检索结果-内蒙古大学图书馆

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Anh Nguyen Seoyoung Kang Woontack Woo Kangsoo Kim Department of Computer Science University of Calgary UVR Lab KAIST Department of Electrical and Software Engineering University of Calgary

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

As real-life social interactions transition to virtual environments using Virtual/Augmented Reality (VR/AR) technologies, understanding how gender representation in virtual avatars affects choices and behaviors becomes increasingly relevant. In this paper, we investigate the combined impact of avatars' gender representation and their owner's gender on virtual partner selection in physical, intellectual, social, and romantic scenarios. We introduce our preliminary research plan, outlining both the study design and system development. The research will contribute to our understanding of social dynamics and gender effects in avatar-mediated interactions.

关键词： Three-dimensional displays Avatars Design methodology Conferences Virtual environments User interfaces

来源：评论

学校读者我要写书评

暂无评论

Modeling and Simulation of Electric Vehicle using MATlab

Modeling and Simulation of Electric Vehicle using MATLAB

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Bahuguna, Neeraj Kumar Sati, Govind Gusain, Raj Vidyarthi, Anurag Prakash, Rishi Gowri, R. Gb Pant University of Agriculture and Technology Department of Electrical Engineering Pantnagar India Vaaman Engineers India Ltd. Hindustan Zinc Pmp Department of Instrumentation Engineering Pantnagar India Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350373783

With the progression of technology, the automobile industry has experienced a significant transformation due to the emergence of Electric Vehicles (EVs). EVs offer a sustainable means of transportation that doesn't harm the environment. The decline of global oil reserves and the detrimental effects of air pollution caused by conventional motor vehicles have motivated numerous researchers to explore alternative energy sources for vehicle propulsion. This study aims to investigate power flow calculations to ensure that the amount of electrical energy aligns with the requirements of the electric vehicle. Using MATlab/Simulink software, a model of a small electric vehicle is designed to achieve optimal power flow response within the EV energy system and this article outlines the process of modeling an Electric Vehicle using MATlab and Simulink, elucidating different components such as the Motor Model, Battery Model, Power Model, and Regenerative System. By employing Simulink, the designed Electric Vehicle is simulated, and important metrics such as Motor Torque, Vehicle Speed, and State of Charge of the Battery are calculated using a given Driving Cycle. The collected data is then analyzed to enhance the model's performance. © 2024 IEEE.

关键词： MATlab

来源：评论

学校读者我要写书评

暂无评论

A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

引用

Control Theory and Technology 2021年第2期19卷 283-294页

作者： Behzad Farzanegan Mohsen Zamani Amir Abolfazl Suratgar Mohammad Bagher Menhaj Distributed Intelligent Optimization Research Lab Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran Computational Intelligence Lab Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran The School of Electrical Engineering and Computer Science The University of NewcastleNewcastleAustralia Department of Medical Physics and Engineering Shiraz University of Medical SciencesShirazIran

In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input ***–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control *** consists of both analytical and algebraic *** the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB *** the algebraic part,the optimal control input that does not exceed the saturation bounds is *** weights of two NNs associated with observer and controller are simultaneously updated in an online *** ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct ***,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.

关键词： Input constraints Optimal control Neural networks Nonaffine nonlinear systems Reinforcement learning Unknown dynamics

来源：评论

学校读者我要写书评

暂无评论

Assessment of Serum Creatinine and Serum Sodium Prognostic Potential in Heart Failure Patients Using Machine Learning 46

Assessment of Serum Creatinine and Serum Sodium Prognostic P...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Alyounis, Sona Khandoker, Ahsan Stefanini, Cesare Hadjileontiadis, Leontios Scuola Superiore Sant'Anna Creative Engineering Design Lab The BioRobotics Institute Pontedera Italy Khalifa University of Science and Technology Healthcare Engineering Innovation Center Department of Biomedical Engineering P.O. Box 127788 Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (纸本)9798350371499

Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF). This study investigates the prognostic impact of serum creatinine and serum sodium levels in HF patients across these three classes using machine learning tools. A comprehensive dataset of HF patients' medical records including serum sodium and serum creatinine was utilized. Machine learning regression models were employed to predict the LVEF levels. Additionally, classification models were implemented to categorize patients into HFpEF, HFmEF, and HFrEF classes. Regression analyses revealed the predictive capabilities of serum sodium and serum creatinine in estimating the progression of HF severity. Furthermore, classification models successfully differentiated between the three EF classes, providing valuable insights into the classification patterns of HF patients based on these biomarkers. The results demonstrated the significance of serum sodium serum creatinine as prognostic markers in HF, and this contributes to a more nuanced approach to HF management, paving the way for targeted interventions and improved patient outcomes. Moreover, this study highlights the potential of machine learning techniques to enhance risk stratification and classification in HF patients, enabling personalized prognostication and treatment strategies. © 2024 IEEE.

