检索结果-内蒙古大学图书馆

5th International Conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Turky, Ayad Hussain, Abir Bettayeb, Anissa M. Serhani, Mohamed Adel Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Information Technology Center University of Sharjah Sharjah United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400708923

Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynamic factors such as weather, time, and day. This paper introduces a novel two-stage approach that integrates deep learning and ant colony optimization to address this limitation. Firstly, deep learning is employed to predict travel times, incorporating weather conditions as input for improved accuracy. Secondly, an ant colony optimization algorithm is utilized to identify the shortest route based on the predicted travel times. We evaluated the proposed approach using the New York City taxi benchmark dataset and observed superior performance compared to current state-of-the-art algorithms. © 2023 ACM.

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

A Multi-Carrier Deep Learning CSK Communication System

引用

IEEE Communications Letters 2025年第6期29卷 1191-1195页

作者： Mobini, Majid Kaddoum, Georges Herceg, Marijan Babol Noshirvani University of Technology Department of ECE Babol Iran University of Québec École de technologie supérieure Département de génie électrique MontréalQC Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon University of Osijek Faculty of Electrical EngineeringComputer Science and Information Technology Department of Communications Croatia

A new Multi-Carrier Deep Learning Chaos Shift Keying (MC-DLCSK) modulation is designed in this paper. The proposed receiver includes a Neural Network (NN)-based classifier that recovers MC-DLCSK signals. The results demonstrate that the MC-DLCSK system achieves a considerable Bit Error Rate (BER) gain compared to all existing similar systems under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. © 1997-2012 IEEE.

关键词： White noise

来源：评论

学校读者我要写书评

暂无评论

Efficient Multi-user Offloading of Personalized Diffusion Models: A DRL-Convex Hybrid Solution

引用

IEEE Transactions on Mobile Computing 2025年

作者： Yang, Wanting Xiong, Zehui Guo, Song Mao, Shiwen Kim, Dong In Debbah, Merouane Singapore University of Technology and Design Pillar of Engineering Systems and Design Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong Auburn University Department of Electrical and Computer Engineering Auburn36830 United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Khalifa University KU 6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Generative diffusion models like Stable Diffusion are at the forefront of the thriving field of generative models today, celebrated for their robust training methodologies and high-quality photorealistic generation capabilities. These models excel in producing rich content, establishing them as essential tools in the industry. Building on this foundation, the field has seen the rise of personalized content synthesis as a particularly exciting application. However, the large model sizes and iterative nature of inference make it difficult to deploy personalized diffusion models broadly on local devices with heterogeneous computational power. To address this, we propose a novel framework for efficient multi-user offloading of personalized diffusion models. This framework accommodates a variable number of users, each with different computational capabilities, and adapts to the fluctuating computational resources available on edge servers. To enhance computational efficiency and alleviate the storage burden on edge servers, we propose a tailored multi-user hybrid inference approach. This method splits the inference process for each user into two phases, with an optimizable split point. Initially, a cluster-wide model processes low-level semantic information for each user's prompt using batching techniques. Subsequently, users employ their personalized models to refine these details during the later phase of inference. Given the constraints on edge server computational resources and users' preferences for low latency and high accuracy, we model the joint optimization of each user's offloading request handling and split point as an extension of the Generalized Quadratic Assignment Problem (GQAP). Our objective is to maximize a comprehensive metric that balances both latency and accuracy across all users. To solve this NP-hard problem, we transform the GQAP into an adaptive decision sequence, model it as a Markov decision process, and develop a hybrid solution combining dee

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Efficient Coordination and Synchronization of Multi-Robot systems Under Recurring Linear Temporal Logic

arXiv

引用

arXiv 2025年

作者： Peron, Davide Fernandez-Ayala, Victor Nan Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Department of Information Engineering University of Padova Padova35122 Italy Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work. © 2025, CC BY.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

Real-Time Sensor Fault Tolerant Control of DC-DC Converters in DC Microgrids Using a Switching Unknown Input Observer

引用

IEEE Access 2025年 13卷 95837-95850页

作者： Ouahabi, Mohammed Said Benyounes, Abdelhafid Barkat, Said Ihammouchen, Syphax Rekioua, Toufik Rabehi, Abdelaziz El-Kenawy, El-Sayed M. Alharbi, Amal H. University of M’sila Electrical Engineering Laboratory Faculty of Technology M’sila28000 Algeria University of Bejaia Laboratoire de Technologie industrielle et de l’Information Bejaia06000 Algeria University of Djelfa Telecommunications and Smart Systems Laboratory PO Box 3117 Djelfa17000 Algeria PO Box 33349 Isa Town Bahrain Applied Science Private University Applied Science Research Center Amman Jordan Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

DC Microgrids are rapidly developing, driven by the increasing integration of renewable sources and dynamic loads. Their growing adoption in critical applications raises the need for operational safety and reliability, particularly under fault conditions. This paper presents a sensor fault-tolerant control (FTC) for DC-DC power converters in DC Microgrids designed to maintain resilient and reliable operation. The proposed FTC leverages a single Switching Unknown Input Observer (SW-UIO) at the bottom control level to estimate arbitrary sensor faults with a high accuracy (0.3% fault estimation error), allowing a real-time identification and precise control reconfiguration to accommodate sensor faults with minimal computational burden. The approach is validated under diverse conditions, including time-varying loads, complete sensor failure, noise abnormality, and simultaneous faults/failure, achieving a consistent fault accommodation of 1μs from fault occurrence to compensation. Real-time simulation has been performed in the OPAL-RT environment to assess the efficacy and superiority of the proposed sensor fault tolerant strategy in comparison to reference FTC strategies. © 2013 IEEE.

