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2023 China Automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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Cloud Energy Storage Management Including Smart Home Physical Parameters

Cloud Energy Storage Management Including Smart Home Physica...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Vikash Kumar Saini Srinivas Yelisetti Rajesh Kumar Ameena S. Al-Sumaiti Center for Energy and Environment Malaviya National Institute of Technology Jaipur Advanced Power and Energy Center Electrical Engineering and Computer Science Khalifa University UAE Department of Electrical Engineering Malaviya National Institute of Technology Jaipur

Consumption of green energy in residential communities is increasing compared to conventional supply. However, the variability in generation due to different weather parameters is a significant challenge to their growth rate. Energy storage has the potential to address this issue, and sharing economy-based cloud energy storage (CES) has gained popularity as a way to reduce energy consumption costs and increase revenue. This study analyzes the deployment of CES infrastructure operation in seven residential smart homes community. All seven homes utilized CES services based on their daily net load demand. In the first phase, the PSO algorithm optimized the load trajectory, taking into account all smart home parameters and physical components. Subsequently, the operational cost of CES in the community is evaluated in scenarios with and without home energy management systems. Simulation results indicated that the energy consumption cost of the residential community from the grid is zero, and revenue is 43.59% higher when home energy management systems are employed. The modeled home parameters significantly affected total community energy costs.

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Short-Term Load Forecasting Employing Recurrent Neural Networks

Short-Term Load Forecasting Employing Recurrent Neural Netwo...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Tanzim Mostafa Mostafa M. Fouda Mohammad G. Abdo Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

This research paper explores the domain of Short-Term Load Forecasting (STLF) through the application of Long Short- Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. Focused on the west region of the Electric Reliability Council of Texas (ERCOT), the study employs a dataset spanning 2012–2015, enriched with new features such as day of the month, month of the year, and a detailed holiday classification. The dataset also incorporates hourly temperature, humidity, time of the day, day of the week, and hourly load values, offering a comprehensive perspective for load forecasting. Through meticulous hyperparameter tuning, LSTM and GRU models are optimized to find their best architecture. Four evaluation metrics have been employed to assess the performance of our models, namely, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination or R Squared (R2). With the identified optimal hyperparameters and look-back window, the obtained values for MAE, RMSE, MAPE and R2 for the LSTM model are 8.94, 12.24, 0.80 and 0.9973, respectively. The GRU model also achieves good performance with MAE, RMSE, MAPE, and R2 of 10.72, 14.48, 0.96, and 0.9962, respectively. Our research shows that the accuracy of STLF on the dataset has been improved by adding new features and fine-tuning the LSTM and GRU models, with the LSTM model achieving the best performance.

关键词： Measurement Recurrent neural networks Load forecasting Predictive models Logic gates Reliability Root mean square

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Modular Multilevel DC-DC Converter with Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

Modular Multilevel DC-DC Converter with Inherent Bipolar Ope...

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作者： Zhao, Shanshan Chen, Yu Cui, Shenghui Hu, Jingxin State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Seoul National University Department of Electrical and Computer Engineering Korea Republic of Institute for Power Generation and Storage Systems E. on Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-Tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype. © 2017 CPSS.

关键词： Poles

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advanced FFT architecture based on cordic method for brain signal encryption system 2

Advanced FFT architecture based on cordic method for brain s...

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2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Pal, Souvik Suseendran, G. Akila, D. Jayakarthik, R. Jabeen, T. Nusrat Global Institute of Management and Technology Department of Computer Science and Engineering India Vels Institute of Science Technology Advanced Studies Department of Information Technology Chennai India Anna Adarsh College for Women Department of Computer Science Chennai India

ISBN: (纸本)9781728194912

The human brain usually generates brain wave signals used for medical research to study the state of the human body. Most common diseases like seizures, insomnia, or other diseases such as brain tumors can be diagnosed using brain wave signals captured with a device's help. Apart from these, nowadays, many devices are invented that operates with brain signals for people with disabilities. And now, in this paper, we will use brain signals for authenticating high-security devices using a network since secondary damage cannot be caused in brain wave signals like generated in a fingerprint, iris, face, etc. Brain wave serves as high-security biometric data. However, brain signals can also be hacked once captured by a malicious personality [14]. Here we will encrypt Brain wave signal with an advanced FFT architecture that incorporates the Cordic system in it. His method enhances high-security transmission of Brain signals over the network for authenticating a high-security device. © 2021 IEEE.

