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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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A Defense Framework for Backdoor Attacks in Federated Learning Based on Client-Server Detection

A Defense Framework for Backdoor Attacks in Federated Learni...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Ding Chen Tianrui Li Cai Yu Rasha Al-Huthaifi Wei Huang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Sichuan Provincial Key Laboratory of Cyberspace Security School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Cybersecurity Chengdu University of Information Technology Chengdu China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Addressing the challenge of backdoor attacks in federated learning (FL), various defense schemes are examined, resulting in the proposal of a comprehensive strategy that incorporates both client-side and server-side measures. This defense strategy is based on Dropout and LOF (Local Outlier Factor) outlier detection techniques. The model's resilience against data poisoning is enhanced by applying the Dropout technique on the client side. At the same time, the LOF outlier detection algorithm is utilized on the server side to identify potential abnormal model updates. Experimental results highlight the effectiveness of the proposed strategy, demonstrating a significant reduction in the Attack Success Rate (ASR) from 88.11% to 1.62% on the FMNIST dataset, and from 95.45% to only 0.25% on the EMNIST dataset. The success rate of backdoor attacks is notably diminished, confirming the strategy's strong backdoor defense capabilities.

关键词： Resistance Federated learning Service robots Data models Stability analysis Real-time systems Servers Anomaly detection Thermal stability Resilience

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Robustness of Large Language models Against Adversarial Attacks

Robustness of Large Language Models Against Adversarial Atta...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yiyi Tao Yixian Shen Hang Zhang Yanxin Shen Lun Wang Chuanqi Shi Shaoshuai Du Johns Hopkins University Maryland USA University of Amsterdam Amsterdam Netherlands University of California San Diego California USA Simon Fraser University Canada Duke University North Carolina USA

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

The increasing deployment of Large Language models (LLMs) in various applications necessitates a rigorous evaluation of their robustness against adversarial attacks. In this paper, we present a comprehensive study on the robustness of GPT LLM family. We employ two distinct evaluation methods to assess their resilience. The first method introduces character-level text attack in input prompts, testing the models on three sentiment classification datasets: StanfordNLP/IMDB, Yelp Reviews, and SST-2. The second method involves using jailbreak prompts to challenge the safety mechanisms of the LLMs. Our experiments reveal significant variations in the robustness of these models, demonstrating their varying degrees of vulnerability to both character-level and semantic-level adversarial attacks. These findings underscore the necessity for improved adversarial training and enhanced safety mechanisms to bolster the robustness of LLMs.

关键词： Training Adaptation models Reviews Large language models Robustness Safety Robots Resilience Testing Context modeling

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Overview of Underwater Image Enhancement and Restoration methods 12

Overview of Underwater Image Enhancement and Restoration Met...

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12th international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Cheng, Chenyu Zhang, Huiying Li, Gang School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132000 China School of Mechanical Control Engineering Baicheng Normal University Baicheng137000 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Marine resources are rich and diverse, which has great value and research significance. In recent years, underwater exploration and research has become a hot direction. As the carrier of information, underwater image plays a vital role in underwater exploration and research. However, due to the influence of unclear underwater image and low contrast, it has caused great obstacles to the exploration of underwater resources. This paper summarizes the traditional image enhancement methods and image enhancement methods under deep learning for the past few years, and makes a comprehensive introduction to several algorithms and models. With the rapid and comprehensive development of deep learning for the past few years, this paper focuses on the image enhancement and restoration algorithm based on deep learning, summarizes the present public data sets in detail, introduces two mainstream objective evaluation systems, and finally looks forward to the future research. © 2022 IEEE.

关键词： Image enhancement

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Industrial Camera-Aided Trajectory Planning for Robotic Welding on Reflective Surfaces

Industrial Camera-Aided Trajectory Planning for Robotic Weld...

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IEEE international conference on Real-time Computing and robotics (RCAR)

