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International Conference on Internet of Things: Systems, Management and Security

作者： Shih-Man Chang Hao Pu Lin Chin-Chuan Han Yu-Chi Wu Dept. of Computer Science and Information Engineering National United University Miao-Li Taiwan Program in Material and Chemical Engineering National United University MiaoLi Taiwan Dept. of Electrical Engineering National United University Miao-Li Taiwan

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

Internet of Things (IoT) technology has widely used in industrial production. Most cases focused on designing new equipment or processes. However, small and medium enterprises especially the powder metallurgy manufacturers in Taiwan would not replace existing production equipment due to the finical problems. In this study, an IoT-based platform is conducted to extract the sintering parameters for technology to augment sintering furnaces by installing sensors and data transmission modules, setting a sintering parameter extraction platform for powder metallurgy sintering furnace. Long-term monitoring and remote control are the main technologies for solving three problems during the sintering process, i.e., temperature, atmosphere, and time. Monitoring targets include checking whether the heaters are damaged, ensuring stable air flow meter readings, and verifying the normal speed of the conveyor belt. Additionally, remote control technology has been developed to execute cooling and reheating processes on the sintering furnace. The staying time on-site for personnel is reduced and the sintering time is increased for production increase during working hours.

关键词： Temperature sensors Powders Furnaces Sintering Process control Metallurgy Internet of Things Remote control Monitoring Parameter extraction

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Surface phase diagram of CsSnI3 from first-principles calculations

arXiv

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arXiv 2024年

作者： Li, Kejia Khamdang, Chadawan Wang, Mengen Department of Electrical and Computer Engineering State University of New York at Binghamton BinghamtonNY13902 United States Materials Science and Engineering Program State University of New York at Binghamton BinghamtonNY13902 United States

CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance. © 2024, CC BY-NC-SA.

关键词： Tin compounds

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Statistical 3D Device Simulation of Full Fluctuations of Gate-All-Around Silicon Nanosheet MOSFETs at Sub-3-nm Technology Nodes

Statistical 3D Device Simulation of Full Fluctuations of Gat...

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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Kola, Sekhar Reddy Li, Yiming Chen, Chieh-Yang Chuang, Min-Hui National Yang-Ming Chiao Tung University Electrical Engineering and Computer Science International Graduate Program Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Department of Electrical Engineering and Computer Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Parallel and Scientific Computing Laboratory Hsinchu300 Taiwan

ISBN: (纸本)9781665409230

We for the first time study characteristic fluctuation of gate-all-around silicon nanosheet MOSFETs induced by random dopants fluctuation (RDF), interface trap fluctuation (ITF), and work function fluctuation (WKF), and their combinations. The explored devices affected by the WKF and its combination result in sizeable variability. Because of enigmatical interactions among the aforementioned random factors, overestimated variabilities occur if we only consider one random source each time, compared with the statistical 3D device simulation for full RDF, ITF and WKF at the same time. Notably, 10.78% and 103% overestimations are found for the threshold voltage and off-state current fluctuations. © 2022 IEEE.

关键词： Nanosheets

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Channel Modeling for 6G Communications: A Survey

Channel Modeling for 6G Communications: A Survey

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2021 International Conference on computer System, Information Technology, and electrical engineering, COSITE 2021

作者： Walidainy, Hubbul Adriman, Ramzi Away, Yuwaldi Nasaruddin, Nasaruddin Univerisity of Syiah Kuala Doctoral Program School of Engineering Banda Aceh Indonesia Univerisity of Syiah Kuala Dept. Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9781665425094

The development of communications technology is overgrowing, making disseminating information so fast, widespread, and massive. A network of communication systems starting from the first generation to the current sixth-generation (6G). Fifth-generation communication systems have been implemented in several places in the world today. However, research continues to develop the sixth-generation communication system. This paper aims to survey and design a channel modeling for a 6G communication system. Moreover, the channel model has also simulated the system in Banda Aceh city as a case study, using the NYUSIM simulator. In this paper, the channel model is considered in the form of Urban Microcell (Umi) and Line of Sight (LOS) with a frequency of 90 GHz. Based on the simulation, this paper found that the path loss value is 124.7 dB, and the received power is -45.5 dBm. The simulation result has also shown that the maximum distance from the transmitter to the receiver for 6G communication is 123.5 m with a delay of 6.4 ns on directional power delay profile (PDP). While on omnidirectional PDP, the delay is 21.9 ns. Thus, the resulting attenuation is relatively high for 6G communication systems due to high rainfall in Banda Aceh. © 2021 IEEE.

