scholarly journals Development of a Routing Software for Inshore Match Races

2016 ◽  
Author(s):  
Francesca Tagliaferri ◽  
Ignazio Maria Viola
Keyword(s):  
Risk Measures ◽  
Optimal Strategies ◽  
Risk Attitude ◽  
Ann Model ◽  
Expected Time ◽  
Coherent Risk ◽  
Wind Measurements ◽  
Artificial Neural Network Ann ◽  
Different Levels ◽  
Better Than

Yacht races are won by good sailors racing fast boats. A good skipper takes decisions at key moments of the race based on the anticipated wind behavior and on his position on the racing area and with respect to the competitors. His aim is generally to complete the race before all his opponents, or, when this is not possible, to perform better than some of them. In the past two decades some methods have been proposed to compute optimal strategies for a yacht race. Those strategies are aimed at minimizing the expected time needed to complete the race and are based on the assumption that the faster a yacht, the higher the number of races that it will win (and opponents that it will defeat). In a match race, however, only two yachts are competing. A skipper’s aim is therefore to complete the race before his opponent rather than completing the race in the shortest possible time. This means that being on average faster may not necessarily mean winning the majority of races. This papers present the development of software to compute a sailing strategy for a match race that can defeat an opponent who is following a fixed strategy that minimizes the expected time of completion of the race. The proposed method includes two novel aspects in the strategy computation: A short-term wind forecast, based on an Artificial Neural Network (ANN) model, is performed in real time during the race using the wind measurements collected on board. Depending on the relative position with respect to the opponent, decisions with different levels of risk aversion are computed. The risk attitude is modeled using Coherent Risk Measures. The software is tested in a number of simulated races. The results confirm that maximizing the probability of winning a match race does not necessarily correspond to minimizing the expected time needed to complete the race.

scholarly journals Screening for Prediabetes Using Machine Learning Models

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soo Beom Choi ◽  
Won Jae Kim ◽  
Tae Keun Yoo ◽  
Jee Soo Park ◽  
Jai Won Chung ◽  
...  
Keyword(s):  
External Validation ◽  
Systematic Evaluation ◽  
Support Vector ◽  
Ann Model ◽  
Internal Validation ◽  
Health And Nutrition ◽  
Svm Model ◽  
Score Model ◽  
Artificial Neural Network Ann ◽  
Better Than

The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data(n=4685)were used for training and internal validation, while data from KNHANES 2011(n=4566)were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

scholarly journals Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications

2020 ◽  
Vol 11 (1) ◽  
pp. 112-119
Author(s):  
Nurhazimah Nazmi ◽  
Mohd Azizi Abdul Rahman ◽  
Saiful Amri Mazlan ◽  
Dimas Adiputra ◽  
Irfan Bahiuddin ◽  
...  
Keyword(s):  
Classification Performance ◽  
Visual Observation ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Ann Model ◽  
Classification Rate ◽  
Time Domains ◽  
Artificial Neural Network Ann ◽  
Limb Rehabilitation ◽  
Better Than

AbstractThe development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs.

scholarly journals Estimating Inside Air Temperature of a Glasshouse Using Statistical Models

2017 ◽  
Vol 12 (1) ◽  
pp. 01-05 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Konstantinos Chronopoulos
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Predictor Variable ◽  
Absolute Error ◽  
Meteorological Station ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Better Than

The efficiency of applying linear regression (LR) and artificial neural network (ANN) models to estimate inside air temperature (T) of a glasshouse (37o48΄20΄΄N, 23o57΄48΄΄E), Lavreotiki, was investigated in the present work. The T data from an urban meteorological station (MS) at 37058΄55΄΄N, 23o32΄14΄΄E, Athens, Attica, Greece, about 30 Km away from the glasshouse, were used as predictor variable, taking into account the actual time of measurement (ATM) and two hours earlier (ATM-2), depending on the case. Air temperature data were monitored in each examined area (glasshouse and MS) for four successive months (July-October) and averages on a two-hour basis were used for the aforementioned estimation. Results showed that ANN were better than LR models, considering their better performance as shown in the scatterplots of the distribution of observed versus estimated inside T data of the glasshouse, in terms of both higher coefficient of determination (R2) and lower mean absolute error (MAE). The best ANN model (highest R2 and lowest MAE) was achieved by using as predictor variables the T at ATM and the T at ATM-2 from MS. The findings of our study may be a first step towards the estimation of inside T of a glasshouse in Greece, from outside T data of a remote MS. Thus, the operation of the glasshouse could be improved noticeably.

Classifiers for Ground-Based Cloud Images Using Texture Features

2014 ◽  
Vol 931-932 ◽  
pp. 1392-1396 ◽  
Author(s):  
Thitinan Kliangsuwan ◽  
Apichat Heednacram
Keyword(s):  
Nearest Neighbor ◽  
Texture Features ◽  
Correct Classification ◽  
Ann Model ◽  
Cloud Type ◽  
Artificial Neural Network Ann ◽  
Sky Conditions ◽  
Type Classification ◽  
Better Than

The classification of ground-based cloud images has received more attention recently. The result of this work applies to the analysis of climate change; a correct classification is, therefore, important. In this paper, we used 18 texture features to distinguish 7 sky conditions. The important parameters of two classifiers are fine-tuned in the experiment, namely, k-nearest neighbor (k-NN) and artificial neural network (ANN). The performances of the two classifications were compared. Advantages and limitations of both classifiers were discussed. Our result revealed that the k-NN model performed at 72.99% accuracy while the ANN model has higher performance at 86.93% accuracy. We showed that our result is better than previous studies. Finally, seven most effective texture features are recommended to be used in the field of cloud type classification.

