检索结果-内蒙古大学图书馆

Securing Mobile Robotic Networks Against Replacement Attack

IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Predict of the Hidden Drug Interactions Based on Similarity Utilizing the Knowledge Graph

Predict of the Hidden Drug Interactions Based on Similarity ...

引用

2024 International Conference on computer and Applications, ICCA 2024

作者： Elfiky, Mariam Gamal Ghoniemy, Samy Eldin, Ahmed Sharaf University of Hertfordshire hosted by Gaf Dept. of Information Technology Management for Business Cairo Egypt Galala University Faculty of Computer Science and Engineering Cairo Egypt Sinai University Faculty of Information Technology and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Drug-drug interactions (DDIs) can significantly impact treatment outcomes by causing adverse effects or reducing therapeutic efficacy. Whereas many DDIs are already known, there are numerous hidden interactions. Therefore, this study tries to predict unknown interactions based on drug structure and target information from the Drug Bank dataset. This paper trains the random forest model integrated with a knowledge graph on a dataset containing 922 unique drugs and 731,196 drug interactions. To illustrate the complex interactions between these drugs, we created a knowledge graph with Neo4j. This graph integrates information gathered from the dataset and extracts relevant data using Cypher queries. The inference engine was created according to the model's predictions on unknown DDIs. The random forest model, with an F1-score of 0.82, achieved a recall of 0.99 and a precision of 0.70. Also, 34,580 potential DDIs were extracted from the sample dataset of drugs. By embedding the knowledge network, our inference engine significantly enhanced predicting the hidden interactions and was found strong in the balance of precision and recall. Our research is applicable to the enhancement of drug safety and effectiveness via mitigating potential DDIs by identifying them. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Deep Learning: A New Way of Looking into Fashion 4

Deep Learning: A New Way of Looking into Fashion

引用

4th International Conference on Advances in Computing, Communication control and Networking, ICAC3N 2022

作者： Thomas, Josh Hanley Roopashree, S. Joy, Jacob Charles Abdullah Kollarath, Haseeb Siby, Arun Dept. CSE-AI and Ml Bengaluru India Rv Institute of Technology and Management Dept. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

Currently, Fashion-Fit using AI focuses on finding the perfect fit for users using GAN's (General Adversarial Net¬works) and various methods proposed in the Artificial Intelligence Industry. However, due to changes in fashion trends and various preferences of the "type of fit"users want for their clothing the prior methodology is not meant for the perfect fit. These days a lot of people wear tighter or even get an oversized fit based on the user's preference. In this paper we use Deep Learning techniques like CNNs and GANs to implement neural body fit by looking at various human body poses from large datasets and concatenating this data to make an accurate "desired"fit based on different human poses. This will set a new benchmark for virtual try-on. In the model being proposed, we are building Convolutional Neural Networks (CNNs) for body segmentation to make it easier to map a human pose. We will then map the human body using CNNs but not necessarily to find the "perfect fit". The CNNs will be used to make measurements that are similar to sizes available within the fashion industry allowing users to see how they would fit within those garments virtually. We will then use 2D renders, using uploaded pictures of the user from 4-5 different angles in order to allow the user to see how he/she would look in the outfit of their choice. These methods can give an accurate prediction helping every fashion e-commerce business. Personalized size and fit recommendations are critical for a fashion e-commerce business. Predicting the optimal fit increases consumer happiness while lowering expenses associated with size-related returns. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Accounting for systemic risk in analysis of crisis scenarios 21

Accounting for systemic risk in analysis of crisis scenarios

引用

21st International ISCRAM Conference, ISCRAM 2024

作者： Gonzalez, Jose J. Abbas, Reem Paulsen, Sigurd University of Agder Kristiansand Norway Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Dept. of Organization Crisis Management and Information Security Kristiansand Norway

Systemic risk is ubiquitous in our increasingly globalised world owing to the interconnections within and across sectors. However, a review of National Risk Assessments performed in OECD member countries reveals that systemic risk is rarely mentioned, and it is never accounted for in those assessments. A powerful method accounts for systemic risk and identifies mitigating strategies using participatory modelling with stakeholders: strategy mapping with Strategyfinder. The risk model is represented as a directed graph consisting of risk nodes and directed edges (arrows) expressing causal influences. The method originated in the management of large, complex projects, and it has been applied to systemic risks in critical infrastructures and pandemics. We demonstrate the Strategyfinder method using a scenario describing a cyberattack on electronic communication infrastructure leading to serious consequences for health and lives. The scenario is borrowed from the report Analysis of Risk Scenarios by the Norwegian Directorate for Civil Protection. © 2024 Information Systems for Crisis Response and management, ISCRAM. All rights reserved.

关键词： Cascading effects Disaster risk reduction National Risk Assessments Risk scenarios Vicious cycles

来源：评论

学校读者我要写书评

暂无评论

USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education 1

USDOT Tier-1 University Transportation Center for Advancing ...

引用

1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Zhang, Yunpeng Jack Wang, Kailai Song, Lingguang Lendasse, Amaury Song, Houbing Herbert Han, Zhu Verma, Rakesh Silva, Arlei Rubio-Medrano, Carlos Li, Zhixia Richard Zhang, Guohui University of Houston Dept. of Information Science Technology HoustonTX United States University of Houston Dept. of Construction Management HoustonTX United States University of Maryland Baltimore County Dept. of Information Systems BaltimoreMD United States University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Computer Science HoustonTX United States Rice University Dept. of Computer Science HoustonTX United States Texas A&m University-Corpus Christi Dept. of Computer Science Corpus ChristiTX United States University of Cincinnati Dept. of Civil and Architectural Engineering and Construction Management CincinnatiOH United States University of Hawai'i at Mānoa Dept. Civil and Environmental Engineering HonoluluHI United States

ISBN: (纸本)9798350319958

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of 'Reducing Transportation Cybersecurity Risks.' CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity. © 2023 IEEE.

关键词： Technology transfer

来源：评论

学校读者我要写书评

暂无评论

Investigation of the Necessity of Past Input/output Information in Reinforcement Learning based Robust control

引用

Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 1953-1957页

作者： Shim, Hyungbo Kim, Jeong Woo Park, Jooyoung Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Dept. of Control & Instrumentation Engineering Korea University Korea Republic of

Reinforcement learning yields a feedback controller that achieves specific control goal (which is often translated as a reward function). However, it often suffers from the Sim2Real gap, and domain randomization is known to be a method to overcome this issue. In this paper, we demonstrate necessity of input/outpu history when domain randomization is employed by a formal example and a simulation result. This is equivalent to the necessity of dynamic feedback controller in terms of control theory. © The Korean Institute of Electrical Engineers

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Design and Application Research of Bionic Mechanical Finger 5

Design and Application Research of Bionic Mechanical Finger

引用

5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Shu, Kuen-Ming Li, Meng-Hua Yeh, Chung-Yi Yan, Yuan-Shao Chen, Zhi-Kun Cheng, Po-Hung Lin, Po-Yung National Formosa University Dept. of Mechanical and Computer-Aided Engineering Yunlin Taiwan National Formosa University Smart Machinery and Intelligent Manufacturing Research Center Department of Industrial Management Yunlin Taiwan National Changhua University of Education Dept. of Computer Science and Information Engineering Changhua Taiwan

ISBN: (纸本)9798350314694

We designed and manufactured a bionic mechanical finger made of aluminum as the skeleton. Its front end was equipped with a piezoelectric ceramic sheet as a pressure sensor, and then the whole finger was covered with silicone and made into a similar shape as a human finger. The elasticity of the silicone makes it smooth to avoid damage to objects or discomfort to the human body in applications. The stepping motor was used to control the finger to grasp the object. When the fingertip touches the object, the pressure sensor generates a feedback signal to the controller and calculates, integrates, and analyzes the measured value. When the value exceeds, the stepper motor stops rotating, and the bionic finger is positioned to clamp the object. In this study, ANSYS Workbench software was used to simulate the pressure test of various parts of the silicone-coated mechanical finger. Compared with the actual pressing experiment, the designed bionic finger in this study was successfully applied to fragile objects. © 2023 IEEE.

