ASK Labs Company We help machines make decisions.

Slides:



Advertisements
Similar presentations
Implementing Corporate Project Management System Spider Project Team
Advertisements

SUSTAINABLE TECHNOLOGIES AS A BASIS OF CLEANER PRODUCTION Vineta Srebrenkoska 1, Kiril Lisichkov 2, Emilija Fidancevska 2, Jadranka Blazevska Gilev 2 1.
4 Intelligent Systems.
Decision Making: An Introduction 1. 2 Decision Making Decision Making is a process of choosing among two or more alternative courses of action for the.
CHAPTER 4 ANALYTICS, DECISION SUPPORT, AND ARTIFICIAL INTELLIGENCE
C SC 421: Artificial Intelligence …or Computational Intelligence Alex Thomo
INDUSTRIAL & SYSTEMS ENGINEERING
1 SYS366 Week 1 - Lecture 2 How Businesses Work. 2 Today How Businesses Work What is a System Types of Systems The Role of the Systems Analyst The Programmer/Analyst.
Chapter 3 Growth These slides supplement the textbook, but should not replace reading the textbook.
Supporting Decision Making Chapter 10 McGraw-Hill/IrwinCopyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved.
Introduction to Quantitative Techniques
Unit 3a Industrial Control Systems
Dr. Osama Al-Habahbah Automation Chapter 1 Introduction.
INTEGRATION OF ARTIFICIAL INTELLIGENCE [AI] SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE & DIAGNOSTICS.
We provide Intelligence to Services Forecasting Optimization Bussines Intelligence Software Solutions FOBISS Cash Management Enterprise for ATM networks.
CIS 429—Chapter 9 Enabling the Organization— Decision Making.
Study of Chinese Seed Project Informationization Shang Shuqi Qingdao Agricultural University Shandong,China Tel
Succeeding with Technology Information, Decision Support… Decision Making and Problem Solving Management Information Systems Decision Support Systems Group.
Tennessee Technological University1 The Scientific Importance of Big Data Xia Li Tennessee Technological University.
MSIS 110: Introduction to Computers; Instructor: S. Mathiyalakan 1 An Introduction to Information Systems Chapter 1.
11.1 Ch. 11 General Equilibrium and the Efficiency of Perfect Competition.
11 C H A P T E R Artificial Intelligence and Expert Systems.
Implementation of international management experience to Ukrainian companies performed: Degtyareva N.V. Supervisor: Magdich A.S., Ph.D. in Economics.
Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. Decision Support Systems Chapter 10.
OBJECT ORIENTED SYSTEM ANALYSIS AND DESIGN. COURSE OUTLINE The world of the Information Systems Analyst Approaches to System Development The Analyst as.
Fundamentals of Information Systems, Third Edition2 Principles and Learning Objectives Artificial intelligence systems form a broad and diverse set of.
Chapter 4 MODELING AND ANALYSIS. Model component Data component provides input data User interface displays solution It is the model component of a DSS.
Engineering Overview © 2012 Project Lead The Way, Inc.Introduction to Engineering Design.
The automated system of maintenance of reliability and quality of equipment ASONIKA
MBAA 607- Operations Analysis & Decision Support Systems Spring 2008 Tuesday 4:25-7:05 Dr. Linda Leon.
Problem Formulation Elastic cloud infrastructures provision resources according to the current actual demand on the infrastructure while enforcing service.
Economy Julia Shukhman,287 English supervisor Julia Shtaltovna Economical supervisor Vasyl Homenko.
Ch. 11 General Equilibrium and the Efficiency of Perfect Competition
AQA A2 Business Studies Unit 3: Strategies for success Operational strategies.
Artificial Intelligence, Expert Systems, and Neural Networks Group 10 Cameron Kinard Leaundre Zeno Heath Carley Megan Wiedmaier.
COMPUTER SCIENCE Computer science (CS) is The systematic study of algorithmic.
Principles of Information Systems, Sixth Edition An Introduction to Information Systems Chapter 1.
Milestone Developments in Operations Management Industrial Revolution, Growth of Railroads, Scientific Management Movement
A field of study that encompasses computational techniques for performing tasks that require intelligence when performed by humans. Simulation of human.
ENGINEERING What is Engineering? The application of mathematics and scientific principles to better or improve life To equip creative minds with the mathematical.
ERP and Related Technologies
LOAD FORECASTING. - ELECTRICAL LOAD FORECASTING IS THE ESTIMATION FOR FUTURE LOAD BY AN INDUSTRY OR UTILITY COMPANY - IT HAS MANY APPLICATIONS INCLUDING.
The article written by Boyarshinova Vera Scientific adviser: Eltyshev Denis THE USE OF NEURO-FUZZY MODELS FOR INTEGRATED ASSESSMENT OF THE CONDITIONS OF.
1 Study on Strategy of Chinese Technical Standards China National Institute of Standardization Jan 18 th 2005.
Theme Guidance - Network Traffic Proposed NMLRG IETF 95, April 2016 Sheng Jiang (Speaker, Co-chair) Page 1/6.
Engineering Overview. ENGINEERING Engineering is the application of mathematics and scientific principles to better or improve life.
Postgraduate stud. Al-Ahnomi Montaser Don State Technical University Department “Computer-aided design" Theme:- "development and research of intelligent.
Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 6: Artificial Neural Networks for Data Mining.
Introduction to Machine Learning, its potential usage in network area,
Management Information Systems (MIS)
The University of Jordan Mechatronics Engineering Department
RESEARCH APPROACH.
Prof. Elmira RAMAZANOVA, Dr. B. IBISHOV, Dr. T. RZAEV, Dr. H. MELIKOV
2 Our Global Economy 2-1 Economics and Decision Making
TECHNOLOGY AND OPERATIONS MANAGEMENT
First work in AI 1943 The name “Artificial Intelligence” coined 1956
Engineering Overview Introduction to Engineering Design
CHAPTER TWO OVERVIEW SECTION DECISION-MAKING SYSTEMS
OVERVIEW OF BIOLOGICAL NEURONS
Engineering Overview Introduction to Engineering Design
Study of Chinese Seed Project Informationization
Introduction to Scheduling Chapter 1
Stevenson 5 Capacity Planning.
Study of Chinese Seed Project Informationization
Fields of Engineering Principles of EngineeringTM
© 2016 Global Market Insights, Inc. USA. All Rights Reserved Fuel Cell Market size worth $25.5bn by 2024 Low Power Wide Area Network.
Engineering Overview.
Engineering Overview.
Engineering Overview.
CHAPTER 6 Process Planning.
Presentation transcript:

