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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Takahashi, Shun Oguchi, Kenichi Kamei, Kento Mizuguchi, Takaha Spratt, Spencer J. Ozeki, Yasuyuki Department of Electrical Engineering and Information Systems The University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo153-8904 Japan Department of Electrical and Electronic Engineering The University of Tokyo Bunkyo-ku Tokyo113-8656 Japan

We present a widely tunable fiber optical parametric oscillator with a tuning range of 1470 cm−1. By synchronizing it with a Ti:sapphire laser, we succeed in stimulated Raman scattering imaging across the broad spectr... 详细信息

ISBN: (纸本)9781957171395

关键词： Sapphire

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All-Polarization-Maintaining L-Band Fiber Ring Laser Mode-Locked by Nonlinear Polarization Rotation

All-Polarization-Maintaining L-Band Fiber Ring Laser Mode-Lo...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Ye, Guanyu Chow, Kin Kee Dai, Maolin Shirahata, Takuma Yamashita, Shinji Set, Sze Yun Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Manchester Metropolitan University Department of Engineering Manchester United Kingdom The University of Tokyo Department of Electrical Engineering and Information Systems Tokyo Japan

We report the first all-polarization maintaining (all-PM) nonlinear polarization rotation (NPR) mode-locked fiber ring laser in the all-anomalous dispersion regime. The laser generates pulses at 1575 nm with a 5 nm sp... 详细信息

ISBN: (纸本)9798350312614

关键词： all-polarization maintaining fiber laser mode-locked nonlinear polarization rotation

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research on workflow recognition for liver rupture repair surgery

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Mathematical Biosciences and engineering 2024年第2期21卷 1844-1856页

作者： Men, Yutao Zhao, Zixian Chen, Wei Wu, Hang Zhang, Guang Luo, Feng Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Medical Support Technology Research Department Systems Engineering Institute Academy of Military Sciences People’s Liberation Army Tianjin300161 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons.

关键词： Image classification

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Read Voltage Dependency of Random Telegraph Noise in the Intermediate State of TaOx-based ReRAM

Read Voltage Dependency of Random Telegraph Noise in the Int...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Yamauchi, Kenshin Misawa, Naoko Shima, Hisashi Naitoh, Yasuhisa Akinaga, Hiroyuki Matsui, Chihiro Takeuchi, Ken The University of Tokyo Dept. of Electrical Engineering and Information Systems Tokyo Japan Device Technology Research Institute National Institute of Advanced Industrial Science and Technology Ibaraki Japan

ISBN: (纸本)9798331504779

This work investigates random telegraph noise (RTN) behavior in the intermediate resistance states of 40nm TaOx-based analog ReRAM. Uniquely in the intermediate resistance states, both the frequency and amplitude of RTN fluctuations tend to increase with the read voltage. Considering the trade-off between increasing them and enhancing margins among adjacent resistance states as read voltage rises, a 0.2V readout minimizes RTN-induced misdetection of resistance states, achieving a significantly low error rate of 0.19%. © 2025 IEEE.

关键词： Fluctuation Pattern Classifier Multi-Level Cell Random Telegraph Noise ReRAM

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Model Predictive Control of a wastewater treatment process using neural networks 28

Model Predictive Control of a wastewater treatment process u...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Vasiliev, Iulian Luca, Irina Condrachi, Larisa Luca, Laurentiu Barbu, Marian Vilanova, Ramon Caraman, Sergiu Dunǎrea de Jos University of Galati Department of Automation and Electrical Engineering Galati Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain

ISBN: (纸本)9798350339918

This paper deals with the increasing of the operation efficiency of a wastewater treatment plant by reducing the level of pollutant concentration in the effluent. In essence, the main goal is the reduction of the concentration of organic substrate, Model Predictive Control algorithm being used for this, considering as a model of the wastewater treatment process a neural network model based on a simplified mathematical model of the 4th order. For the implementation of the control algorithm, two neural networks were trained. In the first version of the neural network, it was considered that all 4 state variables are measurable and in the second version, only the organic substrate was considered measurable. Regarding the training of the two neural networks, good results were obtained in the case of both versions. Instead, the control algorithm gave better results when the second neural network was used (lower values of the substrate, lower variations of the aeration rate, which means a lower cost of operation). © 2023 IEEE.

关键词： Neural networks

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An analysis towards integrated control of wastewater collecting and treatment systems 27

An analysis towards integrated control of wastewater collect...

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27th International Conference on System Theory, Control and Computing, ICSTCC 2023

作者： Vasiliev, Iulian Luca, Laurentiu Barbu, Marian Vilanova, Ramon Caraman, Sergiu Department of Automation and Electrical Engineering Dunărea de Jos University of Galati Galati Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain

ISBN: (纸本)9798350337983

This paper deals with an analysis regarding the effect of Sewer Network operation on the performances of the integrated wastewater collecting and treatment system. The analysis is done by considering various control actions for the output storage tank of the sewer network, comparing a series of performance indicators defined for both subsystems with their "no control" value. Two cases have been considered regarding the control actions for the output storage tank of the sewer network: 1. keeping the output valve at a constant opening and 2. keeping the output valve at the maximum opening possible such as the outflow is limited to a constant value. While the second case did not prove advantageous, the first one showed that lowering the sewer network output valve opening would improve the integrated system performances. © 2023 IEEE.