Prediction of friction capacity of driven piles in clay using the support vector machine

2008 ◽  
Vol 45 (2) ◽  
pp. 288-295 ◽  
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Model Complexity ◽  
Support Vector ◽  
Ann Model ◽  
Driven Piles ◽  
Learning Technique ◽  
Practical Tool ◽  
Artificial Neural Network Ann ◽  
Friction Capacity ◽  
Better Than

The support vector machine (SVM) is an emerging machine learning technique where prediction error and model complexity are simultaneously minimized. This paper examines the potential of SVM to predict the friction capacity of driven piles in clay. This SVM is firmly based on the statistical learning theory and uses the regression technique by introducing accuracy (ε) insensitive loss function. The results are compared with those from a widely used artificial neural network (ANN) model. Overall, the SVM showed good performance and is proven to be better than ANN model. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The study shows that SVM has the potential to be a useful and practical tool for prediction of friction capacity of driven piles in clay.

scholarly journals Do coherent risk measures identify assets risk profiles similarly? Evidence from international futures markets

2017 ◽  
Vol 14 (3) ◽  
pp. 361-380
Author(s):  
Sharif Mozumder ◽  
M. Humayun Kabir ◽  
Michael Dempsey
Keyword(s):  
Risk Measures ◽  
Extreme Value ◽  
Full Density ◽  
Conditional Distributions ◽  
Risk Profiles ◽  
Coherent Risk ◽  
Spectral Risk Measures ◽  
Lévy Measures ◽  
Compare And Contrast ◽  
Better Than

The authors consider Lévy processes with conditional distributions belonging to a generalized hyperbolic family and compare and contrast full density-based Lévy-expected shortfall (ES) risk measures and Lévy-spectral risk measures (SRM) with those of a traditional tail-based unconditional extreme value (EV) approach. Using the futures data of leading markets the authors find that ES and SRM often differ in recognizing the risk profiles of different assets. While EV (extreme value) is often found to be more consistent than Lévy models, Lévy measures often perform better than EV measures when compared with empirical values. This becomes increasingly apparent as investors become more risk averse.

scholarly journals Portfolio optimization with optimal expected utility risk measures

2021 ◽  
Author(s):  
S. Geissel ◽  
H. Graf ◽  
J. Herbinger ◽  
F. T. Seifried
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Expected Utility ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Attitude ◽  
Risk Averse ◽  
Coherent Risk ◽  
Copula Approach

AbstractThe purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the expected portfolio return is maximized. We compare the portfolio optimization with OEU constraint to a portfolio selection model using value at risk as constraint. The former is a coherent risk measure for utility functions with constant relative risk aversion and allows individual specifications to the investor’s risk attitude and time preference. In a case study with three indices, we investigate how these theoretical differences influence the performance of the portfolio selection strategies. A copula approach with univariate ARMA-GARCH models is used in a rolling forecast to simulate monthly future returns and calculate the derived measures for the optimization. The results of this study illustrate that both optimization strategies perform considerably better than an equally weighted portfolio and a buy and hold portfolio. Moreover, our results illustrate that portfolio optimization with OEU constraint experiences individualized effects, e.g., less risk-averse investors lose more portfolio value in the financial crises but outperform their more risk-averse counterparts in bull markets.

Questions to the Article: An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples (Preprint)

2020 ◽  
Author(s):  
Shu-Chun Kuo ◽  
CHIEN WEI ◽  
Willy Chou
Keyword(s):  
Data Entry ◽  
Ann Model ◽  
Model Accuracy ◽  
Data Format ◽  
Raw Data ◽  
Probability Of Survival ◽  
Testing Data ◽  
Laboratory Results ◽  
Data Yield ◽  
Artificial Neural Network Ann

UNSTRUCTURED The recent article published on December 23 27 in 2020 is well-written and of interest, but remains several questions that are required for clarifications, including (1) 30 feature variables with normalized format(mean=0 and SD=1) required to compare model accuracy with those with the raw-data format; (2)inconsistency in variable numbers between entry and preview panels in Figure 4 and reference typos; and (3) data-entry format with raw blood laboratory results in Figure 4 inconsistent with the model designed using normalized data to estimate parameters. We conducted a study using the training and testing data provided by the previous study. An artificial neural network(ANN) model was performed to estimate parameters and compare the model accuracy with those eight models provided by the previous study. We found that (1) normalized data yield higher accuracy than that with the raw data; (2) typos definitely exist at the bottom review (=32>30 variables in the entry) panels in Figure 4 and typos in Table 6; and (3)the ANN earns a probability of survival(=0.91) higher than that(=0.71) in the previous study using the similar entry data when the raw data are assumed in the app. We also demonstrated an author-made app using the visualization to display the prediction result, which is novel and innovative to make the result improved with a dashboard in comparison with the previous study.

Sign in / Sign up

Export Citation Format

Share Document