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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Rochadiani, Theresia Herlina Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wawan Bina Nusantara University Binus Graduate Program- Computer Science Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350369922

Our surroundings' auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models - three, four, and five classes - with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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A Developed PLL Control Technique for Distorted and Unbalanced Grid Voltages with a Three-Level NPC Converter-Based Off-Board Battery Charger

A Developed PLL Control Technique for Distorted and Unbalanc...

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2022 International electrical engineering Congress, iEECON 2022

作者： Thanakam, Thidarat Kumsuwan, Yuttana Chiang Mai University Graduate Program in Electrical Engineering Department of Electrical Engineering Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand

ISBN: (纸本)9781665402064

As electric vehicle technologies advance, the number of off-board battery charger stations grows. Because they can provide a high-power level that is suitable for fast charging. The main factor of high-power transferring is power converters that should serve the grid power quality, including the reactive power, the power factor, and the harmonic distortion. In this paper, the development of the control algorithm for the three-phase three-level neutral point clamped converter-based off-board battery charger to support the utility grid when the three-phase grid voltage distortion or unbalance occurs is presented. By applying the standard phase-locked loop controller into the conventional control algorithm, it is possible to obtain more sinusoidal and balanced grid currents along with distorted or unbalanced grid voltages to conserve the grid power quality. The system operations, along with the comparison of the conventional control algorithm and the proposed algorithm, are provided in the simulation results. © 2022 IEEE.

关键词： Charging (batteries)

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A High-Performance Grid-Tied Three-Level T-Type Converter for Off-Board Battery Chargers under AC-Side Voltage Distortions and Unbalances 19

A High-Performance Grid-Tied Three-Level T-Type Converter fo...

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

ISBN: (纸本)9781665485845

The conversions are a key factor in the battery charging of electric vehicles. The off-board battery chargers, which are composed of converters and controller units, are needed to fast charge by transferring high-power. The converters should serve applications even with unusual grid systems where non-sinusoidal grid currents occur. This paper describes a high-performance, three-level T-type converter for off-board battery chargers. It can operate under conditions of grid voltage distortions and grid voltage unbalances. The proposed control algorithm provides the three-phase sinusoidal currents to prevent their effect on the grid system and the equipment, which is an improvement from the conventional control algorithm. The constant current and constant voltage modes are applied for charging applications. Moreover, the power factor correction can be obtained. The simulation results are provided to investigate the performance of the off-board battery charger based on the three-level T-type converter. © 2022 IEEE.

关键词： Charging (batteries)

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Sensing beyond the exceptional point for high detectivity

Sensing beyond the exceptional point for high detectivity

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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： Li, Zhichao Prasad, Ciril S. Wang, Xielin Zhang, Ding Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States

Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity. Slightly off EP operation boosts detectivity without much loss in sensitivity. We experimentally demonstrate a high-detectivity-off-EP plasmonic-photonic hybrid sensor for anti-mouse IgG protein. © Optica Publishing Group 2024

关键词： Hybrid sensors

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Development of a Mixed-Reality Dual-User Training System for Robot Laparoscopic Surgery

Development of a Mixed-Reality Dual-User Training System for...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Ting-You Young, Kuu-Young Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering Hsinchu Taiwan

ISBN: (纸本)9781665410205

As robot surgery becomes more popular these days, it consequently solicits a high demand on its training system. Currently, most of the systems are intended for personal training, while it may be more effective if an experienced expert can also be involved. It thus motivates us to propose a dual-user robotic surgical training system that can closely link the mentor and trainee together. The system is developed in a mixed-reality environment that integrates the information from both the virtual and real worlds. The haptic feedback and virtual fixtures are employed for assistance during skill transfer. The proposed system can assess the proficiency level of the trainee for the mentor to provide proper assistance during training. The experimental results demonstrate that the proposed system is capable of enhancing the trainee's ability for surgery. Especially, similar effectiveness is observed during comparative studies with direct hands-on guidance frequently adopted for skill transfer during sport training. © 2024 IEEE.

关键词： Robotic surgery

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Sensing beyond the exceptional point for high detectivity

Sensing beyond the exceptional point for high detectivity

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Laser beams

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An Improved I-V model for Resistive Random Access Memory

An Improved I-V model for Resistive Random Access Memory

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2022 IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2022

作者： Chung, Harry Shin, Hyungsoon Sun, Wookyung Park, Jisun Graduate Program in Smart Factory Ewha Womans University Dept. of Electronic and Electrical Engineering Seoul Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, we performed a simulation for a physical resistive random-access device (RRAM) to propose an improved model representing its electrical characteristics. Voltage ThrEshold Adaptive Memristor (VTEAM) model that operates based on the voltage threshold of the device adapted for the RRAM simulation. We applied some physical features to the model to adjust the internal state variable to improve the simulation results. The internal state variable used in the model was designed after the physical feature such as the distance between the end of the filament and the electrode of the device. Therefore, applying some of these physical conditions affects the simulation results. Simulation results with the newly applied model showed optimized I-V characteristics that resemble the physical device's behavior more than the existing model. © 2022 IEEE.

关键词： Memristors

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A Design for Improvement of Visual SLAM in Dynamic Environments Using Feature-Point Removal of Moving Persons

A Design for Improvement of Visual SLAM in Dynamic Environme...

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2023 International Automatic Control Conference, CACS 2023

作者： Song, Kai-Tai Meng, Ching-Hao National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering College of Electrical and Computer Engineering Hsinchu886-3-5731865 Taiwan National Yang Ming Chiao Tung University Graduate Degree Program of Robotics College of Engineering Hsinchu Taiwan

ISBN: (纸本)9798350306354

This paper presents a design to improve the robustness of visual SLAM(vSLAM). A processing step of feature-removal is added to the tracking thread of the conventional ORB-SLAM2 algorithm to improve the localization accuracy of a mobile robot in an environment with moving persons. Instance segmentation and motion tracking are intergrated to identify motion state of people in an image. ORB feature points belonging to moving persons are removed for further processing of the vSLAM pipeline. The advantage of this method is that the vSLAM can remove feature points of moving people, while retain those belonging to static people in the environment, which improves the accuracy of robot pose estimation. The improved ORB-SLAM2 algorithm has been implemented in a NVIDIA Xavier embedded system, which is integrated to a mobile robot. In practical robot navigation experiments, the average positioning error of the proposed method is within 4cm for 22.4m travel distance. Compared with conventional ORB-SLAM2, the average accuracy of our vSLAM method improves 97% in a dynamic environment with moving people. © 2023 IEEE.

关键词： Mobile robots

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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