检索结果-内蒙古大学图书馆

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Khan, Sohaib Ali Rehman, Hafiz Zia Ur Waqar, Asad Khan, Zeashan Hameed Hussain, Muntazir Masud, Usman Karachi Pakistan Air University Department of Mechatronics & Biomedical Engineering Islamabad Pakistan Bahria University Department of Electrical Engineering Islamabad Pakistan National University of Science & Technology Dept of Avionics Engineering CAE Islamabad Pakistan Air University Department of Electrical & Computer Engineering Islamabad Pakistan Faculty of Electrical & Electronics Engineering UET Taxila Pakistan University of Kassel Germany

ISBN: (纸本)9781665472753

This paper presents a framework for the implementation of a digital twin (DT) in electrical grid management. Automation in the electrical energy network has resulted in the transformation into Smart grid, which is utilized for the generation, transmission, and distribution of electrical power as well as interconnecting microgrids with dynamic scheduling and trading options. The evolution of the digital twin offers added advantages including real-time condition monitoring based maintenance of assets based on data analytics, energy forecasting, and prediction for appropriate decision making by investors. Thus, fault diagnosis and detection can be easily handled in the advanced automated future grid. These features have enhanced reliability and offer optimized energy management by incorporating a virtual DT domain. In this paper, some major benefits of establishing a digital twin for the smart-grid is highlighted followed by the case study on monitoring a single component of the Smart grid that is evaluated for the remaining useful life (RUL) of the equipment by using artificial intelligence (AI) algorithm. This approach of preventive maintenance based on DT can be effectively utilized for all the key components in the smart grid-connected via a sensor network for data sampling to reduce downtime and improve the reliability of the overall system. © 2023 IEEE.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

引用

IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 295-312页

作者： Sarah P.Madruga Augusto H.B.M.Tavares Saulo O.D.Luiz Tiago P.do Nascimento Antonio Marcus N.Lima Graduate Program of Electrical Engineering Universidade Federal de Campina GrandeCampina Grande-PBBrazil Department of Electrical Engineering Universidade Federal de Campina GrandeCampina Grande-PBBrazil IEEE Department of Computer Systems Universidade Federal da ParaibaJoao Pessoa-PBBrazil

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural ***,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations *** network training is performed offline,which requires low computational *** target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation *** a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small *** compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the *** was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve *** allocation error was also determined in both ***,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.

关键词： Aerodynamics effects control allocation minidrone multi-rotor UAV neural networks

来源：评论

学校读者我要写书评

暂无评论

Analysis of Knee Osteoarthritis Grading Using Deep Learning 15th

Analysis of Knee Osteoarthritis Grading Using Deep Learning

引用

15th International Conference on Application of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools, ICAFS 2022

作者： Akila, Serag Mohamed Imanov, Elbrus Almezhghwi, Khaled Department of Biomedical Engineering Near East University Mersin-10 Turkey Department of Computer Engineering Near East University Mersin-10 Turkey Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya

ISBN: (纸本)9783031252518

Knee osteoarthritis severity grading from plain radiographs and magnetic resonance (MR) images is of great significance in the diagnosis of osteoarthritis (OA). Recently, deep learning had a great impact on improving the Kellgren and Lawrence (KL) grading scheme of Knee osteoarthritis KOA using models that acquire the contextual features spontaneously without the need for any conventional high computational spatial configuration modeling. In this study, we review the state-of-the-art deep learning methods that enhanced the knee osteoarthritis severity KL grading. Pre-trained models such as Resnet18, VGG, DenseNet, Convolutional Siamese neural network, ResNet34, Squeeze-and-excitation ResNet (SE-ResNet) were found to be employed to extract valuable data for clinical images in the surveyed papers. The survey concludes that some very significant sophisticated deep learning methods were employed in some studies to grade KOA, which may also work on grading other diseases. Moreover, we show that applying Vision Transformer (ViT) for this specific task can be a better option than most of the convolutional neural networks (CNNs) based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

The good, the bad and the ugly sides of data augmentation: an implicit spectral regularization perspective

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2024年第1期25卷 4470-4554页

作者： Chi-Heng Lin Chiraag Kaushik Eva L. Dyer Vidya Muthukumar School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA Coulter Department of Biomedical Engineering School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA School of Electrical & Computer Engineering School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta GA

Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of prevalent augmentations in modern machine learning (e.g. randomized masking, cutout, mixup), that greatly alter the training data distribution. In this work, we develop a new theoretical framework to characterize the impact of a general class of DA on underparameterized and overparameterized linear model generalization. Our framework reveals that DA induces implicit spectral regularization through a combination of two distinct effects: a) manipulating the relative proportion of eigenvalues of the data covariance matrix in a training-data-dependent manner, and b) uniformly boosting the entire spectrum of the data covariance matrix through ridge regression. These effects, when applied to popular augmentations, give rise to a wide variety of phenomena, including discrepancies in generalization between over-parameterized and under-parameterized regimes and differences between regression and classification tasks. Our framework highlights the nuanced and sometimes surprising impacts of DA on generalization, and serves as a testbed for novel augmentation design.

关键词： data augmentation generalization analysis overparameterized models spectral analysis regression classification

来源：评论

学校读者我要写书评

暂无评论

Post-Stroke Lesion Segmentation using 2D and 3D Dense U-net 7

Post-Stroke Lesion Segmentation using 2D and 3D Dense U-net

引用

7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Mansour, Rena Khreiss, Zeinab Sbeity, Fatima Ali, Mohamad Abou Kassem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon Lebanese University Department of Physics & Electronics Lebanon University of the Basque Country Department of Computer Science & Artificial Intelligence Spain Notre Dame University Department of Electrical &computer Engineering Lebanon

ISBN: (纸本)9798350325850

Strokes are brain attacks that cause death. Survivors suffer from damage to their brain (known as lesions). Detecting and segmenting these lesions using imaging techniques is crucial for medical diagnosis and treatment. However, manual post-stroke lesion segmentation is challenging and not yet fully automated. This research aims to assess the effectiveness of the Dimensional Dense U-Net architecture in this task. The architecture has shown promising results in other segmentation tasks, and we aim to evaluate its performance on the Anatomical Tracing of Lesion after Stroke (ATLAS) dataset. Initially, we employed a 3D version of the model and later switched to a 2D version when the 3D results were unsatisfactory. Among numerous trials, the 3D dense architecture achieved its highest dice score of 13% on patients with a single lesion in the left hemisphere, using 110 images. The 2D dense architecture achieved the highest dice score among all using 1000 random images. Each set was split into 70% for training, 20% for validation and 10% for testing. The training dice score was 87%, with a validation dice score of 9.8%. © 2023 IEEE.

关键词： Architecture

来源：评论

学校读者我要写书评

暂无评论

The comparison of EEG features in specific Mandarin words' speech

The comparison of EEG features in specific Mandarin words' s...

引用

2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Tsai, Wen-Shan Huang, Syue-Guan Huang, Kuan-Chih Chen, Jung-Chih Lin, Chin-Teng National Yang Ming Chiao Tung University Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Brain Science and Technology Center College of Electrical and Computer Engineering Hsinchu300093 Taiwan Faculty of Engineering and Information Technology UltimoNSW2007 Australia

ISBN: (纸本)9798350305791

Due to advancements in brain signal application technology, there has been a growing focus on the human speech Brain-computer Interface (BCI) in recent years. An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing. To ensure the decoding capability of human speech BCI, researchers have to investigate the differences between overt speech and imagined speech, and they also need to find the best paradigm to build a reliable BCI. Thus, this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words ""(up), ""(down), ""(left), and ""(right). We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca's and Wernicke's areas (specifically F5, F7, FC3, FC5, and C5), to analyze the brain activity changes while speaking or imagining speech. Second, we used Daubechies 4 (db4) wavelet to do a 7-level decomposition to the EEG signal. The corresponding discrete wavelet transform (DWT) coefficients were further calculated with six statistical features for support vector machine (SVM) classification. We found that the "speak (overt speech)"task and the "imagine (imagined speech)"task resulted in significantly different alpha band power changes, and the beta band shows the same phenomenon. The result of average classification accuracy is 71.97% for "speak"and "imagine"distinction. In conclusion, we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features. This may pave the way to establish a Mandarin imagined speech BCI. © 2023 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning

Semi-Supervised Generalized Adaptive Weighted Recursive Leas...

引用

International Image Processing, Applications and Systems Conference (IPAS)

作者： Mohadeseh Yousefi Yashar Naderahmadian Soosan Beheshti Department of Electrical and Computer Engineering University of Guilan Rasht Iran Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This work presents a novel semi-supervised dictionary learning framework that updates the dictionary by online learning and is efficient in utilizing the training data. The method employs a two-stage process to train the dictionary: initial training with limited labeled data, followed by online refinement using abundant unlabeled data. We introduce an adaptive correction weight to control the influence of new unlabeled data on the dictionary update based on its consistency with the current model estimate. This approach enables efficient use of the training data set. Moreover, results in faster dictionary convergence and improves data representation accuracy, especially in scenarios with limited training data. Experimental results demonstrate significant enhancement in the classification accuracy of the proposed method compared to the state-of-the-art semi-supervised dictionary learning methods, particularly when dealing with a limited number of training samples.

关键词： Training Dictionaries Accuracy Sparse approximation Image processing Training data Data visualization Machine learning Data models Convergence

来源：评论

学校读者我要写书评

暂无评论

UV-SAFE: Advancing Market Product Disinfection with Automated UV-Technology Device 7

UV-SAFE: Advancing Market Product Disinfection with Automate...

引用

7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023

作者： Erazo, Andres Allauca, Luis Guerra, Gabriel Ko, Seok-Bum University of Saskatchewan Division of Biomedical Engineering SaskatoonSK Canada Aasc-Tronix Department of Investigation Quito Ecuador Universidad de Salamanca Science Faculty Salamanca Spain University of Saskatchewan Department of Electrical and Computer Engineering Division of Biomedical Engineering SaskatoonSK Canada

ISBN: (纸本)9798350338232

We present the design and implementation of an automated device for viruses and bacterial disinfection for use in market products. This document includes the design considerations of our innovative solutions and its proof-of-concept testing to ensure its desired operation. Results of an experimental study, conducted with regular and irregular objects, were utilized to ascertain the optimal positioning of the UV lamps within the disinfection chamber. Likewise, the light incidence in the object was validated by these same tests. Collectively, our findings underscore that our device is adept at effectively disinfecting market products, offering a robust and dependable operational capacity. The disinfection process takes 10 seconds per product, achieving a log-reduction dose. © 2023 IEEE.

关键词： Disinfection

来源：评论

学校读者我要写书评

暂无评论

RoRankNet: Modeling Ordinal Progression in Alzheimer's Disease with Plackett-Luce Ranking and Hard Instance Learning

RoRankNet: Modeling Ordinal Progression in Alzheimer's Disea...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yu, Minghang Gao, Linlin Wu, Dan Wang, Jianhua Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China The First Affiliated Hospital Xiamen University Department of Radiology Xiamen China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is a chronic neurodegenerative disorder characterized by progression from normal control (NC) to mild cognitive impairment (MCI), and ultimately to AD. These stages reflect ordinal deterioration of specific brain tissues. Timely and accurately identifying these stages is crucial for effective treatment. While existing multi-classification methods using structural magnetic resonance imaging show promise, they often overlook the inherently ordinal progressive relations. Addressing this, our study introduces RoRankNet, a robust ranking network designed to model the ordinal progression of AD. Specifically, we propose a distance-based Plackett-Luce ranking module to effectively represent the distance relations of these stages, enhancing the alignment between extracted features and disease progression. Additionally, we present a hard instance rectification module to identify and correct challenging instances, enhancing classification robustness. Our extensive experiments demonstrate RoRankNet's superiority in classifying NC, MCI, and AD. Notably, our approach not only preserves the ordinal nature in feature space but also excels in localizing discriminative biomarkers essential for accurate stage prediction in clinical practice. Source codes will be publicly available soon. © 2024 IEEE.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

3D Image Generation from X-ray Projections Using Generative Adversarial Networks 23

3D Image Generation from X-ray Projections Using Generative ...

引用

23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AlShurbaji, Mohammad Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350393118

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0 &90 to 60 &150, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections. © 2023 IEEE.

关键词： Generative adversarial networks

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：