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4th International conference on Machine Learning and Computer Application, ICMLCA 2023

作者： He, Xinxin Zhang, Zhifeng Jing, Liwei Zhou, Hao Zhu, Zhiqin Zhou, Weiwei Institute of Electrical Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

With the rapid acceleration of urbanization, the scale of urban distribution networks has experienced significant growth. However, the prevalence of issues related to cable equipment quality disparities, inadequate construction process oversight, unfavorable operating conditions, and other factors has led to a pronounced increase in safety concerns for both individuals and the grid resulting from cable failures. Furthermore, the limitations of conventional online monitoring methods have posed considerable challenges in meeting the monitoring demands of widely distributed and large-scale urban distribution networks. To address these multifaceted challenges, this paper introduces the design of a low-power wireless online monitoring system grounded in the internet of Things (IoT) paradigm. This system is adept at concurrently monitoring multiple status signals within urban distribution networks. The proposed methodology entails the establishment of a monitoring sensing network for distribution networks, built upon IoT principles. It encompasses the optimization of low-power hardware circuitry, power management strategies, data communication protocols, and software control mechanisms, among other facets. Through the aforementioned research endeavors, the successful realization of a low-power design and application of IoT-based distributed monitoring systems for multiple signals within power distribution networks has been achieved. This accomplishment serves as a robust technical foundation for ensuring the dependable operation of urban power distribution networks. © 2024 SPIE.

关键词： Power quality

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A bio-inspired design of real-time control learning algorithm in model-free drone manipulation

A bio-inspired design of real-time control learning algorith...

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2024 International conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Wang, Jiaguo Li, Wenheng Pei, Yang Northwestern Polytechnical University Shaanxi Xi’an710072 China AVIC Xi’an Aeronautics Computing Technique Research Institute Shaanxi Xi’an710068 China

ISBN: (纸本)9781510685437

In recent years, neural-network-based adaptive dynamic models are commonly used to estimate and control flight dynamics for drones and multi-copters. However, most of them simply use networks to optimize few parameters in control policy. There are still large improvements on the structural layout for robust model-free control applications. Therefore, to achieve a similar intelligence of human is still challenging due to their difference in basic mechanisms and difficulty in network modeling. In this paper, we design a control learning algorithm which combines reinforcement learning with neural networks simplified from human cerebellar motor learning model. The algorithm learns parameters by statistically measuring the performance and analyzing the input-output relationship on real-time episodes. In local linear systems, parameters are learned with respect to a spatial function of environment state and subjective expectation. Compared with other methods using static models, the most obvious advantage of this algorithm is that it can learn complex dynamics of alternative degrees of freedom while the dynamics are difficult to be formulated by equation set. Besides, the algorithm is suitable for individual systems, without prior knowledge about system geometry, centre of gravity as well as installation error, since it learns the dynamic effects directly relevant to the optimal guidance and control behavior in unknown or partially known environments instead. Experiment verifies the algorithm in a practical way. In the experiment, the algorithm is implemented to a quad-copter and it can learn the flight control policy from zero-state without any prior knowledge. The flight quality is tested to be equal to accurate control model at outdoor flight experiences. By repeated experiment, the algorithm is demonstrated to have good robustness to control different physical models and the potential to explore alternative dimensionality. © 2024 SPIE.

关键词： Drones

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Multi-Tag Selection for IRS-Assisted Ambient Backscatter Communication networks 14

Multi-Tag Selection for IRS-Assisted Ambient Backscatter Com...

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14th International conference on Ubiquitous and Future networks, ICUFN 2023

作者： Yoon, Jeong Woo Kim, Junsu Kim, Su Min Department of Electronics Engineering Siheung Korea Republic of

ISBN: (纸本)9798350335385

In this paper, we propose multi-tag selection and transmission protocol for intelligent reflecting surface (IRS)-assisted ambient backscatter communication (AmBC) networks with multiple tags. More specifically, we propose a frame structure, IRS phase control methods, and the best tag selection scheme for two different scenarios according to the number of IRSs. The performance of the proposed IRS-assisted AmBC network is evaluated in terms of bit error rate (BER) and achievable rate through simulations. With increasing the number of AmBC tags and the number of IRS elements, the performance is significantly improved since the received signal quality is enhanced by the gains from IRS beamforming and multi-tag selection diversity. Especially, when two IRSs exist, it is dramatically improved, and a few tags are enough to achieve the maximum performance. © 2023 IEEE.

关键词： internet of things

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International conference on Recent Evolutions in Energy, Drives and E-Vehicles, REED-EV 2022

International Conference on Recent Evolutions in Energy, Dri...

