Evaluation of the Main Causes of Diesel Engine Injector Failure using Fault Tree Analysis

This paper is based on the application of the root cause analysis principle of diesel engine injector failure in diesels Haditha station. The maintenance of the diesel engine injector contains many problems that lead to the injector stopping; several reasons lead to such Problems. Fault tree analysis (FTA) is one of the most widely used methods in the industrial sector to perform reliability analysis of complex engineering systems. A fault tree is a logical representation of the relationship of primary/basic events that lead to a given undesirable event (i.e., top event). This research aims to present the (FTA) technology and how to use it in analyzing the causes of problems that lead to the injector stop working, and how to calculate the probability of occurrence of such problems. Implementation of FTA based on the probabilities of the causes under the top event and canalization lead to the calculated probability value (0.80). The plant management can define a specific plan to reduce these problems, because failure of an important engine part (injector) with such a large value leads to long downtime hours compared to operating hours.

Logical Representation of Sensor Data, Preferences, and Personalized Activity Recommendations in an eCoach System: An Ontology-based Proof-of-Concept Study (Preprint)

UNSTRUCTURED An automatic electronic coaching (eCoaching) can motivate individuals to lead a healthy lifestyle through early health risk prediction, customized recommendation generation, preference setting (such as, goal setting, response, and interaction), and goal evaluation. Such an eCoach system needs to collect heterogeneous health, wellness, and contextual data, and then convert them into meaningful information for health monitoring, health risk prediction, and the generation of personalized recommendations. However, data from various sources may cause a data compatibility dilemma. The proposed ontology can help in data integration, logical representation of sensory observations and customized suggestions, and discover implied knowledge. This "proof of concept (PoC)" research will help sensors, personal preferences, and recommendation data to be more organized. The research aims to design and develop an OWL-based ontology ("UiA Activity Recommendation Ontology/UiAARO") to annotate activity sensor data, contextual weather data, personal information, personal preferences, and personalized activity recommendations. The ontology was created using Protégé (V. 5.5.0) open-source software. We used the Java-based Jena Framework (V. 3.16) to build a semantic web application, which includes RDF API, OWL API, native tuple storage (TDB), and SPARQL query engine. The HermiT (V. 1.4.3.x) ontology reasoner available in Protégé 5.x has implemented the logical and structural consistency of the proposed ontology. The ontology can be visualized with OWLViz and OntoGraf, and the formal representation has been used to infer the health status of the eCoach participants with a reasoner. We have also developed an ontology verification module that behaves like a rule-based decision making (e.g., health state monitor and prediction), which can evaluate participant’s health state based on the evaluation of SPARQL query results, activity performed and predefined goal. Furthermore, the “UiAARO” has helped to represent the personalized recommendation messages beyond just “String” values, rather more meaningful with object-oriented representation. The scope of the proposed ontology is limited neither to specific sensor data nor only activity recommendations; instead, its scope can be further extended.

Digital twin of the process of thermal cutting of flat material into figured parts

The designing a digital twin of the process of the thermal cutting of sheet material using laser or gas equipment for its figure cutting is considered. The solution to the problem of optimizing the path of the cutting tool taking into account the thermal effects on the material to be cut is discussed. The solution of the problem of modeling the temperature change of the material to be cut is considered on the basis of a discrete - logical representation of information about the state of the technological system of sheet cutting. The results of a computational experiment are presented.

Modelling Virtual Bargaining using Logical Representation Change

We discuss work in progress on the computational modelling of virtual bargaining: inference-driven human coordination under severe communicative constraints. For this initial work we model variants of a two-player coordination game of item selection and avoidance taken from the current virtual bargaining literature. In this range of games, human participants collaborate to select items (e.g. bananas) or avoid items (e.g. scorpions), based on signalling conventions constructed and updated from shared assumptions, with minimal information exchange. We model behaviours in these games using logic programs interpretable as logical theories. From an initial theory comprised of rules, background assumptions and a basic signalling convention, we use automated theory repair to jointly adapt that basic signalling convention to novel contexts, with no explicit coordination between players. Our ABC system for theory repair delivers spontaneous adaptation, using reasoning failures to replace established conventions with better alternatives, matching human players’ own reasoning across several games.

Gamification Framework for E-Learning Systems in Higher Education

This paper explains the concept of gamification, lists the current models according to which educational e-courses can be designed, and proposes a conceptual eRIOOS model aimed at higher education. The aim of the research as well as the purpose of creating a conceptual model of gamification is to standardize the elements of computer games that can be used in educational e-courses at higher education institutions. In the preparation of this research, the emphasis was placed on the invention and analysis of professional and scientific literature for creating a conceptual model. The model contains a logical representation of two levels of complexity. Three separate e-courses have been created in different courses within the two University institutions, which serve as a tool to check the correctness of the conceptual eRIOOS model. The result of the research is a confirmed conceptual model that is suitable for creating Moodle e-courses of IT teaching orientation in higher education institutions.

