm Using Siamese Attention Mechanism based Key Point Network 23

Object Tracking Algorithm Using Siamese Attention Mechanism ...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Zhang, Feng Liu, Guifu Yuan, Shuai Kong, Xiangjie Geng, Jinyu School of Electrical and Control Engineering Shenyang Jianzhu University China School of Computer Science and Engineering Shenyang Jianzhu University China

ISBN: (纸本)9798400708237

The tracker based on Siamese network usually adopts the cross-correlation of convolutional features between the object branch and the search branch to describe the visual object tracking task as a similarity matching. However, most trackers apply backbone networks to represent multi-scale features in a hierarchical manner, which has limitations in distinguishing similar object. In addition, the feature information on each channel and spatial region is treated equally in the calculation process. In fact, the feature importance of these locations is not the same, and the anchor box detection mechanism requires setting a large number of anchor boxes with redundant hyperparameters, which can lead to computational and memory overload. As for these problems, this paper proposes an object tracking algorithm using siamese attention mechanism based key point network, called SAMR (Siamese Attention Mechanism Reppoints). First of all, the backbone network res2net is used to represent fine-grained multi-scale features on a hierarchical basis for increasing the receptive field of each layer. Then the channel and spatial attention information of the attention mechanism is to enhance the discriminant ability and positioning ability of the feature map. Finally, a set of key points anchor-free frames is adopted to detect and track the object. Experiments on challenging datasets such as VOT2018, VOT2019, and OTB100 illustrate that the SAMR proposed in this paper is superior to other advanced trackers in tracking accuracy. © 2023 ACM.

关键词： Object detection

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