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Deep Reinforcement Learning (DRL) based data analytics framework for Edge based IoT devices latency and resource optimization

Deep Reinforcement Learning (DRL) based data analytics frame...

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Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), International conference on

作者： Sudhakar Majjari Koteswara Rao Anne Joseph George Department of CSE Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of CSE Adi Shankara Institute of Engg. And Technology Kaladyi India

Internet of Things (IoT) trends show rising data processing computational needs. Sensor data is uploaded to backend cloud nodes before data analyses at the network edge. IoT devices are usually resource-constrained and unable to execute operations quickly and accurately. Cloud servers are impractical and increase communication overhead. Cloud platforms offer machine learning services with pretrained models to understand IoT data. To use the cloud service, personal data must be transferred, and network problems may impede timely analysis results. Data and analysis are shifting to edge platforms to solve these concerns. Most edge devices can't analyze and train a lot of data. Edge-enabled systems provide efficient compute and control at the network edge to reduce scalability and latency. IoT applications provide large heterogeneous data, which makes edge computing difficult. To solve this issue, Deep Reinforcement Learning (DRL) based data analytics framework for Edge based IoT devices to enable devices to execute tasks jointly, leveraging proximity and resource complementarity. It supports parallel data input and strengthen the comprehensive communication overhead handling through data scheduling optimization. The simulation results conveys that the proposed approach uses DRL to optimize execution accuracy and time without requiring a priori IoT node information. Moreover, the average delay time, percentage of failure and cost of rewards are computed in which being compared with the existing scheduling methods includes Proximal Policy optimization technique (PPO), and Deep Deterministic Policy Gradient technique (DDPG).

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Chance-constrained quasi-convex optimization with application to data-driven switched systems control 3

Chance-constrained quasi-convex optimization with applicatio...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Berger, Guillaume O. Jungers, Raphael M. Wang, Zheming UCLouvain Inst Informat & Commun Technol Elect & Appl Math Dept Engn Math ICTEAM INMA B-1348 Louvain Belgium

We study quasi-convex optimization problems, where only a subset of the constraints can be sampled, and yet one would like a probabilistic guarantee on the obtained solution with respect to the initial (unknown) optimization problem. Even though our results are partly applicable to general quasi-convex problems, in this work we introduce and study a particular subclass, which we call "quasi-linear problems". We provide optimality conditions for these problems. Thriving on this, we extend the approach of chance-constrained convex optimization to quasi-linear optimization problems. Finally, we show that this approach is useful for the stability analysis of black-box switched linear systems, from a finite set of sampled trajectories. It allows us to compute probabilistic upper bounds on the JSR of a large class of switched linear systems.

关键词： Data-driven control chance-constrained optimization quasi-convex programming switched systems

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3rd International conference on Neural Computing for Advanced Applications, NCAA 2022

3rd International Conference on Neural Computing for Advance...

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3rd International conference on Neural Computing for Advanced Applications, NCAA 2022

ISBN: (纸本)9789811961410

The proceedings contain 77 papers. The special focus in this conference is on Neural Computing for Advanced Applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis;a Perception Method Based on Point Cloud Processing in Autonomous Driving;many-Objective Artificial Bee Colony Algorithm Based on Decomposition and Dimension Learning;LCSW: A Novel Indoor Localization System Based on CNN-SVM Model with WKNN in Wi-Fi Environments;large Parallax Image Stitching via Structure Preservation and Multi-matching;traffic Congestion Event Mining Based on Trajectory Data;many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition;a New Unified control Approach for Finite-/Fixed-Time Synchronisation of Multi-weighted Dynamical networks;aperiodic Sampling Based Event-Triggering for Synchronization of Nonlinear Systems;two-Stream 3D MobileNetV3 for Pedestrians Intent Prediction Based on Monocular Camera;Research on Non-intrusive Household Load Identification Method Applying LightGBM;design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm;an Ensemble Deep Learning Model Based on Transformers for Long Sequence Time-Series Forecasting;multi-layer Integrated Extreme Learning Machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker;wind Power Forecast Based on Multiple Echo States;UAV-Assisted Blind Area Pedestrian Detection via Terminal-Edge-Cloud Cooperation in VANETs;a Survey of Optimal Design of Antenna (Array) by Evolutionary Computing Methods;a Span-Based Joint Model for Measurable Quantitative Information Extraction;bayesian optimization Based Seq2Seq network Models for Real Estate Price Prediction in Hong Kong;ensemble Learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects;Cage Mass Center Capture for Whirl Analysis Using an Improved MultiResUNet from the Multimodal Biomedical Image Segmentation;master Multiple Real-Time Strategy Game

