No Arabic abstract
We investigate the influence of the specimen velocity on the magnetic flux leakage with the aim of selecting the optimum sensor locations. Parametric numerical simulations where the specimen velocity was in the range [0.1-20] m$cdot$s$^{-1}$ were carried out. As the specimen velocity is increased, the magnetic field varies from being symmetrical to being asymmetric. For the radial magnetic induction, the peak to peak value moves from the centre of the bridge towards the direction of the specimen movement. For the axial magnetic induction, the specimen velocity influence is dependent on the sensor location and a signal-velocity independent region was discussed.
This novel work investigates the influence of the inspection system acceleration on the leakage signal in magnetic flux leakage type of non-destructive testing. The research is addressed both through designed experiments and simulations. The results showed that the leakage signal, represented by using peak to peak value, decreases between 20% and 30% under acceleration. The simulation results indicated that the main reason for the decrease is due to the difference in the distortion of the magnetic field for cases with and without acceleration, which is the result of the different eddy current distributions in the specimen. The findings will help to allow the optimisation of the MFL system to ensure the main defect features can be measured accurately during the machine acceleration. It also shows the importance of conducting measurements at constant velocity, wherever possible.
In this paper, we introduce a new acoustic leakage dataset of gas pipelines, called as GPLA-12, which has 12 categories over 684 training/testing acoustic signals. Unlike massive image and voice datasets, there have relatively few acoustic signal datasets, especially for engineering fault detection. In order to enhance the development of fault diagnosis, we collect acoustic leakage signals on the basis of an intact gas pipe system with external artificial leakages, and then preprocess the collected data with structured tailoring which are turned into GPLA-12. GPLA-12 dedicates to serve as a feature learning dataset for time-series tasks and classifications. To further understand the dataset, we train both shadow and deep learning algorithms to observe the performance. The dataset as well as the pretrained models have been released at both www.daip.club and github.com/Deep-AI-Application-DAIP
Leakage currents through insulators received continuous attention for decades, owing to their importance for a wide range of technologies, and interest in their fundamental mechanisms. This work investigates the leakage currents through atomic layer deposited (ALD) $Al_2O_3$, grown on $SrTiO_3$. This combination is not only a key building block of oxide electronics, but also a clean system for studying the leakage mechanisms without interfacial layers that form on most of the conventional bottom electrodes. We show how tiny differences in the deposition process can have a dramatic effect on the leakage behavior. Detailed analysis of the leakage behavior rules out Fowler-Nordheim tunneling (FNT) and thermionic emission, and leaves the trap-related mechanisms of trap-assisted tunneling (TAT) and Poole-Frenkel as the likely mechanisms. After annealing the sample in air, the currents are reduced, which is ascribed to transition from trap-based mechanism to FNT, due to the elimination of the traps. The dramatic role of the assumptions regarding the flat-band voltage used for analysis is critically discussed, and the sensitivity of the extracted parameters on this magnitude is quantitatively described. We show that field effect devices based on structures similar to those described here, should be able to modulate $>10^{13} cm^{-2}$ electrons. These results provide general guidelines for reducing and analyzing leakage currents in insulators, and highlight some of the possible approaches and pitfalls in their analysis.
We consider information leakage to the user in private information retrieval (PIR) systems. Information leakage can be measured in terms of individual message leakage or total leakage. Individual message leakage, or simply individual leakage, is defined as the amount of information that the user can obtain on any individual message that is not being requested, and the total leakage is defined as the amount of information that the user can obtain about all the other messages except the one being requested. In this work, we characterize the tradeoff between the minimum download cost and the individual leakage, and that for the total leakage, respectively. New codes are proposed to achieve these optimal tradeoffs, which are also shown to be optimal in terms of the message size. We further characterize the optimal tradeoff between the minimum amount of common randomness and the total leakage. Moreover, we show that under individual leakage, common randomness is in fact unnecessary when there are more than two messages.
Side channels represent a broad class of security vulnerabilities that have been demonstrated to exist in many applications. Because completely eliminating side channels often leads to prohibitively high overhead, there is a need for a principled trade-off between cost and leakage. In this paper, we make a case for the use of maximal leakage to analyze such trade-offs. Maximal leakage is an operationally interpretable leakage metric designed for side channels. We present the most useful theoretical properties of maximal leakage from previous work and demonstrate empirically that conventional metrics such as mutual information and channel capacity underestimate the threat posed by side channels whereas maximal leakage does not. We also study the cost-leakage trade-off as an optimization problem using maximal leakage. We demonstrate that not only can this problem be represented as a linear program, but also that optimal protection can be achieved using a combination of at most two deterministic schemes.