scholarly journals ON SOME PROPERTIES OF TIMED FINITE STATE MACHINES

2020 ◽  
Author(s):  
Evgeniy Maximovich Vinarskii ◽  
◽  
Vladimir Anatolyevoch Zakharov ◽  
Keyword(s):  
Real Time ◽  
Computational Linguistics ◽  
Input Data ◽  
Formal Model ◽  
Reactive Systems ◽  
Real Life ◽  
Reactive System ◽  
Formal Models ◽  
Reactive Behavior ◽  
Finite State

Sequential reactive systems are formal models of programs that interact with the environment by receiving inputs and producing corresponding outputs. Such formal models are widely used in software engineering, computational linguistics, telecommunication, etc. In real life, the behavior of a reactive system depends not only on the flow of input data, but also on the time the input data arrive and the delays that occur when generating responses. To capture these aspects, a timed finite state machine (TFSM) is used as a formal model of a real-time sequential reactive system. However, in most of known previous works, this model was considered in simplified semantics: the responses in the output stream, regardless of their timestamps, follow in the same order in which the corresponding inputs are delivered to the machine. This simplification makes the model easier to analyze and manipulate, but it misses many important aspects of real-time computation. In this paper we study a refined semantics of TFSMs and show how to represent it by means of Labelled Transition Systems. This opens up a possibility to apply traditional formal methods for verifying more subtle properties of real-time reactive behavior which were previously ignored.

scholarly journals On the Modeling of Sequential Reactive Systems by Means of Real Time Automata

2020 ◽  
Vol 27 (4) ◽  
pp. 396-411
Author(s):  
Evgeney Maximovich Vinarskii ◽  
Vladimir Anatolyevich Zakharov
Keyword(s):  
Real Time ◽  
Reactive Systems ◽  
Finite State Machines ◽  
Reactive System ◽  
State Machines ◽  
Time Model ◽  
Transition Systems ◽  
Input Signals ◽  
Finite State ◽  
Definition Of

Sequential reactive systems include hardware devices and software programs which operate in continuous interaction with the external environment, from which they receive streams of input signals (data, commands) and in response to them form streams of output signals. Systems of this type include controllers, network switches, program interpreters, system drivers. The behavior of some reactive systems is determined not only by the sequence of values of input signals, but also by the time of their arrival at the inputs of the system and the delays in computing the output signals. These aspects of reactive system computations are taken into account by real-time models of computation which include, in particular, realtime finite state machines (TFSMs). However, in most works where this class of real-time automata is studied a simple variant of TFSM semantics is used: the transduction relation computed by a TFSM is defined so that the elements of an output stream, regardless oftheir timestamps, follow in the same order as the corresponding elements ofthe input stream. This straightforward approach makes the model easier to analyze and manipulate, but it misses many important features of real-time computation. In this paper we study a more realistic semantics of TFSMs and show how to represent it by means of Labeled Transition Systems. The use of the new TFSM model also requires new approaches to the solution of verification problems in the framework of this model. For this purpose, we propose an alternative definition of TFSM computations by means of Labeled Transition Systems and show that the two definitions of semantics for the considered class of real-time finite state machines are in good agreement with each other. The use of TFSM semantics based on Labeled Transition Systems opens up the possibility of adapting well known real-time model checking techniques to the verification ofsequential reactive systems.

ON THE NECESSITY OF FORMAL MODELS FOR REAL-TIME PARALLEL COMPUTATIONS

2001 ◽  
Vol 11 (02n03) ◽  
pp. 353-361 ◽  
Author(s):  
STEFAN D. BRUDA ◽  
SELIM G. AKL
Keyword(s):  
Positive Integer ◽  
Real Time ◽  
Complexity Theory ◽  
Turing Machine ◽  
Formal Model ◽  
Parallel Computations ◽  
General Nature ◽  
Turing Machines ◽  
Formal Models ◽  
Language L

We assume the multitape real-time Turing machine as a formal model for parallel real-time computation. Then, we show that, for any positive integer k, there is at least one language Lk which is accepted by a k-tape real-Turing machine, but cannot be accepted by a (k - 1)-tape real-time Turing machine. It follows therefore that the languages accepted by real-time Turing machines form an infinite hierarchy with respect to the number of tapes used. Although this result was previously obtained elsewhere, our proof is considerably shorter, and explicitly builds the languages Lk. The ability of the real-time Turing machine to model practical real-time and/or parallel computations is open to debate. Nevertheless, our result shows how a complexity theory based on a formal model can draw interesting results that are of more general nature than those derived from examples. Thus, we hope to offer a motivation for looking into realistic parallel real-time models of computation.

scholarly journals On the Expressive Power of Some Extensions of Linear Temporal Logic

2018 ◽  
Vol 25 (5) ◽  
pp. 506-524
Author(s):  
Anton Gnatenko ◽  
Vladimir Zakharov
Keyword(s):  
Temporal Logic ◽  
Reactive Systems ◽  
Expressive Power ◽  
Linear Temporal Logic ◽  
Reactive System ◽  
Formal Specifications ◽  
Control Signals ◽  
Finite State ◽  
Finite State Transducer ◽  
Output Alphabet

