CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Layer of protection analysis applied to ammonia refrigeration systems

Ammonia refrigeration systems are widely used in industry. Demand of these systems is expected to increase due to the advantages of ammonia as refrigerant and because ammonia is considered a green refrigerant. Therefore, it is important to evaluate the risks in existing and future ammonia refrigeration systems to ensure their safety. LOPA (Layer of Protection Analysis) is one of the best ways to estimate the risk. It provides quantified risk results with less effort and time than other methods. LOPA analyses one cause-consequence scenario per time. It requires failure data and PFD (Probability of Failure on Demand) of the independent protection layers available to prevent the scenario. Complete application of LOPA requires the estimation of the severity of the consequences and the mitigated frequency of the initiating event for risk calculations. Especially in existing ammonia refrigeration systems, information to develop LOPA is sometimes scarce and uncertain. In these cases, the analysis relies on expert opinion to determine the values of the variables required for risk estimation. Fuzzy Logic has demonstrated to be useful in this situation allowing the construction of expert systems. Based on fuzzy logic, the LOPA method was adapted to represent the knowledge available in standards and good industry practices for ammonia refrigeration. Fuzzy inference systems were developed for severity and risk calculation. Severity fuzzy inference system uses the number of life threatening injuries or deaths, number of injuries and type of medical attention required to calculate the severity risk index. Frequency of the mitigated scenario is calculated using generic data for the initiating event frequency and PFD of the independent protection layers. Finally, the risk fuzzy inference system uses the frequency and severity values obtained to determine the risk of the scenario. The methodology was applied to four scenarios. Risk indexes were calculated and compared with the traditional approach and risk decisions were made. In conclusion, the fuzzy logic LOPA method provides good approximations of the risk for ammonia refrigeration systems. The technique can be useful for risk assessment of existing ammonia refrigeration systems

Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks

The concepts of event and anomaly are important building blocks for developing a situational picture of the observed environment. We here relate these concepts to the JDL fusion model and demonstrate the power of Markov Logic Networks (MLNs) for encoding uncertain knowledge and compute inferences according to observed evidence. MLNs combine the expressive power of first-order logic and the probabilistic uncertainty management of Markov networks. Within this framework, different types of knowledge (e.g. a priori, contextual) with associated uncertainty can be fused together for situation assessment by expressing unobservable complex events as a logical combination of simpler evidences. We also develop a mechanism to evaluate the level of completion of complex events and show how, along with event probability, it could provide additional useful information to the operator. Examples are demonstrated on two maritime scenarios of rules for event and anomaly detection

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk