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Anywhere Projected AR for Robot Communication: A Mid-Air Fog Screen-Robot system 25

Anywhere Projected AR for Robot Communication: A Mid-Air Fog...

引用

ACM/IEEE International conference on Human-Robot Interaction (HRI)

作者： Adrian Lozada Uthman Tijani Villa Keth Hong Wang Zhao Han Department of Computer Science and Engineering RARE Lab University of South Florida Tampa Florida USA

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Augmented reality (AR) allows visualizations to be situated where they are relevant, e.g., in a robot's operating environment or task space. Yet, headset-based AR suffers a scalability issue because every viewer must wear a headset. Projector-based spatial AR solves this problem by projecting augmentations onto the scene, e.g., recognized objects or navigation paths, viewable to crowds. However, this solution mostly requires vertical flat surfaces that may not exist in large open areas like auditoriums, warehouses, construction sites, or search and rescue scenes. Moreover, when humans are not co-located with the robot or situated at a distance, the projection may not be legible to humans. thus, there is a need to create a projectable, viewable surface for humans in such scenarios. In this HRI systems paper, we introduce a fog screen-robot system that integrates a mid-air fog screen device into a robot to create such a projectable surface and presents two evaluations in a construction site and a search and rescue scenario for high-stakes communication needs. Specifically, we implemented an existing fog screen device, which can only project one-third of a meter (33cm). We improved it to achieve a fog screen length of half a meter (53cm). In the noisy construction site scenario, the robot inspected the site and projected icons for missing wall sockets and plumbing fixtures. In the unstructured search and rescue scenario, the robot was able to project a person icon for a first responder to save life. All 3D models and code are available at https://***/theRARELab/fog-screen-robot-system. Videos are available at https://***/b4efu/.

关键词： Meters Visualization Solid modeling three-dimensional displays Sockets Scalability Human-robot interaction Robots Augmented reality Videos

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Optimizing Latency and Communication in Federated Edge Computing with LAFEO and Gradient Compression for Real-Time Edge Analytics

Optimizing Latency and Communication in Federated Edge Compu...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： Namratha P Koushik Reddy Chaganti Sivananda Lahari Reddy. Elicherla Surekha Guddati Akarapu Swarna P. thiramal Reddy Department of CSE ATES Institute of Technology and Science Anantapur Dept. of Computer and Information Systems University of the Cumberlands KY USA Dept of CSE Dayananda Sagar University Bangalore Dept of CSE Vidya Jyothi Institute of Technology Hyderabad Dept of CSE Vignan Institute of Technology and Science Hyderabad

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the increasing need for real-time data analytics in edge computing is furthered by the latency pressures from computational and communication bottlenecks, thus demanding novel solutions. Fundamental approaches to federated learning and distributed machine learning do not accommodate changes in dynamic latency conditions very well, thereby being inefficient in terms of synchronization, communication overhead, and delay in task execution. this paper presents a holistic framework consisting of three innovative approaches to solve these problems: Latency-Aware Federated Edge-Aware optimization (LAFEO), Latency-Driven Gradient Compression with Adaptive Sparsity (LAGCAS), and Predictive Latency Compensation with Dynamic Replication (PLCDR). the LAFEO framework consists of a double-layer optimization framework, combining latency-weighted scheduling with reinforcement learning to dynamically adapt the synchronization frequencies. this results in 25-30% end-to-end latency reduction with an accuracy deviation that is capped at 2% centralized models. LAGCAS reduces communication overhead by applying adaptive gradient scarification methods, bringing compressions of gradients under real-time accuracy thresholds: it reduces 40% communication volume and accelerates convergence by 20%. PLCDR uses predictive modelling to predict latency bottlenecks and dynamically replicates critical computations across the edge nodes. It hence offers 15-25% decrease in the completion time of tasks and up to 30% enhancement in the resilience of system sets. the existing methods will be addressed with the key limitations due to the introduction of latency-aware mechanisms that adapt to real-time edge conditions along with high accuracy and efficiency. Simulation results have shown marked scalability improvements, latency reductions, and resilience; in its positioning, this work's effort is a transformative advancement in the latency-sensitive edge computing process.

关键词： Adaptation models Accuracy Computational modeling Simulation Scalability Real-time systems Synchronization optimization Edge computing Resilience

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Sizing and operation of a residential PV-battery system: Rule-Based and real time vs optimization and perfect foresight 20

Sizing and operation of a residential PV-battery system: Rul...

引用

20th IEEE Mediterranean Eletrotechnical conference (IEEE MELECON)

作者： Sedano Ruiz, Elena Huete Cubillo, Jesus de la Vega Rodriguez, Jorge Martin, Sebastian Univ Malaga Dept Elect Engn Malaga Spain

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152004

Many residential customers are becoming prosumers, because of environmental concerns and falling prices of renewable generation technologies, as photovoltaic. In this context, typical residential installations consists of photovoltaic (PV) generation and batteries. Battery energy management poses a challenge because of the variability and uncertainty related to PV generation and customer consumption. Here, we propose easy to implement rules for the system (PV + battery) operation, to minimize the total cost for the customer. the main contribution is to use the rule-based operation for system sizing and operation, and to compare the results to those for an optimization based sizing and operation with perfect foresight. Based on this comparison, the value of information for the system operation is quantified and discussed. the optimization problem and the rule-based operation are applied to a case study for a residential customer in Southern Spain.