作者： Kristoffer Klingenberg Karl Victor Johannessen Ozan Kaya Lars Tingelstad Department of Mechanical and Industrial Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Recent advancements in robotic welding, vision-aided control, and automation strategies have gained prominence due to their inherent advantages, such as high precision, rapid detection capabilities, and increased versatility, surpassing traditional manual welding operations. This shift towards automation is driven by the quest for efficiency and the need for enhanced safety, particularly in hazardous working conditions characterized by fumes, high temperatures, and pressures. This study considers the vision-aided trajectory generation strategies, which represent a combination of software and hardware-based approaches designed for automatic robotic welding. The proposed process involves the utilization of image processing techniques facilitated by an industrial camera (Zivid 2) to compute essential parameters. These parameters include the generation of a welding trajectory, determination of the initial welding position, and compensation for positioning errors arising from the workpiece’s placement within the working environment. Furthermore, our research addresses several challenges inherent to the implementation of a vision-aided trajectory planner, such as visual calibration, defining welding workpieces, and control of the industrial robot arm (Yaskawa GP25-12). While these aspects are pivotal, the primary focus remains on effectively compensating for misalignment in the workpiece’s position, which is crucial for achieving optimal welding outcomes. In this study, we present the proposed architecture, elucidate challenges associated with various approaches and techniques, provide kinematic models, and delve into the intricacies of the robotic system’s automatic welding framework. The objective of this research is to contribute to the ongoing discourse on the advancement of automation technologies in welding, with the aim of fostering efficiency, safety, and precision in challenging industrial environments. Additionally, the research seeks to enhance the reliability

关键词： automation Service robots Trajectory planning Welding Robot vision systems Real-time systems Trajectory

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CNN-XGBoost Based Ensemble Model for Short-Term Load Forecasting

CNN-XGBoost Based Ensemble Model for Short-Term Load Forecas...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Kazi Reyad Hossain Md Samin Safayet Islam Md Rashidul Islam Md. Rafiqul Islam Sheikh Md. Omer Faruque Department of EEE Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Precise short-term load forecasting is crucial in the electric power industry, which serves a vital purpose in maintaining a balance between demand and supply. Maintaining this equilibrium is essential for ensuring the stability and efficiency of power systems. This forecasting task, however, poses significant challenges due to its inherent complexity. This paper presents a new approach for short-term load forecasting through the combination of convolutional neural networks (CNN) and extreme gradient boosting (XGBoost) algorithms. The model incorporates the feature extraction skills of CNN with the sequential learning strength of XGBoost. Both the techniques are ensembled with gradient boosting, which functions as a meta-learner. Gradient boosting combines the individual predictions done by the CNN and XGBoost and enhances the accuracy. The research used the daily load consumption data of Queensland, Australia. This proposed ensemble method is evaluated on the dataset and compared with other forecasting techniques. The outcomes indicated that the suggested ensemble model surpasses the existing forecasting algorithms in terms of various evaluation metrics.

关键词： Accuracy Load forecasting Predictive models Power system stability Boosting Prediction algorithms Complexity theory Convolutional neural networks Forecasting Load modeling

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Online Tool Selection with Learned Grasp Prediction models

Online Tool Selection with Learned Grasp Prediction Models

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IEEE international conference on robotics and automation (ICRA)

作者： Khashayar Rohanimanesh Jake Metzger William Richards Aviv Tamar Osaro Inc. San Francisco CA USA Accenture Inc. San Francisco CA USA Technion - Israel Institute of Technology Haifa Israel

Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped with several end-effector tools that can be swapped on-the-fly, based on the target object. Tool-change, however, takes time. Choosing the order of grasps to perform, and corresponding tool-change actions, can improve system throughput; this is the topic of our work. The main challenge in planning tool change is uncertainty - we typically cannot see objects in the bin that are currently occluded. Inspired by queuing and admission control problems, we model the problem as a Markov Decision Process (MDP), where the goal is to maximize expected throughput, and we pursue an approximate solution based on model predictive control, where at each time step we plan based only on the currently visible objects. Special to our method is the idea of void zones, which are geometrical boundaries in which an unknown object will be present, and therefore cannot be accounted for during planning. Our planning problem can be solved using integer linear programming (ILP). However, we find that an approximate solution based on sparse tree search yields near optimal performance at a fraction of the time. Another question that we explore is how to measure the performance of tool-change planning: we find that throughput alone can fail to capture delicate and smooth behavior, and propose a principled alternative. Finally, we demonstrate our algorithms on both synthetic and real world bin picking tasks.

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Penetration of Artificial Intelligence Techniques to Enhance the Agricultural Productivity and the Method of Farming;Opportunities and Challenges 3

Penetration of Artificial Intelligence Techniques to Enhance...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Muramulla, Sai Mounika Ponugoti, Sreelekha Srinivas, O. Dhinakaran, K. Kuchipudi, Ramu Robin, C.R. Rene Symbiosis Institute of Business Management Pune India Symbiosis International Pune India Acharya Nagarjuna University Department of Zoology and Aquaculture Guntur India Rmk College of Engineering and Technology Department of Computer Science Thiruvallur India Hasanparthy India Misrimal Navajee Munoth Jain Engineering College Department of Computer Science Chennai India Chaitanya Bharathi Institute of Technology Department of Information Technology Hyderabad India Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350336238

