检索结果-内蒙古大学图书馆

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

引用

computers, Materials & Continua 2023年第5期75卷 4553-4571页

作者： Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad Tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad Khan Tauheed Khan Mohd Department of Health Information Management and Technology University of Hafr Al-BatinSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing AbdulazizJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdul Aziz UniversityJeddahSaudi Arabia College of Medicine King Khalid UniversityAbhaSaudi Arabia Department of Physics College of ScienceJazan UniversityP.O.Box.114Jazan45142Saudi Arabia Department of Biology College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Math and Computer Science Augustana CollegeIL61201United States

Artificial Intelligence(AI)is finding increasing application in healthcare *** learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health ***,early detection of any disease or derangement can aid doctors in saving patients’***,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper *** propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health *** begin with,various patient datasets were collected and trained into the system using IoT *** a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result ***,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant ***,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a *** found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

关键词： Healthcare system health monitoring clinical decision support internet of things artificial intelligence machine learning diagnosis

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Nature-Inspired Algorithms for Task Assignment Problem 5th

Comparative Analysis of Nature-Inspired Algorithms for Tas...

引用

5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Mishra, Pawan Pooja Jolly Tripathi, Shashi Prakash Upadhyay, Kamal Kishor Department of Electronics and Communication Engineering University of Allahabad Uttar Pradesh Prayagraj India Department of Applied Mathematics and Computer Science Liverpool John Moores University Liverpool United Kingdom Department of Electronics and Communication Engineering JSS Academy of Technical Education Noida India

ISBN: (纸本)9789819719228

In modern task assignment scenarios, the effective allocation of tasks to individuals with suitable skill levels is crucial to ensure efficient execution and resource utilization. This research addresses the problem of task assignment optimization, focusing on minimizing the discrepancy between the assigned skill levels of individuals and the required skill levels for each task. In this research, a comprehensive comparative analysis of nature-inspired algorithms applied to the TAP, to iteratively refine task assignments and improve skill matching. Nature-inspired algorithms (NIA) draw inspiration from biological and ecological processes, mimicking patterns observed in nature to solve complex problems. This study investigates four prominent nature-inspired algorithms: Jaya algorithm and Ant Colony Optimization (ACO) algorithm from Swarm Intelligence, genetic algorithm (GA), and Differential evolution from Evolutionary algorithm. These NIA approaches demonstrate its potential to provide practical solutions to complex task assignment challenges by mitigating skill mismatches and fostering efficient task execution. The significance of this research lies in its applicability to real-world scenarios such as project management, workforce scheduling, and resource allocation. By optimizing task assignments based on skill levels, organizations can enhance productivity, minimize skill wastage, and improve resource utilization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Sliding mode control rotor flux MRAS based speed sensorless induction motor traction drive control for electric vehicles

AIMS Electronics and Electrical Engineering

引用

AIMS Electronics and Electrical engineering 2023年第4期7卷 354-379页

作者： Rind, Saqib J. Javed, Saba Rehman, Yawar Jamil, Mohsin Department of Automotive & Marine Engineering NED University of Engineering & Technology University Road Karachi Pakistan Department of Electronics Engineering NED University of Engineering & Technology University Road Karachi Pakistan Department of Electrical & Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland 240 Prince Phillip Drive St. John'sNLA1B 3X5 Canada

Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PIMRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics. © 2023 the Author(s).

关键词： Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Contact-Rich Task Learning on an Articulated Soft Robot Arm Through Simulation 25th

Contact-Rich Task Learning on an Articulated Soft Robot Ar...

引用

25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Elstner, Laurenz Kyrkjebø, Erik Stoelen, Martin F. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway Fieldwork Robotics Ltd. Cambridge United Kingdom

ISBN: (纸本)9783031720611

Traditional rigid robots face limitations in exploration during learning processes in dynamic and unknown environments where contact and impacts are common. Soft robots that leverage compliant materials, for example as part of variable stiffness actuators (VSAs), offer promising avenues for enhanced adaptability and safety. Deep Reinforcement Learning (RL), while proven to be a powerful tool, requires a lot of training data that is time-consuming to gather. This paper introduces a novel approach to simulating robots driven by antagonistic VSAs, as well as full-task learning with a soft robot arm in three environments with different contact types: path following (no contact), door opening (kinematic constraint), and surface wiping (continuous contact). We show that the tendon-driven robot can learn all three tasks, even if learning in a very low-level actuation space. The stretchable tendons enables it to perform relatively well on the contact-rich tasks. Further, using the proposed variable stiffness controller the robot is able to develop a stable behaviour in terms of forces applied to the environment. The results are promising for extending the approach to the physical robot. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tendons

