Total Quality Management and Six Sigma

In order to survive in a modern and competitive environment, organizations need to carefully organize their activities regarding quality management. TQM and six sigma are the approaches that have been successful in solving intricate quality problems in products and services. This volume can help those who are interested in the quality management field to understand core ideas along with contemporary efforts done in the field and authored as case studies in this volume. This volume may be useful to students, academics and practitioners across diversified disciplines

Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems

As a result of the analysis of dispatcher intelligence centers and aerial, land, underground, underwater, universal, and functionally focused artificial intelligence robotics systems, the problems of rational control, due to be performed under specific conditions of uncertainties, are chosen for probabilistic study. The choice covers the problems of planning the possibilities of functions performance on the base of monitored information about events and conditions and the problem of robot route optimization under limitations on risk of “failure” in conditions of uncertainties. These problems are resolved with a use of the proposed probabilistic approach. The proposed methods are based on selected probabilistic models (for “black box” and complex systems), which are implemented effectively in wide application areas. The cognitive solving of problems consists in improvements, accumulation, analysis, and use of appearing knowledge. The described analytical solutions are demonstrated by practical examples

Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

The approach for probabilistic rationale of artificial intelligence systems actions is proposed. It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling. The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.  The approach includes description of the proposed probabilistic models, optimization methods for rationale actions and incremental algorithms for solving the problems of  supporting decision-making on the base of monitored  data and rationale a robot actions in uncertainty conditions. The approach means practically a proactive commitment to excellence in uncertainty conditions. A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems

Models for Testing Modifiable Systems

The work describes reliability and security growth models for modifiable software systems as a result of revisions and tests performed for specified input data areas. The work shows that the known reliability growth models are of monotonically increasing type, which is not in line with current multi-version team technologies of software development that are primarily based on the open-source code. The authors suggest new non-monotonically increasing models of software reliability evaluation and planning that allow taking into account the effect of decreased reliability resulting from updates or wavefront errors. The work describes the elaborated bigeminal and generic reliability evaluation model as well as the models and test planning procedures. The work includes calculated expressions for the evaluation of the model accuracy and shows that the developed models are adequate to real data. An example is given of transition from probability models to fuzzy models in case of incomplete basic data. The work provides general recommendations for selection of software tool testing models

Probabilistic Methods and Technologies of Risk Prediction and Rationale of Preventive Measures by Using “Smart Systems”: Applications to Coal Branch for Increasing Industrial Safety of Enterprises

Abilities of “smart systems” for processing information, adaptation to conditions of uncertainty, and performance of scientifically proven preventive actions in real time are analyzed. Basic probabilistic models and technologies for the analysis of complex systems, using “smart systems,” ways of generation of probabilistic models for prognostic researches of the new systems projected, modernized, or transformed, are proposed. The proposed methods are described to predict risks to lose integrity for complex structures on the given prognostic time and rationale of preventive measures considering admissible risk, estimate “smart system” operation quality, and predict in real time the mean residual time before the next parameter abnormalities. The methods and technologies are implemented on the level of the remote monitoring systems. The application is illustrated on the examples of the joint-stock company “Siberian Coal Energy Company.

Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University

Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIs’ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the “Critical Infrastructure/Modelling, Simulation and Analysis – Data”. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance. In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: “Critical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuity”. The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet

Probabilistic Predictive Modelling for Complex System Risk Assessments

The risks assessment is described by the action of estimating the probability distribution functions of possible successes or failures of a system during a given prediction period. Typical probabilistic predictive models and methods for solving risks prediction problems are described, and their classification is given. Priority development directions for risks prediction in standard system processes and their implementation procedures are proposed. The reported examples demonstrate the effects and interpretation of the predictive results obtained. Notes: 1. System is a combination of interacting elements organized to achieve one or more stated purposes (according to ISO/IEC/IEEE 15288 “Systems and software engineering—System life cycle processes”). 2. Risk is defined as the effect of uncertainty on objectives considering consequences. An effect is a deviation from the expected — positive and/or negative (according to ISO Guide 73)