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IEEE Region 10 International Conference TENCON

作者： Ha-yeong Cho Ho-jun Lee Jae-Hyung Lee Min-han Yoon dept. of Electric Engineering Kwangwoon University Seoul Korea Energy technology team Samsumg C&T City Korea

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

In this paper, we introduce the problems with long-term period optimization of green hydrogen microgrid system operation and variability inherent in renewable energy. To solve the problems, we proposed a Rolling-Horizon Optimization(RHO) with Chanced-Constrained Optimization(CCO). The validity of our approach is confirmed by comparative analysis with conventional Mixed-Integer Linear Programming(MILP) scheduling by using real PV generation data.

关键词： Systems operation Green products Microgrids Green hydrogen Prediction algorithms approximation algorithms Scheduling Optimization IEEE Regions

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Decentralized Cooperative Control for Multi-Reconfigurable Manipulator Based on Adaptive Dynamic Programing

Decentralized Cooperative Control for Multi-Reconfigurable M...

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Complex Systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Bo Dong Yu Cao Tianjiao An Xinyi He Hongbo Dong Bing Ma Department of Control Science and Engineering Changchun University of Technology Changchun China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

This paper proposes a decentralized cooperative control for multi-reconfigurable manipulator (MRM) based on Adaptive Dynamic Programing (ADP). The control method can achieve both motion path tracking and control the force between the manipulator and the controlled object. The dynamic model of reconfigurable manipulator and controlled object is established using the Newton-Euler algorithm and load distribution. When facing unknown terms in the model, radial basis function neural network (RBFNN) is used for approximation. By taking advantage of the ADP algorithm to obtain the optimal control strategy. The closed-loop MRM system is proved to be stable by using the Lyapunov theory. Finally, the effectiveness of the control method is verified through numerical simulation.

关键词： Adaptation models Tracking Heuristic algorithms Dynamics Optimal control Radial basis function networks approximation algorithms Numerical models Manipulator dynamics Load modeling

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Centroid-Guided YOLO for Plant Stem Detection in Digital Agriculture

Centroid-Guided YOLO for Plant Stem Detection in Digital Agr...

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International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： Rogério Ballestrin Alexandre Balbinot Vitor Camargo Nardelli Ivan Müller Graduate Program in Electrical Engineering Universidade Federal do Rio Grande do Sul (UFRGS) Instituto SENAI de Inovação em Sistemas de Sensoriamento Competence Center For Digital Agriculture

ISBN: (数字)9798350350869

ISBN: (纸本)9798350350876

This study focuses on the utilization of a YOLO (You Only Look Once)-based object detection approach, along with the development of a customized algorithm, to address the identification and localization of invasive plant stems within agricultural crops. The initial phase involved the creation of a dataset comprising 485 manually annotated images, sourced from three distinct data repositories. Subsequent to this, the YOLO model underwent training to accurately recognize the plants depicted in the images. Following the model training, an algorithm was devised that leverages the centroid of the G vector in the RGB image as the designated point of interest for precise stem localization. The algorithm's effectiveness was assessed utilizing key metrics such as mean absolute error (MAE) and root mean square error (RMSE). For images sized at 600×600 pixels, the resulting MAE and RMSE values were calculated at 34.4 pixels and 46.8 pixels respectively. Notably, these outcomes underscored the necessity for refining the detection process, particularly in instances involving plants with delicate branches, where differentiation between the branch and the image back drop proved challenging. In terms of practical application within agrochemical dispersion systems, it was discerned that dispersion takes place across an application area rather than a singular point. By incorporating the proposed algorithm in conjunction with a dispersion cone aperture measuring $100\times 100$ pixels, it was projected that the dispersion system could effectively cover approximately 92.4% of the existing plants. This algorithmic approach exhibits the potential to curtail agrochemical dispersion by a substantial factor of up to 36 times, yielding economic and sustainability advantages through the reduction of waste and environmental contamination.

关键词： YOLO Training Location awareness Transducers Plants (biology) Agrochemicals approximation algorithms Vectors Sustainable development Dispersion

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Discovering Denial Constraints in Dynamic Datasets

Discovering Denial Constraints in Dynamic Datasets

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International Conference on Data Engineering

作者： Eduardo H. M. Pena Fabio Porto Felix Naumann Federal University of Technology - Parana Brazil LNCC-DEXL Brazil Hasso Plattner Institute University of Potsdam Potsdam Germany

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Denial constraints (DCs) are data dependencies with high expressive power, offering great flexibility for modeling data quality rules. Specifying DCs manually is problematic, as the required domain expertise is expensive and scarce. Moreover, database updates can invalidate DCs thought to hold and simultaneously uncover new DCs. This fact leads to burdensome scenarios where experts must often revisit DC specifications. Several algorithms have been devised to discover DCs from data, among which only one considers DC discovery on data updates. However, that solution underperforms in many scenarios due to long runtime and excessive memory use. Also, it targets database inserts only, so no previous solution covers deletions. This paper proposes an efficient and flexible algorithm that covers the earlier limitations regarding performance and scope. The algorithm maintains small-footprint intermediate structures during database updates and a method that exploits the changes in this intermediate to update the DCs incrementally. The results of our extensive experimental evaluation show that our algorithm is orders of magnitude faster than the existing one, with much better scalability in the size of the data updates.