关键词： Surveys

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Defect formation in CsSnI3 from Density Functional Theory and Machine Learning

arXiv

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arXiv 2024年

作者： Khamdang, Chadawan Wang, Mengen Department of Electrical and Computer Engineering State University of New York at Binghamton BinghamtonNY13902 United States Materials Science and Engineering Program State University of New York at Binghamton BinghamtonNY13902 United States

Sn-based perovskites as low-toxicity materials are actively studied for optoelectronic applications. However, their performance is limited by p-type self-doping, which can be suppressed by substitutional doping on the cation sites. In this study, we combine density functional theory (DFT) calculations with machine learning (ML) to develop a predictive model and identify the key descriptors affecting formation energy and charge transition levels of the substitutional dopants in CsSnI3. Our DFT calculations create a dataset of formation energies and charge transition levels and show that Y, Sc, Al, Zr, Nb, Ba, and Sr are effective dopants that pin the Fermi level higher in the band gap, suppressing the p-type self-doping. We explore ML algorithms and propose training a random forest regression model to predict the defect formation properties. This work shows the predictive capability of combining DFT with machine learning and provides insights into the important features that determine the defect formation energetics. © 2024, CC BY-NC-SA.

关键词： Perovskite

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Uncertainty Quantification Kernel PCA: Enhancing Fault Detection in Interval-Valued Data

Uncertainty Quantification Kernel PCA: Enhancing Fault Detec...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Abdelhalim Louifi Abdelmalek Kouadri Mohamed Faouzi Harkat Abderazak Bensmail Majdi Mansouri Hazem Nounou Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M’Hamed Bougara of Boumerdes Boumerdes Algeria Systems and Advanced Material Laboratory Badji Mokhtar - Annaba University Annaba Algeria Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The interval-valued kernel PCA (UQ-KPCA) is a variation of the kernel PCA (KPCA) designed for interval-valued data, designed to handle data uncertainty by defining specific similarity measures and kernel functions for interval data. This paper introduces Uncertainty Quantification KPCA (UQ-KPCA) as a novel method to address uncertainties in data. UQ-KPCA converts the traditional KPCA model from single-valued to interval-valued representations, allowing for accurate error and uncertainty quantification. The process modeling using KPCA is then performed on data based on the interval model, followed by the computation of fault detection statistics such as T 2 , Q, and Φ. The method’s effectiveness is evaluated in the context of the cement rotary kiln process, and compared with the KPCA demonstrating superior performance in accurately identifying faults within a stochastic setting with unknown uncertainties.

关键词： Process monitoring Kilns Uncertainty Fault detection Computational modeling Cement industry Stochastic processes Real-time systems Kernel Principal component analysis

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FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

arXiv

引用

arXiv 2022年

作者： Jang, Jaehee Ha, Heonseok Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Electrical and Computer Engineering Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks. © 2022, CC BY.

关键词： Deep neural networks

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Thermo-optic Properties in PECVD Silicon Rich Silicon Carbide

Thermo-optic Properties in PECVD Silicon Rich Silicon Carbid...

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Novel Optical Materials and Applications, NOMA 2022

作者： Chang, Li-Yang Sunny Pappert, Steve Yu, Paul K.L. Materials Science and Engineering Program University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States

We study the thermo-optic coefficient of silicon carbide with different silicon content. We demonstrate a clear trend between the silicon content and the thermo-optic coefficient which measured as high as 1.88×10... 详细信息

ISBN: (纸本)9781557528209

关键词： Silicon carbide

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac...

ISBN: (纸本)9781957171258

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Quality Improvements in the electrical Design of Typical DC-DC Electronic Power Converter for High-Voltage Applications 8

Quality Improvements in the Electrical Design of Typical DC-...

引用

8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Recto, King Harold A. Escoto, Miguel T. Hernandez, Noel B. Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines Centro Escolar University Post-doctoral Program in Quality Management of the Graduate School Manila Philippines

ISBN: (纸本)9781665491068

The green transition has brought about a worldwide-shift to the use of renewables as alternative energy sources. Because of this, high voltage DC has been a field of interest in power electronics due to its capability in efficiently transmitting power to reach remote areas. Typical DC-DC electronic power converter design has the capability of boosting voltage level to a certain extent but not enough for high-voltage applications. Using the DMAIC Approach of the Six Sigma, quality improvements in the design was proposed to stabilize operating frequency, bring up the output voltage from 100 VDC to 4,000 VDC and provide at least 10% margin of error in the apparent power measured at the output side to offset inherent losses in the electrical design. These proposed quality improvements were sustained by designing a control circuit that would ensure the attainment of the parameters set in the Define Phase of the DMAIC Approach. The result of the study is a practical 12 VDC to 4,000 VDC boost converter design that has incorporated all the proposed quality improvements. © 2022 IEEE.

关键词： Six sigma

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