scholarly journals Learning Nondeterministic Real-Time Automata

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Jie An ◽  
Bohua Zhan ◽  
Naijun Zhan ◽  
Miaomiao Zhang
Keyword(s):  
Active Learning ◽  
Integrated Circuits ◽  
Real Time ◽  
Learning Algorithm ◽  
Timed Automata ◽  
Finite Automata ◽  
Special Kind ◽  
Learning Problem ◽  
Equivalence Queries

We present an active learning algorithm named NRTALearning for nondeterministic real-time automata (NRTAs). Real-time automata (RTAs) are a subclass of timed automata with only one clock which resets at each transition. First, we prove the corresponding Myhill-Nerode theorem for real-time languages. Then we show that there exists a unique minimal deterministic real-time automaton (DRTA) recognizing a given real-time language, but the same does not hold for NRTAs. We thus define a special kind of NRTAs, named residual real-time automata (RRTAs), and prove that there exists a minimal RRTA to recognize any given real-time language. This transforms the learning problem of NRTAs to the learning problem of RRTAs. After describing the learning algorithm in detail, we prove its correctness and polynomial complexity. In addition, based on the corresponding Myhill-Nerode theorem, we extend the existing active learning algorithm NL* for nondeterministic finite automata to learn RRTAs. We evaluate and compare the two algorithms on two benchmarks consisting of randomly generated NRTAs and rational regular expressions. The results show that NRTALearning generally performs fewer membership queries and more equivalence queries than the extended NL* algorithm, and the learnt NRTAs have much fewer locations than the corresponding minimal DRTAs. We also conduct a case study using a model of scheduling of final testing of integrated circuits.

scholarly journals From Timed Automata to Logic - and Back

1995 ◽  
Vol 2 (2) ◽  
Author(s):  
Francois Laroussinie ◽  
Kim G. Larsen ◽  
Carsten Weise
Keyword(s):  
Model Checking ◽  
Real Time ◽  
Timed Automata ◽  
Finite Automata ◽  
Automatic Verification ◽  
Satisfiability Problem ◽  
Timed Automaton

One of the most successful techniques for automatic verification is that<br />of model checking. For finite automata there exist since long extremely<br />efficient model-checking algorithms, and in the last few years these algorithms have been made applicable to the verification of real-time automata using the region-techniques of Alur and Dill.<br />In this paper, we continue this transfer of existing techniques from the<br />setting of finite (untimed) automata to that of timed automata. In particular, a timed logic L is put forward, which is sufficiently expressive that we for any timed automaton may construct a single characteristic L formula uniquely characterizing the automaton up to timed bisimilarity. Also, we prove decidability of the satisfiability problem for L with respect to given bounds on the number of clocks and constants of the timed automata to be constructed. None of these results have as yet been succesfully accounted for in the presence of time.

scholarly journals Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies

2021 ◽  
Author(s):  
Maurice Funk ◽  
Jean Christoph Jung ◽  
Carsten Lutz
Keyword(s):  
Active Learning ◽  
Polynomial Time ◽  
Description Logic ◽  
Learning Algorithm ◽  
Domain Expert ◽  
Conjunctive Queries ◽  
Membership Queries ◽  
Equivalence Queries

We consider the problem to learn a concept or a query in the presence of an ontology formulated in the description logic ELr, in Angluin's framework of active learning that allows the learning algorithm to interactively query an oracle (such as a domain expert). We show that the following can be learned in polynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3) conjunctive queries (CQs) that are chordal, symmetry-free, and of bounded arity. In all cases, the learner can pose to the oracle membership queries based on ABoxes and equivalence queries that ask whether a given concept/query from the considered class is equivalent to the target. The restriction to bounded arity in (3) can be removed when we admit unrestricted CQs in equivalence queries. We also show that EL-concepts are not polynomial query learnable in the presence of ELI-ontologies.

scholarly journals Pre-Training Acquisition Functions by Deep Reinforcement Learning for Fixed Budget Active Learning

2021 ◽  
Author(s):  
Yusuke Taguchi ◽  
Hideitsu Hino ◽  
Keisuke Kameyama
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Learning Problem ◽  
Q Learning ◽  
Fixed Budget ◽  
Active Learner

AbstractThere are many situations in supervised learning where the acquisition of data is very expensive and sometimes determined by a user’s budget. One way to address this limitation is active learning. In this study, we focus on a fixed budget regime and propose a novel active learning algorithm for the pool-based active learning problem. The proposed method performs active learning with a pre-trained acquisition function so that the maximum performance can be achieved when the number of data that can be acquired is fixed. To implement this active learning algorithm, the proposed method uses reinforcement learning based on deep neural networks as as a pre-trained acquisition function tailored for the fixed budget situation. By using the pre-trained deep Q-learning-based acquisition function, we can realize the active learner which selects a sample for annotation from the pool of unlabeled samples taking the fixed-budget situation into account. The proposed method is experimentally shown to be comparable with or superior to existing active learning methods, suggesting the effectiveness of the proposed approach for the fixed-budget active learning.

scholarly journals Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

Smart Cities ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 1220-1243
Author(s):  
Hafiz Suliman Munawar ◽  
Fahim Ullah ◽  
Siddra Qayyum ◽  
Amirhossein Heravi
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Imaging System ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Aerial Images ◽  
Management Systems ◽  
Training Dataset ◽  
Delayed Response

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.

A Case Study on the Effects of Active Learning Classrooms in Higher Education

2018 ◽  
Vol 24 (4) ◽  
pp. 733-754
Author(s):  
Hyeon Woo Lee ◽  
Yoon Mi Cha ◽  
Kibeom Kim Kibeom Kim
Keyword(s):  
Higher Education ◽  
Active Learning

Electrical Probing and Surface Imaging of Deep Sub-Micron Integrated Circuits

1999 ◽  
Author(s):  
Kenneth Krieg ◽  
Richard Qi ◽  
Douglas Thomson ◽  
Greg Bridges
Keyword(s):  
High Resolution ◽  
Integrated Circuits ◽  
Real Time ◽  
High Speed ◽  
Time Signal ◽  
Surface Imaging ◽  
Atomic Force ◽  
Submicron Structures ◽  
High Resolution Images ◽  
Capacitive Loading

Abstract A contact probing system for surface imaging and real-time signal measurement of deep sub-micron integrated circuits is discussed. The probe fits on a standard probe-station and utilizes a conductive atomic force microscope tip to rapidly measure the surface topography and acquire real-time highfrequency signals from features as small as 0.18 micron. The micromachined probe structure minimizes parasitic coupling and the probe achieves a bandwidth greater than 3 GHz, with a capacitive loading of less than 120 fF. High-resolution images of submicron structures and waveforms acquired from high-speed devices are presented.

Magnetic Current Imaging Techniques: Comparative Case Studies

2004 ◽  
Author(s):  
Olivier Crépel ◽  
Philippe Descamps ◽  
Patrick Poirier ◽  
Romain Desplats ◽  
Philippe Perdu ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Integrated Circuits ◽  
Case Studies ◽  
Flip Chip ◽  
Imaging Techniques ◽  
Optimal Domain ◽  
Magnetic Current ◽  
Comparative Case Studies ◽  
Current Flows

Abstract Magnetic field based techniques have shown great capabilities for investigation of current flows in integrated circuits (ICs). After reviewing the performances of SQUID, GMR (hard disk head technologies) and MTJ existing sensors, we will present results obtained on various case studies. This comparison will show the benefit of each approach according to each case study (packaged devices, flip-chip circuits, …). Finally we will discuss on the obtained results to classify current techniques, optimal domain of applications and advantages.

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