检索结果-内蒙古大学图书馆

An Intelligent Framework for Recognizing Social Human-Object Interactions

computers, Materials & Continua 2022年第10期73卷 1207-1223页

作者： Mohammed Alarfaj Manahil Waheed Yazeed Yasin Ghadi Tamara al Shloul Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of KoreaSiheung-si15073Gyeonggi-doKorea

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public *** involves localizing the human and object targets and then identifying the interactions between ***,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient ***,the proposed system offers an automated body-parts-based solution for HOI *** system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed ***,a convex hullbased approach for extracting key body parts has also been *** identifying the key body parts,two types of features are ***,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system *** results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

关键词： Dimensionality reduction human-object interaction key point detection machine learning watershed segmentation

来源：评论

学校读者我要写书评

暂无评论

A High-Efficient Hybrid Traction Inverter in Electric Vehicle Applications

A High-Efficient Hybrid Traction Inverter in Electric Vehicl...

引用

Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Yousefreza Jafarian Omid Salari Praveen Jain Alireza Bakhshai Mohamed Z. Youssef Electrical and Computer Engineering Queen’s University Kingston Canada Electrical and Computer Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper introduces a new multisource inverter (MSI) configuration utilizing multiple energy sources, strategically developed for hybrid storage solutions within electric vehicle (EV) frameworks. The proposed configuration facilitates the integration of multiple DC sources while significantly reducing the number of semiconductor components. This approach optimizes cost, weight, complexity, and power density by eliminating magnetic elements from the circuit architecture. Through precise control of the MSI's switching states, the system can generate the desired AC output voltage and current from any combination of the connected DC sources, offering enhanced flexibility and efficiency in power management for EVs. The efficacy of the proposed topology is validated through different testing, employing both the European Urban Driving Cycle (EUDC) and experimental lab prototype. The EUDC simulation provides a standardized benchmark for assessing the topology's performance under typical urban driving conditions, while the experimental results offer empirical evidence of its practical viability and efficiency in real-world applications.

关键词： Support vector machines Space vehicles Power system measurements Prototypes Switches Inverters Vectors Topology Voltage control Magnetic semiconductors

来源：评论

学校读者我要写书评

暂无评论

Siting and Sizing of EV Charging Stations in Active Distribution Systems Considering V2G Capability

Siting and Sizing of EV Charging Stations in Active Distribu...

引用

Smart Mobility (SM), IEEE International Conference on

作者： Adhip Sreekumar Kizhuthodi Tarlochan Sidhu Sheldon S. Williamson Deptartment of Electrical and Computer Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350390131

ISBN: (纸本)9798350390148

The growing interest in electric vehicles mandates creating better charging infrastructure across the grid. This paper presents a comprehensive approach to siting and sizing of fast charging station in distribution grid with existing distributed energy resources considering the vehicle to grid (V2G) capability of the charging station. In this paper, the IEEE 69 bus radial system is superimposed with road network to consider the availability of land for construction of charging stations in the optimization problem. Shuffled Frog Leaping algorithm is used to optimize with congestion and loss minimization as objectives.

关键词： Vehicle-to-grid Reactive power Roads Distribution networks Minimization Electric vehicle charging Inverters Fast charging Resource management Optimization

来源：评论

学校读者我要写书评

暂无评论

Data-informed Healthy Cell Clustering technique for Second-life Applications of Retired Electric Vehicle Batteries

Data-informed Healthy Cell Clustering Technique for Second-l...

引用

IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Akash Samanta Dominic Karnehm Antje Neve Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Institute of Intelligent Distributed Systems University of the Bundeswehr Munich Neubiberg Germany

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

The electric vehicles (EVs) adoption rate has been significant in the last decade and keeps on increasing. Recently, the retired EV battery packs which no longer provide satisfactory performance to power an EV started appearing in the market. Typically, EV batteries are declared unfit for EV applications when the battery capacity is reduced to 70-80% of its nominal capacity. Various end-of-life (EOL) options are proposed by researchers before sending the retired batteries to recycle such as low-demanding e-mobility, grid-tired energy storage systems, storage of renewable energy, and home emergency power supplies. It is predicted that this may create new value pools in the energy and transportation sectors shortly. It is noticed in lithium-ion battery packs that all cells do not age similarly, thus clustering of healthy cells among the retired batteries is very crucial for safe and reliable operation in second-life applications. Thus, an efficient cell clustering method utilizing voltage relaxation curves powered by a data-driven BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm is proposed in this paper. The efficiency and efficacy of the proposed algorithm compared to the state-of-the-art cell sorting technique are also demonstrated in the paper.

关键词： Renewable energy sources Accuracy Clustering methods Clustering algorithms Transportation Voltage Electric vehicles

来源：评论

学校读者我要写书评

暂无评论

A Prediction Method of Consumer Buying Behavior Based on Attention Mechanism and CNN-BiLSTM

Journal of Network Intelligence

引用

Journal of Network Intelligence 2022年第2期7卷 375-385页

作者： Wang, Jian-Nan Cui, Jian-Feng Chen, Chin-Ling College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

A prediction method of consumer buying behavior based on attention mechanism and CNN-BiLSTM is proposed, to fully extract the effective features of the behavior and make its prediction more accurate and stable. In this paper, the CNN model is used to automatically extract local features of the consumer buying behavior to select and optimize important information. The BiLSTM model is used to collect sequence patterns of buying behavior to find the correlation among features. The attention mechanism is used to assign different weights to the state values of the hidden layers of each neural network so that the hidden layers can better express important information to obtain more accurate and stable predictions. The experiment results demonstrate that the proposed prediction method has advantages in not only accuracy but stability over other models. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada Rhazes Lab Mayo Clinic Department of Artificial Intelligence and Informatics RochesterMN United States

