检索结果-内蒙古大学图书馆

Design and Analysis of a Novel Generalized Continuous Tracking Differentiator

Ain Shams engineering Journal 2022年第4期13卷

作者： Abdul-Adheem, Wameedh Riyadh Ibraheem, Ibraheem Kasim Humaidi, Amjad J. Alkhayyat, Ahmed Maher, Rami A. Abdulkareem, Ahmed Ibraheem Azar, Ahmad Taher University of Baghdad College of Engineering Department of Electrical Engineering Baghdad Al-Jadriyah10001 Iraq Department of Computer Engineering Techniques Al-Rasheed University College Baghdad 10001 Iraq University of Technology Department of Control and Systems Engineering 10001 Baghdad Iraq College of technical engineering The Islamic University Najaf Iraq Faculty of Engineering Isra Univ. P.O. Box 33 Amman11622 Jordan College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha15311 Egypt

In this work, an n-th order Generalized Tracking Differentiator (GTD) is proposed based on sigmoid function with a continuous structure, including both linear and nonlinear parts, thus increasing the estimation accuracy of the input signal and its derivatives, and overcoming the inherent issues related to the classical Tracking Differentiators (TDs). Then, a 2nd-order version of the proposed GTD is derived and optimized with further improvements which are reflected in the excellent behavior in the time and frequency domains. Moreover, stability analysis using the method of Lyapunov analysis is also investigated and the performance of the GTD is proven in the time and frequency domains and revealed that the proposed GTD considerably reduces the "peaking phenomenon" and "noise" and eliminates the "chattering phenomenon" from the signal derivatives. The excellent results of the proposed GTD are demonstrated through simulations on noise-free and noisy signals and compared with the Robust Exact Uniformly Convergent Arbitrary Order Differentiator (REUCAOD). © 2021 Faculty of engineering, Ain Shams University

关键词： Tracking differentiator Sigmoid function Peaking phenomenon Noise Chattering phenomenon Signal derivative

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective DER Planning Framework as a TEP Alternative: An Augmented Epsilon-Constraint Approach

引用

IEEE Transactions on Industry Applications 2025年

作者： Saber, Hossein Ranjbar, Hossein Mazaheri, Hesam Sharif University of Technology Center of Excellence in Power System Control and Management Electrical Engineering Department Tehran11365-11155 Iran University of Adelaide School of Electrical and Mechanical Engineering Adelaide Australia Texas A&M University Department of Electrical and Computer Engineering College StationTX United States

Recently, the construction of new transmission lines has faced extreme challenges due to ever-increasing environmental concerns and construction costs. Investing in distributed energy resources (DERs) offers a viable alternative to reduce reliance on new transmission lines. This study introduces a multi-objective bi-level DER planning model as an alternative to transmission network reinforcement. The upper-level problem aims to minimize the total cost of DERs, mitigate the congestion cost of the transmission network, and improve the system reliability while ensuring the profitability of the DER investment. In the lower-level problem, the wholesale electricity market is cleared to determine the optimal locational marginal prices and the operational status of the bulk power system. A scenario-based approach is employed to account for uncertainties in future loads, renewable generation, and generator bid prices. The bi-level problem is reformulated as a single-level optimization using the strong duality condition, with non-linear terms linearized through the big-M method and complementary slackness conditions. The augmented epsilon-constraint method is then employed to solve the multi-objective optimization problem. Finally, the effectiveness and applicability of the proposed DER planning model are evaluated using the IEEE 24-bus test system, offering insights from both system operators and DER investors. © 1972-2012 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays

arXiv

引用

arXiv 2022年

作者： Ji, Haoqin Liu, Haozhe Li, Yuexiang Xie, Jinheng He, Nanjun Huang, Yawen Wei, Dong Chen, Xinrong Shen, Linlin Zheng, Yefeng Computer Vision Institute College of Computer Science and Software Engineering China AI Research Center for Medical Image Analysis & Diagnosis Shenzhen University China Jarvis Lab Tencent China Academy for Engineering and Technology Fudan University China

Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed by experienced radiologists, and it is tedious and time-consuming, thus difficult to acquire. Such a situation results in a difficulty to develop a fully-supervised abnormality localization system for CXR. In this regard, we propose to train the CXR abnormality localization framework via a weakly semi-supervised strategy, termed Point Beyond Class (PBC), which utilizes a small number of fully annotated CXRs with lesion-level bounding boxes and extensive weakly annotated samples by points. Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost. Particularly, the core idea behind our PBC is to learn a robust and accurate mapping from the point annotations to the bounding boxes against the variance of annotated points. To achieve that, a regularization term, namely multi-point consistency, is proposed, which drives the model to generate the consistent bounding box from different point annotations inside the same abnormality. Furthermore, a self-supervision, termed symmetric consistency, is also proposed to deeply exploit the useful information from the weakly annotated data for abnormality localization. Experimental results on RSNA and VinDr-CXR datasets justify the effectiveness of the proposed method. When ≤20% box-level labels are used for training, an improvement of ∼5% in mAP can be achieved by our PBC, compared to the current state-of-the-art method (i.e., Point DETR). Code is available at https://***/HaozheLiu-ST/Point-Beyond-Class. Copyright © 2022, The Authors. All rights reserved.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Prisilla, Ardha Ardea Pusparani, Yori Chang, Wen-Thong Liau, Ben-Yi Jan, Yih-Kuen Ardhianto, Peter Lin, Chih-Yang Lung, Chi-Wen Asia University Taichung Department of Digital Media Design Taiwan LaSalle College Jakarta Department of Fashion Design Jakarta Indonesia Budi Luhur University Department of Visual Communication Design Jakarta Indonesia Department of Computer Science and Information Engineering Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Asia University Department of Creative Product Design Taichung Taiwan Soegijapranata Catholic University Department of Visual Communication Design Semarang Indonesia National Central University Department of Mechanical Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Discernible image mosaic with edge-aware adaptive tiles

引用

Computational Visual Media 2019年第1期5卷 45-58页

作者： Pengfei Xu Jianqiang Ding Hao Zhang Hui Huang College of Computer Science and Software Engineering Shenzhen University School of Computing Science Simon Fraser University

We present a novel method to produce discernible image mosaics, with relatively large image tiles replaced by images drawn from a database, to resemble a target image. Compared to existing works on image mosaics, the novelty of our method is ***, believing that the presence of visual edges in the final image mosaic strongly supports image perception,we develop an edge-aware photo retrieval scheme which emphasizes the preservation of visual edges in the target image. Secondly, unlike most previous works which apply a pre-determined partition to an input image, our image mosaics are composed of adaptive tiles, whose sizes are determined based on the available images in the database and the objective of maximizing resemblance to the target image. We show discernible image mosaics obtained by our method, using image collections of only moderate size. To evaluate our method, we conducted a user study to validate that the image mosaics generated present both globally and locally appropriate visual impressions to the human observers. Visual comparisons with existing techniques demonstrate the superiority of our method in terms of mosaic quality and perceptibility.

关键词： image mosaic image retrieval image synthesis

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Multiobjective Feature Selection Assisted by Unselected Features

Evolutionary Multiobjective Feature Selection Assisted by Un...

引用

Congress on Evolutionary Computation

作者： Xuan Duan Songbai Liu Junkai Ji Lingjie Li Qiuzhen Lin Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets.

关键词： Accuracy Evolutionary computation Multitasking Feature extraction Classification algorithms Task analysis Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

Matching Based Joint Trading Contract of Energy and Computation in Virtual Power Plant

Matching Based Joint Trading Contract of Energy and Computat...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yanhui Wang Li Wang Lianming Xu Zemin Sun Kuankuan Sima School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT Beijing China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665435413

Electric power grid intelligence and automation are inseparable from the support of computing resources. Electric vehicles (EVs) can provide low-cost and flexible computing offloading services for nearby grid nodes. In this paper, we propose a collaborative model between EVs and intelligent charging stations (CSs) for energy and computation trading, where EVs can contribute their computation resources to CSs during charging at CSs. However, due to selfishness, CSs may refuse to reveal their computing requirements to the EVs, leading to information asymmetry. To cope with this limitation, the contract theory is employed to incentivize interaction between potential CS-EV pair for resource trading. Furthermore, we propose a stable-matching-based algorithm to match CSs and EVs into cooperative groups to achieve mutual-beneficial utilities. Simulation results verify the effectiveness of our algorithm.

关键词： Waste materials Simulation Computational modeling Collaboration Virtual power plants Electric vehicles Power systems

来源：评论

学校读者我要写书评

暂无评论

An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction

An Applied Artificial Intelligence Technique for Early-Stage...

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Hassan Ali Hussain Imtiaz Attique Ur Rehman Sabeen Javaid Tahir Muhammad Ali Menwa Alshammeri Azka Mir Himanshu Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Science and Technology Kuwait Department of Computer Science College of CIS Al Jouf University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. This increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. This research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. The insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease.

关键词： Pathology Logistic regression Computational modeling Predictive models Market research Bayes methods Delays Decision trees Alzheimer's disease Random forests

来源：评论

学校读者我要写书评

暂无评论

PCT-Cap: Point Cloud Transformer for Accurate 3D Capacitance Extraction

PCT-Cap: Point Cloud Transformer for Accurate 3D Capacitance...

引用

Electronics Design Automation (ISEDA), International Symposium of

作者： Ye Cai Yuyao Liang Zhipeng Luo Biwei Xie Xingquan Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China Center of Machine Learning and Optimization Minnan Normal University Zhangzhou China State Key Lab of Processors Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

Accurate parasitic capacitance extraction becomes increasingly essential in advanced technology nodes. 2.5D ex-traction requires more effort to maintain accuracy comparable to the 3D solver. In this work, two experiments reveal that error deviation in the 2.5D method enlarges as wire width shrinks and length increases. A novel Gauss law-based point cloud is proposed to model the 3D capacitance problem. With the proposed data representation, a transformer-based neural network architecture, called PCT-Cap, is designed for 3D pattern matching. PCT-Cap exhibits much better performance than the ResNet-based capacitance models. Extensive experiments on 28nm technology demonstrate that PCT-Cap can accurately predict over 95.41 % of total capacitance with an error margin of less than 5 %. Additionally, PCT-Cap achieves 95.90 % accuracy in predicting coupling capacitance within a margin of error of less than 10%. PCT-Cap achieves at least a 55 × speeding up compared with the commercial extraction tool while consuming negligible memory.

关键词： Point cloud compression Solid modeling Three-dimensional displays Accuracy Capacitance Feature extraction Transformers

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：