检索结果-内蒙古大学图书馆

IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Samia Talmoudi Moufida Lahmari Ksouri Department of Computer Science Applied College University of Tabuk Tabuk Saudi Arabia University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis Tunisia

This paper is focused on uncertain fractional order systems. In this context, a new modelling approach of uncertain fractional order systems represented by an explicit fractional order interval transfer function is proposed. This approach is based, essentially, on multimodel approach. In fact, firstly, two new determination methods of models’ library, inspired from the Kharitonov approach, are exposed. Then, we propose to compute the validity degree of each model of the obtained library, by optimizing a constrained least squares problem. The global model is, finally deduced by fusion of outputs of the different library’ models. To prove its efficiency and its precision, the proposed approach is then compared to the approximation approach of Oustaloup through two simulation examples.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing wastewater treatment plant operations: a machine learning approach for energy consumption and climate dynamics analysis

Optimizing wastewater treatment plant operations: a machine ...

引用

8th IET Smart Cities Symposium (SCS 2024)

作者： Mahmoud Elshabrawy Faris H. Rizk Ahmed Mohamed Zaki Marwa M. Eid Ali E. Takieldeen Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg 24060 Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University 11961 Shaqra Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

ISBN: (数字)9781837243105

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ML-based methods for detection of AD and MCI using blood biomarkers

引用

AIP Conference Proceedings 2023年第1期2834卷

作者： Hussam Daleel Shnawa Al-Kabi Jamal Mustafa Al-Tuwaijari Ali H. Husseen Al-Nuaimi Computer Science Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of computer science College of Science Diyala University Diyala Iraq Department of computer science College of Education Ibn Al-Haitham University of Baghdad Baghdad Iraq

Alzheimer’s disease (AD) is the most prevalent kind of neurodegenerative, and early detection remains a significant challenge in biomarker identification. Neuroimaging technologies are costly and may not be generally available, while cerebrospinal fluid testing is invasive. Blood-based biomarkers have the potential to be developed into a low-cost, time-efficient tool for detecting AD early and facilitating access to suitable care pathways. The goal of this study is to identify the number optimal of biomarkers in the blood in terms of the number of biomarkers, sensitivity, and specificity, which can be utilized to diagnose AD, and MCI. On the ADNI database, we used machine learning-based approaches to find a limited number of biomarkers in the blood for AD, and MCI. We determined a panel of 5 biomarkers (BTC, Calcitonin, EOT3, HBEGF, and PAPP A), which when combined with MMSE and age as two covariates, was able to distinguish between AD, MCI, and normal subjects at a sensitivity, specificity, AUC, and accuracy of 94%, 98%, 99%, and 94% respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enabling Real-time Computing and Transmission Services in Large-Scale LEO Satellite Networks

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Binquan Zhang, Zhou Atapattu, Saman Pan, Miao Yan, Ye Xiong, Zehui Li, Hongyan Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Tianjin300457 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Hunan University College of Computer Science and Electronic Engineering Changsha410012 China University School of Engineering MelbourneVIC3000 Australia University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Tianjin Artificial Intelligence Innovation Center Tianjin300387 China

Enhanced by onboard computing resources and inter-satellite links (ISLs), large-scale satellite networks (SNs) can supplement terrestrial networks, facilitating real-time edge computing and transmission in remote areas at low latency. This capability benefits applications such as real-time remote object tracking, vehicular edge computing, and environmental monitoring. However, different from terrestrial vehicular networks, satellite mobility and the intermittent nature of ISLs often render many computing satellites inaccessible, necessitating efficient scheduling scheme for joint computing and communication resource allocation. To meet this requirement, we investigate joint computing and transmission strategy for real-time computing applications over SNs to reduce end-to-end delays. We formulate the real-time computing and transmission problem over time-varying SNs as an integer linear programming problem. The mathematical optimization method has an exponential time complexity of O(2|L⫟| . (4 |V⫟| + 4∣ L⫟|)2|L∣) in the worst case, which grows rapidly with the growth of network sizes. Here, |V⫟| and |L⫟| denote the number of nodes and links, respectively, within the time window {\tau }. Resorting to the designed k-shortest path-based method yields a worst-case complexity of O(|V⫟|! . |V⫟|3). While efficient under common scenarios, its running time is still relatively unstable and may become tens of times longer when computing resources become scarce. To enhance efficiency, we further design a graph-based algorithm that leverages the solution space's unique structure, thereby achieving optimal solutions with polynomial time complexity O(3|L⫟| + (2\log |V⫟|+1) |V⫟|). Extensive evaluations conducted on the world's largest Starlink system validate the efficacy of the designed methods and highlight their potential synergy. © 1967-2012 IEEE.

