4 research outputs found
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R 2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain
Design of Sail-Assisted Unmanned Surface Vehicle Intelligent Control System
To achieve the wind sail-assisted function of the unmanned surface vehicle (USV), this work focuses on the design problems of the sail-assisted USV intelligent control systems (SUICS) and illustrates the implementation process of the SUICS. The SUICS consists of the communication system, the sensor system, the PC platform, and the lower machine platform. To make full use of the wind energy, in the SUICS, we propose the sail angle of attack automatic adjustment (Sail_4A) algorithm and present the realization flow for each subsystem of the SUICS. By using the test boat, the design and implementation of the SUICS are fulfilled systematically. Experiments verify the performance and effectiveness of our SUICS. The SUICS enhances the intelligent utility of sustainable wind energy for the sail-assisted USV significantly and plays a vital role in shipping energy-saving emission reduction requirements issued by International Maritime Organization (IMO)
Acceleration for the many, not the few
Although specialized hardware promises orders of magnitude performance gains, their
uptake has been limited by how challenging it is to program them. Hardware accelerators
present challenges programmers are not used to, exposing details of the hardware that
are often hidden and requiring new programming styles to use them effectively.
Existing programming models often involve learning complex and hardware-specific
APIs, using Domain Specific Languages (DSLs), or programming in customized assembly languages. These programming models for hardware accelerators present a
significant challenge to uptake: a steep, unforgiving, and untransferable learning curve.
However, programming hardware accelerators using traditional programming models
presents a challenge: mapping code not written with hardware accelerators in mind to
accelerators with restricted behaviour.
This thesis presents these challenges in the context of the acceleration equation, and
it presents solutions to it in three different contexts: for regular expression accelerators,
for API-programmable accelerators (with Fourier Transforms as a key case-study) and
for heterogeneous coarse-grained reconfigurable arrays (CGRAs). This thesis shows
that automatically morphing software written in traditional manners to fit hardware
accelerators is possible with no programmer effort and that huge potential speedups are
available