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American control Conference

作者： Rajarshi Bardhan Debasish Ghose Guidance Control and Decision Systems Laboratory (GCDSL) Dept. of Aerospace Engineering Indian Institute of Science Bangalore India GCDSL the Department of Aerospace Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9781457710957

A guidance law derived by modifying state dependent Riccati equation technique, to enable the imposition of a predetermined terminal intercept angle to a maneuvering target, is presented in this paper. The interceptor is assumed to have no knowledge about the type of maneuver the target is executing. The problem is cast in a non-cooperative game theoretic form. The guidance law obtained is dependent on the LOS angular rotational rate and on the impact angle error. Theoretical conditions which guarantee existence of solutions under this method have been derived. It is shown that imposing the impact angle constraint calls for an increase in the gains of the guidance law considerably, subsequently requiring a higher maneuverability advantage of the interceptor. The performance of the proposed guidance law is studied using a non-linear two dimensional simulation of the relative kinematics, assuming first order dynamics for the interceptor and target.

关键词： Games Trajectory Vectors Equations Geometry Missiles Feedback control

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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Machine Learning Models for Cardiovascular Disease Prediction: A Comparative Study 7

Machine Learning Models for Cardiovascular Disease Predictio...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Yan, Chao Xing, Yiluan Liu, Sensen Gao, Erdi Wang, Jinyin Northeastern University Department of Electrical and Computer Engineering Sunnyvale United States Indiana University Bloomington Economics Department Bloomington United States Washington Univ. in St. Louis Dept. Electr. and Systems Eng St. LouisMO United States New York University Tandon School of Engineering New York United States Stony Brook University Department of Computer Science New York United States

ISBN: (纸本)9798350352009

Cardiovascular diseases (CVDs) pose a significant threat to global public health, affecting individuals across various age groups. Factors such as cholesterol levels, smoking, alcohol consumption, and physical inactivity contribute to their onset and progression. Enhancing our understanding of CVD etiology and informing targeted interventions for disease prevention and management remains a critical challenge. In this study, we address the task of predicting the likelihood of individuals developing CVDs using machine learning techniques. Specifically, we explore three approaches: the k-nearest neighbors (KNN) algorithm, logistic regression, and the random forest algorithm. Leveraging a comprehensive dataset sourced from Kaggle, encompassing 11 relevant factors, we conduct a series of experiments to identify the most influential predictors of CVDs. Our analysis aims not only to forecast disease occurrence but also to elucidate the primary determinants contributing to its manifestation. Through comparative analysis of the three methodologies, we demonstrate that the random forest algorithm exhibits superior performance in terms of predictive accuracy. This research represents a significant step towards leveraging machine learning techniques to enhance our understanding of CVD dynamics and inform targeted interventions for disease prevention and management. © 2024 IEEE.

关键词： Contrastive Learning

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SOLID STATE control FOR SHIPBOARD POWER PLANTS

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NAVAL ENGINEERS JOURNAL 1969年第1期81卷 15-&页

作者： NITSCH, A is presently Manager Systems Engineering Unit Navy and Marine Engineering Industry Control Dept. General Electric Company. He has been with G.E. since receiving a B.S. (EE) in 1951 from Iowa State University working in engineering associated with testing heavy equipment control materials handling equipment and central operations systems.

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Online Trained Model of Tweets Expertise

Online Trained Model of Tweets Expertise

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Information Technology and Nanotechnology (ITNT), International Conference on

作者： Elena Mozaidze Sergei Zuev Petr Kabalyants Dept. of computer software and automated systems Belgorod State Technological University named after V.G. Shukhov Belgorod Russia Dept. of computer engineering and modelling V.I. Vernadsky Crimean Federal University Simferopol Russia

ISBN: (数字)9798350372939

ISBN: (纸本)9798350372946

The paper proposes a model for the examination of tweets (brief texts and messages) in Russian. The model is based on the authors' explorations on the identification of tweets meanings. The main idea is to apply the latest developed quantum machine learning method to the hierarchical classification models of texts. This approach avoids the text corpus necessity and supports the online learning due to the very design. The model proposed is tested in the experiment. The target content was given in the experiment and the model is used to measure the correspondence between the target topics and the test messages. The result of the experiment shows that the proposed method allows to distinguish the target content in the test text.

