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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. this algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. this paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © the Author(s) 2025.

关键词： Federated learning

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A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude control 25th

A Meta-reinforcement Learning Framework for Adaptive Quadro...

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25th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： Zhao, Kaidong Li, Yanjie Liu, Zihan Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China School of Mechanical Engineering and Automation The Harbin Institute of Technology Shenzhen Shenzhen518055 China

ISBN: (纸本)9789819642069

this paper presents a meta-reinforcement learning approach for quadrotor UAV attitude control, addressing the limitations of traditional control methods in complex, dynamic, and uncertain environments. Our method integrates the advantages of context learning, Gaussian sampling, and multi-task training to rapidly adapt to diverse aircraft parameters and environmental conditions. Specifically, we utilize historical information quadruples as contextual data to accurately estimate current task characteristics through real-time observation and learning. In the latent space, we employ Gaussian sampling to effectively handle unknown UAV parameters. To validate the method’s efficacy, we conducted a series of experiments in the Bullet simulator, including single attitude tracking, resultant acceleration control, and continuous trajectory tracking. Experimental results demonstrate that our approach significantly outperforms traditional PID controllers and domain randomization-based SAC methods in terms of stability, rapid response, and trajectory tracking precision. through multi-task training, our controller has developed the ability to swiftly adapt to novel situations. this research provides a robust and flexible solution for adaptive control of quadrotor UAVs, showing promise for more efficient and stable flight control in complex and dynamically changing environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Advancing Object Discovery: Unveiling the Power of YOLO in Computer Vision Applications 6th

Advancing Object Discovery: Unveiling the Power of YOLO in C...

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6th international conference on Intelligent Communication, control and Devices, ICICCD 2024

作者： Vijayalakshmi, A. Ebenezer Abishek, B. Arul Stephen, C. Mohith, K. Sri Niranjan Raja, C.A. Department of Electronics and Communication Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Electronics and Communication Engineering KCG College of Technology Chennai India

ISBN: (纸本)9789819783281

the practical implementations and technological advances of the YOLO method in computer vision for object detection are examined in this article. Santosh Divvala and Joseph Redmon’s YOLO artificial intelligence system can accurately and quickly detect objects in videos and images, eliminating the need for network iterations. Instead of using traditional methods, YOLO directly generates bounding boxes and class probabilities for each object, optimising the procedure. After reading this, the process is clearer. this study examines YOLO’s effectiveness in various situations. this technology has many applications in industrial automation, AR/VR, autonomous vehicles, healthcare, and retail. this article examines YOLO model training and data preparation. COCO datasets are used in this study. the architectural components of YOLO versions 7 and 8 are also examined. the findings show that YOLO v8 is highly accurate in identifying a wide range of objects in real time. this study shows YOLO’s revolutionary potential as object detection software for BV applications, highlighting its outstanding features. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Enhancing the Efficiency of Lead-Less Cs2TiBr6 Solar Cells through Dual Hole Transport Layer Integration 4

Enhancing the Efficiency of Lead-Less Cs2TiBr6 Solar Cells t...

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4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： Agarwala, Raja Sabbir, Safra Binte Tanim, Mehedi Hasan Kumar, Prince Department of Electrical and Electronic Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Electrical Engineering National Institute of Technology Silchar India

ISBN: (纸本)9798331530402

the potential of lead-free perovskite solar cells (PSCs) has garnered prominent interest, particularly due to concerns surrounding the toxicity and instability of lead halide compounds. Enhancing the performance of lead-free, ecologically friendly PSCs utilizing the perovskite absorber layer (PAL) Cs2TiBr6, is the goal of this endeavor. this simulation investigation reveals comprehensive findings from particular modeling techniques for PSCs. the proposed study incorporates an innovative dual-hole transport layer (HTL) to enhance the power conversion efficiency (PCE) of the device. the PSC achieves the highest PCE by combining two HTLs, notably Copper Antimony Sulfide (CuSbS2) and Copper Barium thiostannate (CBTS). the proposed device structure, FTO/TiO2/Cs2TiBr6/CuSbS2/CBTS/Ni, is analyzed using SCAPS-1D simulations to compare and optimize key parameters, including energy band alignment, current-voltage (J-V), and quantum efficiency (QE) properties, absorber and transport layer thickness, parasitic resistances, interface defects, and temperature. Our findings reveal that the dual HTL configuration performs better than previously cited single HTL-based structures. the proposed Cs2TiBr6-based PSC structure improves the device's photovoltaic (PV) parameters, achieving an enhanced open-circuit voltage (Voc) of 1.34 V, a short-circuit current density (Jsc) of 21.28 mA/cm2, and a fill factor (FF) of 86.24%, culminating in PCE of 24.64%. this research demonstrates the efficacy of employing dual HTLs in enhancing the performance and stability of PSCs, providing promising insights for the future advancement of high-efficiency PV devices. © 2025 IEEE.

关键词： Bromine compounds

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Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp Pose Selection Skill 6

Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp ...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lee, Sukhan Jang, Byungwoo Hyeon, Seokjong Lee, Soojin Lee, Jaesun Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Artificial Intelligence Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of AI System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Regional Industry Innovation Korea Institute of Industrial Technology Ulsan44413 Korea Republic of

the edge-brain framework (EBF) is a S/W framework that allows manufacturers to easily integrate AI-empowered robot work skills into a legacy production system in an edge computing environment with minimal time and effort. For EBF-based smart manufacturing, is a skill-based task-level robot programming environment that plays a key role. In this paper, we present the grasp pose selection skill as a case study to demonstrate the proposed skill-based edge-brain smart manufacturing. the proposed grasp-pose selection skill determines the optimal 6D grasp pose associated with the target object in a cluttered workspace by taking into consideration grasp stability, object manipulability, and collision-free path efficacy. To achieve a real-time operation of optimal grasp pose selection skill, deep learning networks are designed to compute, in particular, the manipulability and collision-free path efficacy indices. Experiments demonstrate the effectiveness of the proposed real-time grasp pose selection skill developed for EBF-based smart manufacturing. © 2024 the Authors. Published by Elsevier B.V.

关键词： Smart manufacturing

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Leveraging Blockchain Technology for Secure and Efficient Smart City Applications 6th

Leveraging Blockchain Technology for Secure and Efficient Sm...

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6th international conference on Intelligent Communication, control and Devices, ICICCD 2024

作者： Singh, Tanu Panwar, Arvind Sharma, Neha Sugandh, Urvashi Singh, Manu School of Computer Science UPES Uttarakhand Dehradun India School of Computing Science and Engineering Galgotias University Greater Noida India Bharati Vidyapeeth’s College of Engineering New Delhi India

ISBN: (纸本)9789819783281

the revolution of growing urban population and complicity of the city’s vast amounts of operations has resulted in the establishment of a smart city—one that utilizes the current state-of-the-art technology to enhance the quality of life of the consumers and streamline the use of its resources. However, the smart city faces significant security, privacy, and trust issues due to numerous interconnected systems handling vast amounts of sensitive data. Blockchains developed as a decentralized, transparent, and immutable technology, can solve these issues and revolutionize the vast number of applications of contemporary smart cities. this paper outlines how blockchains can form a basis for the secure and effective deployment of multiple smart city solutions, covering a diverse set of applications, including data management, energy trading, transportation, waste handling, and digital identity. It explains the advantages that blockchains offer, which include increased security, transparency, efficiency, and decentralized decision-making. At the same time, the paper highlights the existing challenges and considerations with the adoption of blockchain for smart cities, such as lack of scalability and regulation, insufficient interoperability, high energy consumption, and low public awareness. Finally, it provides recommendations for their successful deployment and stresses the need for future research and cooperation to enable the full potential of blockchains for safe, effective, and user-driven smart cities. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Data management Decentralization Efficiency Security Smart cities

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Exploring the Influence of Kesterites as Hole Transport Layers in Cs2TiBr6-Based Eco-Friendly Solar Cells 4

Exploring the Influence of Kesterites as Hole Transport Laye...

引用

4th international conference on Power, control and computing Technologies, ICPC2T 2025

ISBN: (纸本)9798331530402

Lead-free, eco-friendly perovskites have gained significant attention for their non-toxicity, addressing environmental concerns while enhancing the sustainability of photovoltaic (PV) technologies and maintaining high power conversion efficiency (PCE). through detailed numerical simulations this study investigates the impact of kesterite materials as hole transport layers (HTLs) in perovskite solar cells (PSCs), utilizing Caesium Titanium Bromide (Cs2TiBr6) as the perovskite absorber layer (PAL) and Titanium Dioxide (TiO2) as the electron transport layer (ETL). the objective is to enhance performance, and stability and reach a PCE greater than 25%. Various kesterite compounds, including copper zinc tin selenide (CZTSe), copper manganese tin sulphide (CMTS), copper iron tin sulphide (CFTS), copper nickel tin sulphide (CNTS), copper zinc tin sulphide (CZTS), and copper barium tin sulphide (CBTS), were analyzed to evaluate their effectiveness as HTLs. Using the SCAPS-1D tool, key factors such as band alignment, optimal layer thickness, and current-voltage (J-V) characteristics, along with quantum efficiency (QE), were evaluated to identify the most effective HTL. Additional simulations investigated the impact of series resistance (Rs), shunt resistance (Rsh) and temperature on device performance. the findings demonstrated that CZTSe outperformed other kesterites as HTL due to its superior absorption coefficient, favorable band alignment, and higher charge carrier formation and extraction capabilities. the configuration resulted in an open-circuit voltage (VOC) of 0.78 V, a short-circuit current density (JSC) of 40.19 mA/cm2 and a fill factor (FF) of 80.30%, leading to an overall PCE of 25.36% these findings highlight the potential of CZTSe as a promising HTL for high-PCE, lead-free PSCs, advancing sustainable solar technology. © 2025 IEEE.

关键词： Titanium dioxide

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Black Widow Optimization Algorithm Tuned IDDF controller for Frequency Regulation of Interconnected Power System with HVDC Line 5th

Black Widow Optimization Algorithm Tuned IDDF Controller for...

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5th international conference on Recent Developments in control, automation and Power engineering, RDCAPE 2023

作者： Kalyan, Ch. Naga Sai Bajaj, Mohit Dixit, Ashutosh Department of EEE Vasireddy Venkatadri Institute of Technology Guntur522 508 India Dehradun248 002 India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Graphic Era Hill University Dehradun248002 India

ISBN: (纸本)9789819600885

this work focuses on the development of a simple and robust secondary regulator for the load frequency control (LFC) of multi-area traditional thermal-hydro (MATth) systems under load disturbances. the integral-double derivative filter (IDDF) based on the recent soft computing approach of the black widow optimization algorithm (BWOA) is proposed whose operational efficacy is demonstrated with the performances of other controllers. the analysis on MATth is initiated with 10%-step load disturbance (SLD). Moreover, the communication time delays (CTDs) are considered with the MATth system while testing the performance of the suggested controller and the subsequent effect on the system performance is revealed. the HVDC line is enacted with MATth as a territory control mechanism, and simulation analysis revealed the achievement of considerable improvement in the system performance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Two term control systems

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Computer Vision Based Quality control for Molding Injection Machines 19th

Computer Vision Based Quality Control for Molding Injection...

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Moreno, Ramón García, Oscar Del Río Cristobal, Miguel Shankar Muthuselvam, Revanth María Sanjuan, José Vallejo, Andrés Wang, Ting Department of Advanced Manufacturing 4.0 Antolin Engineering Burgos Spain Instituto Tecnológico Catilla y Leon Burgos Spain College of Electrical Engineering and Control Science Nanjing Tech University and Southeast University Nanjing China

ISBN: (纸本)9783031750090

this paper presents a comprehensive study on the application of computer vision, specifically Convolutional Neural Networks (CNNs), for quality control in automotive interior manufacturing using Injection Molding Machines (IMMs). Despite the high precision and productivity of IMMs, a small percentage of defects persist. Addressing this, the paper compares four popular CNN algorithms—ResNet-50, SegNet, FCN, and PSPNet—on their ability to detect and analyze defects in post-manufacturing. the study outlines the advantages of IMMs, the challenges in defect management, and the potential of CNNs to revolutionize quality control processes. Experimental results demonstrate the efficacy of these algorithms, paving the way for enhanced quality management and reduced defect rates in the automotive sector. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Design and Analysis of Compact UWB Circular Ring Planar Antenna for mmWave FR-II NR Bands n259, n262, n263 (V-Band), and n260 (Ka-Band) for 5G Applications 6th

Design and Analysis of Compact UWB Circular Ring Planar Ante...

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6th international conference on Intelligent Communication, control and Devices, ICICCD 2024

作者： Kumar, Pradeep Raj, Tej Kapoor, Ankush Krishna, Nippani Satya Discipline of Electrical Electronic and Computer Engineering University of KwaZulu-Natal Durban4041 South Africa Electrical Cluster SoAE UPES Uttarakhand Dehradun India Department of ECE J. N. Government Engineering College Sundernagar India Applied Science Cluster SoAE UPES Uttarakhand Dehradun India

ISBN: (纸本)9789819783281

this manuscript proposes an ultra-wideband circular ring-type planar microstrip patch antenna with a partial ground plane. the antenna achieves an impedance bandwidth of approximately 38.1 GHz, encompassing a frequency range of 36.517–74.624 GHz. the presented radiator attains a fractional bandwidth of 68.57%, with a peak gain of 5.64 dBi, cover FR-II NR frequency bands n259 (39.50–43.50 GHz), n262 (47.20–48.20 GHz), n263 (57–71 GHz), and n260 (37–40 GHz). the proposed antenna is modeled on dielectric material Taconic (TLY-5) with a dielectric constant (Εr) 2.2, a substrate width of 0.51 mm, and a loss tangent of 0.0009. the suggested element features a compact size with substrate dimensions of 10 × 6.30 mm2, exhibiting a quasi-omnidirectional radiation pattern with a maximum radiation efficiency of 96%. the simulation results, including return loss and VSWR, have been validated using CST Microwave Studio and Ansys HFSS, showing close agreement. this indicates that the proposed antenna is a strong candidate for 5G mmWave FR-II NR V-band and Ka-band wireless applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slot antennas

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