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检索条件"任意字段=International Conference on Computer Modeling and Simulation"

共 35261 条记录，以下是4421-4430 订阅

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Functional Interval-Observer-Based Fault Detection for Continuous-time Multivariable Linear Systems

Functional Interval-Observer-Based Fault Detection for Conti...

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international conference on Sciences and Techniques of Automatic Control and computer Engineering (STA)

作者： Hanin Mizouri Rihab Lamouchi Messaoud Amairi Research Laboratory MACS LR16ES22 National Engineering School of Gabes University of Gabes Tunisia

ISBN: (纸本)9781665482622

This paper deals with the problem of the functional interval observer design for continuous-time multivariable linear system applied to fault detection purpose. First, a set of existance conditions is deduced as well as a simple process to built the Luenberger-like interval observer. Then, by utilizing the interval estimation of residual signals a FD procedure is developed. Finally, the validity and the effectiveness of the suggested method is verified by a numerical example.

关键词： Linear systems Actuators Fault detection simulation Computational modeling Estimation Observers

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Classification of LFPs Signals in Autistic and Normal Mice Based on Convolutional Neural Network 7th

Classification of LFPs Signals in Autistic and Normal Mice B...

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7th international conference on Life System modeling and simulation, LSMS 2021, and 7th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Zhang, Guofu Yang, Banghua Chen, Fuxue Zhou, Yu Gao, Shouwei Zan, Peng Wang, Wen Yan, Linfeng School of Mechatronic Engineering and Automation Research Center of Brain Computer Engineering Shanghai University Shanghai200444 China School of Life Sciences Shanghai University Shanghai200444 China Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province Tangdu Hospital Fourth Military Medical University Xi’an710038 China

ISBN: (纸本)9789811672064

In view of the time-consuming and expensive problems that traditional methods is used to diagnose Autism Spectrum Disorder (ASD) through scales or magnetic resonance images, this paper proposes a classification method for ASD based on intrusive Electroencephalogram signals, which assists in determining whether there is ASD. Using artificially induced ASD mice’ local field potentials (LFPs) data set and normal mice’ LFPs data set, this thesis takes advantage of statistical analysis methods to test the significance between the two data sets, designs an architecture based on one-dimensional convolutional neural network, and trains two classification model of LFPs through Adam algorithm. The experimental result shows there is a strong significant difference between the two comparison groups (P © 2021, Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance

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On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI

On a Survival Gradient Boosting, Neural Network and Cox PH B...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Henry Musto Daniel Stamate Doina Logofatu Lahcen Ouarbya Department of Computing Goldsmiths College University of London United Kingdom Department of Computing Goldsmiths College University of London and School of Health Sciences The University of Manchester United Kingdom Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Germany

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

In recent years, attention within the clinical prediction community has turned to the use of survival machine learning as a tool for predicting the risk of developing a disease as a function of time. The current work seeks to contribute to existing literature which demonstrates the utility of these methods when applied to a dementia prediction context. We use the Alzheimer's Disease Neuroimaging Initiative ADNI dataset and model deterioration within two distinct groups, those deemed cognitively normal and those with a formal diagnosis of Mild Cognitive Impairment. In agreement with existing literature we find that survival machine learning outperforms standard survival analysis methods such as Cox PH model, and has very good predictive ability. We propose an innovative approach to predicting dementia diagnosis risk on ADNI, which explores the use of survival neural network and survival extreme gradient boosting techniques that have hitherto seldom been applied to this context. The stability of our models was investigated within a Monte Carlo simulation framework.

关键词： Analytical models Biological system modeling Neural networks Predictive models Boosting Stability analysis Alzheimer's disease

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simulation Based Learning System

Simulation Based Learning System

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international conference on Computational Science and Technology (ICCST)

作者： Vigneshwara K Dineshkumar M J. Julie Joshan Anthanesious J Department of Information Technology Sri Sairam Engineering College Chennai India School of Computer science and Engineering Chennai

ISBN: (纸本)9781665476560

In today's modern world, new discoveries are being made in each and every field and these discoveries led to new opportunities one among those fields is the medical field. This field is further divided into various divisions in which Surgeon's get a special place since they are the one responsible for carrying out most of the emergency cases that arrive at the hospital so to help those surgeons, we have designed a simulation system. Surgical devices nowadays are getting, and the number of people who become physicians are also growing. Medical students must get extensive training, since their job is something which is related to human health major risks can't be taken without proper guidance and also by getting trained regularly their skills can be enhanced. The students are trained by using wide range of equipment, such kind of an equipment is a simulator, which assists the student by providing a real-life experience. Likewise, the simulator we've designed is an EYE Surgery simulator. In this paper, we will discuss about modelling an eye surgery simulator that is both cost-effective and also realistic in every aspect. Immersive VR technology is used in the design process. The main reason of using VR is to get highest level of realism possible while using the simulator. The Unity Game Development Engine is used to produce realistic graphics output while also simplifying the development process. Virtual Reality Surgery simulation is used in this method to deliver realistic visuals that help trainees to train on surgical skills and also it gives the doctors the confidence to operate on patients. To run the simulation, you'll need to create a desktop application, with high-quality 3D images, the simulation sets a new benchmark in the field of virtual reality used for surgical simulation.