关键词： Natural languages Machine learning Information technology Nanotechnology

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Supporting design refinement in MEMS design

Supporting design refinement in MEMS design

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作者： Sinha, Rajarishi Paredis, Christiaan J. J. Khosla, Pradeep K. Institute for Complex Engineered Systems Carnegie Mellon University Pittsburgh PA 15213 United States Institute for Complex Engineered Systems Dept. of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA 15213 United States IC Mechanics 425 N. Craig Street Pittsburgh PA 15213-1154 United States

ISBN: (纸本)0791836215

We present a framework to support design refinement during the virtual prototyping of microelectromechanical systems (MEMS). By instantiating MEMS components and connecting them to each other via ports, the designer can both configure complex systems and simulate them. We examine design refinement in the context of ease of use and representation of the virtual prototype. We propose the use of a common, formal grammar representation for the design entities in the virtual prototype - MEMS components, behavioral models and CAD models. We show that the formal grammar approach leads to easy creation of virtual prototypes. In this paper, we focus on ports - the fundamental building blocks of a virtual prototype. Ports mediate all interactions within and between aspects of the virtual prototype. For even moderately complex designs, there can be many interactions present. The representation and organization of all possible ports is important in the context of design refinement. We provide a set-theoretic formalism that defines the algebra of ports. We present a formal grammar for ports that represents a port as a set of attributes, and provide a design refinement mechanism that involves adding or modifying attributes in the port. We illustrate our framework with a MEMS example. We demonstrate that the MEMS designer can evaluate multiple design alternatives quickly and accurately with our framework. Copyright © 2002 by ASME.

关键词： MEMS

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Neural-fuzzy modelling of lung volume using absolute electrical impedance tomography

Neural-fuzzy modelling of lung volume using absolute electri...

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作者： Samuri, Suzani Mohamad Panoutsos, George Mahfouf, Mahdi Mills, G.H. Denaï, M. Brown, B.H. Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom Dept. of Critical Care and Anaesthesia Northern General Hospital Sheffield United Kingdom School of Science and Eng. Teesside University Middlesbrough United Kingdom Dept. of Medical Physics Royal Hallamshire Hospital Sheffield United Kingdom

ISBN: (纸本)9789898425355

Electrical Impedance Tomography (EIT) has been the subject of intensive research since its development in the early 1980s by Barber and Brown at the Department of Medical Physics and Clinical engineering, Hallamshire Hospital in Sheffield (UK). In particular, pulmonary measurement has been the focus of most EIT related research. One of the relatively recent advances in EIT is the development of an absolute EIT system (aEIT) which can estimate absolute values of lung resistivity and lung volumes. However, there is still active research in the area of validating and improving the accuracy and consistency of the aEIT estimation of lung volumes towards characterising the system as suitable for clinical use. In this paper we present a new approach based on Computational Intelligence (CI) modelling to model the 'Resistivity - Lung Volume' relationship that will allow more accurate lung volume predictions. Eight (8) healthy volunteers were measured simultaneously by the Sheffield aEIT system and a Spirometer and the recorded results were used to develop subject-specific Neural-Fuzzy models able to predict absolute values of lung volume based only on absolute lung resistivity data. The developed models show improved accuracy in the prediction of lung volumes, as compared with the original Sheffield aEIT system. However the interindividual differences observed in the subject-specific modelling behaviour of the 'Resistivity-Lung Volume' curves suggest that a model extension is needed, whereby the modelling structure auto-calibrates to account for subject (or patient-specific) inter-parameter variability.