关键词： Satellite links

来源：评论

学校读者我要写书评

暂无评论

Late-Binding Scholarship in the Age of AI: Navigating Legal and Normative Challenges of a New Form of Knowledge Production

arXiv

引用

arXiv 2023年

作者： Tomlinson, Bill Torrance, Andrew W. Black, Rebecca W. Patterson, Donald J. Informatics at the University of California Irvine United States Te Herenga Waka - Victoria University of Wellington New Zealand Law at the University of Kansas United States The MIT Sloan School of Management United States Department of Mathematics and Computer Science Westmont College United States

Scholarly processes play a pivotal role in discovering, challenging, improving, advancing, synthesizing, codifying, and disseminating knowledge.2 Since the 17th Century, both the quality and quantity of knowledge that scholarship has produced has increased tremendously, granting academic research a pivotal role in ensuring material and social progress.3 Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content.4 New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances that will change the nature of both the scholarly process and the artifacts it produces.5 This article articulates ways in which those artifacts can be written, distributed, read, organized, and stored that are more dynamic, and potentially more effective, than current academic practices. Specifically, rather than the current "early-binding"6 process (that is, one in which ideas are fully reduced to a final written form before they leave an author's desk), we propose that there are substantial benefits to a "late-binding"7 process, in which ideas are written dynamically at the moment of reading. In fact, the paradigm of "binding" knowledge may transition to a new model in which scholarship remains ever "unbound" and evolving. An alternative form for a scholarly work could be encapsulated via several key components: a text abstract of the work's core arguments;hyperlinks to a bibliography of relevant related work;novel data that had been collected and metadata describing those data;algorithms or processes necessary for analyzing those data;a reference to a particular AI model that would serve as a "renderer" of the canonical version of the text;and specified parameters that would allow for a precise, word-for-word reconstruction of the canonical version. Such a form would enable both the rendering of the canonical version, and also the possibility of dynamic AI reimaginings of the text in light of futu

关键词： Abstracting

来源：评论

学校读者我要写书评

暂无评论

TraSeTR: Track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery

arXiv

引用

arXiv 2022年

作者： Zhao, Zixu Jin, Yueming Heng, Pheng-Ann Department Of Computer Science And Engineering The Chinese University Of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory Of Human-Machine Intelligence-Synergy Systems Shenzhen Institute Of Advanced Technology Chinese Academy Of Sciences China Department Of Computer Science University College London United Kingdom

Surgical instrument segmentation - in general a pixel classification task - is fundamentally crucial for promoting cognitive intelligence in robot-assisted surgery (RAS). However, previous methods are struggling with discriminating instrument types and instances. To address above issues, we explore a mask classification paradigm that produces per-segment predictions. We propose TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking cues to assist surgical instrument segmentation. TraSeTR jointly reasons about the instrument type, location, and identity with instance-level predictions i.e., a set of class-bbox-mask pairs, by decoding query embeddings. Specifically, we introduce the prior query that encoded with previous temporal knowledge, to transfer tracking signals to current instances via identity matching. A contrastive query learning strategy is further applied to reshape the query feature space, which greatly alleviates the tracking difficulty caused by large temporal variations. The effectiveness of our method is demonstrated with state-of-the-art instrument type segmentation results on three public datasets, including two RAS benchmarks from EndoVis Challenges and one cataract surgery dataset CaDIS. © 2022, CC BY-NC-SA.

关键词： Surgical equipment

来源：评论

学校读者我要写书评

暂无评论

Hardware Implementation of a Two-dimensional Fractional Map with Hidden Attractors

Hardware Implementation of a Two-dimensional Fractional Map ...

引用

2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023

作者： Ouannas, Adel Khennaoui, Amina Aicha Grassi, Giuseppe Pham, Viet-Thanh Dibi, Zohir Momani, Shaher University of Oum El-Bouaghi Department of Mathematics and Computer Science Oum El Bouaghi Algeria University of Constantine 2 Ntic Faculty Tlsi Department Constantine Algeria Universita Del Salento Dipartimento Ingegneria Innovazione Lecce Italy Ton DucThang University Nonlinear Systems and Applications Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam University of Oum El-Bouaghi Oum El Bouaghi Algeria Ajman University College of Humanities and Sciences Department of Mathematics and Sciencesi Ajman United Arab Emirates

ISBN: (纸本)9798350321685

Several efforts have been recently devoted to the hardware implementations of fractional systems. This manuscript makes a contribution to the topic by introducing the first example of hardware implementation of a 2D fractional map with hidden. Specifically, the paper presents first a new class of two-dimensional fractional map with no equilibruim points using the Grunwald-Letnikov difference operator. Then, the system dynamics are analyzed via numerical simulation, such as bifurcation diagrams, Lyapunov exponents and phase portraits. Finally, the paper illustrates a hardware implementation of the 2D fractional map via a microcontroller, with the aim to show the real presence of chaotic hidden attractors in physical systems described by non-integer order maps. © 2023 IEEE.

