Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Research in nonlinear structural and solid mechanics

Recent and projected advances in applied mechanics, numerical analysis, computer hardware and engineering software, and their impact on modeling and solution techniques in nonlinear structural and solid mechanics are discussed. The fields covered are rapidly changing and are strongly impacted by current and projected advances in computer hardware. To foster effective development of the technology perceptions on computing systems and nonlinear analysis software systems are presented

Comparing Data Mining Classification Algorithms in Detection of Simbox Fraud

Fraud detection in telecommunication industry has been a major challenge. Various fraud management systems are being used in the industry to detect and prevent increasingly sophisticated fraud activities. However, such systems are rule-based and require a continuous monitoring by subject matter experts. Once a fraudster changes its fraudulent behavior, a modification to the rules is required. Sometimes, the modification involves building a whole new set of rules from scratch, which is a toilsome task that may by repeated many times. In recent years, datamining techniques have gained popularity in fraud detection in telecommunication industry. Unlike rule based Simbox detection, data mining algorithms are able to detect fraud cases when there is no exact match with a predefined fraud pattern, this comes from the fuzziness and the statistical nature that is built into the data mining algorithms. To better understand the performance of data mining algorithms in fraud detection, this paper conducts comparisons among four major algorithms: Boosted Trees Classifier, Support Vector Machines, Logistic Classifier, and Neural Networks. Results of the work show that Boosted Trees and Logistic Classifiers performed the best among the four algorithms with a false-positive ratio less than 1%. Support Vector Machines performed almost like Boosted Trees and Logistic Classifier, but with a higher false-positive ratio of 8%. Neural Networks had an accuracy rate of 60% with a false positive ratio of 40%. The conclusion is that Boosted Trees and Support Vector Machines classifiers are among the better algorithms to be used in the Simbox fraud detections because of their high accuracy and low false-positive ratios

Architecture and Design of Medical Processor Units for Medical Networks

This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs). From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs. These specialized processors perform unique medical functions encoded as medical operational codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs. Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s). In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine. Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc. We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.Comment: 17 page

A Bayesian Hierarchical Model for Comparative Evaluation of Teaching Quality Indicators in Higher Education

The problem motivating the paper is the quantification of students' preferences regarding teaching/coursework quality, under certain numerical restrictions, in order to build a model for identifying, assessing and monitoring the major components of the overall academic quality. After reviewing the strengths and limitations of conjoint analysis and of the random coefficient regression model used in similar problems in the past, we propose a Bayesian beta regression model with a Dirichlet prior on the model coefficients. This approach not only allows for the incorporation of informative prior when it is available but also provides user friendly interfaces and direct probability interpretations for all quantities. Furthermore, it is a natural way to implement the usual constraints for the model weights/coefficients. This model was applied to data collected in 2009 and 2013 from undergraduate students in Panteion University, Athens, Greece and besides the construction of an instrument for the assessment and monitoring of teaching quality, it gave some input for a preliminary discussion on the association of the differences in students preferences between the two time periods with the current Greek economic and financial crisis

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security