关键词： Training Solid modeling Visualization Three-dimensional displays Computational modeling Surgery Virtual reality

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4th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th international conference on Medical Imaging Computing and computer-Assisted Intervention, MICCAI 2021

4th International Workshop on Machine Learning in Clinical N...

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4th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th international conference on Medical Imaging Computing and computer-Assisted Intervention, MICCAI 2021

ISBN: (纸本)9783030875855

The proceedings contain 17 papers. The special focus in this conference is on Machine Learning in Clinical Neuroimaging. The topics include: Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation;Deep Stacking Networks for Conditional Nonlinear Granger Causal modeling of fMRI Data;Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling;structure-Function Mapping via Graph Neural Networks;improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity;h3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning;Constrained Learning of Task-Related and Spatially-Coherent Dictionaries from Task fMRI Data;distinguishing Healthy Ageing from Dementia: A Biomechanical simulation of Brain Atrophy Using Deep Networks;towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows;Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients;MRI Image Registration Considerably Improves CNN-Based Disease Classification;dynamic Sub-graph Learning for Patch-Based Cortical Folding Classification;detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models;PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction;multi-modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network.

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Towards a layered architecture for error mitigation in quantum computation

Towards a layered architecture for error mitigation in quant...

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Quantum Software (QSW), IEEE international conference on

作者： José D. Guimarã es Carlos Tavares Centro de Fisica das Universidades do Minho e do Porto International Iberian Nanotechnology Laboratory Braga Portugal High-Assurance Software Laboratory/INESC TEC Braga Portugal

ISBN: (数字)9781665481342

ISBN: (纸本)9781665481359

In the past few years, the first commercially available quantum computers have emerged, in an early stage of development, the so-called Noisy Intermediate-Scale Quantum (NISQ) era. Although these devices are still very prone to errors of different natures, they also have shown to deal successfully with small computational problems. Nowadays, one of the challenges in quantum computation is exactly to be able to show that quantum computers are useful, whereby mitigating the effects of the faulty hardware is pivotal. Recently, a wide range of quantum error mitigation techniques have been proposed and successfully implemented, alternative to quantum error correction codes. Herein, we discuss several types of noise in a quantum computer and techniques available to mitigate them, as well as their limitations and conditions of applicability. We also suggest an hierarchy for them, towards the conception of a layered software framework of error mitigation techniques, and implement some of them in a quantum simulation of the Heisenberg model on an IBM quantum computer, improving the fidelity of the simulation by 2.8x.

关键词： computers Extrapolation Measurement errors Quantum computing Computational modeling Hardware Software

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An Indigenous Flight Control System Test

An Indigenous Flight Control System Test

引用

international Automatic Testing conference, AUTOTESTCON

作者： Şadiye Akdeniz Ağdere Melih Karasubaşi Hüseyin Sağirkaya Turkish Aerospace Avionics Integration Laboratuary and Test Ankara Turkey

ISBN: (数字)9781728154008

ISBN: (纸本)9781728154015

Hardware-in-the-Loop (HIL) simulation technique provides a solution to perform tests without risking damage to Line-Replaceable-Unit (LRU) and reduce costs via increasing the speed of continuous verification and validation. As an important part of verification and validation phase of a system, the Hardware-in-the-Loop simulation is one of the most effective techniques for an LRU system testing and makes a substantial contribution in avionics model integration process and realization of the system. Based on the Hardware-in-the-Loop simulation method, this paper designs the structure and parameters of a Hardware-in-the-Loop simulation test system for an indigenous flight control computer system test. This test system comprises of plant models, signal generation module, data acquisition module, record module, switching module for signal loss and error injection scenarios, and a graphical user interface. Test system includes a Peripheral Component Interconnect Extension for Instrumentation (PXI) chassis with a real-time operating system, hardware capable of converting the digital signals generated by an aircraft model into analog signals proper to LRU input signals and transmitting to LRU, also hardware capable of converting the digital signals generated by the flight control computer into analog signals to the control model and, moreover a recording unit in which all these signals are logged in parallel with data acquisition process.

关键词： Computational modeling Hardware-in-the-loop simulation Atmospheric modeling Operating systems Instruments Data acquisition Aerospace electronics

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Optimized Design of Lower Complexity Reversible Toffoli Gate in QCA Technology

Optimized Design of Lower Complexity Reversible Toffoli Gate...

