Recovery of binary sparse signals from compressed linear measurements via polynomial optimization

The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently proposed to deal with the case of finite-valued sparse signals. In this work, we focus on binary sparse signals and we propose a novel formulation, based on polynomial optimization. This approach is analyzed and compared to the state-of-the-art binary compressed sensing methods

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process

A set-membership approach to direct data-driven control design

L'abstract è presente nell'allegato / the abstract is in the attachmen

Direct data-driven control design through set-membership errors-in-variables identification techniques

In this paper, we propose a non-iterative direct data-driven control approach, such that the controller is directly identified from input/output data without plant identification step. First we formulate the problem of designing a controller in order to match the behavior of an assigned reference model in terms of an equivalent set-membership errors-in-variables problem and we define the feasible controller parameter set. Then, we design the controller parameters by applying previous results by the authors in the field of convex relaxation for errors-in-variables identification. Finally, the effectiveness of the presented technique is shown by means of two simulated examples

A Non-Iterative Approach to Direct Data-Driven Control Design of MIMO LTI Systems

This paper proposes a non-iterative direct data-driven technique that deals with linear time-invariant (LTI) controller design by directly identifying the controller from input-output data without using plant identification. We define the feasible controller parameter set and formulate the problem of designing a controller to match the behaviour of a given reference model as an equivalent set-membership errors-in-variables problem. Then we design the controller parameters by applying recent results in set-membership errors-in-variables identification. Finally, we analyse the effectiveness of the presented technique through simulation examples and experimental results

Set-membership identification of a dry-clutch transmission model

This paper deals with the problem of identifying the mathematical model of a dry-clutch transmission system, from input-output data experimentally collected on a real vehicle. The proposed identification procedure is based on a set-membership identification approach, where a-priori infor- mation on the structure of the physical model are taken into ac- count in the selection of the model class. Parameter uncertainty intervals and the Chebyshev center of the feasible parameter set are computed by applying suitable convex relaxation schemes. The quality of the obtained model is tested on several validation data sets

A kernel-based nonparametric approach to direct data-driven control of LTI systems

In this work, we propose a kernel-based robust nonparametric approach to direct data-driven control of linear systems, in the presence of bounded noise affecting the measurements data. First, we formulate the problem of designing a controller in order to match the behaviour of a given reference model. Then, we design the controller by applying previous results by some of the authors in the field of kernel-based nonparametric error-in-variables identification. Finally, we show the effectiveness of the presented technique by means of two simulation examples

Analytical Study on the Low-Frequency Vibrations Isolation System for Vehicle’s Seats Using Quasi-Zero-Stiffness Isolator

Improving the vibration isolation for the seat of small vehicles under low excitation frequencies is important for providing good comfort for the driver and passengers. Thus, in this study, a compact, low-dynamic, and high-static stiffness vibration isolation system has been designed. A theoretical analysis of the proposed quasi-zero stiffness (QZS) isolator system for vehicle seats is presented. The isolator consists of two oblique springs and a vertical spring to support the load and to achieve quasi-zero stiffness at the equilibrium position. To support any additional load above the supported weight, a sleeve air spring is used. Furthermore, the two oblique springs are equipped with a horizontal adjustment mechanism that is aimed to reach higher frequencies with the existed stroke when a heavy load is applied. The proposed system can be fitted for small vehicles, especially for B-segment and C-segment cars. Finally, the simulation results reveal that the proposed system has a large isolation frequency range compared to that of the linear isolator

Gaining a better understanding of the extrusion process in fused filament fabrication 3D printing : a review

Additive manufacturing is a promising tool that has proved its value in various applications. Among its technologies, the fused filament fabrication 3D printing technique stands out with its potential to serve a wide variety of applications, ranging from simple educational purposes to industrial and medical applications. However, as many materials and composites can be utilized for this technique, the processability of these materials can be a limiting factor for producing products with the required quality and properties. Over the past few years, many researchers have attempted to better understand the melt extrusion process during 3D printing. Moreover, other research groups have focused on optimizing the process by adjusting the process parameters. These attempts were conducted using different methods, including proposing analytical models, establishing numerical models, or experimental techniques. This review highlights the most relevant work from recent years on fused filament fabrication 3D printing and discusses the future perspectives of this 3D printing technology

Gaining a better understanding of the extrusion process in fused filament fabrication 3D printing : a review

Additive manufacturing is a promising tool that has proved its value in various applications. Among its technologies, the fused filament fabrication 3D printing technique stands out with its potential to serve a wide variety of applications, ranging from simple educational purposes to industrial and medical applications. However, as many materials and composites can be utilized for this technique, the processability of these materials can be a limiting factor for producing products with the required quality and properties. Over the past few years, many researchers have attempted to better understand the melt extrusion process during 3D printing. Moreover, other research groups have focused on optimizing the process by adjusting the process parameters. These attempts were conducted using different methods, including proposing analytical models, establishing numerical models, or experimental techniques. This review highlights the most relevant work from recent years on fused filament fabrication 3D printing and discusses the future perspectives of this 3D printing technology