Statistical Estimation and Elimination of Thermal Network Model Errors

1969 ◽  
Vol 91 (4) ◽  
pp. 554-560 ◽  
Author(s):  
B. H. Browne
Keyword(s):  
General Theory ◽  
Complex Systems ◽  
Prediction Accuracy ◽  
Statistical Estimation ◽  
Network Models ◽  
Canonical Forms ◽  
Model Errors ◽  
New Approach ◽  
Thermal Network ◽  
Model Predictions

A new approach is developed for estimating and correcting digital thermal network errors based upon achieving statistical reconciliation between model predictions and observed results. The development is preceded by a review of the general theory of thermal networks and the development of the canonical forms of networks and their error models. Application of the technique promises improved thermal prediction accuracy of complex systems using simplified network models.

scholarly journals Parameter estimation for externally simulated thermal network models

2019 ◽  
Vol 191 ◽  
pp. 200-210 ◽  
Author(s):  
O.M. Brastein ◽  
B. Lie ◽  
R. Sharma ◽  
N.-O. Skeie
Keyword(s):  
Parameter Estimation ◽  
Network Models ◽  
Thermal Network

scholarly journals Multi-model inference of network properties from incomplete data

2006 ◽  
Vol 3 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Michael P. H. Stumpf ◽  
Thomas Thorne
Keyword(s):  
Network Models ◽  
Network Data ◽  
Global Networks ◽  
New Approach ◽  
Model Inference ◽  
Novel Approach ◽  
Eukaryotic Species ◽  
Network Properties ◽  
Experimental Approaches ◽  
Saccaromyces Cerevisiae

Summary It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

scholarly journals A Practical and Adaptive Approach to Predicting Indoor CO2

2021 ◽  
Vol 11 (22) ◽  
pp. 10771
Author(s):  
Giacomo Segala ◽  
Roberto Doriguzzi-Corin ◽  
Claudio Peroni ◽  
Tommaso Gazzini ◽  
Domenico Siracusa
Keyword(s):  
Data Collection ◽  
Prediction Accuracy ◽  
Network Models ◽  
Window Size ◽  
Environmental Data ◽  
Neural Network Models ◽  
Using Data ◽  
Carbon Dioxide Co2 ◽  
Long Term Performance ◽  
Term Performance

COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO2) represents one of the pollutants that most affects environmental health. Therefore, forecasting future indoor CO2 plays a central role in taking preventive measures to keep CO2 level as low as possible. Unlike other research that aims to maximize the prediction accuracy, typically using data collected over many days, in this work we propose a practical approach for predicting indoor CO2 using a limited window of recent environmental data (i.e., temperature; humidity; CO2 of, e.g., a room, office or shop) for training neural network models, without the need for any kind of model pre-training. After just a week of data collection, the error of predictions was around 15 parts per million (ppm), which should enable the system to regulate heating, ventilation and air conditioning (HVAC) systems accurately. After a month of data we reduced the error to about 10 ppm, thereby achieving a high prediction accuracy in a short time from the beginning of the data collection. Once the desired mobile window size is reached, the model can be continuously updated by sliding the window over time, in order to guarantee long-term performance.

scholarly journals A New Approach for Resolving Conflicts in Actionable Behavioral Rules

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Su ◽  
Dan Zhu ◽  
Daniel Zeng
Keyword(s):  
Prediction Accuracy ◽  
New Method ◽  
Best Interest ◽  
New Approach ◽  
Actionable Knowledge ◽  
Empirical Results ◽  
Ranking Procedure ◽  
Valid Method ◽  
Weight Parameter ◽  
Behavioral Rules

Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users’ best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous valid method was proposed. However, inconsistency of the measure for rule evaluating may hinder its performance. To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. We also introduce a tunable weight parameter to allow the flexibility of integration. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results indicate that our approach outperforms those from previous research.

scholarly journals A New Approach to Improve Accuracy of Grey Model GMC(1,n)in Time Series Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sompop Moonchai ◽  
Wanwisa Rakpuang
Keyword(s):  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Prediction Equation ◽  
Grey Model ◽  
System Behavior ◽  
New Approach ◽  
Proposed Model ◽  
Improve Accuracy ◽  
Relative Factors

This paper presents a modified grey model GMC(1,n)for use in systems that involve one dependent system behavior andn-1relative factors. The proposed model was developed from the conventional GMC(1,n)model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n)model was verified by two cases: the study of forecasting CO2emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n)model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n)and D-GMC(1,n)models.

scholarly journals Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50

2019 ◽  
Vol 9 (9) ◽  
pp. 212 ◽  
Author(s):  
Lawrence Fulton ◽  
Diane Dolezel ◽  
Jordan Harrop ◽  
Yan Yan ◽  
Christopher Fulton
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Family Planning ◽  
Prediction Accuracy ◽  
Network Models ◽  
Training Set ◽  
Clinical Dementia Rating ◽  
Cross Sectional ◽  
Term Care ◽  
Class Prediction

Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

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