关键词： battery photovoltaic generation ruled-based operation optimization perfect foresight

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Operation of a Distributed Generation Plant in a Power Supply system with Non-linear and Asymmetric Load

Operation of a Distributed Generation Plant in a Power Suppl...

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International conference on Harmonics and Quality of Power

作者： Yuri N. Bulatov Andrey V. Kryukov Konstantin V. Suslov Bratsk State University Bratsk Russia Irkutsk State Transport University Irkutsk National Research Technical University Irkutsk Russia Irkutsk National Research Technical University Irkutsk Russia

this paper presents the results of research on the influence of asymmetric and nonlinear load on the operating mode of a low-power turbine generator, which simulation takes into account automatic excitation and rotor speed controllers. the results of computer and physical modeling show the occurrence of the following negative effects: a noticeable negative-sequence current appears; the generator begins to generate higher harmonics into the network; the amplitude of the forced oscillations of the excitation current increases; additional mechanical vibrations of the rotor arise, which can lead to the accelerated wear and loss of stability. To ensure the operation of low-power generators with asymmetry and harmonic distortions, it is necessary to limit their load. As a criterion, the maximum current difference in the phases can be used, which should not exceed 12% of the rated current. In addition, it is required to use special means to protect generators: DC links; harmonic filters; negative-sequence protection of generators; automatics for limiting the excitation current.

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Recognition and Application of Learner's Cognitive Ability for Adaptive E-learning 20

Recognition and Application of Learner's Cognitive Ability f...

引用

20th IEEE International conference on Advanced Learning Technologies (ICALT)

作者： Zhang, Bingxue Li, Yuxiang Shi, Yang Hou, Longfeng Univ Shanghai Sci & Technol Dept Opt Elect & Comp Engn Shanghai Peoples R China Univ Shanghai Sci & Technol Dept Energy & Power Engn Shanghai Peoples R China

ISBN: (纸本)9781728160900

In adaptive learning system, the key to promote personalized learning is the learner model. the Elo model has great potential in online learning environment, so based on it, we propose the EELO which is an improved extension of Elo rating system, in view of polychotomously scored items and different granularity evaluations that the Elo rating system do not cover. the performance of the EELO estimating learners' abilities and predicting their future performances are evaluated on two large data set, which demonstrates that the EELO is better-performing. then, we apply it in a real online learning environment to provide an analysis report for different user roles, which also can be used as the reference for the development of adaptive learning applications in the future.

关键词： adaptive learning system learner modeling ELO rating system polychotomously scored item cognitive ability

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optimization of Public Bus Scheduling using Real-Time Online Information

Optimization of Public Bus Scheduling using Real-Time Online...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Teng Hei Ho Ke Wang Man Xu Chan-Tong Lam Benjamin K. Ng Faculty of Applied Sciences Macao Polytechnic University Macao S.A.R China

Due to increasing populations and congested traffics, public transportation arrangements are crucial for smart cities. In this paper, a joint method is proposed to optimize public bus scheduling, using real-time online bus information. Specifically, we first introduce three parameters, i.e., number of buses, number of stops, and dwell time, to be the inputs of the optimization process. then, $K$ -means clustering and genetic algorithms are used to optimize collaboratively. Different from traditional genetic algorithms, the proposed method can effectively avoid local optimal results. Besides, we propose two metrics to evaluate the system performance. Real-time online data-based experiments show the effectiveness and robustness of our scheme. As a result, the number of buses and operation efficiency can decrease by 28% to 47% and enhance by 3% to 12%, respectively.

关键词： Measurement Smart cities system performance Sociology Real-time systems Robustness Statistics

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ANFIS (Adaptive Neuro-Fuzzy Inference system) based on Microgrid’s Reliability and Availability

ANFIS (Adaptive Neuro-Fuzzy Inference System) based on Micro...

引用

IEEE Power India conference

作者： Geeta Yadav Dheeraj Joshi Leena G M K Soni Department of Electrical and Electronics Engineering Manav Rachna International Institute of Research and Studies Faridabad India Department of Electrical Engineering Delhi Technological University Delhi India IIMT Group of Colleges Noida India

ISBN: (纸本)9781665459310

the applicability of machine learning algorithms used to solve microgrid optimization is investigated in this paper. this paper’s main objective is to build a microgrid model to achieve maximum reliability and availability using renewable resources that cater to users' needs with different demands and supplies. the model generated from the adaptive neuro-fuzzy inference system (ANFIS) is used to get the optimum reliability and availability strategy to achieve the user expectations and needs of future microgrids. the ANFIS model is trained with different data sets from Markov modeling. the dataset is divided into three sections, 40% of the data is used to train the model, testing is performed with 40%, and the last 20% of the information is checked. Implementation results show that ANFIS models emulate Markov modeling methods and artificial neural networks model and enhance reliability and availability. Additionally, the ANFIS model is better than the Markov and artificial neural networks models regarding the impact of individual failure rate optimization from sub-models. Comparison is shown with Markov modeling, genetic algorithm, artificial neural networks, and fuzzy system. the optimization toolbox Matlab is used for ANFIS.