关键词： Creatinine

来源：评论

学校读者我要写书评

暂无评论

AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks

arXiv

引用

arXiv 2025年

作者： Li, Jin He, Ziqiang Luo, Anwei Hu, Jian-Fang Wang, Z. Jane Kang, Xiangui Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-Sen University China Electrical and Computer Engineering Dept University of British Columbia Canada

Imperceptible adversarial attacks aim to fool DNNs by adding imperceptible perturbation to the input data. Previous methods typically improve the imperceptibility of attacks by integrating common attack paradigms with specifically designed perception-based losses or the capabilities of generative models. In this paper, we propose Adversarial Attacks in Diffusion (AdvAD), a novel modeling framework distinct from existing attack paradigms. AdvAD innovatively conceptualizes attacking as a non-parametric diffusion process by theoretically exploring basic modeling approach rather than using the denoising or generation abilities of regular diffusion models requiring neural networks. At each step, much subtler yet effective adversarial guidance is crafted using only the attacked model without any additional network, which gradually leads the end of diffusion process from the original image to a desired imperceptible adversarial example. Grounded in a solid theoretical foundation of the proposed non-parametric diffusion process, AdvAD achieves high attack efficacy and imperceptibility with intrinsically lower overall perturbation strength. Additionally, an enhanced version AdvAD-X is proposed to evaluate the extreme of our novel framework under an ideal scenario. Extensive experiments demonstrate the effectiveness of the proposed AdvAD and AdvAD-X. Compared with state-of-the-art imperceptible attacks, AdvAD achieves an average of 99.9% (+17.3%) ASR with 1.34 (-0.97) l2 distance, 49.74 (+4.76) PSNR and 0.9971 (+0.0043) SSIM against four prevalent DNNs with three different architectures on the ImageNet-compatible dataset. Code is available at https://***/XianguiKang/AdvAD. Copyright © 2025, The Authors. All rights reserved.

关键词： HTTP

来源：评论

学校读者我要写书评

暂无评论

Temporal Consistency for LLM Reasoning Process Error Identification

arXiv

引用

arXiv 2025年

作者： Guo, Jiacheng Wu, Yue Qiu, Jiahao Huang, Kaixuan Juan, Xinzhe Yang, Ling Wang, Mengdi Department of Electrical & Computer Engineering Princeton University United States AI Lab Princeton University United States Department of Computer Science & Engineering University of Michigan United States

Verification is crucial for effective mathematical reasoning. We present a new temporal consistency method where verifiers iteratively refine their judgments based on the previous assessment. Unlike one-round verification or multi-model debate approaches, our method leverages consistency in a sequence of self-reflection actions to improve verification accuracy. Empirical evaluations across diverse mathematical process error identification benchmarks (Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements over baseline methods. When applied to the recent DeepSeek R1 distilled models, our method demonstrates strong performance, enabling 7B/8B distilled models to outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the distilled 14B model with our method achieves performance comparable to Deepseek-R1. Our codes are available at https://***/jcguo123/Temporal-Consistency © 2025, CC BY.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

An Ontology Centric Approach for Building Collaborative Tagging-based Systems to Manage Personal Knowledge in KAM

引用

Chinese Journal of Electronics 2023年第3期22卷 442-448页

作者： WANG Xiaoyan BAO Tie Y.V. Ramana Reddy LIU Shufen WANG Luyi College of Computer Science and Technology Jilin University Changchun China Lane Department of Computer Science & Electrical Engineering SIP Lab West Virginia University Morgantown WV USA

This paper propose a framework Knowledge advantage machine (KAM) to help in organizing individually discovered knowledge drawn from a narrowly bounded domain into a personal knowledge network based on personal request and tags. Ontologies, folksonomy and personomy are employed in KAM to constitute the useful repositories of knowledge. Ontologies offer a flexible and expressive layer of abstraction, very useful for capturing the semantics of information repositories, but they can not reflect the user's interest. The user in KAM can freely choose the words to tag the resources which are the reflection of the user's own interest. The set of tags and tagged knowledge of a user comprise the personomy. In a group the shared tags and knowledge are known as folksonomy. Our approach investigates how to map these tags in personomy and folksonomy to existing domain ontology in order to add accurate meanings. The user's behaviors are also used to re-rank the query results. So the user can find the useful knowledge quickly and accurately.

关键词： Knowledge engineering Semantics Collaboration Ontologies Reflection Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Assessing VHF Band Interference in Qatar in Support of Radar Subsurface Probing of Aquifers

Assessing VHF Band Interference in Qatar in Support of Radar...

引用

International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Abdulrahman Abunada Iestyn Pierce Tamer Khattab Nizar Zorba Essam Heggy School of Computer Science and Engineering Bangor University Bangor United Kingdom IIPL Lab Electrical Engineering Qatar University Doha Qatar Electrical and Computer Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

High groundwater is the main natural strategic reserve of fresh water for several hyper-arid nations in the Arabian Peninsula and North Africa. The characterization of the extent, volume, and properties of this groundwater in large aquifer systems primarily relies on sporadic and unmonitored well logs. This, in turn, results in inaccurate groundwater maps in several areas, which compromise effective water management during droughts. It is therefore crucial to have the capability to map shallow aquifers on a large scale with metric resolution that allows a more comprehensive assessment of groundwater resources and dynamic. This research is part of the DesertSEA experiment, a national project in which Qatar is partnering with several US universities and NASA to conceptualize a VHF airborne sounding radar, named Desert Subsurface Exploration of Aquifers (DESERT-SEA), capable of probing shallow aquifers in the first 30 to 50 meters of desiccated grounds. The successful operation of DESERT-SEA requires an accurate assessment of the VHF interference in hyper-arid and low-lying areas, which can compromise the ability to detect shallow aquifers. As such, we build an Interference sensing model (ISM) in the frequency band from 10 to 300 MHz. This frequency covers the two bands operated by Desert-SEA; the first is centered around e40 MHZ and the second at 200 MHz. Herein, we show the results of ISM for the second band based on VHF background noise measured in five locations, which have been identified based on their promising potential for mapping shallow aquifers.

关键词： Radio frequency Transmitting antennas Noise Receiving antennas Interference Sensors Noise measurement Standards Aquifers Noise level

来源：评论

学校读者我要写书评

暂无评论

Efficient Deep CNN Model for COVID-19 Classification

引用

computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

来源：评论

学校读者我要写书评

暂无评论

An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

引用

computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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：