检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Wang, Hongwei Fang, Jun Li, Hongbin Leus, Geert The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States The Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2826 CD Netherlands

In the paper, we consider the line spectral estimation problem in an unlimited sensing framework (USF), where a modulo analog-to-digital converter (ADC) is employed to fold the input signal back into a bounded interval before quantization. Such an operation is mathematically equivalent to taking the modulo of the input signal with respect to the interval. To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on first-order difference, which are robust against noise. Specifically, we show that, with a high probability, the first-order difference of the original samples is equivalent to that of the modulo samples. By utilizing this property, line spectral estimation is solved via a robust sparse signal recovery approach. The second algorithms is built on our finding that, with a sufficiently high sampling rate, the first-order difference of the original samples can be decomposed as a sum of the first-order difference of the modulo samples and a sequence whose elements are confined to be three possible values. This decomposition enables us to formulate the line spectral estimation problem as a mixed integer linear program that can be efficiently solved. Simulation results show that both proposed methods are robust against noise and achieve a significant performance improvement over the higher-order difference-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantization (signal)

来源：评论

学校读者我要写书评

暂无评论

PMU-Based Backup Protection in the Presence of Inverter-Based Resources

PMU-Based Backup Protection in the Presence of Inverter-Base...

引用

Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Mohammad Rezaei Jegarluei Ahmad Salehi Dobakhshari Marjan Popov Vladimir Terzija Sadegh Azizi School of Electronic and Electrical Engineering University of Leeds UK Faculty of Engineering University of Guilan Iran Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands School of Electrical Engineering Shandong University China

Increasing penetration of inverter-based resources (IBRs) undermines the performance of conventional protection systems since IBRs’ fault characteristics are fundamentally different from those of synchronous generators. In this paper, a PMU-based backup protection method is proposed for power transmission systems with high penetrations of IBRs. The method involves replacing all IBRs and the line suspected to be faulty by proper nodal current sources. For accurately detecting the faulted line from the set of candidates, a residual-based index is proposed, utilizing the concept of superimposed circuits and the weighted least-squares method. The method's robustness in the face of influential factors, including input errors, settings, numbers, and locations of IBRs is investigated through extensive simulations on the IEEE 39-bus test system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PAPR Reduction in DCO-OFDM and PAM-DMT Based VLC Systems

PAPR Reduction in DCO-OFDM and PAM-DMT Based VLC Systems

引用

CLEO: Science and Innovations, S and I 2022

作者： Alrakah, Hussien Gutema, Tilahun Offiong, Funmilayo Popoola, Wasiu School of Engineering The University of Edinburgh EdinburghEH9 3FD United Kingdom Faculty of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Electronic and Electrical Engineering Obafemi Awolowo University Nigeria

In this paper, a pilot-assisted scheme is used to reduce the peak-to-average power ratio of multicarrier signalling based visible light communication systems and the system throughput is maximised with bit and power l... 详细信息

ISBN: (纸本)9781557528209

关键词： Peak to average power ratio

来源：评论

学校读者我要写书评

暂无评论

Cloud-based Machine Learning Adoption Model for Higher Education Institutions

Cloud-based Machine Learning Adoption Model for Higher Educa...

引用

2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Kiros, Atakilti Brhanu Salau, Ayodeji Olalekan Vats, Satvik Kaushik, Keshav Tin, Ting Tin Omeje, Crescent Onyebuchi Mekelle University Mekelle Institute of Technology Mit Mekelle Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Department of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248007 India Amity School of Engineering and Technology Amity University Mohali Punjab India Inti International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia University of Port Harcourt Department of Electrical/Electronic Engineering Rivers State Choba500004 Nigeria