ISBN: (纸本)9781665430654

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Artificial Neural Network (ANN) Coordinate Search Expensive Optimization Gradient-free Large-Scale Optimization Stochastic Gradient Descent (SGD)

来源：评论

学校读者我要写书评

暂无评论

A Lumped Element Method for Acoustoelectric Imaging Reconstruction: A Numerical Study

A Lumped Element Method for Acoustoelectric Imaging Reconstr...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Tan, Rick Hao McDermott, Conor Rossa, Carlos Ontario Tech University Faculty of Engineering and Applied Science Oshawa Canada Carelton University Systems and Computer Engineering OttawaON Canada

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Acoustoelectric impedance tomography (AET) is a new non-invasive medical imaging procedure used to map the electrical properties of biological tissues with higher spatial resolution than traditional electrical impedance tomography (EIT). It exploits the acoustoelectric effect where modulated ultrasonic pressure changes the local tissue conductivity. This provides additional information to reconstruct a tomographic image, and has a stabilizing effect on an otherwise highly unstable inverse *** this paper, a novel approach to solving the AET inverse problem for image reconstruction is proposed. In the algorithm, the acoustoelectric effect is assumed to create small perturbations in the local resistance of the medium under observation. A lumped model consisting of a finite mesh of resistors approximates the medium under observation, through which boundary voltage differences between the excited and unexcited medium are calculated. A variation of the Modified Newton Raphson (MNR) algorithm is then proposed, where each pattern in the algorithm is created from small perturbations of the tissue conductivity. A total of eight simulation scenarios are evaluated, where the conductivity perturbations are in the order of 1%, 2.5% to 5% of the nominal tissue conductivity. The algorithm can successfully reconstruct the images in the presence of random noise. The obtained images are compared against traditional EIT where the percentage error is calculated for each simulated tomographic image. The simulation results indicate that the proposed approach is superior to traditional EIT as it constructs more distinct and high contrasting images with less percentage error. © 2022 IEEE.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Differential Privacy Implementations on Synthetic Data

Comparative Analysis of Differential Privacy Implementations...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Huwida E. Said Qusay H. Mahmoud Mandeep Goyal Faiza Hashim Department of Computing and Applied Technolgy College of Technological Innovation Zayed University Dubai UAE Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Computer Science and Software Engineering College of Information Technology UAE University AI-Ain UAE

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments.

关键词： Differential privacy Costs Accuracy Conferences Noise Machine learning software Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based MR Image Re-parameterization

Deep Learning-Based MR Image Re-parameterization

引用

2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Narang, Abhijeet Raj, Abhigyan Pop, Mihaela Ebrahimi, Mehran Dept. of Computer Science & Engineering Dhanbad India Sunnybrook Research Institute TorontoON Canada Ontario Tech University Faculty of Science OshawaON Canada

ISBN: (纸本)9798350327595

Magnetic resonance (MR) image re-parameterization refers to the process of generating an MR image with a new set of MRI scanning parameters using simulations. Different parameter values generate distinct contrast between different tissues, helping identify pathologic tissue. Typically, more than one scan is required for diagnosis;however, acquiring repeated scans can be costly, time-consuming, and difficult for patients. Thus, using MR image re-parameterization to predict and estimate the contrast in these imaging scans can be an effective alternative. In this work, we propose a novel deep learning (DL) based convolutional model for MRI re-parameterization. Based on our preliminary results, DL-based techniques hold the potential to learn the nonlinearities that govern the re-parameterization. © 2023 IEEE.

关键词： Deep Learning Medical Imaging MR Image Re-parameterization

来源：评论

学校读者我要写书评

暂无评论

Investigating the Impact of Bug Dependencies on Bug-Fixing Time Prediction 17

Investigating the Impact of Bug Dependencies on Bug-Fixing T...

引用

17th ACM/IEEE International Symposium on Empirical software engineering and Measurement, ESEM 2023

作者： Li, Chenglin Zhao, Yangyang Yang, Yibiao Zhou, Yuming Nie, Liming Ding, Zuohua School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China Nanjing University State Key Lab for Novel Software Technology Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665452236

Background: Bug dependencies refer to the link relationships between bugs and related issues, which are commonly observed in software evolution. It has been found that bugs with bug dependencies often take longer time to be resolved than other bugs without any dependencies. Despite the potential impact of bug dependencies on bug-fixing time, previous studies use traditional metrics without considering bug dependencies to build bug-fixing time prediction models. As a result, there is currently little empirical evidence to support the use of bug dependencies in improving prediction accuracy. Aims: We aim to conduct a comprehensive empirical study to investigate the value of considering bug dependencies for bug-fixing time prediction. Method: We define a set of bug dependency metrics based on bug dependencies. We first investigate the correlation between bug dependency metrics and bug-fixing time to investigate whether bugs with more complex dependencies are more time-consuming to be fixed. Next, we employ principal component analysis to study whether bug dependency metrics capture additional dimensions of a bug compared to traditional metrics. Finally, we build multivariate prediction models to explore whether considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Results: The experimental results suggest that: (1) bugs with more complex dependencies require more time to be fixed;(2) bug dependency metrics are complementary to traditional metrics;(3) considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Conclusions: These findings highlight the importance of considering bug dependencies in bug-fixing time prediction, and provide valuable insights into the potential impact of bug dependencies on software development processes. © 2023 IEEE.

关键词： Principal component analysis

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：