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In situ Observation of Lithium Stripping and Plating Process in an Open-cell All-solid-state Lithium Metal Battery

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1287-1288页

作者： Wu, C. Zhao, L. Ai, Q. Risal, S. Lou, J. Yao, Y. Fan, Zheng Materials Science and Engineering Program and Texas Center for Superconductivity at the University of Houston University of Houston Houston TX United States Department of Electrical and Computer Engineering University of Houston Houston TX United States Department of Materials Science and Nano Engineering Rice University Houston TX United States Department of Engineering Technology University of Houston Houston TX United States

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Enhancing Efficiency in Yolov8n for Real-Time Detection of Oil Palm Fruits in Plantations Using Mobileone and Cross-Fusion

SSRN

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SSRN 2024年

作者： Hugo, Gregory Mushlik, Muhammad Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Accurate detection of fresh fruit bunches (FFB) ripeness in complex plantation environments is challenging due to occlusion, varying lighting, intricate visual features, and limited computational resources of mobile devices. To address this, a resource-efficient, real-time detection system was developed using an optimized YOLOv8n model. The model integrates MobileOne modules and Cross-Fusion blocks to enhance efficiency without compromising accuracy. Specifically, Conv and C2f modules were replaced with MobileOne modules, reducing parameters by 85% and 81%, respectively. Cross-Fusion improved feature fusion, and its combination with MobileOne (CF-MobileOne) achieved a 51% reduction in parameters while maintaining performance. The dataset comprised images from videos captured in plantations, annotated into five maturity stages: flower, unripe, underripe, ripe, and abnormal, and underwent augmentation with class balancing through manual undersampling. The model outperformed baseline YOLOv8 variants and heavier models like YOLOv9c and Faster R-CNN, achieving an mAP50 of 0.795 and an inference speed of 140.57 FPS. Hyperparameter tuning with a batch size of 64, SGD optimizer, and a learning rate of 0.01 yielded optimal results. Despite challenges in detecting certain classes due to visual similarities and environmental variations, the system effectively enhances productivity and efficiency in precision agriculture for the palm oil industry. © 2024, The Authors. All rights reserved.

关键词： Fruits

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Thermal energy dependent transient permittivity of epsilon-near-zero material

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science China(Physics,Mechanics & Astronomy) 2022年第8期65卷 96-101页

作者： Heng Wang Lixun Sun Kang Du Wending Zhang SooJin Chua Guixin Li Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an710129China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore LEES Program Singapore-MIT Alliance for Research&Technology(SMART)Singapore138602Singapore

Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of conduction electrons. The optical nonlinearity of ENZ materials has been explained by using the overall-effective-mass and the overall-scattering-time of electrons in the extended Drude model. However, their response to optical excitation is yet the last building block to complete the theory. In this paper, the concept of thermal energy is theoretically proposed to account for the total energy of conduction electrons exceeding their thermal equilibrium value. The time-varying thermal energy is adopted to describe the transient optical response of indium-tin-oxide(ITO), a typical ENZ material. A spectrally-resolved femtosecond pump-probe experiment was conducted to verify our theory. By correlating the thermal energy with the pumping density, both the giant change and the transient response of the permittivity of ITO can be predicted. The results in this work provide a new methodology to describe the transient permittivities of ENZ materials, which will benefit the design of ENZ-based nonlinear photonic devices.

关键词： epsilon-near-zero optical nonlinearity transient response thermal energy hot electron indium tin oxide

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A Preliminary Study of Decision Support Model of Photovoltaic for Village Area in South of Sumatera 4

A Preliminary Study of Decision Support Model of Photovoltai...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Ardianto, Feby Ermatita, Ermatita Sofijan, Armin Ayumi, Vina Noprisson, Handrie Purba, Mariana Universitas Sriwijaya Doctoral Program in Engineering Science Palembang Indonesia Universitas Muhammadiyah Palembang Engineering Faculty Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Engineering Faculty South Sumatera Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

Indonesia has a very high potential for solar energy irradiation, including in 66 countries in the sunbelt area. This can be alternative for power generation system that converts energy sources from solar radiation into electricity through solar cells or photovoltaic cells. This research goal is to develop an initial decision support model for the photovoltaic village area in South Sumatra, Indonesia. This research was conducted six months in Pandan Arang Village, Kandis District, Ogan Ilir Regency, South Sumatra. As the result, the proposed model is consisted of four sub-system, including photovoltaic system, data logger, database management and decision support system. Based on the results of experiments on solar panels with the parameters of current, voltage and light intensity, the highest value of light intensity was 1989.1 W/m2, while the voltage and current of 15.49V and current of 14.91 A were displayed through a 16x2 LCD display which served as information display in real time. The expected result in this study is an alternative fault detection PV system in the form of normal conditions, shadows, and overloads. © 2022 IEEE.

关键词： Decision support systems

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Optimizing 3D Clothing Models for VR: A Deep Learning Approach to Triangle Count Reduction

Optimizing 3D Clothing Models for VR: A Deep Learning Approa...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study presents a deep learning framework optimizing 3D clothing models for VR, using a CNN to significantly reduce the triangle count of models from DeepFashion3D and CAP-UDF datasets. Achieving a balance between efficiency and detail, it cuts triangle count from over 160,000 to below 4,000, maintaining high DPI. The approach automates optimization, promising scalability and efficiency in VR fashion, setting a foundation for future 3D content development, enhancing virtual garment realism and interactivity.

关键词： Deep learning Solid modeling Three-dimensional displays Scalability Clothing Optimization Consumer electronics

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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A Hybrid Rolling and Flying Robot for Pipeline Fault Detection Using Deep Learning

A Hybrid Rolling and Flying Robot for Pipeline Fault Detecti...

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2023 Advances in science and engineering Technology International Conferences, ASET 2023

作者： Ghaly, Megan Mitra, Rohan Rayess, Abdullah Al Ahmed, Assem Varam, Dara Jaradat, Mohammad A. Pasquier, Michel Khalil, Ahmed American University of Sharjah Department of Mechanical Engineering Sharjah United Arab Emirates American University of Sharjah Department of Computer Science and Engineering Sharjah United Arab Emirates American University of Sharjah Mechatronics Engineering Gradute Program Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

The Middle Eastern and North African region is highly reliant on the oil and gas industry. Subsequently, the need for pipeline inspection and fault diagnosis has become paramount. Current inspection methods rely on manual procedures that introduce sources of error into the results. In this paper, the design for a hybrid rolling and flying unmanned aerial vehicle (UAV) for pipeline inspection is proposed. The UAV was developed with a rolling frame capable of landing on pipelines to externally inspect them for cracks, leaks, or faults by traversing along the pipeline's surface while maintaining its stability. The hybrid UAV uses a camera to classify cracks along the surface of the pipeline using deep learning models. The paper then tests several models and deploys the best model found during testing -MobileNet100 along with the proposed design for the UAV. Finally, the prototype that was presented in this paper has been tested within a controlled environment to verify the compliance of the flying, rolling and fault detection subsystems. © 2023 IEEE.

关键词： Fault detection

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An Innovative New Open computer Vision Framework Via Artificial Intelligence with Python

An Innovative New Open Computer Vision Framework Via Artific...

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Bonkra, Anupam Dhiman, Pummy Goyal, Shanky Islam, Sardar M. N. Rana, Arun Kumar Sharma, Naman Information Technology Department Chandigarh Engineering College-CGC Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India ISILC & Decision Sciences and Modelling Program Victoria University Footscra Australia Department of Computer Science Galgotias College of Engineering of Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819967544

computer vision has emerged as an important subject of study, with several practical applications in a wide range of domains. OpenCV, a widely used framework, has played an important role in allowing computer vision tasks. This study presents an AI-driven Python implementation of the OpenCV framework to expand its capabilities. Data collecting and pre-processing, feature extraction, model selection and training, and Python-based system implementation are all part of the proposed system. A thorough examination of system performance indicates its advantage over competing approaches. The system’s flexibility enables it to handle a variety of computer vision tasks, such as gesture recognition, face detection, and object recognition. This paper advances AI-powered computer vision systems by providing significant insights into the implementation of OpenCV with Python. The focus of future work will be on improving system accuracy and broadening its functional range. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Transfer Learning Implementation for Batik Besurek Motifs Classification: A Comparative Study

Transfer Learning Implementation for Batik Besurek Motifs Cl...

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International Conference on Informatics and Computing (ICIC)

作者： Marissa Utami Ermatita Ermatita Abdiansyah Abdiansyah Engineering Program Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This research discussion will be a comparative model proposal for introducing Besurek batik fabric by implementing MobileNet, ResNet50, InceptionV3 and VGG16. This research also analyzed the influence of augmentation data methods, namely rotation, zoom, width shift and height shift, on the performance of the Besurek batik fabric recognition algorithm based on accuracy, precision and recall results. The size of the batik besurek fabric motif image as an input image is $224 \times 224$ with three channels consisting of red, green, and blue, totalling five classes, namely kaligrafi, bunga rafflesia, burung kuau, lung paku and rambutan. The ratio of sharing training data and test data is 70:30. Accuracy evaluations are carried out on several epochs and data that have been shared. Based on the experiment, the accuracy value of MobileNetV2 can be obtained by training accuracy of $\mathbf{98.14 \%}$, validation accuracy of $\mathbf{94.60 \%}$, and testing accuracy of $\mathbf{96.46 \%}$. Then, the accuracy value of ResNet50 can be obtained by training accuracy at $\mathbf{99.60 \%}$, validation accuracy at 97.44, and testing accuracy at $\mathbf{98.12 \%}$. The accuracy value of InceptionV3 can be obtained by training accuracy at $96.35 \%$, validation accuracy at $96.59 \%$, and testing accuracy at $\mathbf{92.50 \%}$. Finally, the accuracy value of VGG16 can be obtained by training accuracy at $\mathbf{99.67 \%}$, validation accuracy at $96.02 \%$, and testing accuracy at $\mathbf{95.00 \%}$. The accuracy of VGG 16 can be concluded as the best model for Batik recognition.

关键词： Training Accuracy Lungs Transfer learning Training data Fabrics Proposals Informatics Testing Residual neural networks

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