检索结果-内蒙古大学图书馆

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Kaiwei Zhu Shihan Kong Fang Wu Jinan Sun Junzhi Yu Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China SPIC Nuclear Energy Co. Ltd. China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world.

关键词： Target tracking Observers Path planning software Real-time systems Reliability Vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

Q2oE: Balancing QoE Fairness and Preference for Video Streaming from Cooperative Edge Servers

Q2oE: Balancing QoE Fairness and Preference for Video Stream...

引用

IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Xinwei Huang Yin Xu Jinbo Cai He Sun Jie Wu Mingjun Xiao School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Study University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China Department of Computer and Information Sciences Temple University USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Quality of Experience (QoE) is a pivotal factor that defines the overall quality within the realm of video streaming services. To improve users' QoE indicators, existing methods have proposed many QoE models and Adaptive Bitrate (ABR) algorithms. However, these advanced methods do not account for the tradeoff between users' personalized preferences and fairness, leading to resource wastage on the edge server and an inefficient bitrate allocation. Given resource constraints, non-cooperative edge servers frequently request the cloud to enhance user QoE, consequently resulting in high-latency transmissions. In this paper, we address this issue by introducing collaborative edge servers, which alleviate the burden of video chunk transcoding and caching on individual servers. Specifically, we design a novel QoE model named Q 2 0E that incorporates user-level QoE preference and QoE fairness in this scenario. Furthermore, we propose Q2D3, a Q 2 oE-aware reinforcement learning algorithm based on a video chunk transcoding strategy for mobility-aware vehicular networks, to improve Q 2 oE model performance.

关键词： Measurement Simulation Bit rate Transcoding Reinforcement learning Streaming media Smart systems Quality of experience Servers Resource management

来源：评论

学校读者我要写书评

暂无评论

CoEdge: A Collaborative Architecture for Efficient Task Offloading among Multiple Edge Service Providers

引用

IEEE Internet of Things Journal 2025年

作者： Xie, Xingrui Cheng, Geyao Liu, Han Luo, Lailong Ren, Bangbang Guo, Deke National University of Defense Technology National Key Laboratory of Information Systems Engineering Hunan Changsha410073 China National University of Defense Technology State Key Laboratory of Complex and Critical Software Environment College of Information and Communication Hubei Wuhan430019 China National University of Defense Technology National Key Laboratory of Information Systems Engineering National Laboratory for Parallel and Distributed Processing Hunan Changsha410073 China

Edge computing is an emerging paradigm poised to process a substantial portion of latency-sensitive and computation-intensive tasks through edge service providers (ESPs). However, these ESPs typically operate independently and locally to serve their registered users. When processing burst tasks, the ESPs have to either scale up their respective capacities by introducing additional hardware or compromise user experience by rejecting some user requests, leading to high commercial investment or service degradation. Inspired by the promise of the win-win situation for ESPs and users, we envision a novel task offloading strategy that realizes the following rationales simultaneously: 1) collaborative service, 2) rapid response, and 3) sustainable profitability, while the existing methods fail to achieve them at one shot. To this end, we report CoEdge, a collaborative architecture for efficient task offloading among multiple ESPs in the edge network, aiming at simultaneously minimizing service delay for users and enhancing service profit for ESPs. To achieve this, CoEdge employs a central optimizer to implement a two-stage strategy that determines the task scheduling and service pricing hierarchically. We then formulate these problems and prove their NP-hardness. Additionally, we also propose efficient approximate algorithms to accommodate large-scale computing scenarios with low complexity. Experimental results using real-world datasets demonstrate that our CoEdge can significantly reduce service delay by 2.87x to 4.15x for users and considerably increase service profit by 32% for ESPs. © 2014 IEEE.

关键词： NP-hard

来源：评论

学校读者我要写书评

暂无评论

A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

引用

International Conference on Computer Communication and Informatics (ICCCI)

作者： S. Shanthi K. Nirmaladevi N. Mathiarasi As. Krithika Tharani A. Monisha R. Kuppuchamy N. Krishnamoorthy Department of Computer Science & Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Software Systems and Engineering Vellore Institute of Technology Vellore Campus India

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410

关键词： Training Image segmentation Schedules Pulmonary diseases Neural networks Lung Stochastic processes

来源：评论

学校读者我要写书评

暂无评论

A survey of Blockchain consensus algorithms:mechanism, design and applications

引用

Science China(Information Sciences) 2021年第2期64卷 100-114页

作者： Xiang FU Huaimin WANG Peichang SHI College of Computer National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

In 2008, Blockchain was introduced to the world as the underlying technology of the Bitcoin system. After more than a decade of development, various Blockchain systems have been proposed by both academia and industry. This paper focuses on the consensus algorithm, which is one of the core technologies of Blockchain. In this paper, we propose a unified consensus algorithm process model that is suitable for Blockchains based on both the chain and directed acyclic graph(DAG) structure. Subsequently, we analyze various mainstream Blockchain consensus algorithms and classify them according to their design in different phases of the process model. Additionally, we present an evaluation framework of Blockchain consensus algorithms and then discuss the security design principles that enable resistance from different ***, we provide some suggestions for selecting consensus algorithms in different Blockchain application scenarios.

