Semi-formal verification of closed-loop specifications in the concept design phase

2017 ◽  
Vol 65 (2) ◽  
Author(s):  
Jan H. Richter ◽  
Stefan R. Friedrich
Keyword(s):  
Formal Verification ◽  
Feasibility Problem ◽  
Mixed Integer ◽  
Test Case ◽  
Concept Design ◽  
Simulation Based ◽  
Verification Problem ◽  
Linear Feasibility ◽  
Mixed Logical Dynamical ◽  
Early Phases

AbstractThe article addresses the semi-formal verification of behavioral specifications for subsystems consisting of physical parts and controllers, complemented by simulation-based integration testing. Since design errors in early phases tend to be particularly expensive, the method is tailored towards applicability in these phases. We verify behavioral specifications with proof-like credibility, or falsify them while providing a violation scenario that is reusable as a test case. The system is represented as a mixed logical dynamical (MLD) system, and specifications are expressed by a temporal logic with affine signal abstractions. The verification problem is converted into an equivalent mixed-integer linear feasibility problem solved using off-the-shelf solvers. An example illustrates the effectiveness of the method.

Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems

2020 ◽  
Author(s):  
Michael E. Akintunde ◽  
Elena Botoeva ◽  
Panagiotis Kouvaros ◽  
Alessio Lomuscio
Keyword(s):  
Feasibility Problem ◽  
Mixed Integer ◽  
Linear Programs ◽  
Multi Agent Systems ◽  
Control Logic ◽  
Agent Systems ◽  
Perception System ◽  
Verification Problem ◽  
Multi Agent ◽  
Complex Scenario

We investigate the problem of verifying the strategic properties of multi-agent systems equipped with machine learning-based perception units. We introduce a novel model of agents comprising both a perception system implemented via feed-forward neural networks and an action selection mechanism implemented via traditional control logic. We define the verification problem for these systems against a bounded fragment of alternating-time temporal logic. We translate the verification problem on bounded traces into the feasibility problem of mixed integer linear programs and show the soundness and completeness of the translation. We show that the lower bound of the verification problem is PSPACE and the upper bound is coNEXPTIME. We present a tool implementing the compilation and evaluate the experimental results obtained on a complex scenario of multiple aircraft operating a recently proposed prototype for air-traffic collision avoidance.

scholarly journals Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints

2021 ◽  
pp. 247263032110217
Author(s):  
Takeshi D. Itoh ◽  
Takaaki Horinouchi ◽  
Hiroki Uchida ◽  
Koichi Takahashi ◽  
Haruka Ozaki
Keyword(s):  
Scheduling Algorithms ◽  
Mixed Integer ◽  
Time Constraints ◽  
Live Cells ◽  
Laboratory Automation ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Optimal Allocations ◽  
Scheduling Method ◽  
Time Required

In automated laboratories consisting of multiple different types of instruments, scheduling algorithms are useful for determining the optimal allocations of instruments to minimize the time required to complete experimental procedures. However, previous studies on scheduling algorithms for laboratory automation have not emphasized the time constraints by mutual boundaries (TCMBs) among operations, which is important in procedures involving live cells or unstable biomolecules. Here, we define the “scheduling for laboratory automation in biology” (S-LAB) problem as a scheduling problem for automated laboratories in which operations with TCMBs are performed by multiple different instruments. We formulate an S-LAB problem as a mixed-integer programming (MIP) problem and propose a scheduling method using the branch-and-bound algorithm. Simulations show that our method can find the optimal schedules of S-LAB problems that minimize overall execution time while satisfying the TCMBs. Furthermore, we propose the use of our scheduling method for the simulation-based design of job definitions and laboratory configurations.

scholarly journals Computer Assisted Localization of a Heart Arrhythmia

2018 ◽  
Author(s):  
Chris Vogl ◽  
Peng Zheng ◽  
Stephen P. Seslar ◽  
Aleksandr Y. Aravkin
Keyword(s):  
Diagnostic Procedure ◽  
Feasibility Problem ◽  
Computer Assisted ◽  
Optimization Approach ◽  
Arrival Times ◽  
Heart Arrhythmia ◽  
Simulation Based ◽  
Heart Anatomy ◽  
Using Data ◽  
Diagnostic Catheter

AbstractWe consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival times from a second diagnostic catheter are recorded as the diagnostic catheter moves around within the heart.We model this situation as a nonconvex feasibility problem, where given a set of arrival times, we look for a source location that is consistent with the available data. We develop a new optimization approach and fast algorithm to obtain online proposals for the next location to suggest to the operator as she collects data. We validate the procedure using a Monte Carlo simulation based on patients’ electrophysiological data. The proposed procedure robustly and quickly locates the source of arrhythmias without any prior knowledge of heart anatomy.

