检索结果-内蒙古大学图书馆

2024 Resilience Week, RWS 2024

作者： Boamah, Sharon A. Mathieu, Reynold Faillace, Victor Agnew, Dennis McNair, Janise Bretas, Arturo University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States Sandia National Laboratory Distributed Systems Group United States

ISBN: (纸本)9798350388985

The integration of information communication technology with the power grid exposes it to cyber threats. The network state estimation process provides stability to the smart grid. The communication network plays a major role in ensuring the successful transmission of state information. However, these network measurements are vulnerable to malicious attacks. This subsequently affects the network measurement such as associated high transmission delays and packet losses affecting the reliability of the smart grid. In this work, we propose a hybrid physics-based data-driven model that uses data fusion from the state-of-the-art physics-based Network State Estimation model and a data-driven model to detect false data injection attacks in the communication network layer of the smart grid. The performance of the data fusion method is evaluated and the simulation results show that the proposed model outperforms the standalone approaches in the detection of bad data. This shows that the proposed scheme is able to improve the cyber-physical security of the communication network layer of the smart grid. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Towards Threat Modelling of IoT Context-Sharing Platforms 22

Towards Threat Modelling of IoT Context-Sharing Platforms

引用

22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Goudarzi, Mohammad Shaghaghi, Arash Finn, Simon Stiller, Burkhard Jha, Sanjay School of Computer Science and Engineering UNSW Sydney Australia Australia Communication Systems Group Department of Informatics University of Zurich Switzerland Cisco Australia

ISBN: (纸本)9798331510183

The Internet of Things (IoT) involves complex, interconnected systems and devices that depend on context-sharing platforms for interoperability and information exchange. These platforms are, therefore, critical components of real-world IoT deployments, making their security essential to ensure the resilience and reliability of these "systems of systems." In this paper, we take the first steps toward systematically and comprehensively addressing the security of IoT context-sharing platforms. We propose a framework for threat modelling and security analysis of a generic IoT context-sharing solution, employing the MITRE ATT&CK framework. Through an evaluation of various industry-funded projects and academic research, we identify significant security challenges in the design of IoT context-sharing platforms. Our threat modelling provides an in-depth analysis of the techniques and sub-techniques adversaries may use to exploit these systems, offering valuable insights for future research aimed at developing resilient solutions. Additionally, we have developed an open-source threat analysis tool that incorporates our detailed threat modelling, which can be used to evaluate and enhance the security of existing context-sharing platforms. ©2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

引用

SN computer Science 2024年第4期5卷 363页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama Lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data augmentation Industrial data analysis Machine learning Mineral processing

来源：评论

学校读者我要写书评

暂无评论

Deployment Architectures of MQTT Brokers in Event-Driven Industrial Internet of Things 50

Deployment Architectures of MQTT Brokers in Event-Driven Ind...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Amiri, Amirali Just, Valentin Steindl, Gernot Nastic, Stefan Kastner, Wolfgang Gorton, Ian Institute of Computer Engineering TU Wien Vienna Austria Distributed Systems Group TU Wien Vienna Austria Khoury College of Computer Sciences Northeastern University Seattle United States

ISBN: (纸本)9781665464543

The Industrial Internet of Things (IIoT) involves various standards, protocols, and tools, requiring extensive expertise for system design. The traditional automation pyramid limits scalability due to tightly-coupled components. Integrating IIoT data in the cloud through event-driven communication, like MQTT brokers, provides loose coupling. This integration also aids in resource monitoring and planning, enhancing IIoT application performance. Despite many IIoT architecture studies, there is a lack of empirical comparisons of MQTT broker deployment strategies. This paper examines an edge-cloud aggregation scenario, where IIoT device requests are aggregated at the edge and fog services before being transmitted to the cloud. We study four MQTT deployment architectures and compare the results empirically. We use an Arduino Opta as an IIoT device. Also, we use software-based load generation to support replicability of our experiment and reproducibility of our results. Findings indicate that a central broker on a dedicated virtual machine gives 13.8% improvements of the mean response time compared to the shared deployment of MQTT broker and device gateways. Our results offer insights into effective deployment strategies. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

来源：评论

学校读者我要写书评

暂无评论

Development of a Mobile Monitoring Application for a Message Queuing Telemetry Transport (MQTT)-Based Struvite Fertilizer Production System 10

Development of a Mobile Monitoring Application for a Message...

