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2024 International Symposium on Artificial Intelligence for Education, ISAIE 2024

作者： Abbas, Touqeer Javed, Umair Mehmood, Faisal Raza, Muneeb Li, Hui Department of Computer Science and Technology Beijing University of Chemical Technology Beijing China School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou China Beijing China

ISBN: (纸本)9798400707100

Generative AI is reshaping education by offering personalized learning experiences and innovative teaching methods. In this study, we introduced ChemGenX, an AI tool designed to enhance chemistry education by generating molecular structures with user-defined features. The primary objective is to simplify molecular design and improve student learning. Our approach utilizes the coherently aligned multimodal semantic space using pre-trained CLIP;generate text features from image features to seamlessly alleviate the need for text conditioning, resulting in accurate predictions of bond lengths, angles, and molecular structures. The model was trained on a comprehensive molecular dataset, achieving competitive performance in zero-shot text-to-image conversion. Pilot studies with high school students and teachers demonstrate its potential in generating accurate molecular structures, including key features like bond lengths and angles. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Bond length

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Energy-Efficient Resource Allocation for Smart Grids using Deep Reinforcement Learning in IoT Cloud Systems 8

Energy-Efficient Resource Allocation for Smart Grids using D...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Patil, Harshal Kansal, Vipashi Balasubramani, R. Manohar, Talari Vekariya, Daxa Kannan, Sathish Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Information Science and Engineering Karnataka Nitte574110 India Anantha Lakshmi Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh Ananthapuramu515721 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

This study describes the use of deep reinforcement learning (DRL) models to fine-tune resource allocation for smart grids in order to reduce energy waste and operational costs. The research experiment evaluates four different DRL algorithms, which include SAC, DDPG, PPO, and A3C. It demonstrates performance through various evaluation metrics, which include impeccable energy efficiency, perfect resource utilization, load balancing ratio, average response time, and computational overhead. SAC is proven to achieve 92.5% energy efficiency, 88.7% resource utilization, 90% load balancing ratio, and response time of 120ms. The computational overhead assessed supplements to 15 GFLOPS. DDPG showcased 90.1% energy efficiency and 85.4% resource utilization, 88.9% load balancing, a response time of 110 ms, and a computational overhead of 14.8 GFLOPS. On the other hand, PPO and A3C also proved to work well and have a promising future to be implemented in a dynamic grid. This analysis demonstrates that practical applications from DRL, including real-time decision-making and adaptive control approaches, can prove vital while assigning and working on energy tasks and renewable conduct. These models prove important and beneficial in enhancing the operational efficiency of the smart grid, understanding and learning continuously from real-time data. It will be a vital concept and idea that would be helpful in ensuring that smart grids work perfectly to help in a reduction in carbon footprints and effective energy resource management to ensure end-use service provider satisfactions. © 2024 IEEE.

关键词： Deep reinforcement learning

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Spectral-Efficient Downlink Systems under Imperfect SIC and CSI: MC-NOMA or Partial NOMA?

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IEEE Wireless Communications Letters 2024年第1期13卷 133-137页

作者： Katwe, Mayur Singh, Keshav Li, Chih-Peng Ding, Zhiguo Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates The University of Manchester School of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom

This letter investigates a scalable hybrid non-orthogonal multiple access (NOMA) scheme termed partial NOMA (pNOMA) based on the partial sharing of the frequency resources among the users. In particular, we aim to design a spectrally-efficient multi-user downlink scheme under imperfect successive interference cancellation (SIC) and channel state information (CSI) via a joint control on the power allocation and bandwidth overlap for all users. In particular, we formulate the joint optimization problem of the power allocation for each user and channel allocation for overlapped and non-overlapped signals and later solve it using a meta-heuristic search-based differential evolution algorithm. Simulation results validate that the considered pNOMA scheme flexibly integrates OMA and multi-channel NOMA schemes, and outperforms them in terms of the sum rate under stochastic imperfection in SIC and CSI. © 2012 IEEE.

关键词： Channel state information

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Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

Adaptive Sampling of Algal Blooms Using an Autonomous Underw...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Fonseca, Joana Rocha, Alexandre Aguiar, Miguel Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures StockholmSE-100 44 Sweden

ISBN: (纸本)9798350335446

This paper proposes a method that uses satellite data to improve adaptive sampling missions. We find and track algal bloom fronts using an autonomous underwater vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a concentration indicates the presence of algal blooms. The proposed method learns the kernel parameters of a Gaussian process model using satellite images of chlorophyll a from previous days. The AUV estimates the chlorophyll a concentration online using locally collected data. The algal bloom front estimate is fed to the motion control algorithm. The performance of this method is evaluated through simulations using a real dataset of an algal bloom front in the Baltic. We consider a real-world scenario with sensor and localization noise and with a detailed AUV model. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Solar Irradiance Prediction Using Transformer-based Machine Learning Models

Solar Irradiance Prediction Using Transformer-based Machine ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Demir, Ayda Gutierrez, Luis Felipe Namin, Akbar Siami Bayne, Stephen Texas Tech University Department of Electrical and Computer Engineering United States Texas Tech University Department of Computer Science United States

ISBN: (纸本)9781665480451

This paper presents a study of irradiance prediction using a transformer-based machine learning model for the photovoltaic (PV) renewable energy system. We explore the forecast of irradiance using ten years of data from the Texas Mesonet Data Archive at the Reese Center in Lubbock, Texas. After training our transformer model with 90% of the data, we show that it can fit the irradiance trend successfully, while the testing phase with the remaining 10% of the dataset indicates that the transformer can predict irradiance trends that align with the observed values. Additionally, we borrow the concept of rolling LSTMs to generate a rolling transformer in order to extrapolate values of irradiance even when the observed values are not available. Our extrapolation results show that the transformer can extrapolate irradiance values accurately in the short-term, but it is less precise in the long-term. To remedy this, we aim to explore more thoroughly the hyperparameter configuration of our model in order to move towards our goal of including machine learning methods in the control of PV systems. © 2022 IEEE.

关键词： Forecasting

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Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

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Iran Journal of computer science 2023年第4期6卷 387-396页

作者： Saad, Mohamed N. Zareef, Galena W. Ibrahim, Fatma S. Said, Ashraf M. Hamed, Hisham F. A. Biomedical Engineering Department Faculty of Engineering Minia University Minia Egypt Biomedical Engineering Department Misr University for Science and Technology 6th of October City Egypt Bioinformatics Department Faculty of Computer and Information Minia University Minia Egypt Computer and Software Engineering Department Misr University for Science and Technology 6th of October City Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Electrical and Electronics Engineering Department Faculty of Engineering Minia University Minia Egypt

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Genome-wide association study Genome-wide exploratory analysis Minor allele frequency Single nucleotide polymorphism Variant annotation

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ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities 25th

ModDrop++: A Dynamic Filter Network with Intra-subject Co-tr...

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25th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2022

作者： Liu, Han Fan, Yubo Li, Hao Wang, Jiacheng Hu, Dewei Cui, Can Lee, Ho Hin Zhang, Huahong Oguz, Ipek Department of Computer Science Vanderbilt University Nashville United States Department of Electrical and Computer Engineering Vanderbilt University Nashville United States

ISBN: (纸本)9783031164422

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease and multi-modality MRIs are routinely used to monitor MS lesions. Many automatic MS lesion segmentation models have been developed and have reached human-level performance. However, most established methods assume the MRI modalities used during training are also available during testing, which is not guaranteed in clinical practice. Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality. In this paper, we present a novel method dubbed ModDrop++ to train a unified network adaptive to an arbitrary number of input MRI sequences. ModDrop++ upgrades the main idea of ModDrop in two key ways. First, we devise a plug-and-play dynamic head and adopt a filter scaling strategy to improve the expressiveness of the network. Second, we design a co-training strategy to leverage the intra-subject relation between full modality and missing modality. Specifically, the intra-subject co-training strategy aims to guide the dynamic head to generate similar feature representations between the full- and missing-modality data from the same subject. We use two public MS datasets to show the superiority of ModDrop++. Source code and trained models are available at https://***/han-liu/ModDropPlusPlus. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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A Low-Cost Flexible Wrist ECG Sensor for Wearable Devices Application 5

A Low-Cost Flexible Wrist ECG Sensor for Wearable Devices Ap...

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5th IEEE International Flexible Electronics Technology Conference, IFETC 2023

作者： Kim, Taeil Yi, Qian Hassanpoor, Sina Esfandyarpour, Rahim Baylor University Department of Mechanical Engineering TX76798 United States University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States

ISBN: (纸本)9798350332094

A low-cost flexible wrist electrocardiogram (ECG) sensor is fabricated on flexible Au-coated PET film. The circular electrode of ECG sensor is covered with Ag/AgCl. The diameter of circular electrodes is varied to find an optimized diameter which provides ECG signals including PQRST peaks with minimum intrusiveness for the user. The effects of applying artificial sweat between electrodes and skin, placing electrodes in different positions, and using a third electrode connected to the ground on wrist ECG signals are studied to find the best condition to obtain stable wrist ECG signal with minimum intrusiveness. © 2023 IEEE.

关键词： Electrocardiograms

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Devices, Materials, Process Technologies, and Microelectronic Ecosystem Beyond the Exit of the Device Miniaturization Tunnel

IEEE Transactions on Materials for Electron Devices

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IEEE Transactions on Materials for Electron Devices 2024年 1卷 160-167页

作者： H.-S. Philip Wong Subhasish Mitra Department of Electrical Engineering Stanford University Stanford CA USA Department of Electrical Engineering and Department of Computer Science Stanford University Stanford CA USA

Many innovations across the system stack – from algorithms and architectures to circuits, devices, fabrication processes, and materials – will provide large synergistic benefits at the system level. While conventional wisdom optimizes for cost per transistor and economizes on the use of transistors, other optimization targets are becoming highly relevant moving forward. Examples include power and energy consumption, complexity of fabrication process, design, manufacturing and testing flows, and cycle time for development and productization. These all have important implications for devices, materials, process technologies, and the microelectronics ecosystem of the future.

关键词： Three-dimensional displays Silicon Integrated circuit interconnections System-on-chip Foundries Metals Circuits Transistors Random access memory Logic

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Design and Implementation of a Multi-Band Quad-Port MIMO Antenna for the 5G Applications 2

Design and Implementation of a Multi-Band Quad-Port MIMO Ant...

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2nd International Conference on Advanced computer Applications, ACA 2023

作者： Mohamed, Ali Jassem Khazaal, Hasan Fahad Abed, Thanaa Hussein Kareem, Qasim Hadi Wasit University Electrical Engineering Department Wasit Iraq Ministry of Science and Technology Baghdad Iraq Al-Farabi University College Computer Engineering Department Baghdad Iraq

ISBN: (纸本)9798350341829

In this paper, we present a new dual-configuration antenna designed to meet the demands of 5G applications. This antenna is based on a square shape, with multiband and various frequencies, and multiple slots with dimensions of 37.4mm x 39.4mm x 1.6mm. It has a low reflection coefficient (S 11 ≤-15dB, making it suitable for multi-band applications. The square shape was transformed into a four-port MIMO antenna, which provides good performance for 5G applications with adequate bandwidth for increased data rates and reduced antenna size. The MIMO antenna was implemented on the Roger RT5880 dielectric with a size of 80mm x 80mm x 1.6mm. It offers fewer return losses, high efficiency, and high radiation pattern coverage, covering the bands of 2.57 GHz, 2.89 GHz, 3.27 GHz, 3.92 GHz, 5.05 GHz, 5.53 GHz, and 6.09 GHz. The antenna design was developed using the CST software 2021, and the prototypes were measured by a Vector Network Analyzer (VNA). Both the simulation results and the measurement results have been met. © 2023 IEEE.

关键词： 5G mobile communication systems

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