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International conference on Recent Evolutions in Energy, Drives and E-Vehicles, REED-EV 2022

ISBN: (纸本)9789819707621

The proceedings contain 56 papers. The special focus in this conference is on Recent Evolutions in Energy, Drives and E-Vehicles. The topics include: Comparative Analysis of Mobile Substation;solar Photovoltaic Technology and Future Trends;Crop Leaf Disease Detection Using DCCN;Extraction of Seven Parameters of Double Diode Model Using Analytical and Optimization Method with RTC FRANCE PV Module Manufacturing Data;ioT in Agriculture: A Review;Development and Characterization of Electrode Using PAN/TiO2 Nanofibers to Improve the Battery Capacity;Enhancement of Power quality of PV-Interfaced Distribution System Using Adaptive Backpropagation;Power quality Improvement in a Transmission System Using (Series-Active Filter or DVR);breast Cancer Detection Using Deep Learning and Thermal Images;a Harmonic Current Compensation on a Grid-Connected Electric Vehicle;application of Electro-Mechanical Kinetic Energy Regenerative System and E-Lock Differential for an Electric Vehicle: A Review;challenges and Future Scope in Fake News Detection: A Survey;a Novel Analysis of Urban and Rural Thermal Comfort Levels as a Futuristic Human Dwelling Strategy;a Survey on performance of Broadcasting Protocol for Ad hoc and Sensor network;wheelchair control Using IoT and Fall Detection Mechanism;modeling and Simulation of a Distributed Generation System for Power quality Assessment;A Comprehensive PVsyst Design for a College Campus in Baghdad;control of VSI in Microgrid Using Improved Particle Swarm Optimization;Emmet Territory Optimization Algorithm for Reduction of Pilot Contamination in Massive MIMO systems;Assessment of FSPV-Grid System for Electrification of Industrial Sections with Shrinking Derating Factor;Modeling and Speed control of DC Motor;PV-STATCOM in Distribution System.

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2nd International conference on Mechanical and Energy Technologies , ICMET 2021

2nd International Conference on Mechanical and Energy Techno...

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2nd International conference on Mechanical and Energy Technologies , ICMET 2021

ISBN: (纸本)9789811901072

The proceedings contain 59 papers. The special focus in this conference is on Mechanical and Energy Technologies. The topics include: Role of Artificial Intelligence in Energy and Power Engineering;the Opportunities and Challenges of Implementing Green internet of Things (IoT) Towards Energy Saving Practices in the Current Environment;Influential Effects of Process Parameters of Fused Deposition Modelling on Wear of a PLA Specimen: A Comprehensive Review;review Based on Named Entity Recognition for Hindi Language Using Machine Learning Approach;effects of Covid-19 Lockdown: A Perception Survey;Improving Energy Efficiency in Piezoelectric Effect Based Synchronous Multicast Protocol (PESM) in Wireless Sensor networks;a Critical Analysis on the Application of Next-Generation internet of Things (Iot) for Building Efficient Energy systems for Improved Temperature control and Better Energy Consumption;exploring the Essentials Elements of Data Analysis and Its Impact on performance Management Framework;role of Data Mining in Detecting Theft and Making Effective Impact on performance Management;railway Track Monitoring System;an Analytical Approach of Crime Prediction Using Machine Learning;consumer Behaviour on Digital Marketing Platforms—Specifically in Terms of Consumer Loyalty Using Machine Learning;a Review on Various Aerodynamics control Techniques;the Role of Blockchain Technology on Human Rights Management and Business Ethics—Utopia or Dystopia;machine Learning for the Manufacturing of Digital Marketing Techniques and Its Impact on Health Care System;benefits of Digital Education Over the Impact of Pandemic on Indian Higher Education System;decomposition Makes Better Rain Removal: An Enhanced Attention-Guided Image De-raining Using Deconvolutions network;preface.

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A Smart Trap for Counting Olive Moths Based on the internet of Things and Deep Learning 19

A Smart Trap for Counting Olive Moths Based on the Internet ...

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19th IEEE/ACS International conference on Computer systems and Applications (AICCSA)

作者： Mdhaffar, Afef Zalila, Bechir Moalla, Racem Kharrat, Ayoub Rebai, Omar Hsairi, Mohamed Melek Sallemi, Ahmed Kobbi, Hsouna Kolsi, Amel Chatti, Dorsaf Jmaiel, Mohamed Freisleben, Bernd Univ Sfax ReDCAD Lab ENIS BP 1173 Sfax Tunisia Olive Inst Sfax Sfax Tunisia Digital Res Ctr Sfax Sfax Tunisia Univ Marburg Dept Math & Comp Sci Marburg Germany

ISBN: (纸本)9798350310085

We present a novel smart trap for counting olive moths using internet of Things principles and deep learning algorithms. The smart trap takes a picture of the captured insects once per day and processes it using a deep convolutional neural network to detect "Prays oleae" insects and count their number. Then, the results are transferred via a wireless connection to a backend cloud server. The proposed smart trap is designed to reduce power consumption as much as possible. Two deep neural network models (i.e., YOLO V5 and YOLO V7) are employed to detect and count Prays oleae insects, using our newly created Prays oleae dataset, PraysDB. Our experimental results demonstrate the detection quality, energy efficiency, and computational performance of our smart trap.