引用

10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Maulana, Ilham Fitria, Maya Faizah, Cut Nurul Adharani, Daffa Hirta Jinan, Sarah Nada Dawood, Rahmad Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Interactive Intelligent Systems Reseach Group Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia

ISBN: (纸本)9798350380439

Struvite fertilizer, characterized by its slow-release qualities, is comprised of magnesium, ammonium, and phosphate and is vital for plant growth. The Integrated Laboratory Unit of Universitas Syiah Kuala (USK) has developed a prototype for struvite fertilizer production equipment, but the production process requires manual monitoring. This project seeks to create an MQTT (Message Queuing Telemetry Transport)-based mobile application for the real-time monitoring and control of struvite fertilizer production equipment. The application employs the MQTT communication protocol, known for its quick data transmission and minimal bandwidth requirements. This application incorporates sensors on the production machine, which includes a reactor, filter, and dryer, and employs an Amazon Web Service's (AWS) IoT Core as the MQTT broker. Findings from the application's evaluation indicate that the mobile application can efficiently monitor and control several machine components. Data transmission to MQTT topics was successful, facilitating seamless communication. Functional and integration testing demonstrated that the system operates effectively. Usability testing outcomes yielded an average score of 77.75 with a system usability scale (SUS) approach, signifying that the application possesses a satisfactory degree of usability classified within the 'Grade B' category. © 2024 IEEE.

关键词： Telemetering systems

来源：评论

学校读者我要写书评

暂无评论

Towards Adaptive Trajectories for Mixed Autonomous and Human-Operated Ships 5

Towards Adaptive Trajectories for Mixed Autonomous and Human...

引用

5th IEEE International Conference on Autonomic Computing and Self-Organizing systems Companion, ACSOS-C 2024

作者： Pianini, Danilo Tomforde, Sven Alma Mater Studiorum - Università di Bologna Department of Computer Science and Engineering Cesena Italy Intelligent Systems Group Kiel University Kiel Germany

ISBN: (纸本)9798350389760

We are witnessing the rise of autonomous cars, which will likely revolutionize the way we travel. Arguably, the maritime domain lags behind, as ships operate on many more degrees of freedom (thus, a much larger search space): there is less physical infrastructure, and rules are less consistent and constraining than what is found on roads. The problem is further complicated by the inevitable co-existence of autonomous and human-operated ships: the latter may take unpredictable decisions, which require adjustments on the autonomous ones. Finally, the problem is inherently decentralised, there is no central authority, and communication means can be very diverse in terms of communication distance and performance, mandating special care on which information is shared and how. In this work, we elaborate on the challenges of trajectory prediction and adaptation for mixed autonomous and human-operated ships, and we propose initial ideas on potential approaches to address them. © 2024 IEEE.

关键词： Decentralized control

来源：评论

学校读者我要写书评

暂无评论

Development of a Bias Compensating Q-Learning Controller for a Multi-Zone HVAC Facility

引用

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1704-1715页

作者： Syed Ali Asad Rizvi Amanda J.Pertzborn Zongli Lin IEEE the Department of Electrical and Computer Engineering Tennessee Technological UniversityCookevilleTN 38505 USA the Mechanical Systems and Controls Group in the Building Energy and Environment Division at the National Institute of Standards and Technology(NIST) GaithersburgMD 20899 USA the Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743 USA

We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zone heating,ventilation,and air-conditioning(HVAC)lab facility subject to unmeasurable disturbances and unknown *** is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias(even with integral action support),and in the extreme case,the divergence of the learning *** demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation(LQR)of a multi-zone HVAC environment and showing that,even with integral support,the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains,occupancy variations,light sources,and outside weather *** address this difficulty,we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances(and possibly other sources)in conjunction with the optimal control *** results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances,demonstrating the effectiveness of the algorithm in addressing the above challenges.