来源：评论

学校读者我要写书评

暂无评论

Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第3期6卷 690-699页

作者： Xie, Tianyu Sun, Yue Yang, Hongxu Li, Shuo Song, Jinhong Yang, Qimin Chen, Hao Wu, Mingxiang Tan, Tao Macao Polytechnic University Faculty of Applied Sciences 999078 China Eindhoven University of Technology EindhovenAZ 5612 Netherlands Case Western Reserve University Department of Computer and Data Science Department of Biomedical Engineering ClevelandOH44106 United States Jiangsu JITRI Sioux Technologies Company Ltd. Suzhou215000 China University of Twente Department of Biomechanical Engineering EnschedeNB 7522 Netherlands Shenzhen People's Hospital Department of Radiology Shenzhen518020 China

This article introduces an enhanced teacher-student model featuring a novel Vnet architecture that integrates high-pass and low-pass filters to improve the segmentation of breast magnetic resonance imaging (MRI) images. The model effectively utilizes finely annotated, roughly annotated, and unannotated data to achieve precise breast tissue density segmentation. The teacher-student framework incorporates three specialized Vnet networks, each tailored to different types of annotations. By integrating cosine contrast loss functions between finely and roughly annotated models, and innovatively applying high-pass and low-pass filters within the Vnet architecture, the segmentation performance is significantly enhanced. This hybrid filtering approach enables the model to capture both fine-grained and coarse structural details, leading to more accurate segmentation across various MRI image datasets. Experimental results demonstrate the superiority of the proposed method, achieving Dice values of 0.833 on the finely annotated Shenzhen dataset and 0.780 on the Duke dataset, using 15 finely annotated, 15 roughly annotated, and 58 unlabeled samples provided by Shenzhen People's Hospital. These findings underscore its potential clinical application in breast density assessment. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

引用

computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

来源：评论

学校读者我要写书评

暂无评论

LRP-based network pruning and policy distillation of robust and non-robust DRL agents for embedded systems

LRP-based network pruning and policy distillation of robust ...

引用

作者： Luan, Siyu Gu, Zonghua Xu, Rui Zhao, Qingling Chen, Gang Department of Applied Physics and Electronics Umeå University Umeå Sweden School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Reinforcement learning (RL) is an effective approach to developing control policies by maximizing the agent's reward. Deep reinforcement learning uses deep neural networks (DNNs) for function approximation in RL, and has achieved tremendous success in recent years. Large DNNs often incur significant memory size and computational overheads, which may impede their deployment into resource-constrained embedded systems. For deployment of a trained RL agent on embedded systems, it is necessary to compress the policy network of the RL agent to improve its memory and computation efficiency. In this article, we perform model compression of the policy network of an RL agent by leveraging the relevance scores computed by layer-wise relevance propagation (LRP), a technique for Explainable AI (XAI), to rank and prune the convolutional filters in the policy network, combined with fine-tuning with policy distillation. Performance evaluation based on several Atari games indicates that our proposed approach is effective in reducing model size and inference time of RL agents. We also consider robust RL agents trained with RADIAL-RL versus standard RL agents, and show that a robust RL agent can achieve better performance (higher average reward) after pruning than a standard RL agent for different attack strengths and pruning rates. © 2022 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

引用

IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Windows Registry Attack and Defense: Behavior Analysis and Persistence Detection Based on ETW 2nd

Windows Registry Attack and Defense: Behavior Analysis and P...

引用

2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Chen, Jian-Xin Chang, Yang-Hsin Hwang, Yan-Ling Hunag, Jian-Hong Wang, Hao-Jyun Hsu, Fu-Hau Department of Computer Science and Information Engineering National Central University Taoyuan32001 Taiwan School of Applied Foreign Languages Chung Shan Medical University Taichung40201 Taiwan

ISBN: (纸本)9789819777853

Windows Registry contains various information about users and can be viewed as a database. While Microsoft gives users customization choices, it also unintentionally turns into a tool that attackers may fully use to carry out fileless assaults, incursion persistence, and other harmful actions. The opening section of this paper introduces the registry's logical structure and internal data structures. The following section examines registry-related threats and how the current Windows 10 operating system responds to them. Finally, it shows how a system based on Windows’ integrated logging system, ETW, has been implemented. The monitoring application records and filters registry write operation data, and the filtered data is then examined and sent to VirusTotal to ascertain if the written data was malicious. This study implements an ETW-based registry attack detection system that is effective and dependable. By integrating with VirusTotal, we can more quickly and correctly identify and stop registry assaults or misuse, protecting the security of user PCs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer viruses

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：