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Showing 1-25 of 21,326 resultsfor "Index Terms":Previous Models
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Strong scaling and speedup to 16,384 processors in cardiac electro — Mechanical simulations
Matthias Reumann;Blake G. Fitch;Aleksandr Rayshubskiy;David U. J. Keller;Gunnar Seemann;Olaf Dossel;Michael C. Pitman;John J. Rice
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2009 | Conference Paper |
Cited by: Papers (14)
High performance computing is required to make feasible simulations of whole organ models of the heart with biophysically detailed cellular models in a clinical setting. Increasing model detail by simulating electrophysiology and mechanical models increases computation demands. We present scaling results of an electro-mechanical cardiac model of two ventricles and compare them to our previously pu...Show More
New Short Term Load Forecasting models based on growth rate scaling and simple averaging
2016 IEEE 6th International Conference on Power Systems (ICPS)
Year: 2016 | Conference Paper |
Cited by: Papers (2)
Load Forecasting is the link joining the strategic power system operation with the almighty mathematic algorithms in order to bring greater reliability, efficiency and economy in the system. This paper brings two novel forecasting engines, named SYGRSA and SYGRSAWP, for Short Term Load Forecasting (STLF), i.e., load forecasting with time leads ranging from one day to one week. STLF itself plays a ...Show More
How Big Can It Get? A comparative analysis of LLMs in architecture and scaling
2023 International Conference on Computer and Applications (ICCA)
Year: 2023 | Conference Paper |
Large Language models (LLMs) are increasingly becoming an integral part of our society. They play important roles in education, marketing and healthcare. These models exhibit emergent behavior when scaled, however such scaling might have its disadvantages as well. As a way to combat such disadvantages, new architectures and types of models have been designed. In this paper, we show the different a...Show More
Valid (Re-)Use of Models-of-the-Physics in Cyber-Physical Systems Using Validity Frames
Bert Van Acker;Paul De Meulenaere;Joachim Denil;Yuri Durodie;Alexander Van Bellinghen;Kris Vanstechelman
Modelling and Simulation techniques play a key role in the design of Software-Intensive and Cyber-Physical Systems. They enable not only early virtual integration of the different parts of the system but also the use of the dynamic behavior of the physical system in the control algorithms, to cope with the continuous demand for improved system control and performance. These physics-based high-fide...Show More
Imitation modeling of compatitive market equilibrium
2012 6th International Conference on Application of Information and Communication Technologies (AICT)
Year: 2012 | Conference Paper |
This paper is about questions of the computer modeling of the influence hypothesis for demand and supply at the dynamic of market equilibrium prices. By result of competitive market is worked out mathematical model, on which base composed functional scheme. By means of functional scheme of market equilibrium and mathematical model is composed system computer model Simulink as block-scheme. Automat...Show More
Hierarchical Dynamic Neuro-neighborhood Models
2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
This article will describe the developed approach of hierarchical neuro-neighborhood models. A review of previous studies in the field of neighborhood modeling is carried out. The idea of hierarchical dynamic neuro-neighborhood models is the ability to represent complex systems and their sub-processes using a combination of the neighborhood structure with various models of neural networks. Neighbo...Show More
Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting
Stylianos I. Vagropoulos;G. I. Chouliaras;E. G. Kardakos;C. K. Simoglou;A. G. Bakirtzis
2016 IEEE International Energy Conference (ENERGYCON)
Year: 2016 | Conference Paper |
Cited by: Papers (104)
This paper compares four practical methods for electricity generation forecasting of grid-connected Photovoltaic (PV) plants, namely Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling, SARIMAX modeling (SARIMA modeling with exogenous factor), modified SARIMA modeling, as a result of an a posteriori modification of the SARIMA model, and ANN-based modeling. Interesting results regar...Show More
Incorporating local word relationships into probabilistic topic models
2015 7th Conference on Information and Knowledge Technology (IKT)
Year: 2015 | Conference Paper |
Probabilistic topic models have been very popular in automatic text analysis since introduction. As a dimensionality reduction method, they are similar to term clustering methods. These models work based on word co-occurrence but are not very flexible with context in which co-occurrence is defined. Probabilistic topic models do not let us to bring local or spatial data into account and therefore t...Show More
