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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhou, Yang Zhu, Shiqiang Huang, Huang Li, Yuehua Gu, Jason The Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China The Beijing Institute of Control Engineering Beijing100094 China The Beijing Research Institute Zhejiang Laboratory Beijing100094 China The Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory. © 2022 IEEE.

关键词： Motion planning

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Faculty of Electrical Engineering Visual Recognition Group Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Department of Computer Science Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://***/danini/absolute-pose-from-oriented-and-sealed-features.

关键词： Location awareness Accuracy Simultaneous localization and mapping Measurement units Pose estimation Noise Inertial navigation

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On the Stability Analysis of Open Federated Learning systems

On the Stability Analysis of Open Federated Learning Systems

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American Control Conference (ACC)

作者： Youbang Sun Heshan Fernando Tianyi Chen Shahin Shahrampour the Department of Mechanical and Industrial Engineering Northeastern University Boston Massachusetts USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York USA

We consider the open federated learning (FL) systems, where clients may join and/or leave the system during the FL process. Given the variability of the number of present clients, convergence to a fixed model cannot be guaranteed in open systems. Instead, we resort to a new performance metric that we term the stability of open FL systems, which quantifies the magnitude of the learned model in open systems. Under the assumption that local clients’ functions are strongly convex and smooth, we theoretically quantify the radius of stability for two FL algorithms, namely local SGD and local Adam. We observe that this radius relies on several key parameters, including the function condition number as well as the variance of the stochastic gradient. Our theoretical results are further verified by numerical simulations on synthetic data.

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques

Enhancing Data Security and Privacy in Cloud Computing: A Su...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Suganya P. Gouthami R. Sathiya S. Sivaranjani M. Murugesan Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources; they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user's data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness.

关键词： Surveys Cloud computing Data privacy Privacy Reviews Scalability Software Encryption Servers Identity management systems

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Prompt Baking

arXiv

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arXiv 2024年

作者： Bhargava, Aman Witkowski, Cameron Detkov, Alexander Thomson, Matt Caltech United States University of Toronto Canada Computation & Neural Systems California Institute of Technology United States Department of Electrical and Computer Engineering University of Toronto Canada

Two primary ways to change LLM behavior are prompting and weight updates (e.g. fine-tuning). Prompting LLMs is simple and effective, specifying the desired changes explicitly in natural language, whereas weight updates provide more expressive and permanent behavior changes, specified implicitly via training on large datasets. We present a technique for "baking" prompts into the weights of an LLM. Prompt Baking converts a prompt u and initial weights θ to a new set of weights θu such that the LLM with weights θu behaves like the LLM with weights θ and prompt u. Mathematically, we minimize the KL divergence between Pθ(·|u) and Pθu(·), where P is the LLM’s probability distribution over token sequences. Across all our experiments, we find prompts can be readily baked into weight updates, often in as little as 5 minutes. Baking chain-of-thought prompts improves zero-shot performance on GSM8K, ASDiv, MBPP, ARC-Easy, ARC-Challenge, and CommonsenseQA benchmarks. Baking news headlines directly updates an LLM’s knowledge. And baking instructions & personas alleviates "prompt forgetting" over long sequences, as measured on a Persona Drift benchmark. Furthermore, stopping baking early creates "half-baked" models, allowing for continuous scaling of prompt strength. Baked models retain their sensitivity to further prompting and baking, including re-prompting with the prompt already baked in – thus amplifying the prompt’s strength. Surprisingly, the re-prompted models yield further performance gains in instruction following, as well as math reasoning and coding benchmarks (GSM8K, ASDiv, and MBPP). Taking re-prompting and re-baking to the limit yields a form of iterative self-improvement we call Prompt Pursuit, and preliminary results on instruction following exhibit dramatic performance gains with this technique. Finally, we discuss implications for AI safety, continuous model updating, improving LLM recency, enhancing real-time learning capabilities in LLM-based agents, and metho

关键词： Zero-shot learning

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Learning Based Dynamic Cluster Reconfiguration for UAV Mobility Management with 3D Beamforming

Learning Based Dynamic Cluster Reconfiguration for UAV Mobil...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Irshad A. Meer Karl-Ludwig Besser Mustafa Ozger Dominic Schupke H. Vincent Poor Cicek Cavdar Department of Electrical and Computer Engineering Princeton University USA Division of Communication Systems KTH Royal Institute of Technology Sweden Airbus Central Research and Technology Munich Germany

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In this work, we address the challenge of dynamic clustering and power allocation for unmanned aerial vehicle (UAV) communication in wireless interference networks. Our objective encompasses meeting varying reliability demands, minimizing power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we introduce a novel approach based on reinforcement learning using a masked soft actor-critic algorithm, specifically tailored for dynamic clustering and power allocation.

关键词： Heuristic algorithms Wireless networks Clustering algorithms Materials reliability Reliability engineering Dynamic scheduling Autonomous aerial vehicles

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Smooth Quadrature-Inspired Generalized Choquet Integral in an Application to Anomaly Detection

Smooth Quadrature-Inspired Generalized Choquet Integral in a...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Paweł Karczmarek Michał Dolecki Łukasz Gałka Witold Pedrycz Dariusz Czerwinski Department of Computer Science Lublin University of Technology Lublin Poland Department of Electrical & Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Polish Academy of Sciences Systems Research Institute Warsaw Poland

In this study, we consider a new approach to the enhancement of classic Choquet integral as a vehicle in the processes of aggregation of classifiers o r i nformation fusion. The improvement of classification result o n a b asis o f classifier ensambles is one of the most important tasks of machine learning research community. In the previous series of works, we have introduced a conception of building Choquet-like aggregation operator using the idea inspired by one of the most common numerical methods, namely quadratures. Here, we extend this technique by using the concept which we call smoothing. We use this term to express the idea of smoothing the function under the integral symbol, and thus triggering processes that increase the elasticity of the Choquet integral. In a series of numerical experiments with anomaly detection problem, we show that the new approach is better than the existing ones in terms of accuracy and f1 score.

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Design and Implementation of Multi-mode Scanning Atomic Force Microscope

Design and Implementation of Multi-mode Scanning Atomic Forc...

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2021 International Automatic Control Conference, CACS 2021

作者： Wu, Jim-Wei Weng, Jui-Tse Chao, Shao-An Cen, Wen-Shan Peng, Yuan-Chih Department of Electrical Engineering National Central University Taoyuan City32001 Taiwan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

ISBN: (纸本)9781665444125

Atomic force microscope (AFM) is used to scan the topography of samples using a sharp probe;however, conventional AFM devices are limited by XY scanners based on piezoelectric actuation. This paper proposes a novel AFM system featuring multi-mode scanning via two high-precision positions called piezoelectric actuation positioner (PAP) and precision electromagnetic positioner (PEP). The 3 degrees of freedom (DOF) provided by the PAP and the 2 DOF provided by the PEP greatly enhances the flexibility of the scanning positioners, allowing operation in the following three modes: 1) Short-range scanning;2) Long-range scanning;3) Image-series scanning. Extensive experimentation demonstrated the feasibility of the proposed system. © 2021 IEEE

关键词： Scanning

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement

arXiv

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arXiv 2024年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node’s local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3-bits). Our proposed algorithm respects the network’s bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm’s operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm’s unique characteristics. © 2024, CC BY.

关键词： Optimization algorithms

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