One of the most simple models of computation which is suitable for representation of reactive systems behaviour is a finite state transducer which operates over an input alphabet of control signals and an output alphabet of basic actions. The behaviour of such a reactive system displays itself in the correspondence between flows of control signals and compositions of basic actions performed by the system. We believe that the behaviour of this kind requires more suitable and expressive means for formal specifications than the conventionalLT L. In this paper, we define some new (as far as we know) extensionLP-LT Lof Linear Temporal Logic specifically intended for describing the properties of transducers computations. In this extension the temporal operators are parameterized by sets of words (languages) which represent distinguished flows of control signals that impact on a reactive system. Basic predicates in our variant of the temporal logic are also languages in the alphabet of basic actions of a transducer; they represent the expected response of the transducer to the specified environmental influences. In our earlier papers, we considered a model checking problem forLP-LT LandLP-CT Land showed that this problem has effective solutions. The aim of this paper is to estimate the expressive power ofLP-LT Lby comparing it with some well known logics widely used in the computer science for specification of reactive systems behaviour. We discovered that a restricted variant LP-1-LT Lof our logic is more expressive thanLTLand another restricted variantLP-n-LT Lhas the same expressive power as monadic second order logic S1S.

scholarly journals USING AN EXTENSIONS OF CTL* FOR SPECIFICATION AND VERIFICATION OF SEQUENTIAL REACTIVE SYSTEMS

2020 ◽  
Author(s):  
Anton Romanovich Gnatenko ◽  
◽  
Vladimir Anatolyevoch Zakharov ◽  
Keyword(s):  
Model Checking ◽  
Formal Model ◽  
Reactive Systems ◽  
Expressive Power ◽  
Binary Relations ◽  
Temporal Logics ◽  
Finite State Transducers ◽  
Control Signals ◽  
Data Control ◽  
Finite State

Sequential reactive systems such as controllers, device drivers, computer interpreters operate with two data streams and transform input streams of data (control signals, instructions) into output streams of control signals (instructions, data). Finite state transducers are widely used as an adequate formal model for information processing systems of this kind. Since runs of transducers develop over time, temporal logics, obviously, could be used as both simple and expressive formalism for specifying the behavior of sequential reactive systems. However, the conventional applied temporal logics (LTL, CTL) do not suit this purpose well, since their formulae are interpreted over omega-languages, whereas the behavior of transducers are represented by binary relations on infinite sequences, i.e. omega-transductions. To provide temporal logic with the ability to take into account this general feature of the behavior of reactive systems, we introduced new extensions of this logic. Two distinguished features characterize these extension: 1) temporal operators are parameterized by sets of streams (languages) admissible for input, and 2) sets (languages) of expected output streams are used as basic predicates. In the previous series of works we studied the expressive power and the model checking problem for Reg-LTL and Reg-CTL which are such extensions of LTL and CTL where the languages mentioned above are regular ones. We discovered that such an extension of temporal logics increases their expressive capability though retains the decidability of the model checking problem. Our next step in the systematic study of expressive and algorithmic properties of new extensions temporal logics is the analysis of the model checking problem for finite state transducers against Reg-CTL* formulae. In this paper we develop a model checking algorithm for Reg-CTL* and show that this problem is in ExpSpace.

scholarly journals A Case Study in Automated Verification Based on Trace Abstractions

1995 ◽  
Vol 2 (54) ◽  
Author(s):  
Nils Klarlund ◽  
Mogens Nielsen ◽  
Kim Sunesen
Keyword(s):  
Reactive Systems ◽  
Main Tool ◽  
Automated Analysis ◽  
Reactive System ◽  
Finite State Automaton ◽  
Symbolic Language ◽  
Temporal Properties ◽  
Finite State ◽  
High Level ◽  
Recursive Data Structures

In [14], we proposed a framework for the automatic verification of reactive<br />systems. Our main tool is a decision procedure, Mona, for Monadic<br />Second-order Logic (M2L) on finite strings. Mona translates a formula in<br />M2L into a finite-state automaton. We show in [14] how traces, i.e. finite<br />executions, and their abstractions can be described behaviorally. These<br />state-less descriptions can be formulated in terms of customized temporal<br />logic operators or idioms.<br />In the present paper, we give a self-contained, introductory account of<br />our method applied to the RPC-memory specification problem of the 1994<br />Dagstuhl Seminar on Specification and Refinement of Reactive Systems.<br />The purely behavioral descriptions that we formulate from the informal<br />specifications are formulas that may span 10 pages or more.<br />Such descriptions are a couple of magnitudes larger than usual temporal<br />logic formulas found in the literature on verification. To securely<br />write these formulas, we introduce Fido [16] as a reactive system description<br />language. Fido is designed as a high-level symbolic language for<br />expressing regular properties about recursive data structures.<br />All of our descriptions have been verified automatically by Mona from<br />M2L formulas generated by Fido.<br />Our work shows that complex behaviors of reactive systems can be<br />formulated and reasoned about without explicit state-based programming.<br />With Fido, we can state temporal properties succinctly while enjoying<br />automated analysis and verification.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

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