Monitoring solvent-mediated phase transitions using acoustic emission

National audienceDu fait de son caractère non intrusif et non destructif, l'émission acoustique (EA) est une technique attractive pour le suivi de cristallisations, bien que les rares résultats publiés à ce jour ne soient pas très probants. L'émission acoustique est produite par les processus mécaniques locaux qui résultent de l'apparition ou de la disparition de la phase solide, et de son interaction avec son environnement. Elle se présente sous la forme d'un grand nombre de salves pseudo-périodiques séparées ou continues. Les signaux acoustiques sont acquis par un capteur piézo-électrique externe et traités en temps réel par un système d'acquisition et de traitement qui calcule et enregistre des paramètres globaux caractéristiques des salves (nombre de coups, amplitude, fréquence, énergie ...) A partir de la cristallisation de l'acide citrique présentée ici, on montre que les paramètres caractéristiques des salves pendant la cristallisation peuvent être reliés aux différentes étapes élémentaires de la transition de l'anhydre vers le monohydrate. Une analyse en composante principale des variables caractéristiques des salves a été effectuée qui permet de repérer les différentes étapes du procédé

Etude de l'émission acoustique lors de la cristallisation d'acide citrique avec transition de phase

National audienceDu fait de son caractère non intrusif et non destructif, l'émission acoustique (EA) est une technique attractive pour le suivi de cristallisations, bien que les rares résultats publiés à ce jour ne soient pas très probants. L'émission acoustique est produite par les processus mécaniques locaux qui résultent de l'apparition ou de la disparition de la phase solide, et de son interaction avec son environnement. Elle se présente sous la forme d'un grand nombre de salves pseudopériodiques séparées ou continues. Les signaux acoustiques sont acquis par un capteur piezo-électrique externe et traités en temps réel par un système d'acquisition et de traitement qui calcule et enregistre des paramètres globaux caractéristiques des salves (nombre de coups, amplitude, fréquence, énergie ...) A partir de la cristallisation de l'acide citrique présentée ici, on montre que les paramètres caractéristiques des salves pendant la cristallisation peuvent être reliés aux différentes étapes élémentaires de la transition de l'anhydre vers le monohydrate. Une analyse en composante principale des variables caractéristiques des salves a été effectuée qui permet de repérer les différentes étapes du procédé

CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control

Intrinsic motivation is a promising exploration technique for solving reinforcement learning tasks with sparse or absent extrinsic rewards. There exist two technical challenges in implementing intrinsic motivation: 1) how to design a proper intrinsic objective to facilitate efficient exploration; and 2) how to combine the intrinsic objective with the extrinsic objective to help find better solutions. In the current literature, the intrinsic objectives are all designed in a task-agnostic manner and combined with the extrinsic objective via simple addition (or used by itself for reward-free pre-training). In this work, we show that these designs would fail in typical sparse-reward continuous control tasks. To address the problem, we propose Constrained Intrinsic Motivation (CIM) to leverage readily attainable task priors to construct a constrained intrinsic objective, and at the same time, exploit the Lagrangian method to adaptively balance the intrinsic and extrinsic objectives via a simultaneous-maximization framework. We empirically show, on multiple sparse-reward continuous control tasks, that our CIM approach achieves greatly improved performance and sample efficiency over state-of-the-art methods. Moreover, the key techniques of our CIM can also be plugged into existing methods to boost their performances

IMAP: Intrinsically Motivated Adversarial Policy

Reinforcement learning agents are susceptible to evasion attacks during deployment. In single-agent environments, these attacks can occur through imperceptible perturbations injected into the inputs of the victim policy network. In multi-agent environments, an attacker can manipulate an adversarial opponent to influence the victim policy's observations indirectly. While adversarial policies offer a promising technique to craft such attacks, current methods are either sample-inefficient due to poor exploration strategies or require extra surrogate model training under the black-box assumption. To address these challenges, in this paper, we propose Intrinsically Motivated Adversarial Policy (IMAP) for efficient black-box adversarial policy learning in both single- and multi-agent environments. We formulate four types of adversarial intrinsic regularizers -- maximizing the adversarial state coverage, policy coverage, risk, or divergence -- to discover potential vulnerabilities of the victim policy in a principled way. We also present a novel Bias-Reduction (BR) method to boost IMAP further. Our experiments validate the effectiveness of the four types of adversarial intrinsic regularizers and BR in enhancing black-box adversarial policy learning across a variety of environments. Our IMAP successfully evades two types of defense methods, adversarial training and robust regularizer, decreasing the performance of the state-of-the-art robust WocaR-PPO agents by 34%-54% across four single-agent tasks. IMAP also achieves a state-of-the-art attacking success rate of 83.91% in the multi-agent game YouShallNotPass

A Lightweight Approach for Network Intrusion Detection based on Self-Knowledge Distillation

Network Intrusion Detection (NID) works as a kernel technology for the security network environment, obtaining extensive research and application. Despite enormous efforts by researchers, NID still faces challenges in deploying on resource-constrained devices. To improve detection accuracy while reducing computational costs and model storage simultaneously, we propose a lightweight intrusion detection approach based on self-knowledge distillation, namely LNet-SKD, which achieves the trade-off between accuracy and efficiency. Specifically, we carefully design the DeepMax block to extract compact representation efficiently and construct the LNet by stacking DeepMax blocks. Furthermore, considering compensating for performance degradation caused by the lightweight network, we adopt batch-wise self-knowledge distillation to provide the regularization of training consistency. Experiments on benchmark datasets demonstrate the effectiveness of our proposed LNet-SKD, which outperforms existing state-of-the-art techniques with fewer parameters and lower computation loads.Comment: Accepted to IEEE ICC 202

Improved Key Distributed Storage Mechanism Based on Android System

Abstract-Mobile devices are playing a more and more important role in people's daily lives, mobile payment, watching video on mobile is increasingly common. Android system has occupied more than eighty percent of the mobile devices. Copyright protection of products and privacy protection of Android users is becoming a matter of growing concern, Researchers continue to improve the security of Android, and Digital Right Management (DRM) is an effective program, which private key protection is the most important part. This paper presents an improved key distributed storage solution to protect the private key based on Android system

Data De-Duplication with Adaptive Chunking and Accelerated Modification Identifying

The data de-duplication system not only pursues the high de-duplication rate, which refers to the aggregate reduction in storage requirements gained from de-duplication, but also the de-duplication speed. To solve the problem of random parameter-setting brought by Content Defined Chunking (CDC), a self-adaptive data chunking algorithm is proposed. The algorithm improves the de-duplication rate by conducting pre-processing de-duplication to the samples of the classified files and then selecting the appropriate algorithm parameters. Meanwhile, FastCDC, a kind of content-based fast data chunking algorithm, is adopted to solve the problem of low de-duplication speed of CDC. By introducing de-duplication factor and acceleration factor, FastCDC can significantly boost de-duplication speed while not sacrificing the de-duplication rate through adjusting these two parameters. The experimental results demonstrate that our proposed method can improve the de-duplication rate by about 5 %, while FastCDC can obtain the increase of de-duplication speed by 50 % to 200 % only at the expense of less than 3 % de-duplication rate loss