关键词： Forecasting

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RTBERT: A Transformer Based Approach for Improved Rumor Classification from Tweet

RTBERT: A Transformer Based Approach for Improved Rumor Clas...

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International Conference on computer and Information Technology (ICCIT)

作者： Shakib Mahmud Dipto Mostofa Kamal Sagor Anatte Rozario Sahal Bin Saad Department of Computer Science and Engineering University of Liberal Arts Bangladesh (ULAB) Dhaka Bangladesh Dept. of CSE Computer Vision and Intelligent Systems (CVIS) Brac University Dhaka Bangladesh Department of Computer Science and Engineering Primeasia University Dhaka Bangladesh

In an era where both the general public and established news outlets increasingly rely on social media for real-time information, the abundance of rumors offers a huge difficulty. False information can have far-reaching implications, affecting individuals, communities, and even entire countries. To address this issue, a low-cost, self-regulating, and forward-thinking rumor detection technique is required. This research performs an intensive analysis of the performance of three robust machine learning algorithms, including XGBoost, SVM, and Random Forest, as well as two deep learning-based transformers, namely BERT and DistilBert. In this research, the models are trained and evaluated on a combined dataset comprising data from Twitter15 and Twitter16 datasets. Support Vector Machine (SVM) and Random Forest exhibit the best accuracy among classical machine learning models, reaching 89.05%. In comparison, among the transformer-based deep learning models, BERT achieves the best accuracy of 90.20%. In conclusion, the learning-based transformers beat its competitors in terms of accuracy, recall, precision, and F-measure, proving its efficacy in minimizing the detrimental impact of rumors on people, communities, and society as a whole.

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A support vector machine-based method for LPV-ARX identification with noisy scheduling parameters

A support vector machine-based method for LPV-ARX identifica...

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European control Conference (ECC)

作者： Farshid Abbasi Javad Mohammadpour Roland Tóth Nader Meskin College of Engineering The Univ. of Georgia Athens GA Control Systems Group Eindhoven University of Technology Eindhoven MB The Netherlands Dept. of Electrical Engineering Qatar University Doha Qatar

In this paper, we present a method that utilizes support vector machines (SVM) to identify linear parameter-varying (LPV) auto-regressive exogenous input (ARX) models corrupted by not only noise, but also uncertainties in the LPV scheduling variables. The proposed method employs SVM and takes advantage of the so-called “kernel trick” to allow for the identification of the LPV-ARX model structure solely based on the input-output data. The objective function, as defined in this paper, allows to consider uncertainties related to the LPV scheduling parameters, and hence results in a new formulation that provides a more accurate estimation of the LPV model in the presence of scheduling uncertainties. We further demonstrate the viability of the proposed LPV identification method through numerical examples, where we show that higher best fit rate (BFR) can be achieved under realistic noise conditions using the proposed method compared to the method initially proposed in [6].

关键词： Kernel Support vector machines Noise Uncertainty Mathematical model Noise measurement Cost function

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A service level agreement layer for the D-Grid infrastructure

A service level agreement layer for the D-Grid infrastructur...

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作者： Kuebert, Roland Tenschert, Axel Wäldrich, Oliver Ziegler, Wolfang Battré, Dominic HLRS - High Performance Computing Center Stuttgart Nobelstraße 19 70569 Stuttgart Germany Fraunhofer Institute SCAI Schloss Birlinghoven 53754 Sankt Augustin Germany Technische Universität Berlin Faculty IV - Eletrical Engineering and Computer Science Dept. of Telecommunication Systems Complex and Distributed IT Systems Einsteinufer 17 10587 Berlin Germany

ISBN: (纸本)9781905824212

D-Grid is a German national grid initiative founded by the German Federal Ministry of Education and Research. It provides a frame for the collaboration of researchers across Germany and offers access to distributed services and resources. The SLA4D-Grid project will enhance D-Grid by creating a service level agreement (SLA) layer which is transparently integrated into D-Grid and accessible for the D-Grid community. Copyright © 2010 The Authors.

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