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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

computers, Materials & Continua 2023年第2期74卷 3167-3181页

作者： Mesfer Al Duhayyim Hadeel Alsolai Siwar Ben Haj Hassine Jaber SAlzahrani Ahmed SSalama Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent *** remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic *** order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and ***,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on *** this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing *** presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing *** accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature ***,Elman neural network(ENN)model is applied to allot proper class labels into the input ***,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this *** experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct *** comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

关键词： Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning

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Multi-Timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2023年第4期1卷 322-335页

作者： Wu, Sirui Lin, Jin Li, Jiarong Liu, Feng Song, Yonghua Xu, Yanhui Cheng, Xiang Yu, Zhipeng Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100087 China Tsinghua University Sichuan Energy Internet Research Institute Chengdu610213 China University of Macau Department of Electrical and Computer Engineering Macau999078 China Tsinghua University Department of Electrical Engineering Beijing100087 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China

Renewablepower to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified timeframe. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of the RePtA VPP joining multiple markets over the conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor flexibility. © 2023 IEEE.

关键词： Ammonia

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Modular Delay Audio Effect System on FPGA

Modular Delay Audio Effect System on FPGA

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Cannon, Dean Fang, Tianyang Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper describes a System-on-Chip implementation of a Modular delay audio effect system. The Zedboard Zynq development board was used to implement the design. The programmable logic on the Zynq processor was used to handle the signal processing and the processing system was used to communicate between the programmable logic and software running on a computer to control different audio effect parameters in real-time. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Epileptic Seizure Detection Using the EEG Signal Empirical Mode Decomposition and Machine Learning

Epileptic Seizure Detection Using the EEG Signal Empirical M...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Nassir, Jana Alasabi, Majeda Qaisar, Saeed Mian Khan, Muzammil Effat University Computer Science Department Jeddah22332 Saudi Arabia Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia Cesi Engineering School Lineact Laboratory Villeurbanne69100 France University of Swat Department of Computer and Software Technology Swat Pakistan

ISBN: (纸本)9798350347050

Epileptic seizures affect millions of people worldwide. Medical treatments exist to help lessen the severity of the damage caused by these seizures. However, people with epilepsy still struggle with unexpected seizures. People who experience epileptic seizures have Electroencephalogram (EEG) signals that show different features in comparison to a healthy brain. In this study, EEG signals are studied to detect the seizures. The incoming signals are denoised by using linear phase filters. In next step these are divided in fix-length segments. Then, each segment is broken down using the Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs). For an automatic identification of EEG signals, features are extracted from the collected IMFs and then processed using machine learning techniques. A dataset on epilepsy that is available to the public is used for evaluation. To determine which is the best predictor for the under-consideration dataset, four different classification methods are performed and the results are examined. The system achieves a classification accuracy of 96.70%. © 2023 IEEE.

关键词： Empirical mode decomposition

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Monocular vision navigation system for UAV autonomous mission: a real-time window-based obstacle avoidance approach

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Neural Computing and Applications 2025年第16期37卷 9843-9860页

作者： Javaid, Abdulrahman Alnaser, Mustafa Baroudi, Uthman Alfaraj, Amjad Research and Development Department Yokogawa Saudi Arabia Company Al Khobar Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Center for Intelligent Secure Systems King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia

The need for autonomous unmanned aerial vehicles (UAVs) is rapidly increasing for various industrial applications today. However, the realization of UAV autonomy requires a careful mix of Artificial Intelligence (AI) and computer vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low-cost hardware components. We have infused state-of-the-art AI and CV technologies into our developed system architecture to improve autonomous navigational capabilities of UAVs. The proposed architecture has been implemented and tested on the DJI Tello UAV equipped only with a monocular camera on-board and an inertial measurement unit (IMU). We focused on solving multiple practical technical problems that have arisen during this study due to the hardware limitations of the UAV. The study investigated methods for representing obstacles within an environmental map, which are crucial for effective avoidance and path planning, such as their edges, width, and shape, to ensure accurate detection and navigation. The experiments were carried out in an environment that included dynamic obstacles that were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has a high potential to achieve autonomous UAV operation in the field with low hardware and energy requirements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning 35

Microservice Deployment for Satellite Edge AI Inference via ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Yu, Zhiyong Cheng, Hei Victor Yang, Zhanpeng Liu, Xin Jiang, Yuning Zhou, Yong Shi, Yuanming ShanghaiTech University School of Information Science and Technology Shanghai201210 China Aarhus University Department of Electrical and Computer Engineering Denmark Automatic Control Laboratory Switzerland

ISBN: (纸本)9798350362244

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices' communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time. © 2024 IEEE.

