检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Wu, Zijie Wang, Yaonan Zhu, Qing Mao, Jianxu Wu, Haotian Feng, Mingtao Mian, Ajmal College of Electrical and Information Engineering Hunan University Changsha410082 China School of Computer Science and Technology Xidian University Xian710071 China Department of Computer Science and Software Engineering University of Western Australia Perth CrawleyWA6009 Australia

—In this paper, we propose a novel minimum gravitational potential energy (MPE)-based algorithm for global point set registration. The feature descriptors extraction algorithms have emerged as the standard approach to align point sets in the past few decades. However, the alignment can be challenging to take effect when the point set suffers from raw point data problems such as noises (Gaussian and Uniformly). Different from the most existing point set registration methods which usually extract the descriptors to find correspondences between point sets, our proposed MPE alignment method is able to handle large scale raw data offset without depending on traditional descriptors extraction, whether for the local or global registration methods. We decompose the solution into a global optimal convex approximation and the fast descent process to a local minimum. For the approximation step, the proposed minimum potential energy (MPE) approach consists of two main steps. Firstly, according to the construction of the force traction operator, we could simply compute the position of the potential energy minimum;Secondly, with respect to the finding of the MPE point, we propose a new theory that employs the two flags to observe the status of the registration procedure. The method of fast descent process to the minimum that we employed is the iterative closest point algorithm;it can achieve the global minimum. We demonstrate the performance of the proposed algorithm on synthetic data as well as on real data. The proposed method outperforms the other global methods in terms of both efficiency, accuracy and noise resistance. Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Cooperative Game-based Approach for Local Path Planning of Multiple Mobile Robots in Opposite-direction Scenarios

Cooperative Game-based Approach for Local Path Planning of M...

引用

Chinese Automation Congress (CAC)

作者： Quan Yuan Lina Li Chen Wang Guangming Xie College of Engineering Peking University Beijing China College of Computer Science and Technology Changchun University Changchun China National Engineering Research Center for Software Engineering Peking University Beijing China College of Engineering Institute of Ocean Research Peking University Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper presents a cooperative game-based motion planning (CGP) approach for multiple mobile robots to planning paths in an opposite-direction scenario, such as human walk on the pedestrian passage on both sides of roads. Robots in each direction seek to reach the destination in the shortest time, while avoiding collisions with other robots in the same and opposite directions. Each robot has to consider other robots' intentions and responses in the process of moving. We use cooperative game theory to describe a series of intentions and responses among the robots. Our algorithm can find an approximate generalized Nash equilibrium of the cooperative game by using an iterative best response scheme. Theoretical analysis is provided to show the rationality of the proposed objective functions, and our approach is demonstrated through multi-agent bidirectional motion simulations. Agents using CGP approach exhibit dodge behaviors to avoid collisions with other agents from the same and reverse directions, similar to what we observe from face-to-face motion with human participants. Statistics show that our cooperative game planning approach outperforms a baseline model predictive control approach, and the other agents' responses are not considered in this approach.

关键词： Collision avoidance Robots Nash equilibrium Games Planning Linear programming Trajectory

来源：评论

学校读者我要写书评

暂无评论

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

引用

International Conference on computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

来源：评论

学校读者我要写书评

暂无评论

QoS-aware flow scheduling for energy-efficient cloud data centre network

QoS-aware flow scheduling for energy-efficient cloud data ce...

引用

作者： Wang, Songyun Yuan, Jiabin Zhang, Xiaoda Qian, Zhuzhong Li, Xin You, Ilsun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China CCST Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University Nanjing210000 China Department of Information Security Engineering Soonchunhyang University Asan-si Chungcheongnam-do Korea Republic of

It is highly valuable to achieve energy-efficient cloud data centres, which always act as the basic infrastructures. This paper thus aims at reducing the energy consumption of network devices in cloud data centres by flexible flow scheduling. For such an aim, it is necessary to guarantee the flow-level performance, which is a critical requirement for quality of service (QoS) in production data centres. Hence, this paper takes both energy reduction and QoS into account for flow scheduling, and then proposes a three-phase framework to control the energy consumption for cloud data centre network (DCN) while guaranteeing flow-level performance. The core idea is to turn off the unnecessary switches off. From extensive simulations, it is shown that our solution could not only reduce about 20% of energy on average than the case with all switches on, but also maintain good flow-level performance, stability and fault tolerance simultaneously. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Security aspects of internet of things aided smart Grids: a bibliometric survey

arXiv

引用

arXiv 2020年

作者： Sakhnini, Jacob Karimipour, Hadis Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam School of Engineering University of Guelph GuelphON Canada Cyber Science Lab School of Computer Science University of Guelph GuelphON Canada College of Computing and Software Engineering Kennesaw State University GA United States Department of Mathematics and Computer Science Brandon University MB Canada

The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors’ knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security. Copyright © 2020, The Authors. All rights reserved.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

arXiv

引用

arXiv 2022年

作者： Hennebelle, Alain Ismail, Leila Materwala, Huned Kaabi, Juma Al Ranjan, Priya Janardhanan, Rajiv School of Computing and Information Systems The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates College of Health Sciences Department of Internal Medicine United Arab Emirates University United Arab Emirates Mediclinic Al Ain Abu Dhabi United Arab Emirates Bhubaneswar Institute of Technology India Faculty of Medical & Health Sciences SRM Institute of Science & Technology India

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user’s data. Furthermore, we provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out between our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system using RF predicts diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. The performance is improved by 1.14% and 0.02% after feature selection for PIMA Indian and Sylhet datasets respectively, while it reduces by 0.89% for MIMIC III. © 2022, CC BY.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial

引用

International Journal of Pattern Recognition and Artificial Intelligence 2021年第12期35卷

作者： Lu, Yue Vincent, Nicole Suen, Ching Y. Wang, Patrick S. P. School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Université de Paris Rue des Saint-Pèeres 45 Paris Cedex 0675270 France Department of Computer Science and Software Engineering Centre for Pattern Recognition and Machine Intelligence Gina Cody School of Engineering and Computer Science Concordia University 1515 St. Catherine W. MontrealQCH3G 1M8 Canada College of Computer and Information Science Northeastern University BostonMA02115 United States

来源：评论

学校读者我要写书评

暂无评论

Augmented random search to reinforcement learning parameters

引用

AIP Conference Proceedings 2024年第1期2919卷

作者： Bonthu Kotaiah B. Padmaja Amitabha Yadav Department of Computer Science & Information Technology Maulana Azad National Urdu University Hyderabad. India Department of CSE (AI & ML Institute of Aeronautical Engineering Hyderabad India Department of Software DDUKK National P.G. College Lucknow Uttar Pradesh India

In a simple view, we introduce and evaluate a model-free of Hierarchical Reinforcement-Learning with parameters by using Augmented Random Search. The purpose is to improve the model based on Augmented Random Search Algorithm and Reinforcement Learning Algorithm we are using for those who are unableto stand on their feet or Physically Challenges and, who lose physical part due to accidents. Linear-Quadratic or classic complicationcontrolling method. Our simulations variability in performance in these approach tasks, the algorithm is Fifteen times high active than the quickest competing model-free approaches. Our approaches function generally use evaluation theReinforcement Learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Behavioral-based real-time cheating detection in academic exams using deep learning techniques

引用

AIP Conference Proceedings 2023年第1期2909卷

作者： Zouheir Trabelsi Medha Mohan Ambali Parambil Fady Alnajjar Luqman Ali Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

Recently, with the development of Artificial Intelligence (AI), the use of automated evaluation in education has increased. Maintaining academic integrity is one of the most challenging aspects of higher education. Cheating is rampant in academic examinations and other forms of educational assessment. The vast majority of students believe that it is unethical to tolerate cheating; therefore, it is vital to devote a significant amount of effort to identifying and avoiding instances of cheating. Examining the student’s behavior is one way to determine whether they are engaged in cheating or not. This paper proposes a deep learning-based cheating detection system that can identify instances of students engaging in dishonest behavior. A YOLOv7 model is trained on a custom dataset collected from various resources. The dataset comprises two classes, i.e., cheating and not cheating, and 2565 images. Evaluation criteria like precision, F1 score, recall, and mAP (mean average precision) are used to validate the performance of the proposed model. The proposed model shows promising performance in categorizing the student’s visible actions into cheating or not cheating and achieved an overall mAP@0.5 of 0.719. Overall, the proposed method can be utilized to reduce the error rate associated with human monitoring by alerting the proper authorities whenever unusual behavior is observed during academic tests.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Revisiting Depth-based Face Recognition from a Quality Perspective

Revisiting Depth-based Face Recognition from a Quality Persp...

引用

IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops

作者： Zhenguo Hu Qijun Zhao Feng Liu College of Computer Science Sichuan University College of Computer Science and Software Engineering Shenzhen University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Despite the large number of depth-based face recognition methods in the literature, high quality data are usually required for high recognition accuracy. In this paper, we measure the quality of 3D face data in terms of resolution and precision, and evaluate how the accuracy of three deep face recognition models varies on several benchmark databases as the facial depth data resolution changes from dense to sparse and as the precision changes from high to low. From the experimental results, several observations are made. (i) Given a high precision, a low resolution of 3K is sufficient to represent a 3D face;when the precision decreases, using higher resolutions can benefit face recognition, but the recognition accuracy becomes saturated as the resolution reaches 10K. (ii) Depth precision is more critical than resolution in depth-based face recognition, and a precision of 1mm is generally preferred as a good balance between accuracy and cost. (iii) The deep models trained with low-quality data perform more stable across data of different quality levels. We believe that these observations are beneficial for both depth sensor manufacturers and depth-based face recognition system developers.

关键词： Facial Recognition Precision Data Quality Resolving power Quality standards Face recognition techniques Face

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：