ISBN: (纸本)9798350379716

As a new concept, cloud computing has attracted the IT Initiations attention especially the Mobile learning (M-learning). At present, there Higher Education Institutions (HEI's) have struggle with limited IT budgets, difficulties in obtaining and managing infrastructure licenses, and the complexities of software and hardware management. The coming of cloud computing, all of the above challenges was solved. The migration to cloud based M-learning in HEI's is a significant stage in terms of online education, especially during the COVID-19 pandemic. Cloud based M-learning approach has the potential to be a powerful and rapid solution to the many challenges faced by universities in this unprecedented time. This study starts with some brief concepts of Cloud Computing and M-learning for HEI's, mentioning for the significance of the results obtained so far. Additionally, it starting point as strategy for HEI's to adopt Cloud based M-learning is provided, by comparisons of existing models and select DOI model to propose cloud based adoption model. But, the DOI model has its own weaknesses;for example, it does not take into account the localization as Innovation determinants. This study introduces localization phase for the optimal performance of the adopted model. The adoption model was evaluating the importance of the proposed model by IT domain experts and it initiates to be both useful and suitable for a successful cloud based M-learning adoption Ethiopian perspective. Therefore, the research results obtained are promising and funding the use of Cloud based M-learning solutions for Ethiopia HEI's by improving knowledge and skill in this field provides a useful strategy for the HEI's structure. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Hybrid LSTM-Attention-CatBoost Model for Complex Energy Behavior Forecasting

Leveraging Hybrid LSTM-Attention-CatBoost Model for Complex ...

引用

Smart Grids and Energy Systems (SGES), International Conference on

作者： Lu Lu Zhengdong Shen Shuting Li Weili Wang Faculty of Science and Technology BEIJING NORMAL UNIVERSITY HONG KONG BAPTIST UNIVERSITY UNITED INTERNATIONAL COLLEGE Zhuhai China Department of Electronic and Information Engineering NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY Nanjing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao China School of Computer Science South China Normal University Guangzhou China

ISBN: (数字)9798331539634

ISBN: (纸本)9798331539641

We present an advanced energy prediction model that addresses energy imbalance by integrating Long Short-Term Memory (LSTM) with Attention Mechanism for time-series analysis and categorical boosting (CatBoost) for categorical feature optimization. This model accurately predicts the complex energy behaviors of prosumers who both generate and consume energy. Simulation results show that our model outperforms traditional methods with the lowest Mean Absolute Error (MAE) of 48.514. By achieving such a high level of accuracy, this model improves grid operation efficiency, offers a practical solution for smart grid management.

关键词： Analytical models Accuracy Simulation Time series analysis Weather forecasting Predictive models Data models Smart grids Forecasting Long short term memory

来源：评论

学校读者我要写书评

暂无评论

SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification

arXiv

引用

arXiv 2024年

作者： Talukder, Md Simul Hasan Akter, Sharmin Nur, Abdullah Hafez Aljaidi, Mohammad Sulaiman, Rejwan Bin Alkoradees, Ali Fayez Department of Electrical and Electronic Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh Department of Biomedical Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer science and Technology Northumbria University United Kingdom Unit of Scientific Research Applied College Qassim University Saudi Arabia

Sugarcane, a key crop for the world's sugar industry, is prone to several diseases that have a substantial negative influence on both its yield and quality. To effectively manage and implement preventative initiatives, diseases must be detected promptly and accurately. In this study, we present a unique model called SugarcaneNet that outperforms previous methods for automatically and quickly detecting sugarcane disease through leaf image processing. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models, particularly InceptionV3, InceptionResNetV2, DenseNet201, DenseNet169, Xception, and ResNet152V2. Initially, we added three more dense layers with 0.0001 LASSO regularization, three 30% dropout layers, and three batch normalizations with "renorm" enabled at the bottom of these pre-trained models to improve the performance. The accuracy of sugarcane leaf disease classification was greatly increased by this addition. Following this, several comparative studies between the average ensemble and individual models were carried out, indicating that the ensemble technique performed better. The average ensemble of all modified pre-trained models produced outstanding outcomes: 100%, 99%, and 99.45% for f1 score, precision, recall, and accuracy, respectively. Performance was further enhanced by the implementation of an optimized weighted average ensemble technique incorporated with grid search. This optimized "sugarcaneNet" model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100%, respectively. © 2024, CC BY.