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Nature Inspired Routing in Mobile Ad Hoc network 3

Nature Inspired Routing in Mobile Ad Hoc Network

引用

3rd International conference on Advances in Computing, Communication control and networking, ICAC3N 2021

作者： Dash, Yajnaseni Indian Institute of Technology New Delhi India

ISBN: (纸本)9781665438117

Mobile ad hoc network (MANET) is an ad hoc network having inherent flexibility, autoconfiguration, selforganization, self-administration, self-creation, short of infrastructure, little cost, easier deployment, and used for prospective applications. The aforementioned qualities put together in MANETs, as the essence of imminent pervasive computing environments. It is an emergent research field considering routing. As the network topology, bandwidth, and energy efficiency change consistently, designing an effective routing protocol becomes a challenging task in the case of MANETs. This paper provides the basic concepts of wireless networks and an overview of bio-inspired ant colony based routing algorithms for MANETs. Ant colony optimization is based on the probabilistic way to find the shortest path between an ant colony and its food source. A comparison between ant-based routing algorithms such as AntNet (Adaptive routing in ad hoc network), ARA (Ant colony based routing algorithm), and AntHocNet (Ant agents for hybrid multipath routing) is also presented. AntNet uses probabilistic forwarding of data packets using forward and backward ants. In ARA, multiple paths are set by ants in an on-demand manner between source and destination, and AntHocNet is a hybrid combination of both reactive and proactive routing components. In MANETs, nature-inspired routing algorithms are used for proper decision-making, robustness, self-healing, control, coordination, and adaptation purposes. © 2021 IEEE.

关键词： Mobile ad hoc networks

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3rd International conference on Neural Computing for Advanced Applications, NCAA 2022

3rd International Conference on Neural Computing for Advance...

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3rd International conference on Neural Computing for Advanced Applications, NCAA 2022

ISBN: (纸本)9789811961342

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Robust Power Management and control for Hybrid AC-DC Microgrid 3

Robust Power Management and Control for Hybrid AC-DC Microgr...

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3rd IEEE International conference on Energy, Power and Environment - Towards Clean Energy Technologies (ICEPE)

作者： Buduma, Parusharamulu Das, Madan Kumar Mishra, Sukumar Panda, Gayadhar Indian Inst Technol Delhi Dept Elect Engn New Delhi India Natl Inst Technol Meghalaya Dept Elect Engn Shillong Meghalaya India

ISBN: (纸本)9781665425360

In recent years, Hybrid AC-DC Microgrid (HM) system is a potential approach that can facilitate for connection of AC loads to AC-Microgrid (ACM) and DC loads to DC-Microgrid (DCM). The HM can share power between ACM and DCM via a Bidirectional Power Converter (BPC). The interconnection of ACM and DCM by the BPC requires an efficient supervisory power management share power between its different resources. In this paper, a Robust Optimal Power Management Strategy (ROPMS) is proposed for the HM, along with a robust tracking commitment for BPC. The ROPMS is implemented using a counterpart robust optimization problem for uncertainty in a constrained data set. The objective of the optimization is to share power while maximum utilizing the renewable power, minimum utilizing the conventional power and minimum power through BPC. The robust power tracking for BPC is achieved by a nested loop Robust control Scheme (RCS). The RCS consists of an inner loop with a Robust LQR (RLQR) optimal controller and an outer loop with Mixed H-infinity/H-2 Optimal (MO) controller. Performance of ROPMS for power-sharing and RCS for tracking the references of BPC in the HM is analyzed in both grids connected and islanded modes with different operating case studies and scenarios using MATLAB/SIMULINK.

关键词： Robust control Power management Hybrid AC-DC Microgrid, and Bidirectional power converter

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Research on Intelligent Energy Management of Fuel Cell Vehicle

Research on Intelligent Energy Management of Fuel Cell Vehic...

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Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Tian Yang Songmao Wei Zilong Wang Jiashuai Li Peiliang Yu Department of Electrical & Electronic Control Development China Automotive Technology & Research Centre Co. Ltd Tianjin China

With the development of fuel cell technology, how to effectively reduce hydrogen consumption of fuel cell vehicles has become a technical focus and difficulty. This paper proposed an adaptive energy management algorithm which was suitable for fuel cell vehicles. First, the ECMS algorithm considering the durability of the fuel cell was established. In order to ensure the good performance of the ECMS algorithm, an equivalent factor optimization model was established, which adjusted the equivalent factor according to the changes of battery SOC and future velocity. Considering that in the actual driving environment, it was impossible to accurately obtain the upcoming driving velocity in the future. Therefore, it was hard to quickly adjust and select the equivalent factor. This article established a neural network velocity prediction model, and the predicted velocity were provided to the equivalent factor optimization model, and the adaptive energy management algorithm was accomplished. Simulation results showed that the adaptive algorithm reduced hydrogen consumption by 2.6% compared to the basic ECMS algorithm, while battery SOC was in a suitable wording window.