Each nation's economic sector depends heavily on agriculture. Every day, more people live on the planet, which raises the requirement of food. For the time being, farmers' conventional methods are unable to meet the demand. As a result, new automation techniques are launched to meet these needs and give many people in this industry excellent job prospects. In every industry, including banking, robotics, agriculture, and education, artificial intelligence(AI) has emerged as one of the most crucial technologies. It is playing a very important part in the agriculture sector and changing the agriculture industry. AI protects the agricultural sector from a variety of concerns, including food safety, population expansion, climate change, and employment problems in this industry. Due to AI, the agriculture system of today has advanced to a new level. Real-time crop monitoring, production, harvesting, processing, and marketing have all been enhanced by artificial intelligence. Numerous cutting-edge computer-based systems are created to identify several significant factors, including weed identification, yield identification, crop quality, and many others. © 2023 IEEE.

关键词： Crops

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A Configuration Measurement Method of Cable-Driven Space Manipulator by Link Equivalent Center Points Recognition

A Configuration Measurement Method of Cable-Driven Space Man...

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2022 IEEE international conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Fengxu Yan, Lei Li, Junxiang Lin, Boyang Xu, Wenfu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

A cable-driven space manipulator has great application potential in space operations due to its advantages of flexible movement and slender body. However, the pose accuracy of its end-effector is worse than that of traditional space manipulators because it's difficult to measure the joint angles. The lack of shape information will increase the risk of colliding with others. However, the method of measuring configuration by adding cooperative markers on its body is complex and not suitable in space. To solve the above problems, this paper proposes a method of configuration measurement for the natural characteristics. This method solves the links' equivalent center points observed by the stereovision instead of adding cooperative markers. The kinematics model is derived to reconstruct the shape of the manipulator. The center points of links identification and kinematics modeling adopt the series-by-segment solution to greatly improve the computational efficiency and avoid the multiply solutions of redundant manipulators' inverse kinematics. A simulation system for configuration measurement of cable-driven space manipulator is established, and the above methods is verified by simulation and experiment. The simulation and experimental results show that the proposed method has high accuracy. © 2022 IEEE.

关键词： Stereo vision

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TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems

TPTU-v2: Boosting Task Planning and Tool Usage of Large Lang...

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2024 conference on Empirical methods in Natural Language Processing, EMNLP 2024

作者： Kong, Yilun Ruan, Jingqing Chen, Yihong Zhang, Bin Bao, Tianpeng Shi, Shiwei Du, Guoqing Hu, Xiaoru Mao, Hangyu Li, Ziyue Zeng, Xingyu Zhao, Rui Wang, Xueqian Shenzhen International Graduate School Tsinghua University China SenseTime Research China Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Cologne Germany Qingyuan Research Institute Shanghai Jiaotong University China

ISBN: (纸本)9798891761667

Large Language models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools, such as weather and calculator APIs. However, real-world industrial systems present prevalent challenges in task planning and tool usage: numerous APIs in the real system make it intricate to invoke the appropriate one, while the inherent limitations of LLMs pose challenges in orchestrating an accurate sub-task sequence and API-calling order. This paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents in industry. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs among the extensive API set;(2) the Demo Selector retrieves task-level demonstrations, which is further used for in-context learning to aid LLMs in accurately decomposing subtasks and effectively invoking hard-to-distinguish APIs;(3) LLM Finetuner tunes a base LLM to enhance its capability for task planning and API calling . We validate our methods using a real-world industry system and an open-sourced academic dataset, demonstrating the efficacy of each individual component as well as the integrated framework. The code is available at here. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A survey of image clustering: Taxonomy and recent methods

A survey of image clustering: Taxonomy and recent methods

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2021 IEEE international conference on Real-Time Computing and robotics, RCAR 2021

作者： Liu, Jiaxin Wang, Dongwei Yu, Siquan Li, Xueliang Han, Zhi Tang, Yandong State Grid Liaoning Electric Power Research Institute Shenyang China School of Technology Beijing Forestry University Beijing China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Grid Shandong Electric Power Company Shandong China

ISBN: (纸本)9781665436786

Image clustering is a fundamental problem in computer vision domains. In this survey, we provide a comprehensive overview of image clustering. Specifically, we first discuss the applications of image clustering across various domains. Then, we summarize the common algorithms and propose a classification of image clustering. The existing methods are classified from four aspects: autoencoder based methods, subspace clustering, graph convolution network (GCN) based methods and some other clustering methods. We introduce the main research contents and existing problems of various image clustering methods. We also introduce some recent methods and summarize the experimental results. Based on our taxonomy and analysis, creating and verifying new methods is more straightforward. Finally, we propose the future opportunities in this fast developing field. © 2021 IEEE.

关键词： Convolution

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