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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Dong, Yunxuan Zhou, Binggui Yang, Guanghua Hou, Fen Ma, Shaodan School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China University of Macau Department of Electrical and Computer Engineering 999078 China

ISBN: (纸本)9781665494571

Accurate forecasting of tourism demand is important to the development of tourism. However, the difficulties in recognizing complex spatial and temporal features make it challenging to accurately forecast tourism demand. In addition, existing methods are not practical and flexible enough since they usually established multiple models for different scenic spots. In this paper, we propose a novel method for tourism demand forecasting based on the fully connected long short-term neural network, which enables simultaneous identification of spatial and temporal features for better forecasting accuracy. To enhance the practicality and flexibility of our method, we propose to establish one general model for multiple scenic spots. Experimental results demonstrate that the proposed method outperforms other models in the daily tourism demand forecasting for the Wanshan Archipelago, an emerging tourism spot in Zhuhai, China. © 2021 IEEE.

关键词： Smart cities Neural networks Demand forecasting Transportation Predictive models

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Quaternion-Based Irradiance Calculation Method Applicable to Solar Power Plants Energy Production

SSRN

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SSRN 2024年

作者： Knolmajer, Attila Bálint, Roland Fodor, Attila Vathy-Fogarassy, Ágnes University of Pannonia Department of Electrical Engineering and Information Systems Hungary University of Pannonia Department of Computer Science and Systems Technology Hungary

Precise forecasting of renewable energy production is crucial for ensuring the reliable operation of the electricity grid. Developing more accurate predicive models and computational methodologies can enhance energy production and improve overall system stability. This paper introduces a novel computational approach utilizing quaternions to calculate expected solar irradiance on a given surface. The proposed method incorporates quaternion rotations and translation vectors to model the motion of astronomical objects, both independently and in relation to each other, a factor critical for accurate irradiance estimation. This approach has the potential to replace the commonly used equatorial coordinate system-based computations, offering improvements in computational scalability and efficiency. An additional advantage of the proposed quaternion-based calculation is its adaptability to estimate the irradiation received by solar panels on a satellite following an orbital trajectory. The article presents the calculation methodologies of both models, performs a comparative analysis of the computational results, and presents the differences in computational efficiency between the two approaches. © 2024, The Authors. All rights reserved.

关键词： Solar energy

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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Path-guided particle-based sampling 24

Path-guided particle-based sampling

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Proceedings of the 41st International Conference on Machine Learning

作者： Mingzhou Fan Ruida Zhou Chao Tian Xiaoning Qian Department of Electrical & Computer Engineering Texas A&M University College Station Texas Department of Electrical and Computer Engineering University of California Los Angeles CA Department of Electrical & Computer Engineering and Department of Computer Science and Engineering Texas A&M University College Station TX and Computational Science Initiative Brookhaven National Laboratory Upton NY

Particle-based Bayesian inference methods by sampling from a partition-free target (posterior) distribution, e.g., Stein variational gradient descent (SVGD), have attracted significant attention. We propose a path-guided particle-based sampling (PGPS) method based on a novel Log-weighted Shrinkage (LwS) density path linking an initial distribution to the target distribution. We propose to utilize a Neural network to learn a vector field motivated by the Fokker-Planck equation of the designed density path. Particles, initiated from the initial distribution, evolve according to the ordinary differential equation defined by the vector field. The distribution of these particles is guided along a density path from the initial distribution to the target distribution. The proposed LwS density path allows for an efficient search of modes of the target distribution while canonical methods fail. We theoretically analyze the Wasserstein distance of the distribution of the PGPS-generated samples and the target distribution due to approximation and discretization errors. Practically, the proposed PGPS-LwS method demonstrates higher Bayesian inference accuracy and better calibration ability in experiments conducted on both synthetic and real-world Bayesian learning tasks, compared to baselines, such as SVGD and Langevin dynamics, etc.