关键词： Sugar industry

来源：评论

学校读者我要写书评

暂无评论

CV-MOS: A Cross-View Model for Motion Segmentation

arXiv

引用

arXiv 2024年

作者： Tang, Xiaoyu Chen, Zeyu Cheng, Jintao Chen, Xieyuanli Wu, Jin Xue, Bohuan The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China Xingzhi College South China Normal University Guangdong Guangzhou510000 China The College of Intelligence Science and Technology National University of Defense Technology Changsha China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

In autonomous driving, accurately distinguishing between static and moving objects is crucial for the autonomous driving system. When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes a primary challenge in improving the recognition of moving objects. Previous methods either utilized range view (RV) or bird’s eye view (BEV) residual maps to capture motion information. Unlike traditional approaches, we propose combining RV and BEV residual maps to exploit a greater potential of motion information jointly. Thus, we introduce CV-MOS, a cross-view model for moving object segmentation. Novelty, we decouple spatial-temporal information by capturing the motion from BEV and RV residual maps and generating semantic features from range images, which are used as moving object guidance for the motion branch. Our direct and unique solution maximizes the use of range images and RV and BEV residual maps, significantly enhancing the performance of LiDAR-based MOS task. Our method achieved leading IoU(%) scores of 77.5% and 79.2% on the validation and test sets of the SemanticKitti dataset. In particular, CV-MOS demonstrates SOTA performance to date on various datasets. The CV-MOS implementation is available at https://***/SCNU-RISLAB/CV-MOS Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Tai le character recognition using deep convolution neural networks

Tai le character recognition using deep convolution neural n...

引用

2021 International Conference on computer Communication and Artificial Intelligence, CCAI 2021

作者： Zhao, Jingying Guo, Hai Liu, Yifan Dong, Na Wang, Xinyue Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China College of Computer Science and Engineering Dalian Minzu University Dalian China

ISBN: (纸本)9781728194011

Tai Le characters are used widely in Southwest China and Southeast Asia, and it is necessary to recognize Tai Le characters. In this paper, a Tai Le characters recognition model base on the deep convolution neural network (DCNN) has been established. In addition, to recognize Tai Le characters, the printed Tai Le database YDH2019.1 has been constructed, and image digital processing technology has been used to preprocess the sample images of YDH2019.1. In the modeling of DCNN, 50% data of YDH2019.1 has been used as train data, and the performance of the recognition, model has been evaluated on the remaining 50%. The results show that the recognition accuracy of Tai Le characters reaches 99.98%, which proved the effectiveness of DCNN as a printed Tai Le character recognition model. © 2021 IEEE.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Improving Brain MRI Image Segmentation Quality: A Hybrid Technique for Intensity Inhomogeneity Correction 34

Improving Brain MRI Image Segmentation Quality: A Hybrid Tec...

引用

34th Conference of Open Innovations Association FRUCT, FRUCT 2023

作者： Aziz, Samah Abdel Hawbani, Ammar Wang, Xingfu Abdelhamid, Talaat Maolood, Ismail Y. Alsamhi, Saeed Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Hefei23002 China Zagazig University Zagazig44519 Egypt Menoufia University Faculty of Electronic Engineering Menoufia Egypt -System Information Ministry of Higher Education and Scientific Research Kurdistan Region Erbil Iraq National University of Ireland Insight Centre for Data Analytics Galway Ireland

ISBN: (纸本)9789526524603

Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the image. This artifact not only hampers accurate diagnosis by radiologists but also negatively impacts the performance of computer-aided diagnosis algorithms, particularly in tasks like segmentation. In our proposed approach, we use a hybrid technique called KIFCM, which integrates K-means and Fuzzy C-means to enhance brain tumor segmentation. K-means provides computational efficiency, while Fuzzy C-means improves accuracy by detecting missed tumor cells. We employ a bias correction method based on the level set framework, removing noise with a median filter and applying the hybrid KIFCM technique for optimal segmentation. Our method effectively addresses intensity variation challenges, ensuring precise brain tumor region segmentation. We compare our results with DFCM and MFFLs, and the comparison shows the efficiency of our proposed method by highlighting the superior quality and accuracy of 81% achieved, requiring less computational time. Consequently, our results demonstrate KIFCM's potential to boost both accuracy and speed in MRI-based brain tumor detection through computer-aided diagnosis. © 2023 FRUCT Oy.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

引用

computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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：