ASK Labs Company We help machines make decisions

Base automation deployment on manufacture in the middle of the last century became a considerable stage in development of scientific and technical progress, promoted labor productivity increase, improvement of production quality, management optimization, elimination of the person from the manufactures which are hazardous to health. Industrial automation became a stage of development of scientific and technical progress

Today we are on the threshold of a new coil of scientific-technical progress which will deduce industrial production automation on new level. This coil became possible thanks to deployment of variety of innovative technologies which we unite under the name «Automation 2.0» New stage of industrial automation development

Automation 2.0 To meet the new demands of the market, ASK Labs has developed a product line under the title Automation 2.0. Software and hardware, includes both the proprietary software and third-party equipment manufacturers

What advantages are given to manufacture by deployment of innovative technologies and algorithms of automation? Automation 2.0 copes with problems with which traditional industrial control doesn't cope. Automation 2.0 allows to automate multidimensional processes. Automation 2.0 allows to achieve economic benefit where it can't be made methods of base automation. Automation 2.0 advantages compared with traditional industrial control

New technologies solve problems which not on forces traditional industrial control The new technologies united under the name «Automation 2.0» cope with following tasks: Astable, noisy technological processes. The processes representing a number of alternating modes of steady and unstable movements and self- oscillations. Nonlinear processes.

Multidimensional processes automation Automation 2.0 allows to automate multidimensional processes: The processes depending on several input variables and described by several output variables. There can be difficult dependences between variables. Technological process can make inconsistent demands to variables.

What an essence of the innovative approach used in Automation 2.0? The Automation 2.0 key moment is model construction of technological process. For model construction algorithms of artificial intelligence are used due to which possible to operate multidimensional astable processes with difficult nonlinear dependences, to predict a course of technological process, to stabilize technological process and to find the optimal operating mode for it.

The technological process forecast The technological process forecast allows to realize a principle: «to operate with anticipation instead of to react to a deviation from norm». The prevention of deviations from an optimum operating mode allows to prevent foul-up, not to admit work the equipment in extreme modes, to prevent the excessive consumption of power resources and materials.

Optimization The general approach to optimization of technological process in Automation 2.0: At the first stage we reduce a range of deviations of technological process to minimum values, At a following stage the optimum operating mode of technological process is calculated, Then we shift a working range to a technological limit.

Modern mathematical algorithms Modern mathematical algorithms are used in Automation 2.0 for model construction of technological process. Such as: fuzzy logic, neural networks and genetic algorithms, management by means of the simulated forecast, feedback system, expert systems, management by means of statistical models.

Use of computers in the industry Wide application of Automation 2.0 innovative technologies are possible only owing to wide use of computers on manufacture as new technologies demands volume and fast calculations. For the further development of Automation 2.0 ASK Labs has started development of the national software.

Economic benefit of deployment for the enterprises Refining Petrochemical Metallurgy 4…15% 3…12% 2…10% Economic benefit of Automation 2.0 deployment depends on branch, automation level of the enterprise and other production factors. On the average it is possible to expect following effect from deployment in different sectors:

The total economic effect Experts calculations show that Automation 2.0 deployment of at least on 10 % of the enterprises of the oil, metallurgical and mining industry will give about $2 billions (60 billions RUB) total effect. This effect develops from economy of power resources and materials, increase of labor productivity and production efficiency, decrease in deterioration of the equipment.

Alex Golovin ASK Labs Company