Applying DTGolog to Large-scale Domains

While decision theoretic planning (DTP) offers great potential benefits to elicit purposeful behavior of the agent operating in uncertain environments, state-based approaches to DTP are known to be computationally intractable in large-scale domains. DTGolog is a decision-theoretic extension of a logic-based high level programming language Golog that completes a given partial Golog program using a form of directed value iteration. DTGolog has been proposed to alleviate some of the computational difficulties associated with DTP. The main advantages of DTGolog are that a DTP problem can be formulated using a logical representation to avoid explicit state enumeration, and the programmer can encode domain-specific knowledge in terms of high-level procedural templates to partially specify behavior of an agent. These templates constrain the search space to manageable size. Despite these clear advantages, there are few studies that investigate the applicability of DTGolog to very large-scale practical domains. In this thesis, we conduct two studies. First, we apply DTGolog to the well known case-study of the London Ambulance Service to demonstrate advantages and potentials of DTGolog as a quantitative evaluation tool for designing decision making agents. Second, we develop a software interface that allows to control the well-known Sony's AIBO robotics platform using DTGolog. We show that DTGolog can be used on this platform with a minimal amount of software customization. We run experiments to test functionality of our interface. The main contribution of this thesis is demonstration of applicability of DTGolog to two different large scale domains that are both practical and interesting.

Applying DTGolog to Large-scale Domains

While decision theoretic planning (DTP) offers great potential benefits to elicit purposeful behavior of the agent operating in uncertain environments, state-based approaches to DTP are known to be computationally intractable in large-scale domains. DTGolog is a decision-theoretic extension of a logic-based high level programming language Golog that completes a given partial Golog program using a form of directed value iteration. DTGolog has been proposed to alleviate some of the computational difficulties associated with DTP. The main advantages of DTGolog are that a DTP problem can be formulated using a logical representation to avoid explicit state enumeration, and the programmer can encode domain-specific knowledge in terms of high-level procedural templates to partially specify behavior of an agent. These templates constrain the search space to manageable size. Despite these clear advantages, there are few studies that investigate the applicability of DTGolog to very large-scale practical domains. In this thesis, we conduct two studies. First, we apply DTGolog to the well known case-study of the London Ambulance Service to demonstrate advantages and potentials of DTGolog as a quantitative evaluation tool for designing decision making agents. Second, we develop a software interface that allows to control the well-known Sony's AIBO robotics platform using DTGolog. We show that DTGolog can be used on this platform with a minimal amount of software customization. We run experiments to test functionality of our interface. The main contribution of this thesis is demonstration of applicability of DTGolog to two different large scale domains that are both practical and interesting.

Logic Learning in Adaline Neural Network

In this paper, Adaline Neural Network (ADNN) has been explored to simulate the actual signal processing between input and output. One of the drawback of the conventional ADNN is the use of the non-systematic rule that defines the learning of the network. This research incorporates logic programming that consists of various prominent logical representation. These logical rules will be a symbolic rule that defines the learning mechanism of ADNN. All the mentioned logical rule are tested with different learning rate that leads to minimization of the Mean Square Error (MSE). This paper uncovered the best logical rule that could be governed in ADNN with the lowest MSE value. The thorough comparison of the performance of the ADNN was discussed based on the performance MSE. The outcome obtained from this paper will be beneficial in various field of knowledge that requires immense data processing effort such as in engineering, healthcare, marketing, and business.

AN APPROACH TO THE CONTEXTUAL ANALYSIS OF TIME SERIES

Forecasting methods despite their conventions and limitations are the evolution of descriptive analytics mechanisms. Any model of the real-world objects works only under conditions of restrictions and agreements. The same conclusion can be made for the forecasting process, that it is not possible to forecast future state of the researched objects for 100%. However, building the most accurate forecast under the given conditions is the key. Modern data mining methods are based on a variety of models. However, such models can’t define the components of researched objects and processes except those contained in their models. The context allows using additional domain knowledge in describing the behavior of objects and processes in the form of qualitative assessments of their state. The same dataset in different domains will have various models and analysis results. The article deals with an approach to the domain context formation based on the ontology for analyzing time series of industrial processes indicators. The logical representation of the ontology based on the ALCHI(D) descriptive logic is also considered. The article describes as well experimental results confirming the correctness and effectiveness of the approach proposed.