检索结果-内蒙古大学图书馆

2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Canyakmaz, Ilayda Saner, Can Berk Varvitsiotis, Antonios Singapore University of Technology and Design Engineering Systems and Design Pillar Singapore National University of Singapore Electrical and Computer Engineering Department Singapore

ISBN: (纸本)9789531842976

Data privacy concerns in the power systems sector significantly complicate the sharing and integration of sensitive operational data among various independent entities. This challenge is particularly pronounced when developing system-wide mathematical models, as the reluctance to share sub-system models parametrized by sensitive data hinders effective system analysis and decision-making. To address this issue, this work introduces the application of Physics-Informed Neural Networks (PINNs) to develop surrogate models that accurately replicate power system dynamics without exposing sensitive data, enabling privacy-preserving model sharing. By embedding physical laws into the training process, PINNs utilize both available data and inherent system physics, making them particularly suitable for modeling complex dynamics. We propose a framework for model development, including dataset generation and integration of the physics knowledge during PINN training. As a proof-of-concept, we apply this framework to a simplified Single Machine Infinite Bus (SMIB) system. Case studies demonstrate that the trained PINN model closely follows ground-truth dynamics and consistently achieves higher accuracy compared to generic neural networks, highlighting the potential for accurate, privacy-preserving model sharing and system-wide dynamic simulations in power systems. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Investigation of system-driven talking approaches using depth and color image information for digital signage with speech user interface 13

Investigation of system-driven talking approaches using dept...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Takahashi, Kuuta Kawamoto, Shinichi Gunma College Advanced Production Systems Engineering Course Maebashi Japan Gunma College Department of Information and Computer Engineering Maebashi Japan

ISBN: (纸本)9798350355079

In recent decades, digital signages have been widely used. Specifically, digital signages that have a purpose of either providing information or advertising, it is important to direct people's attention toward that information. Conventionally, some approaches to attract viewer's attention include using animation and audible information such as playing background music or a flashing a catchphrase. However, language information has not been implemented in these approaches. In this study, we propose a method to call out a specific person to the front of a digital signage by using color images and depth information. By calling out a specific person by using this method, it is expected for this person to have an awareness that they are a target intended by a digital signage. In this study, we conducted experiments that use utterances based on a person's position, distance from a digital signage, clothing colors, or a pair of two features. Based on the study results, we confirm that a pair of features, that is, clothing color and depth, is the most effective to call out a specific person to the front of a digital signage © 2024 IEEE.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic systems

引用

computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

来源：评论

学校读者我要写书评

暂无评论

Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

引用

IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Fault-Ride-Through (FRT) Control of a grid-connected Fixed-Speed Wind Energy Conversion System using STATCOM 50

Fault-Ride-Through (FRT) Control of a grid-connected Fixed-S...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Olajube, Ayobami Vedula, Satish Omiloli, Koto Anubi, Olugbenga Moses Department of Electrical and Computer Engineering FAMU-FSU College of Engineering Center for Advanced Power Systems Florida State University United States

ISBN: (纸本)9781665464543

Wind energy conversion system (WECS) is stochastic in nature and has low inertia leading to grid voltage instability, poor reactive power compensation and most importantly fault susceptibility. Variable speed WECS such as the doubly-fed induction generators (DFIG) are well known to reach steady state quickly after fault occurrence without the need for an external reactive power source because of the presence of a back-to-back converter that provides independent control of the active and reactive power unlike in the fixed-speed squirrel cage induction generator (SCIG) counterpart that can’t be stabilized unless an external source of reactive power support is present. However, controlling DFIG is complicated and costly due to complete tripping unlike the fixed-speed generators which doesn’t trip completely when fault occurs. Hence, in this work, a 48-pulse, 3-phase static synchronous compensator (STATCOM) is used to ensure reactive power compensation and fault-ride through (FRT) control of the SCIG against over-voltage emanating from fault occurrence in a grid-connected power system. The goal here is to guarantee voltage stability and fault-ride-through (FRT) control against injected faults within certain time ranges at the point of common coupling (PCC) between the AC source, the load, and the fixed-speed WECS. The numerical simulation shows the fault-ride-through capability of the STATCOM-controlled fixed-speed WECS, good voltage regulation and effective reactive power compensation. © 2024 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

Towards a Software Factory for Developing Recommender systems and Decision Support systems for Smart Tourism

Towards a Software Factory for Developing Recommender System...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Souha, Adnane Naimi, Lahbib Ouaddi, Charaf Benaddi, Lamya Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Sciences and Techniques Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of smart systems is fundamental to improve the competitiveness of tourist destinations, by exploiting technologies such as Recommender systems (RS) and Decision Support systems (DSS). In this article, we propose an approach based on Model-Driven engineering (MDE) for the creation of a software factory dedicated to the design of RS and DSS for tourism destination managers. This software factory is based on two main assets: a Domain-Specific Language (DSL) for recommendation systems and another DSL for decision support systems. The main aim of this approach is to reduce the complexity and development time of this type of application. © 2024 IEEE.

关键词： Digital subscriber lines

来源：评论

学校读者我要写书评

暂无评论

A High-Efficiency, Indirectly-Fed, Dual Moon-Shaped Printed Antenna for Measuring Sand Gradation 7

A High-Efficiency, Indirectly-Fed, Dual Moon-Shaped Printed ...

引用

7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Abbas, Mohammed K. Hammed, Raaed T. Salim, Ali J. Sali, Aduwati University of Technology Department of Electrical Engineering Baghdad Iraq University of Technology Department of Communication Engineering Baghdad Iraq Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

In this paper, an enhanced bandwidth antenna featuring a dual moon-shaped patch design is presented. The antenna occupies an area of 153 mm by 130 mm and operates within the frequency band of 0.69 to 1.19 GHz. The structure is printed on an FR4 substrate with a dielectric constant of 4.3 and a substrate thickness of 1.5 mm. It achieves a bandwidth of 0.5 GHz (50%) and a minimum return loss of-47 dB at the resonant frequency of 1.00 GHz, with a gain of 4.02 dB. The proposed antenna consists of a dual moon-shaped patch positioned on the top of the dielectric substrate. It utilizes an indirect feeding method via a transmission line with two arms to enhance bandwidth. The ground plane is partially reduced, which allows the antenna to resonate at a lower frequency. Additionally, a triangular slot on the backside improves impedance matching from-15dB to-47dB, thus optimizing the electric size. Experimental results confirm the antenna's performance, showing excellent agreement with the predicted parameters. The antenna demonstrates robust performance with effective gain and high bandwidth, making it suitable for practical applications in infrastructure monitoring. An innovative method for measuring sand gradation on-site is being developed by voltage network analyzer Device, eliminating the need for traditional sieve analysis in the laboratory. © 2024 IEEE.

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

Introduction to the Minitrack on Learning Analytics 2024

Proceedings of the Annual Hawaii International Conference on...

引用

Proceedings of the Annual Hawaii International Conference on System Sciences 2024年 1415-1416页

作者： Willermark, Sara Islind, Anna Sigridur Deeva, Galina Óskarsdóttir, María University West School of Business Economics and IT Sweden Reykjavik University Department of Computer Science Iceland KU Leuven Research Centre for Information Systems Engineering Belgium

来源：评论

学校读者我要写书评

暂无评论

A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ouaddi, Charaf Benaddi, Lamya Souha, Adnane Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism. © 2024 IEEE.

关键词： Digital subscriber lines

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

引用

2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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：