Tri-Generation Systems Optimization: Comparison of Heuristic and Mixed Integer Linear Programming Approaches

2014 ◽  
Author(s):  
Aldo Bischi ◽  
Stefano Campanari ◽  
Alberto Castiglioni ◽  
Giampaolo Manzolini ◽  
Emanuele Martelli ◽  
...  
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Storage System ◽  
Simulation Models ◽  
Point Of View ◽  
Mixed Integer ◽  
Test Case ◽  
Optimization Strategy ◽  
Software Codes

This work compares two optimization approaches for combined cooling, heating and power (CCHP or Tri-generation) energy systems scheduling. Both approaches are developed through dedicated software codes and are based on simulation models capable of evaluating of the best operating strategy (both economically and energy-wise) to run a given trigeneration plant while dealing with time-variable loads and tariffs. The simultaneous use of different prime movers operating in parallel is taken into consideration as well as their part load performance, the influence of ambient temperature and the usage of a heat storage system. Cooling may be generated through absorption chillers or electrically driven compression cycles. One of the models is heuristic and adopts an optimization strategy based on a multi-step approach: it simulates several cases according to a pre-defined number of paths, exploring the most reasonable operational modes and comparing them systematically. The other relies on a mathematical approach, based on a Mixed Integer Linear Programming (MILP) optimization model which has been developed in order to deal with more complex systems without the need of predefining a too large variety of operation paths. Results of the two models are compared against a test case based on real plant specifications, discussing their performance by the point of view of simulation capabilities, quality and accuracy of the optimization results (in terms of differences in energy and economic performance) and computational resources.

An MILP Formulation for the Thermal Unit Commitment Problem Considering Start-Up and Shut-Down Power Trajectories

2014 ◽  
Vol 672-674 ◽  
pp. 493-498 ◽  
Author(s):  
Jun Deng ◽  
Hua Wei
Keyword(s):  
Unit Commitment ◽  
Mixed Integer ◽  
Test Case ◽  
Thermal Unit ◽  
Unit Commitment Problem ◽  
Binary Variables ◽  
Computational Performance ◽  
Start Up ◽  
Commitment Problem ◽  
Mixed Integer Linear Formulation

This paper presents a mixed-integer linear formulation for the thermal unit commitment problem considering the start-up and shut-down power trajectories. A realistic and accurate modeling of the unit’s operating phase is given, which includes the phases of start-up, dispatchable and shut-down. The start-up type is decided by the unit’s prior off-line time. The start-up costs and power trajectories depend on the type of start-up. A new set of binary variables is introduced to represent the dispatchable status, which can decrease the binary variables and constraints significantly. Finally, a test case study is analyzed to verify the correctness and show the computational performance of the proposed formulation.

Simulation-Based Design in concept design phase considering uncertainty

2016 ◽  
Vol 2016.12 (0) ◽  
pp. 2111
Author(s):  
Takahiro ICHIMARU ◽  
Hiroshi HASEGAWA ◽  
Yuji KADO
Keyword(s):  
Design Phase ◽  
Concept Design ◽  
Simulation Based Design ◽  
Simulation Based

Calculation Approach Validation for Airblast Atomizers

1991 ◽  
Author(s):  
N. K. Rizk ◽  
H. C. Mongia
Keyword(s):  
Analytical Calculation ◽  
Spray Parameters ◽  
Concept Design ◽  
Air Velocity ◽  
Disturbance Wave ◽  
Mathematical Expressions ◽  
Spray Droplets ◽  
Liquid Preparation ◽  
Analytical Tools ◽  
Early Phases

In order to formulate a common approach that could provide the spray parameters of airblast atomizers, various processes of liquid preparation, breakup and secondary atomization have been included in a semi-analytical calculation procedure. The air velocity components in the atomizer flow field are provided by mathematical expressions, and the spray droplets are considered to form at ligament breakup through a disturbance wave growth concept. The validation of the developed approach included the application to six atomizers that significantly varied in concept, design, and size. They represented both prefilming and plain-jet types, and their data utilized in the present effort were obtained with six different liquids. Satisfactory agreement between the measurements and the predictions has been achieved under wide ranges of air/fuel ratio and air pressure drop for various test liquids. The results of this investigation indicate the potential of using such an approach in the early phases of airblast atomizer design, and may be followed by more detailed calculations using analytical tools.

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