Application of spiking neural networks and the bees algorithm to control chart pattern recognition

Statistical process control (SPC) is a method for improving the quality of products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types of charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application of SNN techniques to control chart pattern recognition. It is designed to present an analysis of the existing learning algorithms of SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation of neural networks. This thesis focuses on the architecture and the learning procedure of the network. Four new learning algorithms arc presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim of the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Application of spiking neural networks and the bees algorithm to control chart pattern recognition

Statistical process control (SPC) is a method for improving the quality of products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types of charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application of SNN techniques to control chart pattern recognition. It is designed to present an analysis of the existing learning algorithms of SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation of neural networks. This thesis focuses on the architecture and the learning procedure of the network. Four new learning algorithms arc presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim of the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies

A Study on Benchmarking Models and Frameworks in Industrial SMEs: Challenges and Issues

This paper is based on a literature review of recent publications in the field of benchmarking methodology implemented in small and medium enterprises with regards to measure and benchmark upstream, leading or developmental aspects of organizations. Benchmarking has been recognized as an essential tool for continuous improvement and competitiveness.  It can also help SMEs to improve their operational and financial performances. However, only few entrepreneurs turn to benchmarking implementation, due to lack of time and resources. In this study current benchmarking models (2005 onwards), dedicated specifically to the SMEs, have been identified and their characteristics and objectives have been discussed.  Key findings from this review confirm that this is an under-developed area of research and that most practitioner approaches are focused on benchmarking practices within SMEs. There is a need to extend theoretical and practical aspects of benchmarking in SMEs by studying the process of benchmarking with regards to the novel concept of lead benchmarking as a possible means of achieving increased radical and innovative transformation in organizational change.   From the review it emerged that, lead, forward looking and predictive benchmarking have not been considered in SMEs, and future researches could include them

Automated Service Identification Methods: A Review

Service identification represents the first phase in service modelling, a necessary step in SOA. This research study reviewed and analyzed the issues related to automation issues of service identification. However, the importance of service identification methods’ (SIM) automation and their business alignment are emphasized in literature, reviewing existing service identification methods (SIMs) reveals the lack of business alignment, automation as challenging issues. We close the gap by proposing ASIF which relies on automating the SIMs’ steps to identify business aligned services based on business processes and business goals

Multiple Descriptors for Visual Odometry Trajectory Estimation

Visual Simultaneous Localization and Mapping (VSLAM) systems are widely used in mobile robots for autonomous navigation.  One important part in VSLAM is trajectory estimation. Trajectory estimation is a part of the localisation task in VSLAM where a robot needs to estimate the camera pose in order to precisely align the real visited image locations.  The poses are estimated using Visual Odometry Trajectory Estimation (VOTE) by extracting distinctive and trackable keypoints from sequence image locations having been visited by a robot. In the visual trajectory estimation, one of the most popular solutions is arguably PnP-RANSCA function. PnP-RANSAC is a common approach used for estimating the VOTE which uses a feature descriptor such as SURF to extract key-points and match them in pairs based on their descriptors. However, due to the sensor noise and the high fluctuating scenes constitute an inevitable shortcoming that reduces the single visual descriptor performance in extracting the distinctive and trackable keypoints. Thus, this paper proposes a method that uses a random sampling scheme to combine the result of multiple key-points descriptors. The scheme extracts the best keypoints from SIFT, SURF and ORB key-point detectors based on their key-point response value. These keypoints are combined and refined based on Euclidean distances. This combination of keypoints with their corresponding visual descriptors are used in VOTE which reduces the trajectory estimation errors. The proposed algorithm is evaluated on the widely used benchmark dataset KITTI where the three longest sequences are selected, 00 with 4541 images, 02 with 2761 images and 05 with 1101 images. In trajectory estimation experiment, the proposed algorithm can reduce the trajectory error of 44%, 8% and 13% on KITTI dataset for the sequence 00, 02 and 05 respectively based on translational and rotational errors. Also, the proposed algorithm succeeded in reducing the number of keypoints used in VOTE as combined with the state-of-the-art RTAB-Map

Critical Success Factors for Enterprise Resource Planning System Implementation: A Case Study in Malaysian SME

Implementing Enterprise Resource Planning (ERP) system for Malaysian Small to Medium Enterprises (SMEs) is not just a technological challenge. It is a socio-technological endeavour which mandates modifying existing applications and redesigning business processes to facilitate ERP system implementation. Most Malaysian SMEs cannot afford to adopt an existing ERP system due to the extremely high cost and complex implementation. The ERP system implementation literature contains many case studies of organizations that have implemented ERP system successfully. However, many Malaysian SMEs do not achieve success in their ERP system implementation. There are very few studies have represented and developed critical success factors of ERP system implementation projects highlighted for SMEs. This research seeks to explore the critical success factors for successful ERP system implementation in Malaysian SMEs. The research method is based on a case study within Malaysian SME to perform a critical success factors model of ERP system implementation adoption which has validated by a number of SMEs in Malaysia. The proposed model will help outline the critical factors that should be considered by Malaysian SMEs in uccessfully adopting ERP system

Local search manoeuvres recruitment in the bees algorithm

Swarm intelligence of honey bees had motivated many bioinspired based optimisation techniques. The Bees Algorithm (BA) was created specifically by mimicking the foraging behavior of foraging bees in searching for food sources.During the searching, the original BA ignores the possibilities of the recruits being lost during the flying.The BA algorithm can become closer to the nature foraging behavior of bees by taking account of this phenomenon.This paper proposes an enhanced BA which adds a neighbourhood search parameter which we called as the Local Search Manoeuvres (LSM) recruitment factor.The parameter controls the possibilities of a bee extends its neighbourhood searching area in certain direction.The aim of LSM recruitment is to decrease the number of searching iteration in solving optimization problems that have high dimensions.The experiment results on several benchmark functions show that the BA with LSM performs better compared to the one with basic recruitment

The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems

Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA), on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others

Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier