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Practical Structural Design Approach of Multicon guration Planar Single-Loop Metamorphic Mechanism with a Single Actuator

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Chinese Journal of Mechanical engineering 2020年第5期 29-43页

作者： Qiang Yang Guangbo Hao Shujun Li Hongguang Wang Haiyang Li College of Mechanical Engineering and Automation Northeastern University Department of Electrical and Electronic Engineering University College Cork State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

As a type of multiconfiguration mechanism that can operate in an under-actuated state, metamorphic mechanisms were proposed more than two decades ago and attracted significant interest. Studies on structural synthesis of metamorphic mechanisms tend to focus more on metamorphic techniques and the structural synthesis of source mechanisms for metamorphic mechanisms. By designing di erent constraint architectures of metamorphic joints, multistructures can be obtained from the same source metamorphic mechanism. To determine the constraint architectures of metamorphic joints and their di erent assembly combinations, a kinematic status matrix and a corresponding constraint status matrix are constructed based on the metamorphic cyclogram of a source mechanism. According to the equivalent resistance gradient model and the constraint status matrix, an equivalent resistance matrix for the metamorphic joints is proposed. A structural synthesis matrix of the metamorphic mechanism is then obtained from the equivalent resistance matrix by deducing the constraint form vectors of the metamorphic joints. Furthermore, a kinematic diagram synthesis of the source metamorphic mechanism of a planar single-loop metamorphic mechanism is proposed, which is based on only the 14 one-or zero-degrees-of-freedom linkage groups. The entire structural design method of a metamorphic mechanism is based on the structural synthesis matrix and is presented as a systematic process. Finally, the proposed structural design approach is illustrated by two examples to verify its feasibility and practicality. This study provides an effective method for designing a practical multi-mobility and multiconfiguration planar single-loop metamorphic mechanism with a single actuator.

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CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption

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CellSecure: Securing Image Data in Industrial Internet-of-Th...

IEEE Conference on Vehicular Technology (VTC)

作者： Hassan Ali Muhammad Shahbaz Khan Maha Driss Jawad Ahmad William J. Buchanan Nikolaos Pitropakis Department of Electrical Engineering HITEC University Taxila Taxila Pakistan School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for realtime sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain.

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A Novel Efficient Signcryption Scheme for Resource-Constrained Smart Terminals in Cyber-Physical Power Systems

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arXiv 2022年

作者： Li, Xue Jiang, Cheng Du, Dajun Wang, Shengyi Fei, Minrui Wu, Lei Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai University Shanghai200072 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

Most of the existing signcryption schemes generate pseudonym by key generation center (KGC) and usually choose bilinear pairing to construct authentication schemes. The drawback is that these schemes not only consume heavy computation and communication costs during information exchange, but also can not eliminate security risks due to not updating pseudonym, which do not work well for resource-constrained smart terminals in cyber-physical power systems (CPPSs). The main objective of this paper is to propose a novel efficient signcryption scheme for resource-constrained smart terminals. First, a dynamical pseudonym self-generation mechanism (DPSGM) is explored to achieve privacy preservation and avoid the source being linked. Second, combined with DPSGM, an efficient signcryption scheme based on certificateless cryptography (CLC) and elliptic curve cryptography (ECC) is designed, which reduces importantly computation and communication burden. Furthermore, under random oracle model (ROM), the confidentiality and non-repudiation of the proposed signcryption scheme are transformed into elliptic curve discrete logarithm and computational Diffie-Hellman problems that cannot be solved in polynomial time, which guarantees the security. Finally, the effectiveness and feasibility of the proposed signcryption scheme are confirmed by experimental analyses. © 2022, CC BY-NC-ND.

关键词： Geometry

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Kepler codebook 24

Kepler codebook

Proceedings of the 41st International Conference on Machine Learning

作者： Junrong Lian Ziyue Dong Pengxu Wei Wei Ke Chang Liu Qixiang Ye Xiangyang Ji Liang Lin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Xi'an Jiaotong University Xi'an China Department of Automation Tsinghua University Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China and Peng Cheng Laboratory Shenzhen China

A codebook designed for learning discrete distributions in latent space has demonstrated state-of-the-art results on generation tasks. This inspires us to explore what distribution of codebook is better. Following the spirit of Kepler's Conjecture, we cast the codebook training as solving the sphere packing problem and derive a Kepler codebook with a compact and structured distribution to obtain a codebook for image representations. Furthermore, we implement the Kepler codebook training by simply employing this derived distribution as regularization and using the codebook partition method. We conduct extensive experiments to evaluate our trained codebook for image reconstruction and generation on natural and human face datasets, respectively, achieving significant performance improvement. Besides, our Kepler codebook has demonstrated superior performance when evaluated across datasets and even for reconstructing images with different resolutions. Codes and pre-trained weights are available at https://***/banianrong/KeplerCodebook

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Multi-Objective Optimization of High-Power Fiber Laser Cutting of Thick Mild Steel by Using Response Surface Methodology

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SSRN

SSRN 2023年

作者： Liu, Yanjie Yoshigoe, Kenji Ullah, Farhan Zhang, Shijin Zhao, Yue Shanghai Key Lab of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Software Northwestern Polytechnical University Xi’an710129 China Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL32114 United States

High-power continuous wave fiber laser cutting is a next-generation cutting technology for rapid prototyping and small-scale fabrication. To utilize the advantage offered by this technology in terms of high-quality cutting, the laser cutting process parameters must be optimized. In this study, separation speed experiment on thick mild steel plates was performed using a 12-kW continuous-wave multimode ytterbium-doped fiber laser, and a high-power fiber laser cutting cut-through criterion for thick carbon steel plates was proposed. The response surface method was used to model and optimize the cut quality characteristics. Material thickness, laser power, cutting speed, gas pressure, and defocus amount were selected as input factors, whereas kerf taper and material removal rate (MRR) were selected as output quality characteristics. The effects of the various factors on the kerf taper and MRR were investigated using analysis of variance, and second-order regression models were developed for the kerf taper and MRR. Furthermore, a multi-objective optimization method was used to optimize the kerf taper and MRR. The percentage errors of the validation experiments and multi-objective optimization results were 5.27% and 1.93% for the kerf taper and MRR, respectively. Moreover, the analysis revealed that the material removal mechanism and the kerf taper formation mechanism are related to the melt flow. The findings presented in this paper contribute to the improvement of quality and accuracy when cutting thick plates by using a high-power fiber laser. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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arXiv 2025年

作者： Fidêncio, Aline Xavier Grün, Felix Klaes, Christian Iossifidis, Ioannis Robotics and BCI Laboratory Institute of Computer Science Ruhr West University of Applied Sciences Mülheim an der Ruhr Germany Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany KlaesLab Department of Neurosurgery University Hospital Knappschaftskrankenhaus Ruhr University Bochum Bochum Germany Faculty of Computer Science Ruhr University Bochum Bochum Germany

Brain-computer interfaces (BCIs) are developed for individuals with motor disabilities to offer alternative methods of communication, such as controlling and interacting with external devices. These systems are designed to improve quality of life, particularly for those with limited mobility. BCIs that rely on non-invasive recordings, such as electroencephalography (EEG), have been developed for a wide range of scenarios due to their practicality and safety. However, BCI performance is often limited by non-stationarities in the EEG data, which can arise from changes in the subject’s mental state or device characteristics, such as electrode impedance. These challenges motivate ongoing research into the development of adaptive BCIs capable of handling such variations. Over the past years, the interest in using the so-called error-related potentials (ErrPs) to improve BCI performance has increased. This is because ErrPs represent the neural response to both self-made and external errors and can be measured using non-invasive techniques. These signals have been combined with different BCI paradigms and used in different works to improve BCI performance via error correction or adaptation. In this work we introduce and explore a new approach for creating an adaptive ErrP-based BCI by evaluating the use of reinforcement learning (RL). This study demonstrates the feasibility of a RL-driven adaptive brain-computer interface (BCI) framework that integrates error-related potentials (ErrPs) and motor imagery. By employing two RL agents to dynamically adapt to EEG non-stationarities, we validate the framework on both a publicly available motor imagery dataset and using a novel experimental protocol involving a fast-paced game designed to enhance user engagement. Results highlight the framework’s potential: RL agents successfully learned control policies from user interactions, achieving robust performance across datasets. However, a critical finding emerged from the game-based p

关键词： Reinforcement learning

Focused Bidirectional Search Trees: A Bidirectional Optimal Fast Matching Method for Robot Path Planning

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Focused Bidirectional Search Trees: A Bidirectional Optimal ...

IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Chenming Li Jiankun Wang Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering University of Alberta Canada

Robot path planning in high-dimensional continuous space is of great significance in robotics. The conventional bidirectional fast marching method utilizes brute-force search to explore the space, which is computationally expensive. Though improving the planning efficiency, heuristic-based bidirectional fast marching search results in a suboptimal path under a predetermined number of random samples. To address these issues, we propose Focused Bidirectional Fast Marching Trees (FBFMT*), developing a novel vertex exploring sequence to constrain the forward and backward searches within the brute-force searched regions. It enables the search trees to meet in the middle of the solution path, which guarantees the optimality under the current set of samples and searches faster than the bidirectional brute-force strategy. Extensive simulations in different scenarios validate the superiority of the proposed method, showing that FBFMT* can speed up the planning process and find a better solution. We also extend the proposed method to general conditions so that the forward and backward searches of FBFMT* can meet at any portion of the path. FBFMT* reveals that the heuristic-based bidirectional fast marching search strategy can be a general and efficient technique for robot path planning in high-dimensional continuous spaces.

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Federated Learning-Empowered AI-Generated Content in Wireless Networks

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arXiv 2023年

作者： Huang, Xumin Li, Peichun Du, Hongyang Kang, Jiawen Niyato, Dusit Kim, Dong In Wu, Yuan State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China The School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer and Information Science University of Macau Taipa China

Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models. Deploying AIGC services in wireless networks has been expected to enhance the user experience. However, the existing AIGC service provision suffers from several limitations, e.g., the centralized training in the pre-training, fine-tuning and inference processes, especially their implementations in wireless networks with privacy preservation. Federated learning (FL), as a collaborative learning framework where the model training is distributed to cooperative data owners without the need for data sharing, can be leveraged to simultaneously improve learning efficiency and achieve privacy protection for AIGC. To this end, we present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content. Furthermore, we conduct a case study of FL-aided AIGC fine-tuning by using the state-of-the-art AIGC model, i.e., stable diffusion model. Numerical results show that our scheme achieves advantages in effectively reducing the communication cost and training latency and privacy protection. Finally, we highlight several major research directions and open issues for the convergence of FL and AIGC. © 2023, CC BY.

关键词： Wireless networks

Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study

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arXiv 2024年

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Wang, Jiacheng Sikdar, Biplab Zhang, Ping College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

Data augmentation is a powerful technique to mitigate data scarcity. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective alternative to wireless data augmentation due to its excellent data generation capability. This article systemically explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks. We first briefly review data augmentation techniques, discuss their limitations in wireless networks, and introduce generative data augmentation, including reviewing GenAI models and their applications in data augmentation. We then explore the application prospects of GenAI-driven data augmentation in wireless networks from the physical, network, and application layers, which provides a GenAI-driven data augmentation architecture for each application. Subsequently, we propose a general generative diffusion model-based data augmentation framework for Wi-Fi gesture recognition, which uses transformer-based diffusion models to generate high-quality channel state information data. Furthermore, we develop residual neural network models for Wi-Fi gesture recognition to evaluate the role of augmented data and conduct a case study based on a real dataset. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss research directions for generative data augmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks