检索结果-内蒙古大学图书馆

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nwafor, Ebelechukwu Baskota, Ujjwal Parwez, Md Salik Blackstone, Jeremy Olufowobi, Habeeb Villanova University Department of Computing Sciences PA United States University of Texas at Arlington Department of Computer Science & Engineering TX United States Howard University Department of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798350351255

The Internet of Things (IoT) landscape has grown exponentially in recent years, making robust and efficient intrusion detection systems (IDS) even more critical. While Large Language Models (LLMs) have gained significant traction, their effectiveness in network intrusion detection remains largely unexplored. This paper proposes an LLM-based framework for enhanced threat detection and analysis in IoT networks. We explore using advanced LLMs like OpenAI's Generative Pre-trained Transformer (GPT) model, focusing on techniques such as fine-tuning and embedding similarity. Using real-world intrusion datasets, we evaluate the proposed LLM's performance in detecting common network attacks and compare it with ensemble-based IDS solutions. We assess the efficiency of the LLM in binary class and multiclass classification task using standard metrics, such as accuracy, recall, precision, and F1 scores. While the fine-tuning approach does not produce comparable results to the current baseline ensemble-based IDS models, the embedding approach, however, yields comparable results. This analysis represents a starting point for exploring the utilization of advanced large language models for intrusion detection within an IoT ecosystem. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Dynamic Payback Period Estimation for Solar Power Plants Through ML Based Solar Power Generation Forecasts

Dynamic Payback Period Estimation for Solar Power Plants Thr...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Singh, Tarun Raj Prajapati, Aryan Himmatlal Kumar, Nishant Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur India Indian Institute of Technology Jodhpur Department of Electrical Engineering Jodhpur India

ISBN: (纸本)9798331510022

Substantial capital is required to invest in solar power plants, which puts estimation of the payback period accurately at primary concern for stakeholders. In this paper, we proposed a novel method to estimate the payback period of a 10MW solar power plant through forecasting solar power generation by an optimized XGBoost Machine learning model. We began by forecasting the Global Horizontal Irradiance (GHI) and temperature with a temporal resolution of 1 hour for the upcoming year. Using these forecasted values, the expected power output of a single solar panel with given properties is calculated, which was further scaled for the entire plant to estimate the total power generated for entire year with a resolution of 1 hour. Monthly income from the plant is estimated by considering current electricity prices and the predicted power of each month. Plant investments accounted for various costs, including panels, land acquisition, maintenance, wiring, and equipment, while also considering compound interest on the initial investment (compounded monthly) with monthly loan repayments. By considering different scenarios, we determined the payback period by varying factors such as interest rates, land costs, panel efficiency, and maintenance expenses. Our findings offer offer valuable insights into the financial feasibility and potentiality of solar power plants and demonstrate how these factors influence the payback period of such plants. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Tensorial Evolutionary Computation for Spatial Optimization Problems

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第1期5卷 154-166页

作者： Si-Chao, Lei Xiaolin, Xiao Yue-Jiao, Gong Yun, Li Jun, Zhang the School of Computer Science and Engineering South China University of Technology Guangzhou510006 China the Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen610056 China the School of Computer Science South China Normal University Guangzhou510006 China the Department of Electrical and Electronic Engineering Hanyang University Seoul15588 Korea Republic of

—Spatial optimization problems (SOPs) refer to a class of problems where the decision variables require spatial organization. Existing methods based on evolutionary algorithms (EAs) fit conventional evolutionary operators by flattening the spatial representations of SOPs to one dimension, which hence loses crucial spatial structures of the potential solutions. To address this issue, this article proposes a tensorial evolutionary algorithm (TEA) by formulating a tensor space of the SOPs, where the population of candidate solutions is represented as a third-order tensor. Accordingly, we design a tensor-product crossover operator and a two-phase mutation consisting of a local convolutional mutation operator and a global Gaussian mutation operator in the tensor space. These operators are all implemented based on tensor operations. Under the consideration of spatial context on individual solutions, we devise three types of evolutionary tensors, namely, the crossover tensor, the convolutional mutant tensor, and the Gaussian mutant tensor for the three tensorial evolutionary operators correspondingly. The resultant TEA is tested using two typical real-world SOPs, namely, the facility layout problem and the image steganography problem. Comparative experimental studies validate the effectiveness and robustness of TEA. © 2024 IEEE.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Battery Monitoring System on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

引用

4th International Conference on Electronic and electrical engineering and Intelligent System, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

来源：评论

学校读者我要写书评

暂无评论

Federated Multi-Agent Reinforcement Learning for AoI Minimization in UAV-Enabled IoV-MEC Systems 22

Federated Multi-Agent Reinforcement Learning for AoI Minimiz...

引用

22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xu, Shoukun Zhang, Yu Wang, Xueyuan Gursoy, M. Cenk Changzhou University Academy of Computer Science and Artificial Intelligence Changzhou China Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY13244 United States

ISBN: (纸本)9798331509712

With the advancement of Mobile Edge Computing (MEC), effective solutions for communication scenarios, including the industrial Internet of Things (IoT) and the Internet of Vehicles (IoV), are becoming increasingly feasible. Unmanned aerial vehicles (UAVs) can further enhance flexibility in delivering computational services within MEC contexts. Addressing the urgent need for information freshness, we propose a three-tier IoV-MEC system, supported by multiple UAVs and a cloud center, aiming to minimize the system's average age of information (AoI). We propose a heterogeneous multi-agent reinforcement learning algorithm based on the actor-critic framework, where vehicles act as data sources, UAVs serve as edge devices, and the cloud acts as a control center. All three tiers learn interaction strategies cooperatively based on the observations. To further enhance system performance, we implement an efficient federated learning method, allowing same-tier agents to share learning parameters, thus improving system performance and convergence speed. Extensive simulation results demonstrate that the proposed algorithm outperforms baseline algorithms in terms of average AoI and convergence speed. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

引用

computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

来源：评论

学校读者我要写书评

暂无评论

An Approximate Method for Spatial Task Allocation in Partially Observable Environments 28

An Approximate Method for Spatial Task Allocation in Partial...

引用

28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Amini, Sara Palhang, Maziar Mozayani, Nasser Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran Iran University of Science and Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350338195

Multi-robot task allocation has many applications in the real world. Robots often have noisy or local sensor readings, making their workspace partially observable. This paper proposes a partially observable spatial task allocation algorithm, called POSA, that extends the subjective self-absorbed view of E-FWD, a task allocation algorithm for a fully observable environment. POSA uses Partially Observable Monte-Carlo Planning (POMCP) to evaluate the value of the successor belief states. Simulations show that POSA can reach the performance of E-FWD, even though it has partial observability rather than full observability. POSA also has a better convergence rate because it uses Monte-Carlo simulations that estimate the value of suitable locations of search space and does not have to evaluate the value of all parts of the search space. © 2023 IEEE.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

Analysis of Microgrids Electric Vehicles Charging Stations with Hybrid Energy Storage System Algorithm with AI

Analysis of Microgrids Electric Vehicles Charging Stations w...

引用

International Conference on Computational Intelligence and Smart Technologies in electrical engineering, CISTEE 2023

作者： Tiwari, Prakash Narayan Tiwari, Kaushal Prasad Dixit, Anshu Bohre, Aashish Kumar Shah, Nand Lal Gupta, Sanjeev Kumar Department of Electrical and Electronics Engineering Rabindranath Tagore University Bhopal India Department of Computer Science and Engineering Bansal Institute of Science and Technology Bhopal India Department of Electrical and Electronics Engineering NIT Durgapur West Bengal Durgapur India