关键词： piezoelectric ceramics pressure sensing robotic fingers silicone

来源：评论

学校读者我要写书评

暂无评论

Bayesian Belief Network Analysis for SPAD System in Railways 4

Bayesian Belief Network Analysis for SPAD System in Railways

引用

4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Krishnamurthy, Manjunath Sargur Rahul, Bommisetty Bondala, Chandrahas Reddy Reddy, M. Vishnu Vardhan Magha Raghul, G. Ambika, B.J. JP Morgan & Chase Co. HoustonTX United States BMS Institute of Technology and Management Dept. of AI&ML Bengaluru India Manipal Institute of Technology Bengaluru Dept. of Computer Science and Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798350375466

Even with a powerful network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even today. These occurrences, known as Signal Passed At Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach Bayesian Network (BN) is considered to be a better model to represent this situation when it comes to handling complexity. Bayesian Network allows for integration and includes systematic knowledge about the system, which helps to get a very flexible concise, and simple graphed representation. This study demonstrates applications of Bayesian Belief Networks (BBN) analysing and enlightening the Signal Passed at Danger (SPAD) system in railway processes. Execution of BBN offers more dynamic and flexible models compared to outmoded Fault Tree Analysis (FTA), struggling with complexity. BBN efficiently catches the inter dependencies between factors that are contributing to SPAD events, such as signal conditions, human errors, and mechanical issues. Over simulations and probability calculations using OpenMarkov software, Study revealed key intuitions, such as identifying highly risk apparatuses within SPAD system. The outcomes include enhanced risk calculation accuracy and recommendations for improving signal systems and safety protocols. These conclusions contribute to a safer and reliable railway structure, highlighting the success of BBN for complex, safety critical systems in real world applications. © 2024 IEEE.

关键词： Derailments

来源：评论

学校读者我要写书评

暂无评论

Transfer and Bayesian Learning Approaches for Land Use and Land Cover Classification

Transfer and Bayesian Learning Approaches for Land Use and L...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Zainab Malik Aisha Sir Elkhatem Gokhan Bilgin Dept. of Computer Engineering Yildiz Technical University Istanbul Turkey Dept. of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Land use and land cover (LULC) classification is essential for understanding the impact of both human activities and natural processes on the Earth’s surface. This classification plays a critical role in deforestation, urban planning, and damage assessment. However, traditional machine learning techniques often face challenges in achieving consistent accuracy in diverse datasets. In this study, we implement transfer learning and Bayesian learning approaches to enhance the accuracy and robustness of LULC classification. Transfer learning leverages knowledge from previous classification problems, while Bayesian learning addresses uncertainties in the classification process. Using the Remote Sensing Image Classification Benchmark (RSI-CB128) dataset, the study evaluates the performance of Bayesian Convolutional Neural Networks (CNN), Mobile Neural Networks (MobileNet), Inception Neural Networks (InceptionNet), Densely Connected Convolutional Networks (DenseNet) and Efficient Neural Networks (EfficientNet). The study also investigates the impact of varying the Bayesian layers on model performance, finding that a medium number of layers optimally captures model uncertainty. The results indicate that the transfer learning models, particularly MobileNet, achieved the highest accuracy of 97. 48% compared to 90. 33% for Bayesian CNN. These findings suggest that transfer learning techniques are highly effective for LULC classification, providing a reliable method for practical applications in remote sensing.

关键词： Deforestation Accuracy Uncertainty Computational modeling Transfer learning Neural networks Urban planning Land surface Bayes methods Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

引用

Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Pavan Kalyan S Arun Kumar B R Satish Kumar T Prem Raj S Pratik B Patil Pothireddy Harshini Dept. Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments.

关键词： Adaptation models Computational modeling Large language models Memory management Data structures Data models Computational efficiency Problem-solving Sustainable development Optimization

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：