关键词： Identification (control systems)

来源：评论

学校读者我要写书评

暂无评论

Advanced Air Mobility: Innovations, Applications, Challenges, and Future Potential

引用

Journal of Air Transportation 2025年第2期33卷 169-187页

作者： Raza, Waleed Renkhoff, Justus Ogirimah, Olayemi Bawa, Gurvir Kaur Stansbury, Richard S. Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Department of Information Systems University of Maryland BaltimoreMD21250 United States

来源：评论

学校读者我要写书评

暂无评论

A Fast CU Partitioning Algorithm Based on Texture Characteristics for VVC 23

A Fast CU Partitioning Algorithm Based on Texture Characteri...

引用

5th International Conference on Image Processing and Machine Vision, IPMV 2023

作者： Li, Wei Jiang, Xiantao Jin, Jiayuan Song, Tian Yu, Fei Richard Department of Information Engineering Shanghai Maritime University NO.1550 Haigang Ave. Shanghai201306 China Department of Electrical and Electronics Engineering Tokushima University 2-24 Shinkura-cho Tokushima770-8501 Japan Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

ISBN: (纸本)9781450397926

Different from the traditional quaternary tree (QT) structure utilized in the previous generation video coding standard H.265/HEVC, a new partition structure named quadtree with nested multi-type tree (QTMT) is applied in the latest codec H.266/VVC. The introduction of QTMT brings in superior encoding performance at the cost of great time-consuming. Therefore, this paper proposes a fast coding unit (CU) partitioning algorithm based on CU texture complexity and texture direction. First, we terminate further splitting of a CU when its texture is judged as simple. Then, we use the gray level co-occurrence matrix (GLCM) to extract the texture direction of the block to decide whether to partition this CU by QT, thus terminating further MT partitions. Finally, a final partition type is selected from the four MT partitions in combination with the multi-level texture complexity and texture direction of the block. The simulation results show that the overall algorithm can significantly reduce the encoding time, while the loss of coding efficiency is reasonably low. In comparison with the reference model, the encoding time is reduced by up to 44.71%, while the BDBR is increased by only 0.84% on average. © 2023 ACM.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

AE-LSTM: Autoencoder with LSTM-Based Intrusion Detection in IoT

AE-LSTM: Autoencoder with LSTM-Based Intrusion Detection in ...

引用

2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Mahmoud, Mohamed Kasem, Mahmoud Abdallah, Abdelrahman Kang, Hyun Soo Chungbuk National University College of Electrical and Computer Engineering Cheongju Korea Republic of Assiut University Multimedia Systems Assiut Egypt Assiut University Information Technology Assiut Egypt

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

The acceleration in the field of the Internet of things had increased security problems, so we find ourselves in need of effective ways to protect IoT systems From intrusions. Recently Machine learning plays an active role in network security and detecting attacks. in this research, we propose a Machine learning method (AE-LSTM) for intrusion detection which uses Autoencoder with LSTM. Our method has 6-layer Autoencoder (AE) model with LSTM that is effective in anomaly detection. To avoid the bias in our model which occur from imbalanced data in the NSL-KDD dataset, we use Standard Scaler in our AE-LSTM model To delete the outliers from the input. AE-LSTM uses the best reconstruction function. It is critical in discovering whether network traffic is normal or abnormal. We use the NSL-KDD test dataset to evaluate our proposed model. Our Model achieved the highest accuracy over other methods with f1-score micro and weight at 98.69% and 98.70% for 5 classes in detection methods (Dos, Probe, R2L, U2R, Normal). Also, we evaluated it with two classes Malicious, Normal) with f1-score micro and weight at 98.78% and 98.78%. © 2022 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Developing High Confinement and Low-loss Silicon Nitride Photonic Devices for Optical Coherence Tomography (Invited)

Developing High Confinement and Low-loss Silicon Nitride Pho...

引用

Novel Techniques in Microscopy, NTM 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Ji, Xingchen Chen, Junzhuo Ling, Yuye Su, Yikai John Hopcroft Center for Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

There is a surging interest in developing integrated Optical Coherence Tomography (OCT) system. However, most components are based on silicon which cannot be used for wavelength below 1.2 µm. Here, we discuss the... 详细信息

ISBN: (纸本)9781957171210

关键词： Breast cancer imaging Frequency combs Optical coherence tomography Optical imaging Photonic devices Supercontinuum sources

来源：评论

学校读者我要写书评

暂无评论

Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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：