Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, the goal is to eliminate noise and improve data quality. Furthermore, we employ a feature selection process to identify the most relevant features derived from the multisensor UP-FALL dataset, which in turn will enhance the performance and efficiency of machine learning models. We then evaluate the efficiency of various machine learning models in detecting the impact moment using the resulting data information from multiple sensors. Through extensive experimentation, we assess the accuracy of our approach using various evaluation metrics. Our results achieve high accuracy rates in impact detection, showcasing the power of leveraging multisensor data for fall detection tasks. This highlights the potential of our approach to enhance fall detection systems and improve the overall safety and well-being of individuals at risk of falls.

In this paper a novel modulation technique is proposed to eliminate instabilities such as subharmonic and chaotic oscillations in dc-dc switching converters. This modulation technique injects a stabilizing signal which is generated internally from its own state variables so that the system is free from the problems due to externally injected signal, such as frequency mismatch, phase shift, etc. Stability analysis of the system is carried out using Floquet theory taking into account its switching nature. Our results show that the system has larger stable region in the parameter space compared to the conventional modulation techniques. Numerical simulations illustrate the performance of the proposed technique under line and load disturbances.

Power electronic systems exhibit different types of fast- and slow-scale instabilities which limit the stable operating range of the parameters. It has been shown that the stability of complex power electronic systems can be fruitfully investigated using the Filippov method, where the stability of the system is given by the eigenvalues of the monodromy matrix, which is a combination of the state transition matrices through each subsystem and those across the switching events, called saltation matrix. In this paper we show that the components of the saltation matrix can be used to change the stability status of the system, and propose three specific techniques, which can be used individually or together to extend the range of stability significantly. The performance of these techniques are shown using line and load disturbances.

In this paper, a typical intermediate bus architecture is modeled from circuit theory and nonlinear dynamics point of view. In the studied system a regulated DC-DC buck converter in the first level supplies two parallel connected regulated buck converters in the second level through an intermediate bus. The loads connected to the second level converters are resistive. The complexity of the system is due to the interaction of the output voltage of the first converter with the downstream converters. Unlike the earlier studies where averaged or simplified reduced-order model were used only for two cascaded converters, we have done our study using the exact switching model of the DC-DC converters. This paper shows different mechanisms of instability when the parallel converters are fed through another converters instead of constant voltage. This knowledge will help in designing more reliable intermediate bus architecture for different application under different operating conditions.

This paper describes a three-level framework for semi-supervised feature selection. Most feature selection methods mainly focus on finding relevant features for optimizing high-dimensional data. In this paper, we show that the relevance requires two important procedures to provide an efficient feature selection in the semi-supervised context. The first one concerns the selection of pairwise constraints that can be extracted from the labeled part of data. The second procedure aims to reduce the redundancy that could be detected in the selected relevant features. For the relevance, we develop a filter approach based on a constrained Laplacian score. Finally, experimental results are provided to show the efficiency of our proposal in comparison with several representative methods.

Variable-weighting approaches are well-known in the context of embedded feature selection. Generally, this task is performed in a global way, when the algorithm selects a single cluster-independent subset of features (global feature selection). However, there exist other approaches that aim to select cluster-specific subsets of features (local feature selection). Global and local feature selection have different objectives, nevertheless, in this paper we propose a novel embedded approach which locally weights the variables towards a global feature selection. The proposed approach is presented in the semi-supervised paradigm. Experiments on some known data sets are presented to validate our model and compare it with some representative methods.

In this paper, a single-inductor single-input boost-type dual-output dc-dc converter is studied. Peak current-mode control is used for the main switch whereas the output switch is controlled by voltage-mode controller. To regulate both the output voltages proportional-integral compensator is introduced. In the state-space the five-dimensional system is divided into four subsystems by four switching surfaces. Due to the large number of subsystems, Filippov's method is used to obtain the stable operating region in the design parameter space. Coexisting attractors, period-doubling and Neimark-Sacker bifurcations are identified as the major causes of the possible instabilities. Detailed studies of the possible bifurcation scenarios (smooth as well as nonsmooth) have been done by detecting the stable as well as unstable orbits and by calculating their eigenvalues.

In this paper, we present a novel feature selection approach based on an efficient selection of pair wise constraints. This aims at selecting the most coherent constraints extracted from labeled part of data. The relevance of features is then evaluated according to their efficient locality preserving and chosen constraint preserving ability. Finally, experimental results are provided for validating our proposal with respect to other known feature selection methods.