Refinement of UAV dynamics model through online identification: a model-data hybrid approach
2020 Chinese Automation Congress (CAC)
Year: 2020 | Conference Paper |
This paper investigates how to precisely and efficiently obtain the dynamics model of Unmanned Aerial Vehicle (UAV) which can used in single or swarm control. The UAVs are homogeneous in the mechanical structure, yet differ in a variety of unmodeled uncertainties, which cannot be ignored in high-fidelity simulation systems. To solve this, we propose a model-data hybrid approach that consists of tw...Show More
Neural Network Model Comparison and Analysis of Prediction Methods Using ARIMA and LSTM Models
2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
Using the relevant data of the main corn futures contract of China Dalian Commodity Exchange from 2018 to 2021, the ARIMA model and the LSTM long short-term memory neural network model were established respectively, the two models were used to predict the daily closing price of corn futures, and compared with the actual. The prediction results of the two models are evaluated using the root mean sq...Show More
Online Sentiment and Topic Dynamics Tracking over the Streaming Data
2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
Year: 2012 | Conference Paper |
Cited by: Papers (3)
We propose a dynamic joint sentiment-topic model (dJST) which is able to effectively track sentiment and topic dynamics over the streaming data. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency i...Show More
Neural Decoding for Macaque’s Finger Position: Convolutional Space Model
In this paper, we study how to use the number of spike signals in a macaque's motor cortex to estimate the position of its finger movement. First, we analyze the time correlation of a traditional state space model (SSM) and derive a convolutional space model (CSM) to decode the movement position of the macaque finger. Compared with the traditional model, the model can correlate the current moment ...Show More
Cross-correlation analyses toward a prediction system of CPU availability in volunteer computing system
2015 International Conference on Industrial Engineering and Systems Management (IESM)
Year: 2015 | Conference Paper |
Computing resources in volunteer computing grid represent a big under-used reserve of processing capacity. However, a task scheduler has no guarantees regarding the deliverable computing power of these resources. Predicting CPU availability can help to better exploit these resources and make effective scheduling decisions. In this paper, we draw up the main guidelines to develop a method to predic...Show More
Modeling Nonlinear Synaptic Dynamics: A Laguerre-Volterra Network Framework for Improved Computational Efficiency in Large Scale Simulations
Eric Y. Hu;Gene Yu;Dong Song;C. Jean-Marie Bouteiller;W. Theodore Berger
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2018 | Conference Paper |
Cited by: Papers (4)
Synapses are key components in signal transmission in the brain, often exhibiting complex non-linear dynamics. Yet, they are often crudely modelled as linear exponential equations in large-scale neuron network simulations. Mechanistic models that use detailed channel receptor kinetics more closely replicate the nonlinear dynamics observed at synapses, but use of such models are generally restricte...Show More
Bayesian statistic model for nurse call data considering time-series, individual patient variabilities and massive zero-count call data
Hiroshi Noguchi;Maki Miyahara;Soo In Kang;Shuhei Noyori;Toshiaki Takahashi;Hiromi Sanada;Taketoshi Mori
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Analysis of nurse call data is important to evaluate nursing management, because nurse calls reflect the fundamental demand of patients. However, the nurse call data include time-series properties and individual patient variabilities. In addition, the calls do not necessarily follow the common single distributions such as normal and Poisson distribution. These characteristics of the nurse call dat...Show More
Various models of fatigue crack growth in different scenarios have been proposed in the literature. Here, in this paper, we propose a general prognostic framework for tracking crack evolution in equipment undergoing fatigue and predicting the Remaining Useful Life (RUL). The main contribution of this work is to integrate Particle Filtering (PF) and a new ensemble model which combines diverse physi...Show More
Comparative analysis of GR2M, Temez and HEC-HMS Hydrological Models for runoff estimation in a high Andean basin
Solange Kate Flores Zuñiga;Elkky Junior Santos de la Cruz;Sissi Santos Hurtado
2022 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI)
Year: 2022 | Conference Paper |
In Peru, high Andean regions have a shortage of hydrometeorological data and monitoring stations, which generates uncertainty for water projects such as hydroelectric power generation, irrigation, among others, in order to satisfy needs for population. For this reason, precipitation-runoff hydrological models represent a way to estimate monthly mean flows based on precipitation, evapotranspiration...Show More
Human segmentation and pose recognition in fish-eye video for assistive environments
13th IEEE International Conference on BioInformatics and BioEngineering
Year: 2013 | Conference Paper |
In this work, we present a system, which uses computer vision techniques for human silhouette segmentation from video in indoor environments and a parametric 3D human model, in order to recognize the posture of the monitored person. The video data are acquired indoors from a fixed fish-eye camera in the living environment. The implemented 3D human model collaborates with a fish-eye camera model, a...Show More
Modeling of air quality prediction for PM2.5 concentration in Chengdu area based on measured data
ChengLin Yu;Ming Zhu;HongYuan Zhang;Ke Liu;YongQiang Liu;He Zhou;Qiang Yang
2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)
Year: 2022 | Conference Paper |
Air quality, especially the concentration of PM2.5, has attracted widespread attention as it affects people's health and may cause health conditions such as allergies, coughs and lung diseases. Chengdu is located in the Sichuan Basin. The unique geographical environment and climatic conditions make Chengdu's PM2.5 changes have its own characteristics. Based on the measured data, this paper propose...Show More
Matrix based univariate and multivariate linear similar day approach towards short term solar radiation forecasting
Sreenu Sreekumar;Ayushi Gupta;Sushila;Varun Agarwal;Rohit Bhakar
Increase in the penetration of renewable generation like solar and wind in power system requires expansion of transmission structure as well as more flexible generation, better cycling of existing generation, accurate spinning reserve planning, optimum power dispatch from renewable plants and security evaluations and stability analysis with high penetrated renewables. Almost all these operations r...Show More
State switching in US equity index returns based on SETAR model with Kalman filter tracking
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Year: 2015 | Conference Paper |
This paper develops a new self-excited threshold autoregressive model (SETAR) for US equity Index returns modeling and analysis. First, a two regime switching model is formulated. The regime state is controlled by a simple piecewise function of lagged values from the time series itself. The hypothesis test is conduct to verify the statistical significance of two regimes in stock index returns. The...Show More
Research on Cloud Computing load forecasting based on LSTM-ARIMA combined model
2022 Tenth International Conference on Advanced Cloud and Big Data (CBD)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
With the continuous development of cloud computing technology, the change of cloud computing resource load presents more and more complex characteristics, and efficient load prediction has become a key technology to solve the imbalance of cloud computing resource utilization. Aiming at the problem of low prediction performance of the current load prediction model, a combined prediction model LSAR ...Show More
This paper presents two language models that utilize a Chinese semantic dependency parsing technique for speech recognition. The models are based on a representation of the Chinese semantic structure with dependency relations. A semantic dependency parser was described to automatically tag the semantic class for each word with 90.9% accuracy and parse the sentence semantic dependency structure wit...Show More
Duhem Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator via Takagi-Sugeno Fuzzy Neural Network
2020 IEEE 15th International Conference on Nano/Micro Engineered and Molecular System (NEMS)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
The magnetic shape memory alloy (MSMA)-based actuator is a promising candidate in the micro positioning field by virtues of its large stroke and small volume. However, the inherent hysteresis nonlinearity between the input current and the output displacement seriously limited the application of the MSMA-based actuator. In this paper, the hysteresis, which is related to the input frequency and work...Show More
In this paper a Model Reference Adaptive Control system is presented which is based on qualitative (less precise) models. These qualitative models can represent the underlying physical system accurately even with low order models. Further, multi-valued performance specifications can be expressed explicitly by a qualitative model in the form of a reference model. The Qualitative Model Reference Ada...Show More
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