Condition monitoring of helical gears using automated selection of features and sensors

The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or red undant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications

The value of theoretical multiplicity for steering transitions towards sustainability

Transition management, as a theory of directing structural societal changes towards sustainable system innovations, has become a major topic in scientific research over the last years. In this paper we focus on the question how transitions towards sustainability can be steered, governed or managed, in particular by governmental actors. We suggest an approach of theoretical multiplicity, arguing that multiple theories will be needed simultaneously for dealing with the complex societal sustainability issues. Therefore, we address the steering question by theoretically comparing transition management theory to a number of related theories on societal change and intervention, such as multi-actor collaboration, network governance, configuration management, policy agenda setting, and adaptive management. We conclude that these related theories put the managerial assumptions of transition management into perspective, by adding other steering roles and leadership mechanisms to the picture. Finally we argue that new modes of steering inevitable have consequences for the actual governance institutions. New ways of governing change ask for change within governance systems itself and vice versa. Our argument for theoretical multiplicity implicates the development of multiple, potentially conflicting, governance capacitie

A FRAMEWORK FOR ONTOLOGY- BASED DIABETES DIAGNOSIS USING BAYELSIAN OPTIMIZATION TECHNIQUE

Diabetes Management System (DMS) is a computer-based system which aid physicians in properly diagnosing diabetes mellitus disease in patients. The DMS is essential in making individuals who have diabetes aware of their state and type. Existing approaches employed have not been efficient in considering all the diabetes type as well as making full prescription to diabetes patients. In this paper, a framework for an improved Ontology-based Diabetes Management System with a Bayesian optimization technique is presented. This helped in managing the diagnosis of diabetes and the prescription of treatment and drug to patients using the ontology knowledge management. The framework was implemented using Java programming language on Netbeans IDE, Protégé 4.2 and mysql. An extract of the ontology graph and acyclic probability graph was shown. The result showed that the nature of Bayesian network which has to do with statistical calculations based on equations, functions and sample frequencies led to more precise and reliable outcome.   &nbsp

Theoretical multiplicity for the governance of transitions. The energy producing greenhouse case

Transition management, as a theory of directing structural societal changes towards sustainable system innovations, has become a major topic in scientific research over the last years. In the Netherlands, the concept of transition management was adopted by several governmental agencies as one of the leading principles for steering sustainable development. In this paper we focus on the governance of transitions. The question is if and how transitions towards sustainability can be steered, governed or managed, in particular by governmental actors. We suggest an approach of theoretical multiplicity, arguing that multiple theories will be needed simultaneously for dealing with the complex societal sustainability issues. Therefore, we address the governance question by theoretically comparing transition management theory to a number of related theories on societal change and intervention, such as multi-actor collaboration, network governance, policy agenda setting and adaptive governance. We argue that these related theories put the managerial assumptions of transition management into perspective, by adding other steering roles and leadership mechanisms to the picture. We will illustrate the advantages of theoretical multiplicity by analysing the case of the greenhouse as a source of energy. The energy producing greenhouse can be considered a revolutionary technology, with the potential of turning the greenhouse horticultural sector from a mass energy consumer into a sustainable energy user and producer

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Diagnosis of sustainable collaboration in health promotion – a case study

<p>Abstract</p> <p>Background</p> <p>Collaborations are important to health promotion in addressing multi-party problems. Interest in collaborative processes in health promotion is rising, but still lacks monitoring instruments. The authors developed the DIagnosis of Sustainable Collaboration (DISC) model to enable comprehensive monitoring of public health collaboratives. The model focuses on opportunities and impediments for collaborative change, based on evidence from interorganizational collaboration, organizational behavior and planned organizational change. To illustrate and assess the DISC-model, the 2003/2004 application of the model to the Dutch whole-school health promotion collaboration is described.</p> <p>Methods</p> <p>The study combined quantitative research, using a cross-sectional survey, with qualitative research using the personal interview methodology and document analysis. A DISC-based survey was sent to 55 stakeholders in whole-school health promotion in one Dutch region. The survey consisted of 22 scales with 3 to 8 items. Only scales with a reliability score of 0.60 were accepted. The analysis provided for comparisons between stakeholders from education, public service and public health.</p> <p>The survey was followed by approaching 14 stakeholders for a semi-structured DISC-based interview. As the interviews were timed after the survey, the interviews were used to clarify unexpected and unclear outcomes of the survey as well.</p> <p>Additionally, a DISC-based document analysis was conducted including minutes of meetings, project descriptions and correspondence with schools and municipalities.</p> <p>Results</p> <p>Response of the survey was 77% and of the interviews 86%. Significant differences between respondents of different domains were found for the following scales: organizational characteristics scale, the change strategies, network development, project management, willingness to commit and innovative actions and adaptations. The interviews provided a more specific picture of the state of the art of the studied collaboration regarding the DISC-constructs.</p> <p>Conclusion</p> <p>The DISC-model is more than just the sum of the different parameters provided in the literature on interorganizational collaboration, organization change, networking and setting-approaches. Monitoring a collaboration based on the DISC-model yields insight into windows of opportunity and current impediments for collaborative change. DISC-based monitoring is a promising strategy enabling project managers and social entrepreneurs to plan change management strategies systematically.</p