关键词： Fast Fourier transforms

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Anisotropy of two-dimensional ReS_(2)and advances in its device application

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Rare Metals 2021年第12期40卷 3357-3374页

作者： Ya-Di Cao Ying-Hui Sun Su-Fei Shi Rong-Ming Wang Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Key Laboratory for Magneto-Photoelectrical Composite and Interface ScienceSchool of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing100083China Department of Chemical and Biological Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic InstituteTroyNY 12180USA

Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted growing interest regarding their potential applications in next-generation electronic and optoelectronic *** to their atomic thickness and tunable bandgap,they exhibit unique mechanical,electrical,and optical *** a specific member of the TMDC family,rhenium disulfide(ReS_(2))has stimulated intensive interest due to its anisotropic crystal structure,weak inter-layer coupling,and anisotropic electrical and optical *** this review,we summarize the distinct crystal structure and intrinsic anisotropic properties of ReS_(2),followed by an introduction to its synthesis *** current applications of ReS_(2)and its heterojunctions are presented based on its anisotropic *** review not only provides a timely summary of the current applications of ReS_(2)and its heterojunctions,but also inspires new approaches to develop other innovative devices based on 2D materials with a low lattice symmetry.

关键词： Transition metal dichalcogenides(TMDCs) Rhenium disulfide Anisotropic property Heterojunction Functional device

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Deep Learning-based Physical Layer Authentication in LiFi Networks Under Multi - User Mobility

Deep Learning-based Physical Layer Authentication in LiFi Ne...

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IEEE Southeastcon

作者： Elmahedi Mahalal Eslam Hasan Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Department of Computer Science University of New Haven West Haven CT USA Department of Computer Science Tennessee Technological University Cookeville TN USA Cybersecurity Education Research and Outreach Center Cookeville TN USA College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Western University London ON Canada

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The open nature of wireless networks poses significant security challenges. Upper-layer authentication (ULA) methods face limitations, including vulnerability to cryptanalysis and replay attacks. Physical-layer authentication (PLA) has emerged as a promising technique to complement the ULA, offering robust two-factor authentication. PLA leverages physical-layer characteristics to authenticate transmitters with reduced complexity and latency. In this paper, we focus on passive PLA schemes for indoor Light Fidelity (LiFi) networks, where approximately 80% of data traffic originates indoors. Existing studies overlook channel impulse response (CIR) similarity under multi-user mobility and varying user densities, resulting in a lack of robust PLA solutions. To address this gap, we investigate CIR similarity in indoor LiFi environments. Our simulation results demonstrated that the CIR similarity is significant under multiuser mobility, where the attacker can share more than 30% of the CIR of a legitimate user. We propose a deep learning-based Long Short- Term Memory (LSTM) model that predicts the next CIR value based on historical data to enhance authentication reliability. Authentication is performed if the predicted CIR matches the actual CIR at the access point. Our numerical results show that the proposed LSTM-based PLA method effectively distinguishes legitimate users, achieving a detection probability exceeding 94% even under high multi-user mobility. Furthermore, the proposed LSTM-based PLA method can maintain the missed detection and the false alarm under 6% and 3% in the worst-case scenario, respectively.

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

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Edge-Computing Enabled Si Photonics Multimodal Sensor with Integrated Photonic Convolutional Processor

Edge-Computing Enabled Si Photonics Multimodal Sensor with I...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Zian Xiao Zhihao Ren Yangyang Zhuge Zixuan Zhang Jingkai Zhou Siyu Xu Cheng Xu Bowei Dong Chengkuo Lee Department of Electrical & Computer Engineering National University of Singapore Singapore NUS Suzhou Research Institute (NUSRI) Singapore Institute of Microelectronics (IME) Agency for Science Technology and Research Singapore National Centre for Advanced Integrated Photonics Singapore

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

Photonic integrated circuits provide compact and efficient solutions for multimodal spectroscopic sensors. However, the resulting sensory data is highly complex and contains significant redundancy. To circumvent high communication cost for redundancy, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal edge computing system. A microring resonator crossbar is used as photonic processor with 5-bit accuracy. The in-situ processing of sensory data is demonstrated in this system, achieving a classification accuracy of 94.27% across 45 different classes. The multimodal edge computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the photonic data processing capability.

关键词： Temperature sensors Accuracy Multimodal sensors Redundancy Sensor systems Silicon Temperature control Optoelectronic and photonic sensors Photonics Edge computing

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Bioinspired UV-Visible Multispectral Imaging Sensor Enabled by Perovskite Nanocrystal Coating

Bioinspired UV-Visible Multispectral Imaging Sensor Enabled ...

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2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Chen, Cheng Wang, Ziwen Wu, Jiajing Deng, Zhengtao Zhang, Tao Zhu, Zhongming Jin, Yifei Lew, Benjamin Srivastava, Indrajit Liang, Zuodong Nie, Shuming Gruev, Viktor Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States College of Engineering and Applied Sciences Nanjing University Jiangsu Nanjing China Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Inspired by the visual system of Papilio xuthus butterfly, w e f abricated a ultraviolet-visible multispectral imager which combines perovskite nanocrystals and vertically stacked silicon photodetectors. High-resoluti... 详细信息

ISBN: (纸本)9781957171289

关键词： Nanocrystals

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