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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Kaito Watanabe Hirotaka Nakayama Atsushi Shiraki Tomoyoshi Ito Katsumi Hirata Naoto Hoshikawa Advanced Course General Engineering Program National Institute of Technology Oyama College Oyama Japan Center for Computational Astrophysics National Astronomical Observatory of Japan Mitaka Japan Graduate School of Engineering Chiba University Chiba Japan Department of Innovative Electrical and Electronic Engineering National Institute of Technology Oyama College Oyama Japan Graduate School of Science and Tecnology University of Tsukuba Tsukuba Japan

In recent years, 3DCG-related industries have grown rapidly and the demand for 3DCG content has increased. However, 3DCG production requires special skills and a lot of time. In particular, the creation of background data requires a lot of human resources at the production site because the data is huge and complex. Therefore, we propose an automatic creation system that generates 3D background data for 3DCG production. The proposed system consists of three functions: 3D model generation, generation parameter optimization, and fitness evaluation. The user inputs a landscape photograph as idea information into the system, and the system optimizes the generation parameters so that the 3D object shape approximates the input image. To evaluate the accuracy of the system's performance, we observe the optimization process and the 3D object output when a previously generated fractal image or landscape photos are input. Experimental results show that the system accurately optimizes the generation parameters and generates 3D objects that are almost identical to the shape of the fractal image. In addition, the parameters were also optimized with a certain degree of accuracy for landscape photographs for which no specific solution had been determined.

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An Optimal Sizing Framework for Autonomous Photovoltaic/Hydrokinetic/Hydrogen Energy System Considering Cost, Reliability and Forced Outage Rate Using Horse Herd Optimization

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SSRN 2022年

作者： Alanazi, Abdulaziz Alanazi, Mohana S. Arabi, S.N. Abdelaziz, Almoataz Y. El-Shahat, Adel Department of Electrical Engineering College of Engineering Northern Border University Ar’ar73222 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University Sakaka Saudi Arabia Institute of Research Sciences Power and Energy Group Johor Bahru Malaysia Faculty of Engineering and Technology Future University in Egypt Cairo11835 Egypt Energy Program School of Engineering Technology Purdue University West LafayetteIN47907 United States

The components outage of an energy system weakens its operation probability, which can affect the sizing of that system. An optimal sizing framework is presented for an autonomous hybrid photovoltaic/hydrokinetic/fuel cell (PV/HKT/FC) system with hydrogen storage to supply an annual load demand with forced outage rate (FOR) of the clean production resources based on real environmental information such as irradiance, temperature, and water flow. The sizing problem is implemented with the objective of cost of energy (COE) minimization and also satisfying probability of load supply (PLS) as a reliability constraint. The FOR effect of the photovoltaic and hydrokinetic resources is evaluated on the hybrid system sizing, energy cost, reliability, and also storage contribution of the system. Meta-heuristic horse herd optimization (HHO) algorithm with perfect capability on exploration and exploitation phases is used to solve the sizing problem. The results proved that the PV/HKT/FC configuration is the optimal option to supply the demand of an autonomous residential complex with the minimum COE and maximum PLS compared with the other system configurations. The results demonstrated the overlap of hydrogen storage with clean production resources to achieve an economic-reliable power generation system. The findings indicated that the COE is increased and the PLS is decreased due to the FOR increasing because of reducing the generation resources operational probability. The results demonstrated that the hydrogen storage level is increased with FOR increasing to maintain the system reliability level. © 2022, The Authors. All rights reserved.

关键词： Optimization

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Comparative Analysis of Real-Time Multi-Person Pose Detection in electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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Joint Receivers for Multiuser MIMO Wireless Communications

Joint Receivers for Multiuser MIMO Wireless Communications

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International Conference on Imaging, Signal Processing and Communication (ICISPC)

作者： James Wang Fang-Biau Ueng Yun-Hsuan Sung Hsuan-Fu Wang Dept. Electrical Engineering National Chung Hsing University Taichung Taiwan Bachelor’s Program in Intelligent Robot National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (纸本)9781665454810

In order to achieve high throughput requirements of next-generation communication systems, Multiple-Input Multiple-Output (MIMO) technology should be employed. Generalized Frequency Division Multiplexing (GFDM) signal has lower Out-Of-Band (OOB) emission and peak-to-average power ratio and can be used for next-generation communications. But the pulse shaping filter used by GFDM will cause Inter Symbol Interference (ISI) and Inter Carrier Interference (ICI). In this paper, we propose low-complexity joint receivers to suppress ISI, ICI and multi-user interference (MUI) for multi-user MIMO-GFDM uplink communication. The traditional solution is to design the equalizer and the GFDM demodulation separately, but this paper deals with the two parts together to reduce the complexity initially. The complexity of the preliminary joint receiver is still not low enough, so we derive two closed-form expressions for the low-complexity joint receivers, which not only greatly reduces the complexity, but also achieves the optimal error rate performance. Some simulation examples are given to demonstrate the effectiveness of the proposed low-complexity multi-user MIMO-GFDM receivers.