ISBN: (纸本)9789819604753

Renewable energy sources, such as hydropower, solar power, and wind power, have the capacity to efficiently supply their respective portions of the world’s energy needs. Since then, the use of renewable energy in electric cars (EVs) has garnered a lot of attention. Significant advancements in lithium-ion batteries have expedited the development of electric vehicles. The issue is that as the number of EVs increases, so does the need for electricity, increasing the burden on the power system. This promotes research into alternative and sustainable energy sources for recharging electric vehicles. In this study, a solar energy system is used to build an EV charging station. The charging station uses many connectors to supply a DC bus with a constant voltage. Power balance and constant current/constant voltage charging are the foundations of the charging controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hybrid vehicles

来源：评论

学校读者我要写书评

暂无评论

TURNING THE CURSE OF HETEROGENEITY IN FEDERATED LEARNING INTO A BLESSING FOR OUT-OF-DISTRIBUTION DETECTION 11

TURNING THE CURSE OF HETEROGENEITY IN FEDERATED LEARNING INT...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Yu, Shuyang Hong, Junyuan Wang, Haotao Wang, Zhangyang Zhou, Jiayu Department of Computer Science and Engineering Michigan State University United States Department of Electrical and Computer Engineering University of Texas Austin United States

Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from out-of-distribution (OoD) samples, misclassifying them with high confidence. Recent advances show promising OoD detection performance for centralized training, and however, OoD detection in federated learning (FL) is largely overlooked, even though many security sensitive applications such as autonomous driving and voice recognition authorization are commonly trained using FL for data privacy concerns. The main challenge that prevents previous state-of-the-art OoD detection methods from being incorporated to FL is that they require large amount of real OoD samples. However, in real-world scenarios, such large-scale OoD training data can be costly or even infeasible to obtain, especially for resource-limited local devices. On the other hand, a notorious challenge in FL is data heterogeneity where each client collects non-identically and independently distributed (non-iid) data. We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL. The key is that for each client, non-iid data from other clients (unseen external classes) can serve as an alternative to real OoD samples. Specifically, we propose a novel Federated Out-of-Distribution Synthesizer (FOSTER), which learns a class-conditional generator to synthesize virtual external-class OoD samples, and maintains data confidentiality and communication efficiency required by FL. Experimental results show that our method outperforms the state-of-the-art for OoD tasks by 2.49%, 2.88%, 1.42% AUROC, and 0.01%, 0.89%, 1.74% ID accuracy, on CIFAR-10, CIFAR-100, and STL10, respectively. Codes are available: https://***/illidanlab/FOSTER. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

GHZ Distillation using Quantum LDPC Codes 12

GHZ Distillation using Quantum LDPC Codes

引用

12th International Symposium on Topics in Coding, ISTC 2023

作者： Rengaswamy, Narayanan Raina, Ankur Raveendran, Nithin Vasic, Bane University of Arizona Tucson Department of Electrical and Computer Engineering Arizona85721 United States Indian Institute of Science Education and Research Department of Electrical Engineering and Computer Science Madhya Pradesh Bhopal462066 India

ISBN: (纸本)9798350326116

Entanglement distribution is a critical task in quantum networks. Since the distributed entanglement can suffer from noise in the channel, it is necessary to develop methods that distill higher quality entanglement from the shared noisy entangled states. In this work, we propose a protocol to distill multi-qubit Greenberger-Horne-Zeilinger (GHZ) states among the nodes of a network using quantum error correcting codes. The method builds upon a Bell state distillation protocol by Wilde et at. (2007) that we recently generalized to 3-qubit GHZ states. The key technical result that enables our protocol shows how measuring a Pauli operator, or in general a set of code stabilizers, on one subsystem of a multipartite GHZ state affects the other subsystems. The design and analysis of the protocol is driven by the stabilizer formalism for measurements, and we provide discussions to elucidate the steps of the protocol. A similar approach can be applied to distill other multipartite entangled states as long as they are stabilizer states. © 2023 IEEE.

关键词： Quantum entanglement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：