关键词： HVAC control optimal tracking Q-learning reinforcement learning(RL)

来源：评论

学校读者我要写书评

暂无评论

Strengthening Plant Disease Detection Using Ensemble Learning 1

Strengthening Plant Disease Detection Using Ensemble Learnin...

引用

1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Rawat, Sur Singh Chand, Aanant Singh, Mahesh Kumar Kumar, Gyanendar Verma, Lal Pratap JSS Academy of Technical Education Computer Science and Engineering Noida India Dronacharya Group of Institutions Computer Science and Engineering Greater. Noida India Manipal University Jaipur IoT and Intelligent Systems Jaipur India Manipal University Jaipur Computer Communication & Engineering Jaipur India

ISBN: (纸本)9798350353648

Deep learning approaches have been applied to the agricultural sector to resolve several challenges, such as crop disease detection, yield prediction, irrigation management, etc., faced by farmers. Detecting and classifying plant diseases at an early stage is crucial for taking measures to prevent crop failure. In this paper, disease detection and classification of tomato plants is discussed. Experimentation involves transfer learning methods using deep learning models such as DenseNet201, InceptionV3, ResNet50, MobileNetV2, InceptionResNetV2, and Xception, along with a proposed ensemble learning model that integrates a voting ensemble of these models. The dataset contains 18,160 images divided into 10 classes. Among the individual models, DenseNet201 achieved the best accuracy at 94.54%whereas the proposed ensemble learning outperformed against the sate-of-the-art approaches. © 2024 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Joakinator: An Interface for Transforming Body Movement and Perception through Machine Learning and Sonification of Muscle-Tone and Force 23rd

Joakinator: An Interface for Transforming Body Movement and ...

引用

23rd International Conference on New Interfaces for Musical Expression, NIME 2023

作者： Díaz-Durán, Joaquín R. Vidal, Laia Turmo Tajadura-Jiménez, Ana DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain

Joakinator is a wearable interactive interface that allows users to activate different media materials, such as sound, music, and video, through body gestures. The device, designed in the context of music and performing arts, integrates surface electromyogram, force sensors, and machine learning algorithms with tailored-made software for sonifying muscle-tone and force. This allows the body to reflect expressively the content of the media and the architecture of the device. Recently, we have started to investigate the potential of Joakinator to alter body perception in the context of BODYinTRANSIT, a European Research Council Project focused on the transformations of body perception through the use of interactive sound/haptics technology. At NIME-2023, we will showcase Joakinator and invite visitors to experience the device firsthand. Visitors will have the opportunity to try on the device, observe others using it, and reflect on its capabilities to transform body movement and perception through the sonification of muscle-tone and force. Overall, Joakinator is a technology that pushes the boundaries of bodycomputer interaction and opens new possibilities for human-computer interaction and expression. © 2023, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Machine Perception

来源：评论

学校读者我要写书评

暂无评论

Modeling and performance analysis of an industrial transport platform manufacturing process 28

Modeling and performance analysis of an industrial transport...

引用

28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Arzola, César Latorre, Daniel Sacramento, Germán Mahulea, Cristian Júlvez, Jorge University of Zaragoza Dept. of Computer Science and Systems Engineering Spain Alimak Group La Muela Spain Spain

ISBN: (纸本)9798350339918

This contribution focuses on the modeling and performance analysis of the manufacturing process for transport platforms at the Alimak group facility in La Muela, Spain. The objective is to leverage formal tools to gain insights into this complex manufacturing process and explore potential applications such as control and optimization. This work presents a preliminary study, where a Generalized Stochastic Petri net model of the manufacturing process is proposed to be used for simulation, performance analysis, and optimization of the system. © 2023 IEEE.

关键词： Stochastic systems

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：