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Gianluca Albanese, Antonio Delle Femine, Daniele Gallo, Claudio Iodice, Massimo Iorizzo, Carmine Landi, Francesco Letizia, Mario Luiso, Bruno Testa
Measurement of the Effect of Pulsed Electric Fields on the Inactivation of Wine Yeasts

Traditional sterilization techniques for wine (more in general liquid food) often compromise the organoleptic properties of the food itself. Research is underway to find alternative solutions. The pulsed electric field has proved to be an alternative technique for food sterilization. However, scientific papers on this topic report conflicting results regarding treatment details such as the duration, amplitude, frequency and number of required field pulses. In this article, some preliminary experiments will be presented in the application of pulsed electric fields in winemaking, taking care of the aspects related to the measurement of the electrical quantities involved. The experiment was carried out in the first place to evaluate the selectivity of the treatment with respect to the different yeast species naturally present in grape juice. The measurement of the electrical quantities involved is not trivial, the article represents a first effort to develop a correct measurement setup, aimed at providing the scientific community with the first experimental measurement results during the treatment with the pulsed electric field.

Maria Emanuela Palmieri, Laura Fabbiano, Antonella Gaspari, Luigi Tricarico
Robust control of the draw-in in a deep-drawing process: a measurement-based approach

A methodology to consider the process variability related to a deep-drawing process is proposed, with reference to specific laboratory conditions. In fact, the integration among numerical simulation, control system and measurements informative flows carried out in controlled conditions is expected to improve - and thus limit, the variability introduced in the conditions of the field, where the machining tool is called to operate. The results obtained from a preliminary experimental campaign aimed at fine-tuning the models and feedback control algorithms appear promising. This is based on the use of the laser triangulation technique to monitor displacement during the draw-in of the blank. A feasibility analysis and a consequent test plan are necessary to evaluate the system signature both in optimal conditions and to identify and implement the practical situations affecting the process (e.g. lubricant conditions and materials properties) in view of a technology transfer in the field. The methodological suggestions are envisaged to optimise the monitoring strategy, as well, to make the control robust and enhance the process sustainability and the product quality.

E. Natale, G. D’Emilia, L. Chiominto, A. Gaspari, S. Marsella, M. Marzoli
Metrological comparison between instruments for status monitoring of buildings after earthquake

This paper aims to investigate the possibility of using measuring instruments to obtain objective indications of the state of a building following an earthquake, to facilitate the planning of interventions in safe conditions and to design provisional structures. The use of such tools would also be useful for monitoring both buildings and provisional structures over time. In particular, a Laser Scanner and a Total Station are examined and compared from a metrological point of view, to evaluate the possibility of using them in emergency situations, which present particular criticalities for measurements, if the reliability of the results has to be guaranteed. The façade of a historical building is used as a test case, and is subjected to measurement campaigns, distributed over time, with the aim of evaluating repeatability and reproducibility of results. Furthermore, applying known thicknesses to the monitoring points, the systems measuring thresholds are evaluated. This work would like to represent a first step towards the standardization of procedures for the adoption of Laser Scanner and Total Stations by rescuers in emergency situations.

Rubens M. B. da S. Lima, Hugo B. S. Araujo, Cleonilson P. de Souza
An Analysis of Block Sizes for Compressive Sensing Reconstruction Applied in Image Processing Optimisation

Signal sampling is a fundamental process of data acquisition systems and several studies have emerged regarding sampling methods following on Nyquist Theorem. Compressive Sensing (CS) proposes sampling of sparse signals with sub-Nyquist sampling rates. In short, CS is composed of a sampling/compress stage and a reconstruction stage. Some algorithms are used where this last. One of these is the Orthogonal Matching Pursuit (OMP) where 2D Discrete Cosine Transform (2D-DCT) can be used. This work compared the application of 2D-DCT transform and CS theory on images as either a whole or split in blocks. As a result, the influence of block size is revealed using the Mean Square Error (MSE) metric for different block sizes.

Marco Carratù, Vincenzo Gallo, Antonio Pietrosanto, Paolo Sommella
An LSTM based soft sensor for rear motorcycle suspension

The increasing development of neural networks for classification and prediction of temporal sequences has opened the way for a new development of mathematical models for soft sensor design. In particular, Long Short-Term Memory (LSTM) networks have greatly improved execution time and reduced error in both single-step and multi-step prediction. In this context, it is therefore possible to improve on the current concept of Instrument Fault Detection and Isolation (IFDI), reducing costs and footprint by not using physical redundancies of sensitive elements but by employing virtual sensors themselves. Therefore, the work aims to develop a soft sensor for rear suspension stroke using an LSTM network. This new approach was trained on over 50000 samples acquired in a real-world environment, and the results were compared with ground truth on a total of over 100000 samples. The results of the analysis showed excellent potential of the method and wide room for improvement in future developments.

