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International Conference on Autonomous Robots and Agents, ICARA

作者： Marco Baracca Andre Meixner Giuseppe Averta Paolo Salaris Tamim Asfour Matteo Bianchi Dept. of Information Engineering Research Center “E. Piaggio” University of Pisa Pisa Italy Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Dep. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coordination between the motion of the hands required for this type of actions makes their planning not trivial. A proper definition of these coordination patterns moving from the human example could simplify their translation on the robot side, fostering the generation of effective bimanual tasks. In this work, we propose Multivariate functional Principal Component Analysis (MfPCA) as a mathematical tool to encode inter-hands temporal kinematic covariations in terms of principal spatiotemporal coordination patterns in the Cartesian domain. We compared these patterns extracted from a dataset of human bimanual tasks with those resulting from the usage of classical fPCA, applied independently to each hand (univariate fPCA). We found that MfPCA allows for a better classification of the tasks, with respect to a state of the art taxonomy. For what concerns motion planning, MfPCA and fPCA yield similar accuracy in the reconstruction of the motion, but with a smaller number of principal components needed in the MfPCA case. These results, although preliminary, can open interesting perspectives for the usage of MfPCA for human-like bimanual motion planning and control of robotic manipulators, as well as for action recognition, to enable a more effective human-robot interaction.

关键词： Hands Translation Shape Robot kinematics Taxonomy Human-robot interaction Functional analysis Planning Spatiotemporal phenomena Principal component analysis

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Effects of Salt Spray on c-Si Photovoltaic Modules in the Brazilian Region

Effects of Salt Spray on c-Si Photovoltaic Modules in the Br...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Mendelsson R.M. Neves Allan Silveira Hugo S. Alvarez Rodrigo M. Garcia Francisco C. Marques Marcelo G. Villalva Dept. of Systems and Energy - DSE School of Electrical and Computer Engineering Campinas Brazil Communications Department - DECOM School of Electrical and Computer Engineering Campinas Brazil Dept. Research and Development - R&D Build Your Dreams Company - BYD Campinas Brazil Dept. of Applied Physics - DFA Institute of Physics Gleb Wataghin Campinas Brazil

Brazil has great potential for this type of energy generation due to its geographic location, allowing the development of viable photovoltaic (PV) projects in several regions. its use in places close to the sea has increased, with its use on boats and even resorts and hotels. This proximity to the sea requires attention to the local salinity, more precisely to the saline mist. This article will describe the methodology used to carry out the salinity resistance test of PV modules, choosing a specific classification of corrosive atmosphere according to the brazilian environment on the coast where the module will be placed in real conditions.

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Robust Speech Analysis Framework Using CNN 3

Robust Speech Analysis Framework Using CNN

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Rupasinghe, Lakmal Alahendra, A.M.A.T.N. Ranathunge, R.A.D.O. Perera, P.S. Kulathunge, N.Y. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice is the main component of human communication and learning about and recognizing somebody's behavior. By listening to people's voices, humans can recognize a person's identity, speech fluency, accent, emotions, and stress level. It is difficult to understand what the speaker is saying when Speech fluency is poor. It varies from person to person. With the help of specific information in a person's voice, we can recognize human emotion, stress level, and identity. Every person has a unique vocal feature that facilitates recognizing them from others. This proposed framework is developed to identify a person's identity, emotions, fluency in speaking, and stress level of the speaker using their voice. The proposed framework is developed using machine learning techniques, and deep learning algorithms are highlighted in this study. Convolution Neural Network (CNN) is the used deep learning algorithm, and Fast Fourier transform (FFT), (MFCC), and Random Forest are machine learning techniques. The proposed AI-based framework provides comparatively accurate results in a user-friendly way. © 2021 IEEE.

关键词： Stress analysis

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Hybrid Sub-Harmonic Synchronous Machines Using Series and Parallel Consequent Permanent Magnet

Hybrid Sub-Harmonic Synchronous Machines Using Series and Pa...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： S M Sajjad Hossain Rafin Qasim Ali Osama A. Mohammed Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Florida USA Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan

This paper presents novel sub-harmonic synchronous machines having series or parallel permanent magnet structures on rotors for better performance in terms of greater torque density. The stator of the machines has two sets of three-phase winding, where the additional winding is used to generate sub-harmonic magnetomotive force components for brushless operation. Similar to the stator, the rotor of them uses an additional winding to induce that sub-harmonic component. Rotor also uses a permanent magnet to improve machine performance in series or parallel hybrid structures having conventional or consequent pole permanent magnet structures. In this paper, 4 different rotors, 3 having hybrid excitation, are used and their performance is compared. To validate the performance, a 2D finite element analysis of 8 poles and 48 slots of sub-harmonic synchronous machines is carried out with different rotors. Finally, the wide-speed operation of the proposed machine is discussed.