关键词： Wireless communication Symbols Receivers Interference Throughput Complexity theory Uplink

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Mechatronic Design of an Amphibious Drone

Mechatronic Design of an Amphibious Drone

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International Conference on Autonomous Robots and Agents, ICARA

作者： Nikolaos Evangeliou Dimitris Chaikalis Nikolaos Giakoumidis Anthony Tzes Electrical Engineering Program New York University Abu Dhabi United Arab Emirates Electrical & Computer Engineering Department New York University Brooklyn NY USA Kinesis Lab Core Technology Platform New York University Abu Dhabi United Arab Emirates Center for Artificial Intelligence and Robotics New York University Abu Dhabi United Arab Emirates

This paper is concerned with the mechatronic design of a tricopter drone which can move as a ground vehicle and/or as a vessel. The tricopter has three pairs of coaxial rotors and is capable of providing 100N of thrust with a 15 minute flight time using an 8S LiPo 12.5Ah battery. Three waterproof motors are attached to an isosceles triangular omniwheel configuration for operating as a ground vehicle after landing. The customized wheels allow the vehicle to move with a linear velocity up to 2 m/sec at a tilt slope up to 20°. Two submersible parallel water thrusters provide 24N collective force leading to a maximum vessel velocity of 1 m/s and a turn radius of 1.5 m. The design alternatives are highlighted and prioritized according to their weight while the overall amphibious drone is shown in several flight/omniwheel vehicle/vessel modes.

关键词： Mechatronics Navigation Force Wheels Rotors Prototypes Land vehicles

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Wideband and Wide Beam-Scanning Phased Array Antenna Design for 5G Applications

Wideband and Wide Beam-Scanning Phased Array Antenna Design ...

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Antennas and Propagation Society International Symposium

作者： Haoran Zhang Atif Shamim Electrical Engineering Program CEMSE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9781728146713

In this work, a $5 \times 5$ stacked patch rectangular phased array, along with its feeding network, is designed and fabricated in a multilayered low-temperature co-fired ceramic (LTCC) substrate. The single element of the phased array is a dual linear polarized stacked patch microstrip antenna which has improved impedance as well as 3-dB gain bandwidth due to the stacked patch approach. Based on the field distributions, the single element's orientation is optimized to reduce the mutual couplings between the adjacent elements for a wide beam scanning range over a wide frequency band. The features of multilayered implementation, low dielectric loss, and excellent packaging properties ensure a compact and high-efficiency phased array design and qualify this design as an antenna-in-packaging (AiP) design.

关键词： Phased arrays Microstrip antenna arrays Mutual coupling 5G mobile communication Patch antennas Microstrip antennas Packaging

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Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting

Quantum Kernel-Based Long Short-term Memory for Climate Time...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Yu-Chao Hsu Nan-Yow Chen Tai-Yu Li Po-Heng Henry Lee Kuan-Cheng Chen NARlabs National Center for High-Performance Computing Hsinchu Taiwan Cross College Elite Program National Cheng Kung University Tainan Taiwan National Synchrotron Radiation Research Center Hsinchu Taiwan Department of Electrical and Electronic Engineering Imperial College London London UK Department of Civil Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

We present the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate time-series forecasting tasks, such as Air Quality Index (AQI) prediction. By embedding classical inputs into high-dimensional quantum feature spaces, QK-LSTM captures intricate nonlinear dependencies and temporal dynamics with fewer trainable parameters. Leveraging quantum kernel methods allows for efficient computation of inner products in quantum spaces, addressing the computational challenges faced by classical models and variational quantum circuit-based models. Designed for the Noisy Intermediate-Scale Quantum (NISQ) era, QK-LSTM supports scalable hybrid quantum-classical implementations. Experimental results demonstrate that QK-LSTM outperforms classical LSTM networks in AQI forecasting, showcasing its potential for environmental monitoring and resource-constrained scenarios, while highlighting the broader applicability of quantum-enhanced machine learning frameworks in tackling large-scale, high-dimensional climate datasets.

关键词： Quantum computing Accuracy Computational modeling Time series analysis Predictive models Computational efficiency Forecasting Kernel Long short term memory Meteorology

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Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

Statistical Analysis of Intrinsic High-Frequency Characteris...

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IEEE Electron Devices technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

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Smart Home Security System Using Face Recognition Based on IoT- CNN

Smart Home Security System Using Face Recognition Based on I...

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Information technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning Computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning Computer Vision holds tremendous benefits for homeowners.

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Learning-Based Fast VVC Affine Motion Estimation

Learning-Based Fast VVC Affine Motion Estimation

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Fernando Sagrilo Marta Loose Ramiro Viana Gustavo Sanchez Guilherme Corrêa Luciano Agostini Graduate Program in Electrical Engineering (PPGEE) UNIPAMPA Alegrete RS Brazil Federal Institute of Farroupilha (IFFar) Jaguari Brazil Graduate Program in Computing (PPGC) UFPel Pelotas RS Brazil Video Technology Research Group (ViTech) UFPel Pelotas RS Brazil Federal Institute of Sertão Pernambucano (IFSertaoPE) Salgueiro PE Brazil

This paper presents a fast Affine Motion Estimation (AME) of Versatile Video Coding (VVC) Standard, based on Machine Learning and using Random Forest (RF) classification method. This encoding approach develops an RF model for each block size. The models were trained with information extracted during the VVC encoding process of the current, parent, and neighboring Coding Units (CU). Each model is applied to predict whether the Affine Motion Estimation (AME) will be skipped or not for that CU size. The proposed solution achieves a reduction of 20% on average in AME encoding time, with an insignificant impact of 0.07% on BD-BR.

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