检索结果-内蒙古大学图书馆

Method of Coding Video Flow Segments Containing Motion Objects 17

Method of Coding Video Flow Segments Containing Motion Objec...

17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Onyshchenko, Roman Barannik, Volodymyr Revva, Kyrylo Kulitsa, Oleh Pertsev, Pavlo Bielous, Mykhailo Kharkov National University of Radio Electronics Dept. of Information network engineering Kharkiv Ukraine National University of Civil Defence of Ukraine Dept. of Information network engineering Cherkasy Ukraine Ivan Kozhedub Kharkiv National Air Force University Dept. of Scientific research department simulation modeling Kharkiv Ukraine

ISBN: (纸本)9798331520564

This article shows that one of the most important directions in the development of information communication systems (ICS) is the construction of new generations of wireless technologies. Including such a direction is important for the creation of information technology support systems and decision-making for the management of critical infrastructure objects. It is shown that increasing the bit rate of the coded video stream is a relevant area of research. Such an advantage is proposed to be formed on the basis of structural and positional coding of segments of dynamic objects. A model for evaluating the informativeness of a transformant is created based on the fact that it is represented in the spectral space according to an uneven diagonal scheme. The construction of the information technology of the code representation of diagonals in the structural-positional space of transformants is considered. © 2024 IEEE.

关键词： Video streaming

来源：评论

学校读者我要写书评

暂无评论

Towards Semi-Automatic Model Specification

Towards Semi-Automatic Model Specification

引用

2021 Winter simulation Conference, WSC 2021

作者： Shuttleworth, David Padilla, Jose J. Old Dominion University Modeling Simulation and Visualization Eng. Dept. NorfolkVA23529 United States Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States

ISBN: (纸本)9781665433112

This paper presents a natural language understanding (NLU) approach to transition a description of a phenomenon towards a simulation specification. As multidisciplinary endeavors using simulations increase, the need for teams to better communicate and make non-modelers active participants on the process increases. We focus on semi-automating the model conceptualization process towards the creation of a specification as it is one of the most challenging steps in collaborations. The approach relies on NLU processing of narratives, create a model that captures concepts and relationships, and finally provide a specification of a simulation implementation. An initial definition set and grammatical rules are proposed to formalize this process. These are followed by a Design of Experiments (DoE) to test the NLU model accuracy and a test case that generates Agent-Based Model (ABM) conceptualizations and specifications. We provide a discussion on the advantages and limitations of using NLUs for model conceptualization and specification processes. © 2021 IEEE.

关键词： Specifications

来源：评论

学校读者我要写书评

暂无评论

Exploring Challenges in Deep Learning of Single-Station Ground Motion Records

SSRN

引用

SSRN 2024年

作者： Çaglar, Ümit Mert Yilmaz, Baris Türkmen, Melek Tileylioglu, Salih Akagündüz, Erdem Dept. of Modeling and Simulation Graduate School of Informatics Middle East Technical University Ankara Turkey Dept. of Civil Engineering Kadir Has University Istanbul Turkey

Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classification, localization, earthquake early warning systems, and structural health monitoring. However, the extent to which these models effectively learn from these complex time-series signals has not been thoroughly *** this study, our objective is to evaluate the degree to which auxiliary information, such as seismic phase arrival times or seismic station distribution within a network, dominates the process of deep learning from ground motion records, potentially hindering its *** perform a hyperparameter search on two deep learning models to assess their effectiveness in deep learning from ground motion records while also examining the impact of auxiliary information on model performance. Experimental results reveal a strong reliance on the highly correlated Primary (P) and Secondary (S) phase arrival *** observations highlight a potential gap in the field, indicating an absence of robust methodologies for deep learning of single-station ground motion recordings independent of any auxiliary information. © 2024, The Authors. All rights reserved.

关键词： Structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

Exploring Challenges in Deep Learning of Single-Station Ground Motion Records

arXiv

引用

arXiv 2024年

作者： Çaǧlar, Ümit Mert Yilmaz, Baris Türkmen, Melek Akagündüz, Erdem Tileylioglu, Salih Dept. of Modeling and Simulation Graduate School of Informatics Middle East Technical University Ankara Turkey Dept. of Civil Engineering Kadir Has University Istanbul Turkey

Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classification, localization, earthquake early warning systems, and structural health monitoring. However, the extent to which these models effectively learn from these complex time-series signals has not been thoroughly analyzed. In this study, our objective is to evaluate the degree to which auxiliary information, such as seismic phase arrival times or seismic station distribution within a network, dominates the process of deep learning from ground motion records, potentially hindering its effectiveness. We perform a hyperparameter search on two deep learning models to assess their effectiveness in deep learning from ground motion records while also examining the impact of auxiliary information on model performance. Experimental results reveal a strong reliance on the highly correlated P and S phase arrival information. Our observations highlight a potential gap in the field, indicating an absence of robust methodologies for deep learning of single-station ground motion recordings independent of any auxiliary information. © 2024, CC BY.

