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A Public Flue Smoke Intelligent Cleaning Device based on Ultrasonic Atomization and computer simulation 2

A Public Flue Smoke Intelligent Cleaning Device based on Ult...

引用

2nd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Tianru, Lan School of Mechanical and Electrical Engineering Wuhan University of Technology Hubei Wuhan China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

This project designs a public flue gas cleaning device based on ultrasonic atomization and photochemical degradation. The design of the device can not only ensure that the flue gas is discharged quickly and reasonably, but also achieve timely capture and treatment during the process of flue gas discharge, coupled with the designed self-cleaning module, which can reduce operation and maintenance costs while timely and reasonably treating flue gas pollutants. It can effectively solve the problem of no treatment measures for grease emission from public grease emission pipes in high-rise residential buildings in China. © 2022 IEEE.

关键词： Flue gases

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A Stair Climbing Floor-sweeping Robot Based on Solidworks and Matlab simulation Platform 2

A Stair Climbing Floor-sweeping Robot Based on Solidworks an...

引用

2nd IEEE international conference on Data Science and computer Application, ICDSCA 2022

作者： Pan, Zhangyilu Yu, Bin Xi, Wanqiang Li, Hailiang School of Automation Nanjing University of Information Science and Technology Binjiang College Jiangsu Wuxi214105 China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Nowadays, most sweeping robots can only clean on flat ground, and due to a large number of steps, manual cleaning is more complicated and requires a lot of work. Therefore, a sweeping robot that can climb stairs is designed, and the solidwoks platform is used to draw the structural model of the sweeping robot. The sweeping robot consists of a robot body, a stair-climbing mechanism, and a cleaning arm. The stair-climbing mechanism is in the existing planetary structure wheel. Based on the combination of the Mecanum wheel, the sweeping robot can perform the lateral movement on the stairs. At the same time, the Mecanum wheel base's kinematic model is established, the control process of climbing and cleaning is introduced, and then the forward kinematics simulation of the cleaning arm is carried out through the matlab simulation platform. The stair-climbing sweeping robot designed in this paper has made several major improvements based on traditional sweeping robots and has the characteristics of adapting to various complex environments. © 2022 IEEE.

关键词： Robots

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Facial Emotion Recognition Prediction Technique: A Review

Facial Emotion Recognition Prediction Technique: A Review

引用

Computational modelling, simulation and Optimization (ICCMSO), international conference on

作者： Oreeti Khajuria Rakesh Kumar Meenu Gupta Department of Computer Science and Enginnering Chandigarh University Punjab India

In human communication, facial expressions can provide a wealth of information about the feelings of others. The most frequent method of communication between people is through their facial expressions. For instance, people open their mouths when astonished, grin to indicate a greeting, and frown to indicate confusion. The contact is greatly enhanced by our understanding of emotions and our ability to respond to other’s facial expressions. Facial emotion helps to analyze the mental state of a person. The various technique for the facial emotion prediction is reviewed. Emotion prediction includes the technique based on Machine learning and Deep Learning.

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Topology Maintenance Optimization Algorithm Based on Deep Reinforcement Learning in High Dynamic Flying Ad-Hoc Networks 2

Topology Maintenance Optimization Algorithm Based on Deep Re...

引用

2nd international conference on Signal processing, computer Networks and Communications, SPCNC 2023

作者： Li, Liheng Qiu, Xiulin Song, Bo Ke, Yaqi Yang, Yuwang School of computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Jiangsu University of Science and Technology Jiangsu Zhenjiang China

ISBN: (纸本)9798400716430

In the realm of high-dynamic-flight self-organizing networks, traditional proactive and reactive routing protocols confront challenges arising from the delayed and inflexible determination of the validity of neighbour nodes. An untimely assessment of node validity may lead to an escalated overall network packet loss rate, consequently causing a proportional decrease in throughput. To mitigate this issue, this paper introduces a Reinforcement Learning based Topoloty Maintenance Algorithm (RLTM). This algorithm integrates the mobility characteristics and remaining energy of neighbour nodes into a stability metric for these nodes. By conceptualizing the validity of neighbour nodes in topology maintenance as a Markov process and incorporating the stability metric as a dimension in the state space, the model undergoes training via Proximal Policy Optimization (PPO) to generate decisions for establishing the effective time of neighbour nodes. simulation results demonstrate that, in comparison to other topology maintenance optimization algorithms, the proposed algorithm amplifies the average packet delivery rate and throughput of unmanned aerial vehicle (UAV) nodes in high-speed mobility scenarios. © 2023 ACM.