关键词： Wastewater treatment

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Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

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Journal of engineering and Applied Science 2024年第1期71卷 12页

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Nounou, Hazem Bouzrara, Kais Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kairouan Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Monastir5019 Tunisia

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time. © 2023, The Author(s).

关键词： Failure analysis

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Low-frequency Oscillation Damping Control for Large-scale Power System with Simplified Virtual Synchronous Machine

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Journal of Modern Power systems and Clean Energy 2021年第6期9卷 1424-1435页

作者： Karim Sebaa Yang Zhou Yong Li Ayetül Gelen Hassan Nouri Advanced Electronics Systems Laboratory University of Medea Institute of Energy Systems Energy Efficiency and Energy EconomicsTU Dortmund University IEEE College of Electrical and Information Engineering Hunan University Department of Electrical Electronic Engineering Bursa Technical University Power Systems and Energy Research Laboratory University of the West of England

This study focuses on a virtual synchronous machine(VSM) based on voltage source converters to mimic the behavior of synchronous machines(SMs) and improve the damping ratio of the power system. The VSM model is simplified according to some assumptions(neglecting the speed variation and the stator transients) to allow for the possibility of dealing with low-frequency oscillation in large-scale systems with many VSMs. Furthermore, a virtual power system stabilizer(VPSS) structure is proposed and tuned using a method based on a linearized power system dynamic model. The linear and nonlinear analyses examine the stability of two modified versions of a 16-machine power system in which, in the first case, partial classical machines are replaced by VSMs, and in the second case, all SMs are replaced by VSMs. The simulation results of the case studies validate the efficiency of the proposed control strategy.

关键词： Synchronous machine(SM) small-signal stability transient stability virtual power system stabilizer(VPSS) virtual synchronous machine(VSM)

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Optimal Dispatch of Cogeneration with Thermal and Battery Energy Storage for BEMS Subject to Load Demand Uncertainty 19

Optimal Dispatch of Cogeneration with Thermal and Battery En...

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19th International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Trairat, Parichada Banjerdpongchai, David Chulalonkorn University Intelligent Control Automation of Process Systems Research Unit Dept of Electrical Engineering Bangkok10330 Thailand

ISBN: (纸本)9781665485845

This paper presents optimal dispatch of cogeneration with thermal energy storage (TES) and battery energy storage (BES) for Building Energy Management System (BEMS) subject to load demand uncertainty. The energy supply of BEMS consists of combined heat and power (CHP), an auxiliary boiler, an absorption chiller, battery energy storage (BES), spinning reserve (SR) and power grids. Previous research related to CHP for BEMS provides optimal operation to minimize total operating cost (TOC), but their operation has only CHP and TES for supply the electrical energy. In this paper, we propose to add BES and SR which aims to supply the electrical energy in the building subject to load demand uncertainty. The load demand uncertainty arises from the difference between the forecasted load demand and the actual load demand. We apply artificial neural network (ANN) to analyze the load demand uncertainty. BES and SR are utilized to tackle the load uncertainty. Then, we design economic optimal operationwith TOC as the objective function. We apply the proposed BEMS with a large shopping mall to demonstrate improvements in energy efficiency. The numerical results manifest that the proposed dispatch can reduce 12.52% of TOC compared to the previous BEMS without BES and SR. © 2022 IEEE.

关键词： Uncertainty analysis

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Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning

Improving Pretrained YAMNet for Enhanced Speech Command Dete...

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Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Sidahmed Lachenani Hamza Kheddar Mohamed Ouldzmirli Department of Electrical Engineering Research Laboratory in Advanced Electronics Systems LSEA University of Medea Medea Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

This work addresses the need for enhanced accuracy and efficiency in speech command recognition systems, a critical component for improving user interaction in various smart applications. Leveraging the robust pretrained YAMNet model and transfer learning, this study develops a method that significantly improves speech command recognition. We adapt and train a YAMNet deep learning model to effectively detect and interpret speech commands from audio signals. Using the extensively annotated Speech Commands dataset (speech_commands_v0.01), our approach demonstrates the practical application of transfer learning to accurately recognize a predefined set of speech commands. The dataset is meticulously augmented, and features are strategically extracted to boost model performance. As a result, the final model achieved a recognition accuracy of 95.28%, underscoring the impact of advanced machine learning techniques on speech command *** achievement marks substantial progress in audio processing technologies and establishes a new benchmark for future research in the field.

关键词： Deep learning Adaptation models Accuracy Transfer learning Speech recognition Speech enhancement Benchmark testing Feature extraction Telecommunications Intelligent systems

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