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C++ and Objective C
Published in Paul W. Ross, The Handbook of Software for Engineers and Scientists, 2018
Scope is the area of a program in which the name of a variable, function, or object is visible. C++ and Objective C offer three kinds of scope: global, local, and class. Global scope is done just as in C, by defining the variable outside of any function or class scope. The static modifier can be used to limit global scope to a single file. Local scope is provided for automatic variables (those defined within functions). Class scope is provided for data members of a class, and encompasses all of the methods of the class.
A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space
Published in Technometrics, 2023
Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi, Robert William Platt
We further extend the Ktweedie model to integrate automatic variable selection. Several methods have considered weighting variables within the kernel to achieve variable selection (Weston et al. 2000; Grandvalet and Canu 2002; Gilad-Bachrach, Navot, and Tishby 2004; Li, Yang, and Xing 2005; Argyriou et al. 2006; Cao et al. 2007), where weights are found using a separate procedure and variable selection is not integrated in the estimation. Other approaches such as COSSO (Lin and Zhang 2006) can simultaneously estimate the nonlinear functional component and select important variables, but they are limited to the additive models. Inspired by the work of Allen (2013) and Chen et al. (2018), we achieve variable selection in the Ktweedie model through a certain sparse penalization on the variable weights in the kernel function. This additional feature can potentially improve the interpretability of the result and the prediction accuracy. Specifically, we modify the objective function (10) in two aspects: First, variable weights are used in the kernel matrix such that where is a p-dimensional weight vector that controls the contribution of each variable in x and “” denotes element-wise multiplication. That is, the kernel matrix K in (9) is replaced by the weighted kernel matrix with the following form,
Big data and reliability applications: The complexity dimension
Published in Journal of Quality Technology, 2018
Yili Hong, Man Zhang, William Q. Meeker
With the increasing development of sensor and communication technologies, multichannel sensor signals arriving at a high rate (e.g., per second or millisecond) can be obtained in real time. Hence, data generated by these sensors are getting bigger due to the high volume. From online signals, certain events can be detected, and it is often of interest to categorize those events into different types. Thus, different actions can be taken for different types of events. These sensor data can be treated as multivariate functional data. Because not all variables are useful for clustering, it is important to include only those variables that are useful in the clustering procedure. Figure 7 shows an example of sensor data for two channels with respect to specific defined events. In the literature, Jacques and Preda (2014) considered multivariate functional data as clustering. Wang and Zhu (2008) studied model-based clustering with variable selections. Here we briefly describe a clustering algorithm for multivariate functional data with automatic variable selection.
A novel continuously variable-speed offshore wind turbine with magnetorheological transmission for optimal power extraction
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2023
In view of this, in this paper, a magnetorheological transmission (MT) type OWT is proposed. The magnetic transmission is integrated into the gear drive train of the OWT to form a controllable power split-type speed increasing CVT to achieve the MT-OWT. The MT is accomplished based on the Magnetorheological fluid, which is a new type of intelligent material with good controllable rheological performance due to the magneto-rheological effect. The MT potentially has good transmission performance and also has potential starting or overloading protection and automatic variable torque speed controllability. The transmitted torque of the MT can be controlled by the magnetic field in the magnetorheological fluid.