关键词： Reinforcement learning

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Enhancing Power Decoupling Control of Grid-Connected Inverters Through Data-Driven Parameter Optimization 2

Enhancing Power Decoupling Control of Grid-Connected Inverte...

引用

2nd IEEE international Power Electronics and Application Symposium, PEAS 2023

作者： Zhu, Jiangge Lin, Gang Dai, Ningyi University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China University of Macau State Key Laboratory of Internet of Things for Smart City China

ISBN: (纸本)9798350313765

Grid-connected inverters typically employ a virtual impedance loop to facilitate power decoupling control. However, designing the control parameters to achieve optimal performance is a challenging task. This paper introduces a novel power decoupling index as a quantitative measure for evaluating the effectiveness of power decoupling control. The training dataset is generated under 800 sets of parameters by using Real Time Digital Simulators (RTDS). A deep neural network (DNN) surrogate model is developed to establish the relationship between control parameters and the power coupling index. To optimize the control parameters effectively, a Genetic Algorithm (GA) optimization process is employed based on the trained surrogate model. simulation results are provided to verify the effectiveness of the control parameter optimization method. © 2023 IEEE.

关键词： Deep Neural Network Genetic Algorithm Parameter Optimization Power decoupling

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ANALYSIS OF INTEGER-N PHASE-LOCKED-LOOP ARCHITECTURE 2

ANALYSIS OF INTEGER-N PHASE-LOCKED-LOOP ARCHITECTURE

引用

2nd international conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Chenguo, Ziyu Ou, Zichen Tao, Xinyu Faculty of Natural Mathematical & Engineering Sciences King's College London LondonWC2R 2LS United Kingdom Samueli School of Engineering University of California IrvineCA92697 United States School of Electrical Engineering and Computer Science University of Ottawa OttawaK1N 6N5 Canada

ISBN: (纸本)9781837242672

Phase-Locked Loops have a wide range of applications as frequency synthesizers. An analysis of Integer-N Phase-Locked-Loop (PLL) is presented in this article. The performance of a phase-locked loop is determined by a number of factors. It is shown whether good locking time performance can be provided by changing the loop bandwidth of the PLL system. The loop filter can be designed to accelerate the lock time because it controls the poles and zeros of the system. The PLL system is a typical Charge-Pump PLL which uses a Ring Voltage-Controlled Oscillator (Ring-VCO) to optimize the area as much as possible and to reduce power consumption. The phase noise of the Ring-VCO at 1MHz is -96.14dBc and the system can deliver a stable output frequency of 1.6GHz with a 100MHz reference clock. The Multi-Modulus Divider (MMD) used can provide divider ratios from 16 to 31. simulation is done by TSMC 40nm process. © The Institution of Engineering & Technology 2024.

关键词： Phase locked loops

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Production Method of Light Field Image simulation Based on 3ds Max 3D Reconstruction 2

Production Method of Light Field Image Simulation Based on 3...

引用

2nd international conference on computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022

作者： Wu, Tianhao Zhang, Yuxiang Yang, Jungang Liu, Jia Zhang, Hongjiang College of Electronic Science and Technology National University of Defense Technology ChangSha China Aerospace Dfh Satellite Co. Ltd. Mechanical Thermal Engineering Department Beijing China School of Electronics and Information Engineering Ankang College Ankang China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

At present, there are various ways to implement a light field imaging system, among which light field array camera as a new system of the light field imaging system is widely concerned and researched, however, the light field image produced based on this method does not provide the demand of scene baseline changeable, which greatly limits the application scene of light field image. To this end, this paper proposes a light field image simulation production method based on 3ds Max 3D reconstruction, which mainly includes five key steps, including 3D scene modeling, scene unit configuration, lighting parameter adjustment, array camera construction, and rendering imaging. The method proposed in this paper solves the shortcomings of the traditional light field image production process that cannot target array camera baseline changes and has advantageous features such as stable camera parameters, idealized rendering imaging, variable imaging baseline, and simultaneous presentation of a depth map. Compared with the traditional method, the method produces images with balanced quality and high automation, which can effectively improve the production efficiency of light field images in specific scenes and meet the requirements of relevant light field image processing tasks. © 2022 IEEE.