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Is Almost Sure Asymptotic Stabilizability Sufficient for Input Delay Tolerability of Nonlinear Stochastic systems?

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IEEE Transactions on Automatic control 2025年

作者： Yu, Xin Lin, Wei Jiangsu Normal University School of Electrical Engineering and Automation Xuzhou221116 China Case Western Reserve University Dept. of Electrical Computer and Systems Engineering ClevelandOH44106 United States

This article considers the problem of input delay tolerance (IDT) for stochastic control systems that are almost surely asymptotically stabilizable but inherently nonlinear (e.g., neither locally Lipschitz continuous nor linear growth and non-smoothly stabilizable). Using the notions of almost sure forward completeness [42] and weighted homogeneity, together with the existence of the solutions of continuous time-delay stochastic systems, we prove under Lyapunov-like conditions that : i) almost sure asymptotic stabilizability by nonsmooth homogeneous state feedback and dominated homogeneity of degree zero imply input delay tolerability of stochastic nonlinear systems, and ii) almost sure asymptotic stabilizability by homogeneous output feedback also implies input delay tolerability of the same class of multi-input/multi-output stochastic nonlinear systems. Our results contain not only the existing IDT results for the global Lipschitz or linear growth stochastic systems as special cases, but also important applications to significant classes of stochastic control systems with unstabilizable and undetectable linearization. © 1963-2012 IEEE.

关键词： Stochastic control systems

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Successive Interference Cancellation Based Defense for Trigger Backdoor in Federated Learning

Successive Interference Cancellation Based Defense for Trigg...

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IEEE International Conference on Communications (ICC)

作者： Yu-Wen Chen Bo-Hsu Ke Bo-Zhong Chen Si-Rong Chiu Chun-Wei Tu Jian-Jhih Kuo Computer Systems Technology New York City College of Technology Brooklyn New York USA Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

Federated Learning (FL) provides a decentralized training mechanism that ensures users' data privacy. However, FL is vulnerable to backdoor attacks, a type of data poisoning attack. The adversaries tampered with the local models by injecting a trigger into a subset of training data. After the aggregation process, the global model would be poisoned and mispredict the input images that injected a trigger designed by an adversary. Unlike the existing defense methods attempting to identify and remove the abnormal model updates on the aggregation step, this paper proposes a Successive Interference Cancellation-based Defense Framework (SICDF) to detect and eliminate the trigger during model inference. SICDF first employs Explainable AI to infer where the trigger is and then uses image processing skills to eliminate potential trigger effects. Experiment results show that SICDF can effectively recover the poisoned data while only slightly reducing the accuracy of the clean model and benign data.

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Enhancing Predictive Model Performance through Comprehensive Pre-processing and Hybrid Feature Selection: A Study using SVM 2

Enhancing Predictive Model Performance through Comprehensive...

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2nd International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Thuraka, Bharadwaj Pasupuleti, Vikram Kodete, Chandra Shikhi Naidu, U. Ganesh Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States B V Raju Institute of Technology Department of Csbs Narsapur Telangana Medak India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350368413

This study aims to achieve the improved performance of heart disease prediction based on the increased level of preprocessing and rather effective feature selection approaches. Starting with data preparation in which the data is first cleaned up the work develops a class balance solution using Synthetic Minority Over-sampling Technique (SMOTE) while the Z-score function identifies outliers. As it will be described in CHAN section that applies Chi-square and ANOVA under a hybrid model, the useful characteristics for model training are identified and prioritized. Using a method derived from the medical records database, the study focuses on the heart health indices using Support Vector Machines (SVM) which is a modelling technique widely known for its effectiveness in handling increased numbers of variables and the relationship between the measurements. Measures such as accuracy, precision, recall and F1-score among others are used in the evaluation of the success of the approach. In particular, the results shed the light to Health Care applications where risk assessment and correct diagnosis is extremely critical;the findings also assert the important roles of strong preprocessing and selective feature selection strategies in enhancing the predictive model's performance. This work contributes to the development of approaches intended for stable and efficient modelling in medical decision support system. © 2024 IEEE.