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Microelectronics (IICM), Iranian international conference on

作者： Saeid Seyedi Hatam Abdoli Department of Computer Engineering Faculty of Engineering Bu-Ali Sina University Hamedan Iran

ISBN: (数字)9798331511371

ISBN: (纸本)9798331511388

Every time an advancing technology gets close to reaching a dead end, a new technological revolution is required. CMOS technology serves as the foundation for current VLSI technology. Modern technology built on quantum dot cellular automata (QCA) has been established in response to new problems in the field. The field of QCA is fascinating in nanocomputing technology as it offers a different way to overcome the physical constraints CMOS systems encounter as they continue to reduce in size. QCA provides significant advantages at the nanoscale, including increased packing density, reduced area, much lower power consumption, and faster working speed. Since current logic gates aren't reversible by nature, and so dissipate energy, they aren't energy-efficient or energy-saving. Consequently, much work is needed to produce an efficient model for designing circuits that retain information while dissipating no energy. Power-efficient circuits may be built with increased accuracy, increasing the circuit's lifespan and speed. This study explores using QCA as an implementation technique for building reversible logic gates that are both power and area-efficient, including QCA cells, which result in improved performance in the suggested design. With 14 cells and a 0.02 μm 2 area, the proposed QCA reversible Toffoli gate architecture achieves a notable size decrease over earlier designs. Using a single-layer design methodology, the circuit is designed to maximize efficiency and minimize cell usage. The proposed reversible Toffoli gate has been shown to have improved functionality and efficiency based on simulation findings using the QCA Designer simulator version 2.0.3. It is therefore a smart choice for anyone interested in studying nanoscale integrated circuit applications.

关键词： Power demand Design methodology Quantum dots Logic gates Very large scale integration CMOS technology Nanoscale devices Energy efficiency Microelectronics Integrated circuit modeling

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Construction of Demand Forecasting Model of Human Resources Professional Structure Based on Deep Learning

Construction of Demand Forecasting Model of Human Resources ...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Qi Chen Xiaorui Wang Rui Feng Party School of State Grid Beijing Electric Power Company Beijing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In order to address the challenge of constructing a demand forecasting model for the professional structure of human resources, in view of the shortcomings of the existing BFS algorithms, this study proposes an innovative structural demand forecasting model construction method based on deep learning algorithms. This new scheme uses the principles of gradient descent theory to accurately identify and locate key influencing factors, and accordingly carries out a sensible classification of indicators to reduce possible interference. At the same time, employing the unique mechanism of deep learning algorithm, the design strategy of demand forecasting model is cleverly constructed. The empirical evidence suggests that the proposed scheme shows significant improvement compared with the traditional BFS algorithm in key performance indicators, including the accuracy of structural demand forecasting model construction and the processing efficiency of key factors, showing its obvious strong advantages. In the human resources profession, the construction of structural demand forecasting model plays a crucial role, which can accurately predict and optimize the growth trend and output results of the structural demand forecasting model construction of human resources professional. However, when faced with complex simulation tasks, traditional BFS algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, our study introduces a new idea of constructing a structural demand prediction model optimized by deep learning algorithms, accurately controls the influencing parameters through gradient descent theory, and uses this as a road map for indicator allocation, and then uses deep learning algorithms to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accu

关键词： Deep learning Accuracy Computational modeling Roads Key performance indicator Demand forecasting Predictive models Prediction algorithms Classification algorithms computer security

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The use of SVM and AR Methods for allotment of Load Scheduling and Energy Management in SPS

The use of SVM and AR Methods for allotment of Load Scheduli...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Sulakshana Bhausaheb Mane S. Thangalakshmi R. Umamageswari C Praveenkumar Asala Riyadh Sarhan Omar Muhsin Department of Computer Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Faculty - Electrical School of Marine Engineering and Technology Indian Maritime University Chennai Campus Chennai EEE Department Annai Mira College of Engineering and Technology Ranipet Department of EEE Sri Ramakrishna Engineering College Coimbatore Collage of Nursing National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Integration of photovoltaic (PV) systems into energy grids to meet the solar energy consumption requires high-performance predictive algorithms Although genetic process (GP) based additive regression (AR) models so provide answers to nonlinear prediction problems For the information Another hybrid model is proposed that combines the SR with a support vector machine (SVM) architecture. The hybrid model seeks to improve accuracy and robustness by using real-world climate data including solar radiation and historical PV power estimates. Using the Elastic Net and Extreme Boosting methods, the hybrid AR-SVM algorithm makes fast feature selection by eliminating redundant inputs. Stability and reliability are ensured by maintaining the hyper-criteria during the training and testing phases. The model reduces the computational complexity and improves accuracy by using fewer layers and neurons. The improved prediction accuracy is demonstrated by simulation results, which show a significant reduction in the absolute error (MAE) and root mean squared error (RMSE) The improvement in the $\mathbf{R} 2$ evaluation measure confirms the power of the model has to recognize the underlying pattern emphasis. By preventing the AR from reaching a local minimum with the contribution of the SVM branch, the hybrid model exhibits high robustness to forecast errors, and shows superiority in PV power forecasting applications.

关键词： Support vector machines Photovoltaic systems Accuracy Computational modeling Predictive models Prediction algorithms Robustness

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