Marco Carratù, Marcantonio Catelani, Lorenzo Ciani, Gabriele Patrizi, Antonio Pietrosanto, Paolo Sommella
Reliability estimation of Inertial Measurement units using Accelerated Life Test

In many different technological and industrial fields microelectronic device reliability is rising up as a fundamental aspect to consider during the design of diagnostic, optimization and control systems. Unexpected failures in diagnostic and control units could lead to a severe impact on the entire system/plant availability. Thus, reliability analysis must be carried out during the early phase of the design. MEMS (Micro-Electro-Mechanical Systems) based Inertial Measurement Units are widespread in diagnostic units to monitor acceleration, position and angular velocity of machinery. However, recent literature lack of a reliability estimation for this kind of devices. Thus, this paper proposes a measurement setup and a customized Accelerated Life Test plan for reliability estimation of a set of Inertial Measurement Units. A temperature-based stress test based on the HTOL (High Temperature Operating Life) protocol have been carried out to age the devices with the aim of obtaining a failure dataset. Results of the test have been used to predict device’s reliability.

Doris Schadler, Michael Wohlthan, Andreas Wimmer
Adaptive Methods for Fault Detection on Research Engine Test Beds

In the case of test beds for research engines, fault detection methods that use models based on historical data face a particular challenge. Due to the experimental design of the test bed, offline training of statistical models with a data set containing all possible variations is simply not possible. The methods must adapt to the current data situation directly on-site. But this involves risks. First, computational time and memory requirements can become extremely large with high data volumes. Second, the data may be faulty and thus negatively affecting the models. To avoid both, a selection of data is made before it is used to build the fault-free reference model. For this purpose, a new statistic is presented as the combination of the Mahalanobis distance and the forecast residual. With it, it is possible to reduce the update frequency and to increase the rate of detected faulty points, since the models are no longer manipulated by faulty data points and thus the residuals provide a better structure for fault detection.

Yukio Hiranaka, Koichi Tsujino, Hidenori Katsumura, Maho Mori
Deterioration Score of Cold Forging Dies by Using Acoustic Emission Signals

It is generally difficult to estimate the degree of deterioration of forging dies, but it is necessary to prevent a large number of defective products. In this study, we propose a deterioration score in cold lateral forging using acoustic emission (AE) signals. From the analysis of the measured data, the transition of the signal from the initial state to the deteriorated state can be observed, and the transition can be numerically evaluated. In the evaluation, variational auto-encoder (VAE) is used for learning the initial distribution, and the deterioration score is calculated by the degree of deviation from the learned distribution. The AE cumulative maximum amplitude and AE cumulative count during the linearly increasing stress period for each forging shot are given to the input of the VAE encoder, and valid deterioration scores are obtained for multiple actual measurements.

Chao-Ching Ho, Wei-Ming Su, Sankarsan Mohanty
Based on Deep Convolutional Neural Network and Machine Vision Applied to the Surface Defect Detection of Hard Disk Metal Gaskets

This Study aims at the surface defects of aluminum gaskets as the detection targets. The types of defects are yellow spots, incomplete grinding and bump damages. The detection method will select image processing or deep learning according to the characteristics of the defects. The characteristic of yellow spots has many variables of random shapes and different shades of color, it is difficult to use image processing to detect defects, therefore, this Study selects deep learning as the detection method of yellow spot and the detection network architecture is a modified architecture based on U-Net. It also proposes the preprocess of removing the background of the image before the model training, by removing the outer pixel value out side the gasket area on the image. It was found that the preprocess can improve the Intersection over Union (IoU) by 0.041. The experiment results showes that using the proposed network architecture the evaluation of yellow spot IoU is 0.611 which is better than the original U-Net with a model accuracy of 99.56%.

Dariusz Zieliński, Damian Grzechca
Energy distribution on surge arrester elements selected by genetic algorithm in railway systems

Communication lines play very important role in railway industry. They enable to exchange data between signalling devices such as wheel detectors, evaluators, signals etc. All of them make it possible to manage rail traffic in a safe and efficient way. Availability which depends on robustness of the communication lines is one of the most important features of the system during its use. In this paper the verification of a surge protection module is presented at two stages, i.e. when the voltage has reached the threshold for a gas discharge tube (GDT) and when it is too low. These two cases have different characteristics and create new challenges during the design process.

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