关键词： Adaptation models Microgrids Artificial neural networks Markov processes Maintenance engineering Predictive models Mathematical models

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Solving Dynamic Bag-of-Tasks Scheduling Problem in Heterogeneous Multi-cloud Environment Using Hybrid Bi-Level optimization Model 1

引用

20th International conference on Hybrid Intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired Computing, NaBIC 2020

作者： Karaja, Mouna Ennigrou, Meriem Said, Lamjed Ben SMART Laboratory Institut Supérieur de Gestion Université de Tunis Tunis Tunisia

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

Task scheduling problem has attracted a lot of attention since it plays a key role to improve the performance of any distributed system. this is again more challenging, especially for multi-cloud computing environment, mainly based on the nature of the multi-cloud to scale dynamically and due to heterogeneity of resources which add more complexity to the scheduling problem. In this paper, we propose, for the first time, a new Hybrid Bi-level optimization model named HB-DBoTSP to solve the Dynamic Bag-of-Tasks Scheduling Problem (DBoTSP) in heterogeneous multi-cloud environment. the proposed model aims to minimize the makespan and the execution cost while taking into consideration budget constraints and guaranteeing load balancing between Cloud’s Virtual Machines. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Budget control

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Discrete Event Controllers for Grids' Lines 20

Discrete Event Controllers for Grids' Lines

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20th IEEE Mediterranean Eletrotechnical conference (IEEE MELECON)

作者： Calin, Ciufudean Corneliu, Buzduga Stefan Cel Mare Univ Fac Elect Engn & Comp Sci Suceava Romania

ISBN: (纸本)9781728152004

Deadlock avoidance of energy delivery through lines with shared resources, such as green energy grids, is an actual, but hard task. We use Petri nets for modeling the controllers of energetic lines capable to provide a restrictive policy which prevents deadlocks in the system. Dealing with Petri net formalism is a versatile and user friendly way for implementing a certain model, concerning the process of model's simulation before building it. A difficult problem when dealing with Petri nets modeling and simulation concerns system's reversibility in order to ensure the system's recoverability from deadlock or failure states. therefore in this paper we propose an approach by using qualitative formalisms for avoiding the bottlenecks of the Petri net model without losing the desired properties for real system. An example illustrates our approach.

关键词： grid system green energy discrete event systems Petri nets Markov chains reversible controller

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20th International conference on Hybrid Intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired Computing, NaBIC 2020

20th International Conference on Hybrid Intelligent Systems,...

引用

20th International conference on Hybrid Intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired Computing, NaBIC 2020

ISBN: (纸本)9783030730499

the proceedings contain 78 papers. the special focus in this conference is on Hybrid Intelligent systems. the topics include: Text Classification Using Convolution Neural Networks with FastText Embedding;deep Convolutional Neural Network Based on Wavelet Transform for Super Image Resolution;optimizing Instance Selection Strategies in Interactive Machine Learning: An Application to Fraud Detection;building IoT Analytics and Machine Learning with Open Source Software for Prediction of Environmental Data;ANFIS-Based Inverse Kinematics and Forward Dynamics of 3 DOF Serial Manipulator;design and Development of Intelligent Pesticide Spraying system for Agricultural Robot;solving Dynamic Bag-of-Tasks Scheduling Problem in Heterogeneous Multi-cloud Environment Using Hybrid Bi-Level optimization Model;Caching Mechanisms Evolution in CCN Architectures;SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews;auto-Encoder Based Wavelet and Extreme Learning Machine for Face Recognition;characterizing Antennas’ Radiation Pattern Using Bernoulli Lemniscates;analysis of Metaheuristics Feature Selection Algorithm for Classification;hybrid Genetic Algorithms to Solve the Traveling Salesman Problem;hybridization of Adaboost with Random Forest for Real-Time Prediction of Online Shoppers’ Purchasing Intention;change Detection in Satellite Imagery: A Multi-label Approach Using Convolutional Neural Network;inverse Neural Control of a Magnetic Levitation system: Experimental Results;COVID-19 Detection Using Deep Learning;an Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies;an Effective Heart Disease Prediction Model Based on Machine Learning Techniques;optimal Design of Fuzzy Controllers Using the Multiverse Optimizer;sparse Wavelet Auto-encoder for Covid-19 Cases Identification;a New Bi-objective Classic Transportation Model Considering Social Justice;emotion Recognition Using Multimodalities.

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