关键词： Markov processes

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Machine Learning Models for Cyberattack Detection in Industrial Control systems

Machine Learning Models for Cyberattack Detection in Industr...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Arnold, David Ford, John Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

Industrial Control systems (ICS) provide a network environment for operator command and control of cyber-physical systems and devices. As such, these systems are common throughout power plants, pipelines, manufactories, and other critical infrastructure. Due to the importance of critical infrastructure in everyday life, hackers of all stripes are taking an increased interest in compromising ICS for personal gain or nefarious purposes. Recent high-profile attacks also highlight the need to monitor system process and sensor data in detecting compromised user accounts or insider threats. Central to ICS data collection and monitoring, Data Historians hold the key to identifying malicious behavior and cyber breaches. Machine Learning techniques may be applied to detect data that is indicative of part failures or cyberattacks, allowing operators to take preventative measures prior to system failure. In this paper, several machine models for cyberattack detection within Industrial Control systems will be introduced. These models will reside within the Data Historian and are implemented through the Apache Spark MLlib Libraries. The machine learning model were applied to an ICS Dataset to determine the system's accuracy at detecting cyberattacks. Naïve Bayes, Logistic Regression, Decision Tree Classifier, and Random Forest Classifier Machine Learning models were tested with the Tree Classifiers producing the most promising results. © 2022 IEEE.

关键词： Critical infrastructures

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Position Measurement Based Slave Torque Feedback Control for Teleoperation systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Study of the Behavior of Water Droplets under the Influence of a Uniform Electric Field on Samples of Borosilicate Glass

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Journal of engineering Science and Technology Review 2022年第7期14卷 67-70页

作者： Baroutis, Ioannis Danikas, Michael G. Sarathi, Ramanujam Democritus University of Thrace School of Engineering Department of Electrical and Computer Engineering Xanthi67100 Greece Indian Institute of Technology Madras Department of Electrical Engineering High Voltage Laboratory Chennai600 036 India

This paper investigates the behavior of water droplets on the surface of borosilicate glass samples. Borosilicate glass is used in MEMS and this is due to its special properties, such as its excellent insulating property, its inertness to most chemical substances, its easy connection to silicon and its optical permeability. In this paper, water droplet conductivities and wet etched borosilicate glass samples– are investigated. It seems that droplet conductivity plays a dominant role in determining the flashover voltage of the aforementioned samples. © 2021

关键词： Surface discharges

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Predicting Vascular Age using PPG Signals and Machine Learning Algorithms: A Non-Invasive Approach for Early Cardiovascular Risk Detection 11

Predicting Vascular Age using PPG Signals and Machine Learni...

引用

11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Abrisham, Kiana Pilevar Alipour, Khalil Tarvirdizadeh, Bahram Ghaman, Mohammad Laboratory Department of Mechatronics Engineering Faculty of New Sciences and Technologies Tehran Iran Kettering University Department of Electrical and Computer Engineering FlintMI United States

ISBN: (纸本)9798350308105

Cardiovascular diseases (CVDs) pose a significant global health challenge, necessitating effective strategies for early risk assessment and intervention. Arterial stiffness, a critical parameter within the cardiovascular system, is considered a primary risk factor for CVD development. This parameter undergoes natural age-related structural and functional changes in arterial walls, resulting in increased arterial stiffness and reduced elasticity. These changes contribute to vascular aging and alter pulse wave velocity. Photoplethysmography (PPG) offers a non-invasive means to assess these alterations. Our research leverages PPG in combination with machine learning to develop a portable, non-invasive method for predicting vascular age. We identify and select 21 features from the fiducial points of the digital artery PPG signals obtained from simulated pulse waves across a diverse sample of healthy adults aged 25 to 75 years, its first derivative, and its second derivative waveforms. We assess the performance of machine learning algorithms, including Decision Tree, k-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Among these algorithms, SVM outperformed the others, achieving an accuracy, precision, recall, and F1 score of 99%. This high accuracy underscores the potential of SVM in predicting vascular age. Accurate vascular age prediction within the healthy population has the potential to establish age-specific benchmarks, aiding in the detection of outliers indicative of accelerated cardiovascular aging or underlying cardiovascular diseases. © 2023 IEEE.

关键词： Support vector machines

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