关键词： Blockchain consensus algorithm Byzantine fault-tolerant process model design principles

来源：评论

学校读者我要写书评

暂无评论

The impact of Information Flow in the Circular Economy of Lithium-Ion- Batteries and how to measure it

引用

Procedia CIRP 2022年 105卷 495-500页

作者： Mathias Nippraschk Sebastian Lawrenz Simon Klode Institute of Mineral and Waste Processing Waste Disposal and Geomechanics (IFAD) Clausthal University of Technology Germany Institute for Software and Systems Engineering (ISSE) Clausthal University of Technology Germany

There is still a difference between the ideal Circular Economy and the one in reality due to a lack of information. The information is available, but there is no exchange between stakeholders. Furthermore, every information has a different value for different stakeholders at different points in time, making it difficult to determine the information value objectively. Therefore, a qualitative valuation scheme for information in the Circular Economy using the example of traction batteries is presented, demonstrating via the system dynamics approach how these can be given a monetary value and subsequently present a marketplace where the information can be traded.

关键词： Circular Economy Lithium-Ion-Battery Information Flow Value of Information Specific Utility Analysis Decision support System Dynamics Information Marketplace Information Trading

来源：评论

学校读者我要写书评

暂无评论

CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum Chemistry software Bug Detection

CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum...

引用

IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Feng Qiu Pu Ji Baojian Hua Yang Wang School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China Computer Science Department The Johns Hopkins University USA

Quantum chemistry software implements the first principle quantum computation and is indispensable in both scientific research and chemical industries. Any bugs in such software will lead to serious consequences, thus defeating its trustworthiness and reliability. However, bug detection techniques for such software have not been fully investigated. In this paper, to fill this gap, we propose a novel approach to fuzz quantum chemistry software with the aid of Large Language Models (LLMs). Our basic idea is utilize LLMs to mutate and generate syntactic and semantic valid input files from seed inputs, by proving valuable domain-specific knowledge of chemistry. With this basic idea, we have designed and implemented CHEMFuzz, a fully automatic fuzzing framework to fuzz quantum chemistry software for bugs. Our evaluation of CHEMFUZZ leverages popular LLMs including GPT3.5, Claude-2, and Bart as test oracles to generate parameters to mutate inputs and analyze computation results. CHEMFUZZ detected 40 unique bugs, which have been classified and reported to developers, with a code coverage of 17.4%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Traffic Surveillance: Deep Learning Approach for Road Anomaly Detection in Videos

Improving Traffic Surveillance: Deep Learning Approach for R...

引用

Computing and Machine Intelligence (ICMI), International Conference on

作者： Sarfaraz Natha Muhammad Arif Syed Shahryar Jamil Fareed Ahmed Jokhio Muslim Jameel Syed Department of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Instituto Politécnico Nacional (IPN) Mexico MAJU Karachi Pakistan Department of Computer Systems Engineering Quaid-e-Awam University of Engineering and Technology Nawabshah Pakistan Department of Enterprise and Technology Atlantic Technological University Galway Ireland

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In recent years, Closed-circuit Television (CCTV) cameras have been playing a vital role in the surveillance of both public and private areas. The primary objective of surveillance is to monitor human behavior and road conditions. In the real-world situation, detecting, and recognizing abnormal activities poses significant challenges due to the densely crowded environment and the complex nature of transportation systems. These factors make it difficult to automatically identify various anomalies that occur while traveling, leading to emergencies, and endangering human life and property. This study introduces an automatic detection framework for recognizing road anomalies such as accidents, fighting, car fires, and armed snatching (gunpoint) in road surveillance videos. After reviewing the literature, the review directs that convolutional neural networks (CNNs) are a specialized deep learning approach well suited for image and video analysis. The proposed methodology combines the pretrained CNN models with Data Augmentation (DA) techniques to fine-tune hyperparameters such as learning rate and momentum that enhance the model learning accuracy and performance for recognizing road anomalies. Furthermore, it introduced a rolling prediction algorithm to solve the flickering problem during testing and created a new road anomaly dataset (RAD) as a benchmark consisting of road surveillance videos and images. Our proposed model combined with the InceptionV3 pre-trained model achieved a best accuracy is $98.81\%$ for detection and classification as compared to other deep learning models.

关键词： Deep learning Accuracy Roads Surveillance Computational modeling Prediction algorithms Data augmentation

来源：评论

学校读者我要写书评

暂无评论

EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

引用

Mathematical Biosciences and engineering 2024年第4期21卷 5712-5734页

作者： Qamar, Hafiz Ghulam Murtza Qureshi, Muhammad Farrukh Mushtaq, Zohaib Zubariah, Zubariah Rehman, Muhammad Zia ur Samee, Nagwan Abdel Mahmoud, Noha F. Gu, Yeong Hyeon Al-Masni, Mohammed A. School of Electrical Engineering Yanshan University Hebei Qinhuangdao066104 China Department of Electrical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems College of Engineering and Technology University of Sargodha Sargodha40100 Pakistan Department of Physiotherapy Isfandyar Bukhari Civil Hospital District Headquarter Hospital Attock43600 Pakistan Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Health Science and Technology Aalborg University Aalborg9220 Denmark Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Artificial Intelligence Data Science College of Software & Convergence Technology Sejong University Seoul05006 Korea Republic of

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN’s superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Predicting the Air Pollution Level and Creating an Awareness Platform

Predicting the Air Pollution Level and Creating an Awareness...

引用

Advancements in Computing (ICAC), International Conference on

作者： M.N Nuha G.K.N.P Wishvajith Amitha Caldera N.T Weerasinghe G.S Weeratunga Shashika Lokuliyana Department of Computer Science and Software Engineering Sri Lanka Institute of Infromation Technology Malabe Sri Lanka Department of Computer Systems & Network Engineering Sri Lanka Institute of Infromation Technology Malabe Sri Lanka

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution.

关键词：

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：