CCA Secure PKE with Auxiliary Input Security and Leakage Resiliency

2015 ◽  
pp. 319-335 ◽  
Author(s):  
Zhiwei Wang ◽  
Siu Ming Yiu
Keyword(s):  
Auxiliary Input

Key-policy attribute-based encryption against continual auxiliary input leakage

2019 ◽  
Vol 470 ◽  
pp. 175-188 ◽  
Author(s):  
Jiguo Li ◽  
Qihong Yu ◽  
Yichen Zhang ◽  
Jian Shen
Keyword(s):  
Attribute Based Encryption ◽  
Key Policy ◽  
Auxiliary Input

scholarly journals Active Fault Diagnosis Based on Stochastic Tests

2008 ◽  
Vol 18 (4) ◽  
pp. 487-496 ◽  
Author(s):  
Niels Poulsen ◽  
Henrik Niemann
Keyword(s):  
Fault Diagnosis ◽  
Change Detection ◽  
Input Signal ◽  
Output Signal ◽  
Active Fault ◽  
Fast Change ◽  
Signal Injection ◽  
Cusum Method ◽  
Cusum Tests ◽  
Auxiliary Input

Active Fault Diagnosis Based on Stochastic TestsThe focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example.

scholarly journals A Pronunciation Prior Assisted Vowel Reduction Detection Framework with Multi-Stream Attention Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8321
Author(s):  
Zongming Liu ◽  
Zhihua Huang ◽  
Li Wang ◽  
Pengyuan Zhang
Keyword(s):  
Prior Knowledge ◽  
Language Learners ◽  
Native Speakers ◽  
Recognition Rate ◽  
Auxiliary Information ◽  
Vowel Reduction ◽  
Phoneme Recognition ◽  
End To End ◽  
The Impact ◽  
Auxiliary Input

Vowel reduction is a common pronunciation phenomenon in stress-timed languages like English. Native speakers tend to weaken unstressed vowels into a schwa-like sound. It is an essential factor that makes the accent of language learners sound unnatural. To improve vowel reduction detection in a phoneme recognition framework, we propose an end-to-end vowel reduction detection method that introduces pronunciation prior knowledge as auxiliary information. In particular, we have designed two methods for automatically generating pronunciation prior sequences from reference texts and have implemented a main and auxiliary encoder structure that uses hierarchical attention mechanisms to utilize the pronunciation prior information and acoustic information dynamically. In addition, we also propose a method to realize the feature enhancement after encoding by using the attention mechanism between different streams to obtain expanded multi-streams. Compared with the HMM-DNN hybrid method and the general end-to-end method, the average F1 score of our approach for the two types of vowel reduction detection increased by 8.8% and 6.9%, respectively. The overall phoneme recognition rate increased by 5.8% and 5.0%, respectively. The experimental part further analyzes why the pronunciation prior knowledge auxiliary input is effective and the impact of different pronunciation prior knowledge types on performance.

A range-extender engine speed control strategy with generator torque as an auxiliary input

2020 ◽  
Vol 103 ◽  
pp. 104596
Author(s):  
Wenyu Xiong ◽  
Jie Ye ◽  
Qichangyi Gong ◽  
Han Feng ◽  
Jinbang Xu ◽  
...  
Keyword(s):  
Control Strategy ◽  
Engine Speed ◽  
Speed Control ◽  
Range Extender ◽  
Auxiliary Input

Proton Exchange Membrane Fuel Cell System Identification and Control: Part I — System Dynamics, Modeling and Identification

2006 ◽  
Author(s):  
Yee-Pien Yang ◽  
Fu-Cheng Wang ◽  
Hsin-Ping Chang ◽  
Ying-Wei Ma ◽  
Chih-Wei Huang ◽  
...  
Keyword(s):  
Fuel Cell ◽  
System Identification ◽  
Proton Exchange Membrane ◽  
Cell System ◽  
Proton Exchange ◽  
Time Varying ◽  
Fuel Cell System ◽  
And Control ◽  
Exchange Membrane ◽  
Auxiliary Input

This paper consists of two parts to address a systematic method of system identification and control of a proton exchange membrane (PEM) fuel cell. This fuel cell is used for communication devices of small power, involving complex electrochemical reactions of nonlinear and time-varying dynamic properties. From a system point of view, the dynamic model of PEM fuel cell is reduced to a configuration of two inputs, hydrogen and air flow rates, and two outputs, cell voltage and current. The corresponding transfer functions describe linearized subsystem dynamics with finite orders and time-varying parameters, which are expressed as discrete-time auto-regression moving-average with auxiliary input models for system identification by the recursive least square algorithm. In experiments, a pseudo random binary sequence of hydrogen or air flow rate is fed to a single fuel cell device to excite its dynamics. By measuring the corresponding output signals, each subsystem transfer function of reduced order is identified, while the unmodeled, higher-order dynamics and disturbances are described by the auxiliary input term. This provides a basis of adaptive control strategy to improve the fuel cell performance in terms of efficiency, transient and steady state specifications. Simulation shows the adaptive controller is robust to the variation of fuel cell system dynamics.

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