关键词： Adaptation models Hydrogen Predictive models Prediction algorithms Electromagnetic compatibility Fuel cell vehicles Batteries

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2020 3rd International conference on Modeling, Simulation and optimization Technologies and Applications, MSOTA 2020

2020 3rd International Conference on Modeling, Simulation an...

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2020 3rd International conference on Modeling, Simulation and optimization Technologies and Applications, MSOTA 2020

The proceedings contain 90 papers. The topics discussed include: modeling analysis of carrier-based aircraft's scheduling on flight deck;modeling on heterosynaptic plasticity based on postsynaptic membrane potential and current density;classification and quantitative modeling analysis of groundwater hardness based on ultraviolet absorption spectrum;congestion control of multi-layer cellular networks based on modeling of transmit power;an evaluation method of dc magnetic bias vibration for transformer based on prior knowledge and neural network modeling;simulation and functional mechanism of tuna median fins on yaw mobility in unsteady flow;modeling and intelligent identification of axis orbit for rotating machinery based on the convolution neural networks;and modeling and robust continuous power allocation strategy with imperfect channel state information in cognitive radio networks.

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An Economic and Low-carbon Dispatch Algorithm for Microgrids with Electric Vehicles

An Economic and Low-carbon Dispatch Algorithm for Microgrids...

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Power Systems and Electrical Technology (PSET), IEEE International conference on

作者： Jiayu Cheng Hao Liang Xiaoying Tang Shuguang Cui Shenzhen Future Network of Intelligence Institute School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Science and Engineering Shenzhen Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Electric vehicles (EVs) are gaining popularity for their energy storage capabilities and environmental advantages. Integrating EVs into microgrids (MGs) enhances MG dispatch in cost-effective and low-carbon operations. However, previous research has not insufficiently explored MGs with non-dispatchable EVs, and the uncertainties introduced by high EV penetration present significant challenges to the joint optimization of MG dispatch. We address these issues by proposing a novel Two-Layer MG Economic and Low-Carbon Dispatch (TL-ELCD) algorithm to optimize MG dispatch and EV regulation. A two-layer control scheme is utilized to promote critical interactions and reduce computational complexity. Additionally, we develop multi-agent pricing strategies employing the price-based demand response, which achieves indirect regulation of EV charging and contributes to carbon emissions reduction. Experiments validate the effectiveness of TL-ELCD in improving system economic and environmental benefits.

关键词： Uncertainty Pricing Microgrids Carbon dioxide Regulation Electric vehicle charging Demand response Power systems optimization Energy storage

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Robustly Coordinated Distributed Voltage control Through Residential Demand Response Under Multiple Uncertainties

Robustly Coordinated Distributed Voltage Control Through Res...

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19th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 3rd IEEE Industrial and Commercial Power Systems europe conference (I and CPS europe)

作者： Sharma, Sumedha Verma, Ashu Panigrahi, B. K. Indian Inst Technol Delhi Ctr Energy Studies New Delhi 110016 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

This article proposes a multilevel coordinated voltage control (CVC) strategy for facilitating distributed residential demand response for network voltage optimization. The proposed CVC scheme coordinates multiple home energy management systems (HEMS) connected on a low-voltage radial distribution network through a sensitivity-based approach to minimize voltage deviations in multiple timescales owing to high photovoltaic (PV) penetrations and load variability. In order to mitigate the effects of uncertain PV output and load during actual system operation, a day-ahead two-stage robust optimization (TSRO) model has been developed, which optimizes network operation in the longer timescale, for worst-case realization of uncertainties in shorter timescales. This is sequentially followed by fully distributed hour-ahead CVC which is decomposed into network optimization and energy management subproblems through alternating direction method of multipliers. In order to counteract the effects of real-time uncertainties, local adjustments are performed by redispatching HEMS at individual households to locally adapt to unforeseen variations, thereby minimizing real-time voltage deviations. Extensive simulations are performed on the modified Dutch LV network. Simulation results demonstrate effectiveness of the proposed multilevel CVC approach to robustly handle uncertainties while ensuring reliable operation and maximum user-comfort.

关键词： Voltage control optimization Uncertainty Real-time systems Inverters Load modeling Load management Alternating direction method of multipliers demand response home energy management sensitivity analysis two-stage robust optimization voltage control

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