Availability Analysis of Redundant and Replicated Cloud Services with Bayesian Networks

Due to the growing complexity of modern data centers, failures are not uncommon any more. Therefore, fault tolerance mechanisms play a vital role in fulfilling the availability requirements. Multiple availability models have been proposed to assess compute systems, among which Bayesian network models have gained popularity in industry and research due to its powerful modeling formalism. In particular, this work focuses on assessing the availability of redundant and replicated cloud computing services with Bayesian networks. So far, research on availability has only focused on modeling either infrastructure or communication failures in Bayesian networks, but have not considered both simultaneously. This work addresses practical modeling challenges of assessing the availability of large-scale redundant and replicated services with Bayesian networks, including cascading and common-cause failures from the surrounding infrastructure and communication network. In order to ease the modeling task, this paper introduces a high-level modeling formalism to build such a Bayesian network automatically. Performance evaluations demonstrate the feasibility of the presented Bayesian network approach to assess the availability of large-scale redundant and replicated services. This model is not only applicable in the domain of cloud computing it can also be applied for general cases of local and geo-distributed systems.Comment: 16 pages, 12 figures, journa

Hospitality unit diagnosis: an expert system approach

Formal methods of management problem-solving have been extensively researched. However, these concepts are incomplete in that they assume a problem has been correctly identified before initiating the problem-solving process. In reality management may not realise that a problem exists or may identify an incorrect problem. As a result, considerable time and effort may be wasted correcting symptoms rather than the true problem. This research describes the development of a computerised system to support problem identification. The system focuses specifically on the area of hospitality management, encompassing causes and symptoms of prominent problems in the hospitality industry. The system is based on knowledge rather than data. Research has shown that Expert Systems allow reasoning with knowledge. As a result, Expert Systems were selected as an appropriate technology for this application. Development is undertaken from the perspective of a hotel manager, using appropriate software development tools. The required knowledge is generally obtained from either expert interviews or textbook analysis. Gaining commitment from sufficient industry experts proved too difficult to allow the use of the former method, and therefore the latter method was utilised. However, knowledge acquired in this manner is limited in both quality and quantity. In addition, essential experience based judgmental knowledge is not available from this source. To counteract this, the personal knowledge of the author, a qualified hotel manager, was used. When developing an Expert System, knowledge acquisition and representation are of paramount importance. In this research, these issues are problematic due to the broad interdisciplinary nature and scope of hospitality management. To counteract this problem, some structure was required. Finance, Marketing, Personnel, Control, and Operations were selected as important functions within the hospitality business and therefore were represented within the system for diagnosis. A modular approach was used with modules being developed for each functional area. An initial top level module performs a general diagnosis, and then separate subordinate modules diagnose the functional areas. This research established that the knowledge required for incorporation into such a system is not available. The possibility of acquiring this knowledge is beyond the bounds of this research. However, sufficient marketing knowledge was sourced to facilitate the development of the Expert System structure. This structure demonstrates the application of the technology to the task and could subsequently be used when more knowledge is elicited. The research findings show that the development of a modular diagnostic system is possible using an Expert System Shell. The major limiting factor encountered is the total lack of the relevant knowledge. As a result, further research is recommended to establish the factors influencing diagnosis in the hospitality industry

Chondrosarcoma: With Updates on Molecular Genetics

Chondrosarcoma (CHS) is a malignant cartilage-forming tumor and usually occurs within the medullary canal of long bones and pelvic bones. Based on the morphologic feature alone, a correct diangosis of CHS may be difficult, Therefore, correlation of radiological and clinicopathological features is mandatory in the diagnosis of CHS. The prognosis of CHS is closely related to histologic grading, however, histologic grading may be subjective with high inter-observer variability. In this paper, we present histologic grading system and clinicopathological and radiological findings of conventional CHS. Subtypes of CHSs, such as dedifferentiated, mesenchymal, and clear cell CHSs are also presented. In addition, we introduce updated cytogenetic and molecular genetic findings to expand our understanding of CHS biology. New markers of cell differentiation, proliferation, and cell signaling might offer important therapeutic and prognostic information in near future

Quantum computing challenges in the software industry. A fuzzy AHP-based approach

publishedVersio