关键词： Lyapunov methods

来源：评论

学校读者我要写书评

暂无评论

FAST CAT: Collaborative data entry and curation for semantic interoperability in digital humanities

arXiv

引用

arXiv 2021年

作者： Fafalios, Pavlos Petrakis, Kostas Samaritakis, Georgios Doerr, Korina Kritsotaki, Athina Tzitzikas, Yannis Doerr, Martin Centre for Cultural Informatics and Information Systems Laboratory FORTH-ICS Greece Computer Science Department University of Crete Heraklion Greece

Descriptive and empirical sciences, such as History, are the sciences that collect, observe and describe phenomena in order to explain them and draw interpretative conclusions about influences, driving forces and impacts under given circumstances. Spreadsheet software and relational database management systems are still the dominant tools for quantitative analysis and overall data management in these these sciences, allowing researchers to directly analyse the gathered data and perform scholarly interpretation. However, this current practice has a set of limitations, including the high dependency of the collected data on the initial research hypothesis, usually useless for other research, the lack of representation of the details from which the registered relations are inferred, and the difficulty to revisit the original data sources for verification, corrections or improvements. To cope with these problems, in this paper we present FAST CAT, a collaborative system for assistive data entry and curation in Digital Humanities and similar forms of empirical research. We describe the related challenges, the overall methodology we follow for supporting semantic interoperability, and discuss the use of FAST CAT in the context of a European (ERC) project of Maritime History, called SeaLiT, which examines economic, social and demographic impacts of the introduction of steamboats in the Mediterranean area between the 1850s and the 1920s. © 2021, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Optimized Attention with Sparse Graph Convolutional Network Based Tumor Segmentation Using Field Programmable Gated Array

Optimized Attention with Sparse Graph Convolutional Network ...

引用

Sustainable Expert systems (ICSES), International Conference on

作者： Vaithianathan V S. Nagarani R. Rajalakshmi Shyamprakash. D G. Aparna S. Rajavelu Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Tamil Nadu India Department of Mathematics Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India Department of Computer science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Telled Marketing Telangana India Department of Physics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

In recent years, deep learning (DL) techniques have become increasingly intriguing for segmentation and classification. A convolutional neural network (CNN) needs to do multiple upsampling operations in order to get the input image from the characteristics map during breast Tumor (BT) detection. In this study, a field programmable gate array (FPGA) for BT segmentation using optimized Residual group attention network based Sparse Graph Convolutional Neural Network (RGAN-SGCNN) is introduced. The optimal trade-off between accuracy and hardware complexity is determined by the FPGA implementation of RGAN-SGCNN taking into account both fixed and floating point operations. Several adder and multiplier units, which demand more space and power, are typically needed for the RGAN-SGCNN network model. A carrier select quality-efficient approximate multiplier with low power binary adder (QEAM-LPBA) is therefore used as the foundation for an optimized approximate multiplier. Furthermore, the Running city game optimizer (RCGO) is presented as a means of fine-tuning the network model's parameters. The proposed model demonstrated high efficiency in an experimental scenario, with an accuracy of 98.89. The proposed model's FPGA design, with a power consumption of 0.32W and a LUT of 12,167, demonstrated efficiency through experimental findings.

关键词： Image segmentation Accuracy Power demand Urban areas Logic gates Hardware Complexity theory Table lookup Field programmable gate arrays Adders

来源：评论

学校读者我要写书评

暂无评论

Retracted: Self-attention Bi-RNN for developer emotion recognition based on EEG

引用

IET Software 2022年第4期17卷 620-631页

作者： Yingdong Wang Yuhui Zheng Lu Cao Zhiling Zhang Qunsehng Ruan Qingfeng Wu School of Informatics Xiamen University Xiamen China School of Information Mechanical and Electrical Engineering Ningde Normal University Ningde China Department of Nature Science and Computer Ganzhou Teachers College Ganzhou China School of Film Xiamen University Xiamen China

Retraction: [Yingdong Wang, Yuhui Zheng, Lu Cao, Zhiling Zhang, Qunsehng Ruan, Qingfeng Wu, Self-attention Bi-RNN for developer emotion recognition based on EEG, IET Software 2022 ( https://***/10.1049/sfw2.12080 )]. The above article from IET Software , published online on 23 December 2022 in Wiley Online Library (***), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

关键词：

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：