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Distributed Consensus Multi-Agent System Control of AC Microgrid Under Various Communication Disturbances

Distributed Consensus Multi-Agent System Control of AC Micro...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ibtissam Kharchouf Mahmoud S. Abdelrahman Hossam M. Hussein S M Sajjad Hossain Rafin Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

The resilience of AC microgrids (MGs) in the face of communication disturbances, including variable delays, packet loss, packet corruption (noise), and limited bandwidth, is studied. A Controller Hardware-in-the-Loop (CHIL) platform is used to investigate the ramifications of these disturbances comprehensively. The MG cyber-physical system (CPS) integrates a real-time islanded AC microgrid deployed on RT-LAB, distributed secondary consensus controllers on single-board computers, and a real communication network infrastructure. Through meticulous analysis and experimentation, this research highlights the vulnerabilities of AC MGs to communication disturbances and their detrimental effects on the system resiliency, stability, and efficiency. The experiments emphasize the need for designing robust and adaptive control strategies that can effectively mitigate the effects of communication disruptions and enhance the overall reliability and performance of AC microgrid systems.

关键词： Noise Packet loss Microgrids Bandwidth Cyber-physical systems Stability analysis Real-time systems Delays Resilience Multi-agent systems

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Self-assembled microtubular electrodes for on-chip low-voltage electrophoretic manipulation of charged particles and macromolecules

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Microsystems & Nanoengineering 2022年第1期8卷 341-352页

作者： Apratim Khandelwal Nagendra Athreya Michael Q.Tu Lukas L.Janavicius Zhendong Yang Olgica Milenkovic Jean-Pierre Leburton Charles M.Schroeder Xiuling Li Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Chemical Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Electrical and Computer Engineering Microelectronics Research CenterUniversity of TexasAustinTX 78758USA Coordinated Science Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA

On-chip manipulation of charged particles using electrophoresis or electroosmosis is widely used for many applications,including optofluidic sensing,bioanalysis and macromolecular data *** hereby demonstrate a technique for the capture,localization,and release of charged particles and DNA molecules in an aqueous solution using tubular structures enabled by a strain-induced self-rolled-up nanomembrane(S-RuM)***-in 3D electrodes that are embedded in cylindrical S-RuM structures and biased by a constant DC voltage are used to provide a uniform electrical field inside the microtubular *** charged-particle manipulation is achieved at a bias voltage of<2-4 V,which is~3 orders of magnitude lower than the required potential in traditional DC electrophoretic ***,Poisson-Boltzmann multiphysics simulation validates the feasibility and advantage of our microtubular charge manipulation devices over planar and other 3D variations of microfluidic *** work lays the foundation for on-chip DNA manipulation for data storage applications.

关键词： electrodes cylindrical planar

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Spatiotemporal Control of Nonlinear Effects in Multimode Fibers for Two-Octave High-Peak-Power Femtosecond Tunable Source

Spatiotemporal Control of Nonlinear Effects in Multimode Fib...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D, COSI, DH, FLatOptics, IS, pcAOP 2023 - Part of Imaging and Applied Optics Congress 2023

作者： Qiu, Tong Cao, Honghao Liu, Kunzan You, Sixian Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

We developed a two-octave high-peak-power femtosecond tunable source by spatiotemporal control of nonlinear effects in a step-index MMF through a customer-designed fiber shaper. Its application to improve label-free i... 详细信息

ISBN: (纸本)9781957171289

关键词： High power fiber lasers Image quality Multimode fibers Nonlinear effects Optical computing Tissue imaging

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Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

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computer systems science & engineering 2022年第10期43卷 397-412页

作者： Sunil Dhankhar Mukesh Kumar Gupta Fida Hussain Memon Surbhi Bhatia Pankaj Dadheech Arwa Mashat Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthan302017India Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Mechatronics Engineering Jeju National UniversityJeju63243Korea Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia

In today’s digital era,the text may be in form of *** research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary *** rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords *** primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant *** second goal is to maintain or improve the classification system’s *** experiment included news articles from various genres,such as Bollywood,politics,and *** proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human *** evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.

关键词： Support vector machine(SVM) optimization precision Hindi character recognition optical character recognition(OCR) automatic summarization and compression ratio

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STAR-RIS Assisted Covert Wireless Communications with Randomly Distributed Blockages

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4690-4705页

作者： Li, Xingwang Zhao, Jingwei Chen, Gaojie Hao, Wanming Da Costa, Daniel Benevides Nallanathan, Arumugam Shin, Hyundong Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shenzhen518107 China 5GIC 6GIC University of Surrey United Kingdom Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS. 2002-2012 IEEE.

关键词： Numerical methods

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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