Henrikas Pranevicius

发表

Guest editorial: On the “modelling of business, industrial and transport systems” international conference

Irina Yatskiv, Eugene Kopytov, Henrikas Pranevicius, 2008 .

The use of aggregate approach for formal specification and simulation of real-time systems

Henrikas Pranevicius, Dalius Makackas, 2001 .

Fuzzy Rule Base Generation Using Discretization of Membership Functions and Neural Network

Henrikas Pranevicius, Tadas Kraujalis, Germanas Budnikas, 2014, ICIST.

PLA‐based formalization of business rules and their analysis by means of knowledge‐based techniques

Henrikas Pranevicius, Germanas Budnikas, 2008 .

Complexity of Embedded Chain Algorithm for Computing steady State Probabilities of Markov Chain

Henrikas Pranevicius, Mindaugas Snipas, Eimutis Valakevicius, 2011, Inf. Technol. Control..

Application of Stochastic Automata Networks for Creation of Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels

Henrikas Pranevicius, Mindaugas Snipas, Mindaugas Pranevicius, 2015, BioMed research international.

Development and Analysis of a Problem Domain Knowledge Base Oriented to PLA Specifications

Henrikas Pranevicius, Germanas Budnikas, 2006, JCKBSE.

Simulation of Protocol for Initiation of Communication Sessions using dynPLA Model

Henrikas Pranevicius, A. Jarutis, A. Paulauskaite–Taraseviciene, 2010 .

Verification of business process workflows

Henrikas Pranevicius, Regina Misevičienė, H. Pranevicius, 2012 .

EXTENSION OF PLA SPECIFICATION FOR DYNAMIC SYSTEM FORMALIZATION

Henrikas Pranevicius, Šarūnas Packevičius, Algirdas Kazla, 2015 .

Simulation of stevedoring work in the Klaipeda oil terminal

Henrikas Pranevicius, Dalius Makackas, 2002 .

Scenario Generation Employing Copulas

Kristina Sutiene, Henrikas Pranevicius, 2007, World Congress on Engineering.

Knowledge Based Verification of Aggregate Specifications

Henrikas Pranevicius, Regina Miseviciene, H. Pranevicius, 2006, 2006 Fifth Mexican International Conference on Artificial Intelligence.

Utility of Statistical Model Checking of Stochastic Hybrid Automata for Patient Controlled Analgesia

Henrikas Pranevicius, Vytautas Pilkauskas, Mindaugas Pranevicius, 2017 .

APPLICATION OF ABSTRACT DATA TYPE IN DYNAMIC PLA APPROACH

Henrikas Pranevicius, Dalius Makackas, Agnė Paulauskaitė-Tarasevičienė, 2009 .

Combining Simulation Models with the Information System for an Operative Control of the Oil Terminal

Henrikas Pranevicius, Vytautas Pilkauskas, 2005 .

Multistage K-Means Clustering for Scenario Tree Construction

Kristina Sutiene, Henrikas Pranevicius, Dalius Makackas, 2010, Informatica.

The Use of Aggregate and Z Formal Methods for Specification and Analysis of Distributed Systems

Henrikas Pranevicius, 2001, ADBIS.

Creation of ESTELLE/Ag Specifications Using Knowledge Bases

Henrikas Pranevicius, Germanas Budnikas, 2003, Informatica.

DATA CLASSIFICATION USING DIRICHLET MIXTURES

Mindaugas Margelevicius, Henrikas Pranevicius, Dovilė Rudokaitė-Margelevičienė, 2015 .

The use UML and aggregate approach for developing Klaipeda oil simulation model

Henrikas Pranevicius, Dalius Makackas, Vytautas Pilkauskas, 2002, SimVis.

Formal specification and analysis of distributed systems

Henrikas Pranevicius, 1998, J. Intell. Manuf..

Creation Simulation Models of Complex Systems Using Piece-linear Aggregare Formalism

Henrikas Pranevicius, 2006, MSV.

Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels

Henrikas Pranevicius, Mindaugas Snipas, Nerijus Paulauskas, 2014, Inf. Technol. Control..

Knowledge based traffic signal control model for signalized intersection

Henrikas Pranevicius, Tadas Kraujalis, 2012 .

Applications of Finite Linear Temporal Logic to Piecewise Linear Aggregates

Henrikas Pranevicius, Stanislovas Norgela, 2012, Informatica.

Piece Linear-Aggregate Approach for Modelling and Analysis of Fuzzy Systems

Henrikas Pranevicius, T. Kraujalis, 2013 .

Behavior Analysis of Real-Time Systems Using PLA Method

Henrikas Pranevicius, Dalius Makackas, Regina Miseviciene, 2013, ICIST.

Stochastic 16-state model of voltage gating of gap-junction channels enclosing fast and slow gates.

Jonas Mockus, Henrikas Pranevicius, Nerijus Paulauskas, 2012, Biophysical journal.

A stochastic four-state model of contingent gating of gap junction channels containing two "fast" gates sensitive to transjunctional voltage.

Henrikas Pranevicius, Nerijus Paulauskas, Mindaugas Pranevicius, 2009, Biophysical journal.