关键词： Runtime Databases Heuristic algorithms Scalability Data integrity Data engineering approximation algorithms

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Study on the Iterative Estimation and Compensation Algorithm for IQ Imbalance in Coherent Optical Communication Systems

Study on the Iterative Estimation and Compensation Algorithm...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Changjun Huang Fengtao He Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Coherent optical communication technology is a key technology for building high-speed optical fibre communication networks, while the amplitude and phase imbalance between the in-phase and quadrature (IQ) signals at the receiving end will lead to the degradation of the received signal quality and affect the performance of the communication system. In this thesis, a narrowband zero-intermediate frequency (ZIF) receiver IQ imbalance estimation and compensation algorithm is proposed with the research objective of designing IQ imbalance estimation and compensation algorithms for the 16 quadrature amplitude modulation (16QAM) signal in coherent optical communication systems that can be implemented in hardware circuits. The algorithm is based on the Gram-Schmidt orthogonalization procedure (GSOP) for approximate estimation of the IQ imbalance parameters and improves the estimation accuracy of the IQ imbalance parameters by means of iterative optimization. The IQ imbalance signal is effectively compensated by the inverse matrix of the IQ mismatch parameter matrix. Based on the estimation error of the IQ imbalance parameters, the bit error rate (BER) of the signal transmission at different signal to noise ratio (SNR), and the simulation results of the 16QAM constellation diagram before and after compensation, it is concluded that the iterative optimized algorithm based on GSOP reduces the complexity of hardware implementation and improves the efficiency and accuracy of the IQ imbalance parameters estimation effectively. The findings of this study hold practical engineering significance for estimating and calibrating IQ imbalance in coherent optical communication systems using high-order modulated signals through digital signal processing techniques.

关键词： Accuracy Circuits Bit error rate Estimation Signal processing algorithms approximation algorithms Optical fiber communication Iterative algorithms Hardware Signal to noise ratio

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Joint Beamforming Design for STAR-RIS Aided Cognitive Radio Systems

Joint Beamforming Design for STAR-RIS Aided Cognitive Radio ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Haochen Li Xidong Mu Yuanwei Liu Yue Chen Zhiwen Pan National Mobile Communications Research Laboratory Southeast University Nanjing China Queen’s University Belfast Belfast U.K The University of Hong Kong Hong Kong Queen Mary University of London London UK Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

A novel multiple-input multiple-output (MIMO) cognitive radio (CR) system is proposed in this work. Specifically, the underly secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The secondary base station (SBS) beamformers and the STAR-RIS coefficients are jointly designed to maximize the sum rate of secondary users (SUs) considering the power limitation at the SBS, interference limitation at the primary users (PUs), and the coefficients constraint for the STAR-RIS. The block coordinate descent method is invoked to tackle the formulated optimization problem. In each iteration, the beamformers at the SBS are optimized by solving a quadratically constrained quadratic program problem, and the passive STAR beamforming problem is solved with the successive convex approximation-based algorithm. Simulation results show that the proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system.

关键词： Array signal processing Simulation Stars Interference Reconfigurable intelligent surfaces approximation algorithms MIMO Cognitive radio Global communication Optimization

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Assessment of the Impact of Date Production on Water Resources Using Remote Sensing Techniques, a Case Study for Madinah, Saudi Arabia

Assessment of the Impact of Date Production on Water Resourc...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Abdulrahman Badaoud Claire Walsh Greg O’Dnnell Newcastle University Newcastle-Upon-Tyne United Kingdom King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In Saudi Arabia, the scarcity of surface water and limited renewable groundwater, heavily used in irrigation, raises concerns for future water management. Due to the limitations of in-situ observations, an accurate estimation of the condition of water resources poses a significant challenge. However, remote sensing platforms provide a mean to fill that gap by providing global estimates of several water components, such as, groundwater storage (GWS), evapotranspiration (ET) and precipitation. This study employs the Gravity Recovery and Climate Experiment (GRACE) satellite for estimating variations in GWS across Madinah. Surface Energy Balance Algorithm for Land (SEBAL) is also applied to estimate agricultural water consumption through ET, while the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) algorithm is used to derive monthly precipitation values. Results show the average annual decline in GWS is -6.7 mm/year, alongside an average annual precipitation rate of approximately 73 mm, while ET in the irrigated area reaches 1715.5 mm/year. Initial findings highlight a downward trend in GWS results, emphasizing the influence of irrigation practices, as revealed by the ET results, and the impact of climate change, as indicated by low rainfall rates.

关键词： Irrigation Renewable energy sources Satellites Rain Land surface Production approximation algorithms

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Does Machine Bring in Extra Bias in Learning? Approximating Fairness in Models Promptly

arXiv

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arXiv 2024年

作者： Bian, Yijun Luo, Yujie College of Design and Engineering National University of Singapore Singapore Department of Mathematics National University of Singapore Singapore

Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly in high-stakes domains. Existing techniques for assessing the discrimination level of ML models include commonly used group and individual fairness measures. However, these two types of fairness measures are usually hard to be compatible with each other, and even two different group fairness measures might be incompatible as well. To address this issue, we investigate to evaluate the discrimination level of classifiers from a manifold perspective and propose a "harmonic fairness measure via manifolds (HFM)" based on distances between sets. Yet the direct calculation of distances might be too expensive to afford, reducing its practical applicability. Therefore, we devise an approximation algorithm named "approximation of distance between sets (ApproxDist)" to facilitate accurate estimation of distances, and we further demonstrate its algorithmic effectiveness under certain reasonable assumptions. Empirical results indicate that the proposed fairness measure HFM is valid and that the proposed ApproxDist is effective and *** Codes 68T01, 68T09, 68T20 Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Controlling DC Motor Speed with FoPID-Controllers

Controlling DC Motor Speed with FoPID-Controllers

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Information Technology (ACIT)

作者： Rania Saadeh Iqbal M. Batiha Ahmad Qazza Iqbal H. Jebri Ali Elrashidi Shaher Momani Department of Mathematics Zarqa University Zarqa Jordan Department of Mathematics Al Zaytoonah University of Jordan Amman Jordan Nonlinear Dynamics Research Center (NDRC) Ajman University Ajman UAE Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mathematics The University of Jordan Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

This work aims to propose several designs for controlling the direct-current motor (or simply DC motor) speed. Such motor is broadly used in numerous applications like blowers, lathe machines, cranes, elevators, milling machines, fans, drilling rigs, etc. To achieve our aim, the DC motor model will be first fractionalized and then the so-called Particle Swarm Optimization (PSO) algorithm will be executed to adjust the proposed controllers’ parameters. This actually would be performed to the resultant fractional-order transfer function. Accordingly, several Fractional-order PID controllers (FoPID-controllers) will be formulated in agreement with the Outstaloup approach, which is typically used to approximate the yielded Laplacian operators s ±α , where 0 < α < 1. The controllers generated in accordance with different values of fractional-order values will be then competed to each other to outline which of them has an ability to provide to the closed-loop system of the DC motor speed model a minimal overshoot, short settling time and short rise time.

关键词： Motor drives Laplace equations Velocity control Transfer functions DC motors approximation algorithms Prediction algorithms Mathematical models Particle swarm optimization Predictive control

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Distributed Functional Compression for Independent Component Analysis in Wireless Networks

Distributed Functional Compression for Independent Component...

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IEEE Conference on Global Communications (GLOBECOM)

作者： A. Alamoudi Y. Saidutta F. Fekri Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah Saudi Arabia School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we consider distributed Independent Component Analysis (ICA) in wireless networks, where data from several geographically distributed wireless nodes (nodes) must be transmitted to a central server (server) to extract original sources through ICA. However, transmitting the vast amount of data over wireless channels to the server poses significant challenges due to limited bandwidth and privacy concerns. Our research addresses how to encode node data to meet channel rate constraints while providing maximally relevant information for ICA. Particularly, we propose a distributed functional compression framework for learning ICA over orthogonal AWGN channels. The framework leverages the Information Bottleneck (IB) principle to encode and compress the data to meet the channel rate constraint while maximally preserving the functionally relevant information for ICA. We train both neural encoders at the nodes and a neural decoder at the server in an unsupervised manner using the IB principle. We consider ICA for both linear and nonlinear mixing setups. Compared to the state-of-the-art, over real dataset, our proposed framework demonstrates a remarkable improvement of up to approximately 43% in accurately estimating the source signals in ICA while meeting the channels’ rate constraints. Finally, we propose a three-stage training algorithm, where the raw sensory data never leaves the nodes either for training or inference, to reduce the communication overhead. We show that our proposed training algorithm notably reduces channel use compared to the traditional cloud-based method, where the observed data from the nodes are compressed and transmitted to the cloud for learning ICA.

关键词： Training Wireless sensor networks Wireless networks Independent component analysis approximation algorithms Inference algorithms Decoding Servers Uplink Unsupervised learning

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