62 research outputs found
Lumped Parameter Modelling of Common Rail High-Pressure Fuel Injection Pump
No full textHigh injection pressure is crucial for both modern and future diesel engines, resulting in enhanced performance, fuel efficiency, and reduced emissions. Fuel atomization, combustion optimization, torque control, and engine NVH are among the critical topics directly influenced by the injection strategy. Common rail diesel injection pumps play a pivotal role in delivering fuel at high pressure, with performance metrics such as flow rate and maximum pressure defining the pump's technology level. Development efforts have consistently focused on improving performance and efficiency since the inception of common rail systems. Future challenges for diesel engines and injection pumps include meeting carbon neutrality goals, which may require adapting to new fuels, engine control strategies (such as hybrid powertrains and drivetrains), and pump drive concepts. In this context, simulating pump operation is essential for optimizing design, predicting performance, and developing control systems. The article deals with the mechanical-hydraulic modelling of a high-pressure pump. The model is based on the lumped parameter simulation of a cylinder-piston pair interacting with intake and delivery volumes via automatically opening valves. Validation of the model relies on dedicated experimental investigation campaigns, enabling measurement of pressure in the pump's working chamber and high-speed visualization of the intake valve's position relative to the pump shaft's angular position. The article reports a detailed description of the model, the experimental approach, and the synthesis of the results
Technologies 4.0: Understanding the evolution of ICT and its role forbusiness development
No full textConfronto tra modelli termofluidodinamici per la convezione forzata di nanofluidi in condotti
No full textPerceptual quality-preserving black-box attack against deep learning image classifiers
No full textDeep neural networks provide unprecedented performance in all image classification problems, including biometric recognition systems, key elements in all smart city environments. Recent studies, however, have shown their vulnerability to adversarial attacks, spawning intense research in this field. To improve system security, new countermeasures and stronger attacks are proposed by the day. On the attacker's side, there is growing interest for the realistic black-box scenario, in which the user has no access to the network parameters. The problem is to design efficient attacks which mislead the neural network without compromising image quality. In this work, we propose to perform the black-box attack along a high-saliency and low-distortion path, so as to improve both attack efficiency and image perceptual quality. Experiments on real-world systems prove the effectiveness of the proposed approach both on benchmark tasks and actual biometric applications
INVESTIGATING EXTERNAL GEAR PUMP EFFICIENCY AT VERY LOW SPEEDS WITH GRAPHENE NANOFLUIDS
No full textExperimental investigations are performed to assess the operational efficiency of external gear pumps under high-pressure conditions, with a specific focus on varying shaft rotation speeds. These analyses encompass a broad range of rotational velocities, spanning from conventional to remarkably low speeds, reaching a minimum threshold of 150 RPM. At such reduced speeds, both volumetric and torque losses rise significantly, posing notable challenges to pump performance. Additionally, the lubrication conditions at such modest velocities are often compromised, further constraining the operational range of gear pumps, and limiting the efficiency of pump-controlled systems reliant on speed modulation. The primary objective of the current investigations is to assess the potential effectiveness of nanofluids in enhancing pump performance, particularly under the demanding operational conditions delineated above. This entails scenarios where pump loads are very high, and operational speeds are diminished to reach markedly off-design operating conditions. Consequently, the study presents a comparative analysis of external gear pumps operating with a commercial fluid that is integrated with graphene nanoparticles. Performance metrics, in terms of volumetric and hydro-mechanical efficiency, are evaluated to assess the impact of the nanofluid on overall pump energy performance. The findings of this study provide a first insight into the influence of nanofluids on gear pump efficiency and offer data for pump modeling at low speed. Such analyses hold relevance when control of gear pumps through variable speed logic is needed, as they provide an understanding of the potential benefits and limitations associated with the adoption of nanofluid-enhanced pump systems
Attacking the triangle test in sensor-based camera identification
No full textDigital camera identification is a very active research area, with important applications in the forensics field. Several approaches have been proposed in recent years for this task. One of the most promising is based on the estimation of the sensor noise pattern, used as a sort of camera fingerprint. However, a clever attacker can estimate a camera fingerprint and use it maliciously: this calls for new countermeasures, and so on, in a typical two-party game. In this paper we consider the triangle test, a well-know countermeasure against fake fingerprint attacks, and propose a new algorithm for improving the attacker's success rate. Numerical experiments show that, in typical scenarios, the proposed algorithm improves significantly the attacker performance
Do GANs Leave Artificial Fingerprints?
No full textIn the last few years, generative adversarial networks (GAN) have shown tremendous potential for a number of applications in computer vision and related fields. With the current pace of progress, it is a sure bet they will soon be able to generate high-quality images and videos, virtually indistinguishable from real ones. Unfortunately, realistic GAN-generated images pose serious threats to security, to begin with a possible flood of fake multimedia, and multimedia forensic countermeasures are in urgent need. In this work, we show that each GAN leaves its specific fingerprint in the images it generates, just like real-world cameras mark acquired images with traces of their photo-response non-uniformity pattern. Source identification experiments with several popular GANs show such fingerprints to represent a precious asset for forensic analyses
Are GAN Generated Images Easy to Detect? A Critical Analysis of the State-Of-The-Art
No full textIn recent years, there has been intense research on the generation of synthetic media, and a large number of deep learning-based methods have been proposed to this end. Generative adversarial networks (GAN), in particular, have brought tremendous quality improvements. There are GAN-based methods to generate images from scratch as well as to modify the attributes of an existing image. A number of exciting applications exist already. However, this technology can also be used for malicious purposes, for example to generate fake profiles on social network or to generate fake news. Even the most careful observer can now be fooled by GAN-generated images, not to mention the average Internet user. Therefore, there is urgent need for automatic tools that can reliably distinguish real content from manipulated content
Offset-compensated nonlocal filtering of interferometric phase
No full textThe nonlocal approach, proposed originally for additive white Gaussian noise image filtering, has rapidly gained popularity in many applicative fields and for a large variety of tasks. It has proven especially successful for the restoration of Synthetic Aperture Radar (SAR) images: single-look and multi-look amplitude images, multi-temporal stacks, polarimetric data. Recently, powerful nonlocal filters have been proposed also for Interferometric SAR (InSAR) data, with excellent results. Nonetheless, a severe decay of performance has been observed in regions characterized by a uniform phase gradient, which are relatively common in InSAR images, as they correspond to constant slope terrains. This inconvenience is ultimately due to the rare patch effect, the lack of suitable predictors for the target patch. In this paper, to address this problem, we propose the use of offset-compensated similarity measures in nonlocal filtering. With this approach, the set of candidate predictors is augmented by including patches that differ from the target only for a constant phase offset, which is automatically estimated and compensated. We develop offset-compensated versions of both basic nonlocal means and InSAR-Block-Matching 3D (BM3D), a state-of-the-art InSAR phase filter. Experiments on simulated images and real-world TanDEM-X SAR interferometric pairs prove the effectiveness of the proposed method
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