关键词： IoT AI smart trap olive moth (Prays oleae) insect detection deep learning PraysDB dataset

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Energy Efficient Hardware Acceleration of Neural networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International conference on Computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Omnidirectional Image quality Assessment by Distortion Discrimination Assisted Multi-Stream network

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2022年第4期32卷 1767-1777页

作者： Zhou, Yu Sun, Yanjing Li, Leida Gu, Ke Fang, Yuming China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Xuzhou Engn Res Ctr Intelligent Ind Safety & Emer Xuzhou 221116 Jiangsu Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Intelligent Percept & Autonomous Con Beijing Artificial Intelligence InstBeijing Lab Minist EducBeijing Key Lab Computat Intelligence Beijing 100124 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Jiangxi Peoples R China

Omnidirectional image (OI) quality assessment is crucial to facilitate the development of virtual reality (VR) related technology. In this work, a distortion discrimination assisted multi-stream network is proposed for OI quality assessment. The multi-stream architecture is constructed by generating the viewport images received by the retina at one point to simulate the characteristics of humans perceiving VR contents. Additionally, the strategy of generating several viewport image sets from one OI is proposed for data augmentation. Furthermore, the facts that the human brain has the ability for both quality assessment and distortion type distinguishment, and the process of human brain handling two tasks exists information interaction inspire us to employ an auxiliary distortion discrimination task to facilitate the quality assessment task learning. Extensive experiments conducted on two public OI databases demonstrate the superiority of the proposed method to both traditional 2D quality metrics and existing metrics specific for OIs. Moreover, utilizing the assistant task is proven to be more effective than the single task learning for OI quality evaluation. Better generalization performance is also verified to be another valuable trait of the proposed method.

关键词： Task analysis Measurement Distortion quality assessment Sun Image coding Visualization Image quality assessment virtual reality (VR) omnidirectional image (OI) viewport generation distortion discrimination

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Safety-Aware Optimal control of Nonlinear systems Using Off-Policy Reinforcement Learning∗

Safety-Aware Optimal Control of Nonlinear Systems Using Off-...

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2023 International Annual conference on Complex systems and Intelligent Science, CSIS-IAC 2023

作者： Lin, Mingduo Zhao, Bo Xia, Hongbing Liu, Derong School of Systems Science Beijing Normal University Beijing China School of Artificial Intelligence Anhui University Hefei China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798350309003

In this paper, we investigate the safety-aware optimal control (SAOC) problem, which attempts to minimize a predefined performance index function while ensuring the safety of nonlinear systems. First, the barrier function-based system transformation is utilized to design an optimal control policy which maintains the system states located in the safety region. To deal with the input constraints, a non-quadratic cost function is imposed to the control input. Then, the Hamilton-Jacobi-Bellman equation is established to provide the solution of the SAOC problem. Moreover, by utilizing the off-policy Bellman equation, a data-based off-policy reinforcement learning (OPRL) algorithm is developed to obtain the safety-aware optimal controller in a model-free manner. To implement this algorithm, a data collection process with the barrier transform is executed to generate the off-policy trajectory data, and an actor-critic neural network structure with the least-square updating law is employed in the off-policy learning phase. Finally, a simulation example is provided to verify the effectiveness of the developed control method. © 2023 IEEE.

关键词： Reinforcement learning

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An IoT-based Wireless Sensor network for Lighting control systems 20

An IoT-based Wireless Sensor Network for Lighting Control Sy...

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IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

作者： Pierleoni, Paola Belli, Alberto Palma, Lorenzo Sabbatini, Luisiana Raggiunto, Sara Univ Politecn Marche Dept Informat Engn DII Ancona Italy

ISBN: (数字)9781665462976

ISBN: (纸本)9781665462976

The fifth industrial revolution brings the focus to well-being, sustainability, and resilient human-centric manufacturing systems. To this end, the quality and quantity of light in the work environment plays a fundamental role for the management of the human body's circadian clock, and hence for the psycho-physical well-being of the worker. In this work we propose a lighting control system based on a wireless sensor network able to realize an architecture fully compliant to the internet of Things requirements, and an inclusive work environment focused on worker's physical and mental well-being. In particular, an ad-hoc node with suitable interface has been developed to interoperate with standard lighting control systems and to extend their functionality to remote monitoring through web platform. The proposed solution has been tested in a critical scenario such as the industrial one in which the performance of the proposed wireless sensor network has been analyzed in terms of Round Trip Time, packet loss and goodput. The obtained results show that the value of the work is its versatility and scalability relatively to the number and density of nodes, network typology and sensing units.

关键词： DALI protocol Industry 5.0 internet of Things Lighting control systems Wireless Sensor networks

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