关键词： Structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records

arXiv

引用

arXiv 2024年

作者： Türkmen, Melek Meral, Sanem Yilmaz, Baris Cikis, Melis Akagündüz, Erdem Tileylioglu, Salih Dept. of Modeling and Simulation Graduate School of Informatics METU Turkey Dept. of Systems Engineering Turkish Aerospace Inc. Turkey Dept. of Systems Engineering ASELSAN Inc. Turkey Dept. Civil Engineering Kadir Has University Turkey

This paper explores the application of deep learning (DL) techniques to strong motion records for single-station epicenter localization. Often underutilized in seismology-related studies, strong motion records offer a potential wealth of information about seismic events. We investigate whether DL-based methods can effectively leverage this data for accurate epicenter localization. Our study introduces AFAD-1218, a collection comprising more than 36,000 strong motion records sourced from Turkey. To utilize the strong motion records represented in either the time or the frequency domain, we propose two neural network architectures: deep residual network and temporal convolutional networks. Through extensive experimentation, we demonstrate the efficacy of DL approaches in extracting meaningful insights from these records, showcasing their potential for enhancing seismic event analysis and localization accuracy. Notably, our findings highlight significant reductions in prediction error achieved through the exclusion of low signal-to-noise ratio records, both in nationwide experiments and regional transfer-learning scenarios. Overall, this research underscores the promise of DL techniques in harnessing strong motion records for improved seismic event characterization and localization. © 2024, CC BY.

关键词： Seismology

来源：评论

学校读者我要写书评

暂无评论

AI-based botnet attack classification and detection in IoT devices

AI-based botnet attack classification and detection in IoT d...

引用

2022 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2022

作者： Puri, Vikram Kataria, Aman Solanki, Vijender Kumar Rani, Sita Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam CSIR-CSIO Chandigarh India CMR Institute of Technology Dept. of Computer Science and Engineering TS Hyderabad India Guru Nanak Dev Engineering College Dept. of Computer Science and Engineering Punjab Ludhiana India

ISBN: (纸本)9781665487016

End-user Internet of Things (IoT) devices, including security cameras, smart appliances, home monitors, and thermostats, are becoming more prevalent in households. Additionally, the proliferation of devices facilitates the propagation of security concerns like DoS and spoofing. However, it is difficult for conventional rule-based security systems to recognize IoT assaults due to the development of heterogenous devices in the IoT ecosystem. Artificial Intelligence (AI) techniques can be a solution which enables the creation of an effective security model based on actual data from each device. In this work, IoT botnets are detected and classified using machine learning (ML) and deep learning (DL) based algorithms. Six ML models and three DL models are used to assess the system's performance. The best-performing model is also implemented as an API. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Blockchain-enabled Transparent and Trustworthy Regulation System for Smart Cities 1

Blockchain-enabled Transparent and Trustworthy Regulation Sy...

引用

1st International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2023

作者： Puri, Vikram Solanki, Vijender Kumar Kataria, Aman Long, Cu Kim Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Cmr Institute of Technology Dept. of Computer Science and Engineering TS Hyderabad India Amity University Amity Institute of Defence Technology Uttar Pradesh India Information Technology Center Hanoi Viet Nam

ISBN: (纸本)9798350323566

The development and growth of the shared economy in the smart cities are significantly influenced by technology. An online community-based ecosystem that enables peer-to-peer trade between buyers and sellers as well as share- and rental-based access to goods and services. Lack of trust and transparency is the main problem in the shared economy system. A blockchain-enabled architecture is created for the shared economy ecosystem to address this problem. In this study, product registration and product services policies are implemented using smart contracts. This technology aids in distinguishing between genuine and counterfeit goods while also giving consumers access to the product's metadata via QR codes. An experimental testbed that makes use of the polygon network is taken into consideration in order to validate the suggested framework. © 2023 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Deep Sequence Models for Predicting Average Shear Wave Velocity from Strong Motion Records

arXiv

引用

arXiv 2025年

作者： Yilmaz, Baris Akagündüz, Erdem Tileylioglu, Salih Dept. of Modeling and Simulation Graduate School of Informatics Middle East Technical University Ankara Turkey Civil Engineering Department Kadir Has University Istanbul Turkey

This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface (Vs30) at strong motion recording stations in Türkiye. Vs30 is a key parameter in site characterization and, as a result for seismic hazard assessment. However, it is often unavailable due to the lack of direct measurements and is therefore estimated using empirical correlations. Such correlations however are commonly inadequate in capturing complex, site-specific variability and this motivates the need for data-driven approaches. In this study, we employ a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and temporal dependencies in strong motion records. Furthermore, we explore how using different parts of the signal influence our deep learning model. Our results suggest that the hybrid approach effectively learns complex, nonlinear relationships within seismic signals. We observed that an improved P-wave arrival time model increased the prediction accuracy of Vs30. We believe the study provides valuable insights into improving Vs30 predictions using a CNN-LSTM framework, demonstrating its potential for improving site characterization for seismic studies. Our codes are available via this repo: https://***/brsylmz23/CNNLSTM_DeepEQ © 2025, CC BY.

关键词： Shear waves

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

引用

2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

DLT Based Smart Medical Ecosystem

DLT Based Smart Medical Ecosystem

引用

2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Puri, Vikram Kataria, Aman Rani, Sita Pareek, Piyush Kumar Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Amity Institute of Defence Technology Amity University Uttar Pradesh Noida India Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India Nitte Meenakshi Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350300826

The healthcare system has become more reliant on the data collection capabilities of fast evolving IoT devices. Patient healthcare records (PHR) now contain additional information as a result. However, securing identities and guaranteeing data security have emerged as critical challenges in the healthcare system. When it comes to regulating PHR, many of the centralized and decentralized methods now in use struggle difficulties such as single point of failure, high transaction fees, slow throughput, and excessive latency. To address these concerns, a distributed ledger technology (DLT)-based framework named IOTA rather than a blockchain-based platform such as Ethereum or Polygon is proposed. The proposed framework considered the example of a hospital with several patients on the same and various floors. Furthermore, the suggested approach employs a real-time experimental setup for calculating the time and power consumption of the devices. © 2023 IEEE.

关键词： Distributed ledger

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：