Application of SVM Classifier in IR Target Recognition

AbstractIn the paper, support vector machine is proposed in IR target recognition. Grid method is used to select the appropriate parameters of SVM to avoid over-fitting due to the choice of inappropriate parameters. We employ coal mine IR monitoring images to testify the IR target recognition ability of SVM. And features and category of the coal mine IR monitoring images are given. The experimental results illustrate that the IR target recognition accuracy of SVM is 100%.Thus, SVM is an excellent IR target recognition method

The Use of a Complexity Model to Facilitate in the Selection of a Fuel Cell Assembly Sequence

Various tools and methods exists for arriving at an optimised assembly sequence with most using a soft computing approach. However, these methods have issues including susceptibly to early convergence and high computational time. The typical objectives for these methods are to minimise the number of assembly change directions, orientation changes or the number of tool changes. This research proposes an alternative approach whereby an assembly sequence is measured based on its complexity. The complexity value is generated using design for assembly metrics and coupled with considerations for product performance, component precedence and material handling challenges to arrive at a sequence solution which is likely to be closest to the optimum for cost and product quality. The case presented in this study is of the assembly of a single proton exchange membrane fuel cell. This research demonstrates a practical approach for determining assembly sequence using data and tools that are used and available in the wider industry. Further work includes automating the sequence generation process and extending the work by considering additional factors such as ergonomics

The use of a complexity model to facilitate in the selection of a fuel cell assembly sequence

Various tools and methods exists for arriving at an optimised assembly sequence with most using a soft computing approach. However, these methods have issues including susceptibly to early convergence and high computational time. The typical objectives for these methods are to minimise the number of assembly change directions, orientation changes or the number of tool changes. This research proposes an alternative approach whereby an assembly sequence is measured based on its complexity. The complexity value is generated using design for assembly metrics and coupled with considerations for product performance, component precedence and material handling challenges to arrive at a sequence solution which is likely to be closest to the optimum for cost and product quality. The case presented in this study is of the assembly of a single proton exchange membrane fuel cell. This research demonstrates a practical approach for determining assembly sequence using data and tools that are used and available in the wider industry. Further work includes automating the sequence generation process and extending the work by considering additional factors such as ergonomi

A new knowledge sourcing framework to support knowledge-based engineering development

New trends in Knowledge-Based Engineering (KBE) highlight the need for decoupling the automation aspect from the knowledge management side of KBE. In this direction, some authors argue that KBE is capable of effectively capturing, retaining and reusing engineering knowledge. However, there are some limitations associated with some aspects of KBE that present a barrier to deliver the knowledge sourcing process requested by the industry. To overcome some of these limitations this research proposes a new methodology for efficient knowledge capture and effective management of the complete knowledge life cycle. Current knowledge capture procedures represent one of the main constraints limiting the wide use of KBE in the industry. This is due to the extraction of knowledge from experts in high cost knowledge capture sessions. To reduce the amount of time required from experts to extract relevant knowledge, this research uses Artificial Intelligence (AI) techniques capable of generating new knowledge from company assets. Moreover the research reported here proposes the integration of AI methods and experts increasing as a result the accuracy of the predictions and the reliability of using advanced reasoning tools. The proposed knowledge sourcing framework integrates two features: (i) use of advanced data mining tools and expert knowledge to create new knowledge from raw data, (ii) adoption of a well-established and reliable methodology to systematically capture, transfer and reuse engineering knowledge. The methodology proposed in this research is validated through the development and implementation of two case studies aiming at the optimisation of wing design concepts. The results obtained in both use cases proved the extended KBE capability for fast and effective knowledge sourcing. This evidence was provided by the experts working in the development of each of the case studies through the implementation of structured quantitative and qualitative analyses

An ontology-based approach for integrating engineering workflows for industrial assembly automation systems

Modern manufacturing organisations face a number of external challenges as the customer-base is more varied, more knowledgeable, and has a broader range of requirements. This has given rise to paradigms such as mass customisation and product personalisation. Internally, businesses must manage multidisciplinary teams that must work together to achieve a common goal despite spanning multiple domains, organisations, and due to improved communication technologies, countries. The motivation for this research is to therefore understand firstly how the multiplicity of stakeholders come together to realise the ever increasing and ever more complex number of product variants that manufacturing systems must now realise. The lack of integration of engineering tools and methods is identified to be one of the barriers to smooth engineering workflows and thus one of the key challenges faced in the current dynamic market. To address this problem, this research builds upon previous works that propose domain ontologies for representing knowledge in a way that is both machine and human readable, facilitating interoperability between engineering software. In addition to this, the research develops a novel Skill model that brings the domain ontologies into a practical, implementable framework that complements existing industrial workflows. The focus of this thesis is the domain of industrial assembly automation systems due to the role this stage of manufacturing plays in realising product variety. Therefore, the proposed ontological models and framework are applied to product assembly scenarios. The key contributions of this work are the consolidation of domain ontologies with a Skill model within the context of assembly systems engineering, development of a broader framework for the ontologies to sit within that complements existing workflows. In addition, the research demonstrates how the framework can be applied to connect assembly process planning activities with machine control logic to identify and rectify inconsistencies as new products are introduced. In summary, the thesis identifies the shortcomings of existing ontological models within the context of manufacturing, develops new models to address those shortcoming, and develops new, useful ways for ontological models to be used to address industrial problems by integrating them with virtual engineering tools

Time Localization of Abrupt Changes in Cutting Process using Hilbert Huang Transform

Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and condition of machining system elements. Different phenomena in cutting zone have signatures in different frequency bands in signal acquired during process monitoring. The time localization of signal’s frequency content is very important. An emerging technique for simultaneous analysis of the signal in time and frequency domain that can be used for time localization of frequency is Hilbert Huang Transform (HHT). It is based on empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMFs) as simple oscillatory modes. IMFs obtained using EMD can be processed using Hilbert Transform and instantaneous frequency of the signal can be computed. This paper gives a methodology for time localization of cutting process stop during intermittent turning. Cutting process stop leads to abrupt changes in acquired signal correlated to certain frequency band. The frequency band related to abrupt changes is localized in time using HHT. The potentials and limitations of HHT application in machining process monitoring are shown