关键词： Cameras

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A Hybrid modelling Approach Based on Deep Learning for the Prediction of the Silicon Content in the Blast Furnace 27

A Hybrid Modelling Approach Based on Deep Learning for the P...

引用

27th international conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Lakshmanan, Kayal Karimi, Aurash Carr, Alex Wauters, Philippe Auinger, Michael Pleydell-Pearce, Cameron Giannetti, Cinzia Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom University of Warwick CoventryCV4 7AL United Kingdom Tata Steel UK Port TalbotSA13 2NG United Kingdom The Alan Turing Institute LondonNW1 2DB United Kingdom Department of Mechanical Engineering Imperial College London LondonSW7 2AZ United Kingdom

The blast furnace is an important part of the steelmaking process, and its main function is to melt and reduce oxygen from the iron ore for subsequent processing into the steel-ironmaking process. Due to its complexity, Blast Furnaces need to operate near their practical limits because of economic and environmental constraints. The capacity to monitor and regulate the process's thermal condition is, however, constrained by the harsh operating conditions inside the furnace. The amount of silicon present in pig iron, which is the metallic iron generated by the blast furnace process, serves as a crucial indicator of the furnace's thermal condition. Therefore, the creation of a predictive model is essential to assist in proactive control of the furnace's thermal condition because measurements of this crucial variable can only be sampled at sporadic and irregular intervals and analysis of the sample introduces a substantial delay. In this paper, an improved hybrid modelling methodology is introduced for blast furnace operation, which integrates physical and data-driven models. Deep Learning based Autoencoders are used for the prediction of the changes in silicon concentration with respect to time and that helps users to avoid running frequent and costly feature pre-processing procedures and correlation studies. Integrating the physical model improved the prediction accuracy compared to a purely data-driven model. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0)

关键词： blast furnace Deep Learning Hybrid modelling Industrial applications physical model

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Simulating Common Indoor Crowd Behaviours in 3D Environments Using ABMU 2

Simulating Common Indoor Crowd Behaviours in 3D Environments...

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2nd international conference on Interactive Media, Smart Systems and Emerging Technologies, IMET 2022

作者： Cheliotis, Kostas National Technical University of Athens Laboratory of Cartography Athens Greece

ISBN: (数字)9781665470162

ISBN: (纸本)9781665470162

Being able to model common crowd behaviours is vital in architecture and similar built environment fields. This is becoming even more pressing, especially in recent years with COVID-19 having a significant impact in how we interact in indoor spaces. As such, common crowd simulation approaches such as Agent-Based modelling (ABM) need to evolve rapidly to be able to accommodate new requirements in crowd interaction;for indoor crowds, this usually requires a model to accommodate three-dimensional spaces, as the existing 2D modelling approaches are often inadequate in capturing the nuances of crowd interaction in indoor spaces, which are inherently three-dimensional. In this paper we present an application of such a 3D ABM modelling framework through a model of queuing behaviour, a common behaviour in indoor crowds. The model is developed in three-dimensional space on the Unity3D development platform, using the Agent-Based modelling Framework for Unity3D (ABMU). © 2022 IEEE.

关键词： Three dimensional computer graphics

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Artificial Intelligence-Based Control of Autonomous Vehicles in simulation: A CNN vs. RL Case Study 2nd

Artificial Intelligence-Based Control of Autonomous Vehicle...

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2nd international conference on Digital Transformation in Education and Artificial Intelligence Applications, MoStart 2024

作者： Vasiljević, Ive Musić, Josip Lima, José Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture University of Split Split21000 Croatia Research Centre of Digitalization and Intelligent Robotics Instituto Politécnico de Bragança Bragança5300-253 Portugal

ISBN: (纸本)9783031620577

The article provides a comparison of Convolutional Neural Network (CNN) and Reinforcement Learning (RL) applied to the field of autonomous driving within the CARLA (CAr Learning to Act) simulator for training and evaluation. The analysis of results revealed CNNs better overall performance, as it demonstrated a more refined driving experience, shorter training durations, and a more straightforward learning curve and optimization process. However, it required data labelling. In contrast, RL relayed on an exhaustive (unsupervised) exploration of different models, ultimately selecting the model at timestep 600,000, which had the highest mean reward. Nevertheless, RL’s approach revealed its susceptibility to excessive oscillations and inconsistencies, necessitating additional optimization and tuning of hyperparameters and reward functions. This conclusion is further substantiated by a range of used performance metrics (objective and subjective), designed to assess the performance of each approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Reinforcement learning

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