关键词： Support vector machines

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Characterization of Solar Cell Busbar Grid for Different Technologies by Time Domain Reflectometry Simulation: Transmission Line Approach

Characterization of Solar Cell Busbar Grid for Different Tec...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： A.M.C. Silveira M.R.M. Neves R. Garcia H. Alvarez M.G. Villalva F.C. Marques L.C. Kretly Communications Department - DECOM School of Electrical and Computer Engineering Campinas Brazil Dept. of Systems and Energy - DSE School of Electrical and Computer Engineering Campinas Brazil Dept. Research and Development - R&D Build Your Dreams Company - BYD Campinas Brazil Dept. of Applied Physics - DFA Institute of Physics Gleb Wataghin Campinas Brazil

This work electromagnetically simulates the design of metallic grids for different Busbar technologies in standard solar cells existing in the photovoltaic market in order to characterize their grid impedance using a signal integrity technique known as Time Domain Reflectometry - TDR and addressing the design of metallized grid of solar cells within the foundations of transmission lines. The objective of the investigation is to parameterize the results obtained so that they can serve as a reference in studies of micro-resistance in series of solar cells and their effects on energy efficiency.

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FedDist-POIRec: Federated Distillation for Point-Of-Interest Recommendation in Human Mobility Prediction

FedDist-POIRec: Federated Distillation for Point-Of-Interest...

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IEEE International Conference on Human-Machine systems

作者： Esmatollah Rezaei S. Jamal Seyedmohammadi Jamshid Abouei Konstantinos N. Plataniotis Dept. of Electrical Engineering Yazd University Yazd Iran Inst. of Information Systems Engineering Concordia University Montreal Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Next Point-of-Interest (POI) recommendation plays a vital role in human mobility prediction within Location-based Social Networks (LSBN), assisting individuals to decide on their next destination. However, traditional centralized systems rely on large datasets of previous POI check-ins, which raises privacy and computation concerns. In this regard, Federated Learning (FL) has emerged as a promising solution, enabling distributed training of machine learning models while preserving user privacy. In this paper, we propose a novel FL-based POI recommendation system, FedDist-POIRec, that utilizes Federated Distillation (FD) in a semi-synchronous framework to promote privacy, recommendation efficiency, and cope with resource heterogeneity by considering heterogeneous model architectures among users. Additionally, to alleviate the sparsity of user checkin lists, we employ a innovative knowledge aggregation technique, where the knowledge of each user is aggregated with that of users who share similar preferences. Simulation results demonstrate that FedDist-POIRec outperforms the baseline methods in terms of recommendation efficiency and communication cost, while keeping the computation cost at an acceptable level.

关键词： Training Privacy Data privacy Costs Social networking (online) Federated learning Simulation

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Learning Location Semantics and Dynamics for Traffic Origin-Destination Demand Prediction

Learning Location Semantics and Dynamics for Traffic Origin-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kuan-Hsuan Yung Josh Jia-Ching Ying Hui-Ting Lin Vincent S. Tseng Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan Dept. Management Information Systems National Chung Hsing University Taichung Taiwan Dept. Computer Science and Information Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traffic Origin-Destination (OD) Demand Prediction is pivotal for real-time ride-hailing services and government traffic management, aiming to anticipate traffic patterns and volumes between specific locations. Traditional grid-based traffic OD demand prediction methods, however, often fail to accurately capture the intricate and context-driven demand patterns inherent in modern urban transportation systems. In this paper, we propose an innovative location semantics and dynamics learning framework for capturing location semantics and dynamics to improving origin-destination traffic demand prediction. Departing from conventional grid-based methods, our approach incorporates a semantic location generation module that dynamically organizes semantic locations based on Points of Interest (POI). Our semantic location generation module is designed to form semantic locations according to POI types, spatial proximity, and demand patterns. This model captures hierarchical relationships and varying importance levels across POI types and domains. To learn the context and function of each area, we design a POI context extractor which can analyze contextual information of POIs within a specified radius. The POI Type Encoder utilizes advanced word embedding techniques to encode the POIs, enabling the model to comprehend the semantic significance of diverse locations and their influence on traffic patterns. Extensive experiments conducted on real-world New York City Taxi datasets demonstrate the superior performance of the proposed framework over state-of-the-art methods, as indicated by experiment results across all evaluation measurement. These findings affirm that our proposed framework could improve effectiveness by learning location semantics and dynamics.

关键词： Accuracy Semantics Urban areas Neural networks Prediction methods Traffic control Data processing

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