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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Tokudome, Yusuke Yamamoto, Yuya Watanabe, Yuta Uahchinkul, Chinvudh Muramatsu, Satoshi Inagaki, Katsuhiko Tokai University Course of Electrical and Electronic Engineering Kanagawa259-1292 Japan Tokai University Course of Science and Technology Kanagawa259-1292 Japan Tokai University Department of Applied Computer Engineering Kanagawa259-1292 Japan

ISBN: (纸本)9798331531614

Wheeled mobile mechanisms are essentially unsuitable for moving over rough terrain, but the rocker bogie mechanism is known as a six-wheeled mobile mechanism with high ground adaptability that solves this problem. However, this mechanism requires a large number of degrees of freedom (DOF), at least 10 DOF, or 12 DOF in the case of an omni-directional moving function. This contains problems in terms of control system configuration, mechanism development, and energy efficiency. In this study, the number of DOFs is reduced to a maximum of 6 DOFs and a minimum of 4 DOFs by applying omni-directional wheels to the rocker bogie mechanism. First, we examined how the two main types of omni-directional wheels should be arranged in relation to the six wheels. Based on the results, an actual machine was developed for evaluation, and its effectiveness was confirmed through step over experiment on several types of stairs. © 2025 IEEE.

关键词： Bogies (railroad rolling stock)

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Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G Networks in Stochastic α-η-µ Fading Channels with Multi-Layer Selection Combining and Cross-Layer Interference Adaptation

Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G N...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Keshav Kaushik Priyanka Gaur Barga Mohammed Mujahid Mudassir Khan Elfatih Elmubarak Mustafa Amrita Singh Sharda School of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science Applied College Khamish Mushaiyt King Khalid University Saudi Arabia Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University Abha Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and Information Technology University of Bisha Saudi Arabia Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The authors proposed to use a meta-learningbased approach for QoS optimization in future 6G networks that are self-optimizing sections, focusing on stochastic α − η − µ fading channels. The proposed model utilizes information learned in the past so that it can rapidly adapt to these changes as they arrive, providing much better area adaptation, energy efficiency and interference mitigation than traditional learning approaches. You push the best and QoS, network throughput, latency abased simulation results are provided under high mobility in addition to severe fading conditions. The metalearning model showed accelerated convergence, adaptive resource allocation and improved interference mitigation which naturally led to QoS improvement up to 80% as well energy efficiency increased up to 65−90%, network reliability were enhanced. This step helps build green and robust communication mechanisms in the next-generation networks.

关键词： Fading channels 6G mobile communication Metalearning Adaptation models Prevention and mitigation Stochastic processes Quality of service Interference Energy efficiency Numerical models

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Soliton solutions to generalized (3 + 1)-dimensional shallow water-like equation using the (ϕ'/ϕ,1/ϕ)-expansion method

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Arab Journal of Basic and applied sciences 2024年第1期31卷 121-131页

作者： Dey, Pinakee Sadek, Lamiaa H. Tharwat, M.M. Sarker, Shuvo Karim, Rezaul Akbar, M. Ali Elazab, Nasser S. Osman, M.S. Department of Mathematics Mawlana Bhashani Science and Technology University Tangail 1902 Bangladesh Department of Mathematics and Computer Science Faculty of Science Beni-Suef University Beni-Suef 62511 Egypt Department of Applied Mathematics Rajshahi University Rajshahi 6205 Bangladesh Department of Mathematics Faculty of Science Cairo University Giza 12613 Egypt Mathematics Department Faculty of Applied Sciences Umm Al-Qura University Makkah 21955 Saudi Arabia

The (3 + 1)-dimensional generalized shallow water equation is a significant mathematical framework for analyzing the dynamic behavior of waves in ocean physics. The purpose of this article is to investigate some more generic soliton solutions of the generalized shallow water-like model in three dimensions. The investigation is conducted utilizing the sophisticated mathematical methodology known as the double variables (Formula presented.) expansion technique. With this approach, we produce new propagating wave solutions for this model in the form of hyperbolic, trigonometric, and rational functions. In addition, we offer two- and three-dimensional graphical representations to help visualize the intricate physical phenomena of the system. We have constructed many soliton solutions, such as kink shape soliton solutions, anti-bell shape solutions, single periodic solutions, singular soliton solutions, and anti-kink shape solutions for different values of the free parameters involved in the obtained solutions. These graphical representations are predicated on certain parameter selections, which facilitate the understanding of the complicated general behavior for this model. Through the presentation of new findings in the field of soliton solutions for the aforementioned equation, this paper offers fresh perspectives and highlights hitherto overlooked aspects of this fascinating mathematical challenge. The paper illuminates new results on soliton solutions with different geometrical structures for the given equation, revealing hitherto overlooked facets of this intriguing mathematical challenge. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： analytical wave solutions The (3 + 1)-dimensional generalized shallow water equation the double variables (ϕ′/ϕ,1/ϕ) expansion method

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Enhancing Scalability in Reinforcement Learning for Open Spaces

Enhancing Scalability in Reinforcement Learning for Open Spa...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Jamshaid Iqbal Janjua Shagufta Kousar Areeba Khan Anaum Ihsan Tahir Abbas Ali Q Saeed Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science The TIMES Institute Multan Pakistan Computer Center Northern Technical University Ninevah Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Reinforcement Learning (RL) has been successful when the environment has specific objectives and boundaries. But with the emerging focus on open-world application which makes all or some of the rules or purpose go to naught, it makes traditional methods of RL a bit more difficult. This paper goes over various advancements and changes in Reinforcement Learning which can be employed for open-ended environments. Among the other strategies, hierarchical reinforcement learning, intrinsic motivation-based exploration, meta-learning and unsupervised skill acquisition are also among the ones that are examined. As a result, such a position based on the technology argues the promising future of open-ended methods for the management of complex problems and high level of uncertainty associated with the preset target or purpose. Also, we study cases in video games, robotics and autonomous systems, where RL is implemented in an open-ended and dynamic environment. We also outline existing limitations and perspectives, highlighting the need for more flexible methods and inter-scientific collaboration to fully realize RL's potential in open-ended contexts.

关键词： Metalearning Video games Uncertainty Autonomous systems Scalability Reinforcement learning Medical services Safety Robots Unsupervised learning

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Model Adaptation for Enhanced Liveness Face Detection: Experimental Findings on SiWMv2 Dataset 8

Model Adaptation for Enhanced Liveness Face Detection: Exper...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Khairnar, Smita Gite, Shilpa Thepade, Sudeep D. Mahajan, Kashish University Symbiosis Institute of Technology Computer Science and Information Technology Department Pune412115 India Pimpri Chinchwad College of Engineering Pune411044 India Pune Symbiosis Centre for Applied Ai Symbiosis Institute of Technology Aiml Department Pune412115 India PCET's Pimpri Chinchwad University Pimpri Chinchwad College of Engineering Pune411044 India Pimpri Chinchwad College of Engineering and Research Computer Science Department Pune412101 India

ISBN: (纸本)9798350391770

The field of liveness face detection, an essential component of security and biometrics, has seen notable advancements through developments in computer vision and deep learning. It's crucial for accurately distinguishing between genuine and spoofed data samples. This study aims to elevate the performance of automated liveness face detection by focusing on model adaptation, utilizing the SiWMv2 dataset. This dataset is particularly valuable as it encompasses 14 kinds of spoofing attacks, that include print, replay attacks, obfuscation makeup, paper glasses, and various others, which significantly enhances the robustness of the detection systems against a wide array of spoofing techniques. By fine-tuning and adapting seven pre-trained architectures - VGG16, DenseNet201, InceptionV3, VGG19, ResNet50, MobileNetV2, and Xception - paper aims to push all boundaries of current automated liveness face detection capabilities. Through systematic experimentation and analysis, this study aims to contribute to the improvement and effectiveness of liveness face detection systems across various applications, ensuring they are more resilient and capable of handling diverse spoofing scenarios. © 2024 IEEE.

关键词： Data obfuscation

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Checking for non-Euclidean latent geometry of biological networks

Checking for non-Euclidean latent geometry of biological net...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lecca, Paola Re, Angela Free University of Bozen-Bolzano Faculty of Computer Science Bolzano Italy Politecnico di Torino Department of Applied Science and Technology Turin Italy

ISBN: (纸本)9781665468190

We present non-iterative methods for graph embedding in flat and curved manifold and acriterion to assess the quality of the embedding for the purpose of determining what is the optimal latent geometry of a graph. To date, graph embedding procedures have been used in data dimension reduction problems for the purpose of simpler and more efficient data analysis, but little has been done to use them as a tool for the estimation of the latent geometry. The latent geometry of a graph is important since it explains graph organizational structure and dynamics. Advancing the state of the art in this direction is of particular importance for the study of biological networks, since these networks exhibit a high degree of organizational complexity that is difficult to identify with standard analyses of node centrality measures. © 2022 IEEE.

关键词： Geometry

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Comparative Analysis of Detector-Tracker Architecture for Object Tracking Based on SBC for UAV

Comparative Analysis of Detector-Tracker Architecture for Ob...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Pylyp Prystavka Andrii Shevchenko Irina Rokitianska Department of Applied Mathematics Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

This paper investigates the tracking technology of moving objects from a UAV camera (or streaming video) for systems with limited computational resources, such as modern SBCs. A detector-tracker architecture is proposed, combining the advantages of object detection methods and tracking algorithms. A comparative analysis of the performance and accuracy of various detector and tracker combinations on modern SBCs is conducted. The results demonstrate the high efficiency of the proposed approach for object-tracking tasks under limited computational resources, which has practical significance for UAV-based systems.

关键词： Accuracy computer architecture Object detection Detectors Streaming media Autonomous aerial vehicles Cameras Computational efficiency Object tracking

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Proportional Feature Pyramid Network for Lane Detection 4

Proportional Feature Pyramid Network for Lane Detection

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4th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Hui, Jiapeng Lian, Guoyun Wu, Jiansheng Ge, Shuting Yang, Jinfeng Shenzhen Polytechnic University Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China University of Science and Technology School of Computer Science and Software Engineering Anshan China

ISBN: (纸本)9798350373233

Lane detection under extreme conditions presents a highly challenging task that requires capturing each crucial pixel to predict the complex topology of lane lines and differentiate the various lane types. Existing methods predominantly rely on deep feature extraction networks with substantial parameters or the fusion of multiple prediction modules, resulting in large model sizes, embedding difficulties, and slow detection speeds. This paper proposes a Proportional Feature Pyramid Network(P-FPN) through fusing the weights into the FPN for lane detection. In our method, the high-level features are explored to predict lanes coarsely while local- detailed features are leveraged to improve localization accuracy. Extensive experiments on the widely used lane detection dataset, CULane dataset, demonstrate that the proposed method achieves the competitive results compared with several state-of-the-art approaches. © 2023 IEEE.

关键词： Feature extraction

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Employee Fingerprint Identification Management System Based on Bidirectional ResNext Network with Triplet Loss

Employee Fingerprint Identification Management System Based ...

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Management Engineering, Software Engineering and Service sciences (ICMSS), International Conference on

作者： Yuezhi Yang Qian Yang Zhihao Su Wenlong Wu Computer Science College Guangzhou College of Applied Science and Technology Guangzhou China La Consolacion University Philippines

ISBN: (数字)9798350393705

ISBN: (纸本)9798350393712

With the increasing digitization and modernization of enterprise management, employee fingerprint identification management systems have become a key tool to ensure the safety and efficiency of the office space. Such a system not only accurately verifies the identity of employees but can also be used for attendance, access control and other related applications. However, improving the accuracy of fingerprint recognition remains a challenge. In this paper, we propose a fingerprint identification method based on a bidirectional ResNext network and combined with triplet loss. Compared with the traditional ResNext network, our method captures richer fingerprint features through bidirectional structure and triplet loss, thereby achieving higher matching accuracy. We conducted extensive experiments on the public FVC (Fingerprint Verification Competition) dataset and our own collected dataset CSFI (Company staff fingerprint identification). Experimental results show that compared with the original ResNext network, our method achieves approximately 3% and 5% performance improvements on the two data sets respectively. These results verify the effectiveness of our proposed method on fingerprint identification tasks and provide strong technical support for the further development of employee fingerprint identification management systems.

关键词： Deep learning Access control Software algorithms Companies Fingerprint recognition Aerospace electronics Safety

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A Hybrid Model for Botnet Detection using Machine Learning 2

A Hybrid Model for Botnet Detection using Machine Learning

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Zaheer, Anam Tahir, Sidra Almufareh, Maram Fahaad Hamid, Bushra University of Arid Agriculture University Institute of Information Technology Rawalpindi Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350335644

Botnet attacks are becoming a growing threat to the security of computer networks, and there is a need for effective and efficient methods for detecting these attacks. In this study, we proposed a hybrid machine-learning model for detecting botnet attacks in network traffic. The approach combines three machine learning algorithms: k-means, rule- based system, and decision tree. The experiment was conducted using the CTU-13 dataset and features extracted from the Barnacles Mating Optimizer for network traffic flow. The experiment results showed that the proposed hybrid machine learning approach can achieve high accuracy levels in detecting botnet attacks, with a computed accuracy of 99%. The combination of different machine learning algorithms can improve the accuracy of the system, making it more robust to different types of botnet attacks. The use of real-world data, such as the CTU-13 dataset, and incorporating features from the Barnacles Mating Optimizer, make the results of this experiment more representative of real-world botnet attack detection computed accuracy 99.32 for kmean, 99.11% for decision tree and 97.14% for rule-based system. Precision for kmean 98.93%, decision tree 98.37% and rule-based 95.93% computed. © 2023 IEEE.

关键词： Decision trees

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