title: Fault detection and path optimisation for a meat-processing robot

authors: Hurd, Shaun A.

abstract: An automated Y-cutting system has been developed by the Automation Systems team of Industrial Research Limited. This robotic device performs the Y-cut operation on sheep carcasses. The robotic Y-cutting system must deal with a variety of carcass shapes and sizes, and it is important that process faults are detected, diagnosed and corrected as quickly as possible. This thesis addresses the fault detection and path optimisation requirements of the Y-cutting system. 

The development of a neural network-based fault detection module is documented. This module classifies process faults using axial motor current data from the Y-cutting robot. The module successfully classifies 98% of the presented cut signals during offline training, and 100% of cuts during an extended trial in an Australian meat-processing plant. An online training scheme is implemented to allow for the retraining of the neural network weights as required. The fault detection module is extended to handle a greater number of fault conditions and to detect variations in the process load. 

A path optimisation algorithm is developed to optimise the parameters that define the cut-path of the robot based on the output of the fault detection module. A line-search within the parameter space is used to estimate the position of the optimum parameter value. The optimisation of fifteen path parameters requires 4760 simulated Y-cuts, equating to approximately 1.5 days of processing in a typical meat-plant. This is significantly faster than the existing method for manually tuning the Y-cutting system. The fault detection and path optimisation systems can be generically applied to other robotic systems produced by Industrial Research Limited for the handling and processing of highly varying natural products.

text: Fault detection and path optimisation for a meat-processing robot An automated Y-cutting system has been developed by the Automation Systems team of Industrial Research Limited. This robotic device performs the Y-cut operation on sheep carcasses. The robotic Y-cutting system must deal with a variety of carcass shapes and sizes, and it is important that process faults are detected, diagnosed and corrected as quickly as possible. This thesis addresses the fault detection and path optimisation requirements of the Y-cutting system. 

The development of a neural network-based fault detection module is documented. This module classifies process faults using axial motor current data from the Y-cutting robot. The module successfully classifies 98% of the presented cut signals during offline training, and 100% of cuts during an extended trial in an Australian meat-processing plant. An online training scheme is implemented to allow for the retraining of the neural network weights as required. The fault detection module is extended to handle a greater number of fault conditions and to detect variations in the process load. 

A path optimisation algorithm is developed to optimise the parameters that define the cut-path of the robot based on the output of the fault detection module. A line-search within the parameter space is used to estimate the position of the optimum parameter value. The optimisation of fifteen path parameters requires 4760 simulated Y-cuts, equating to approximately 1.5 days of processing in a typical meat-plant. This is significantly faster than the existing method for manually tuning the Y-cutting system. The fault detection and path optimisation systems can be generically applied to other robotic systems produced by Industrial Research Limited for the handling and processing of highly varying natural products.

year: 2005

title: Big data analytics role in shaping the work of accounting function and accounting professionals

authors: Mukherjee, Abhishek

abstract: Purpose – This paper aims to examine how big data can be applied in accounting and identifies the competences future accountants at organizational level need to possess to remain competitive. Design/methodology/approach – This study used the association of chartered certified accountants (ACCA’s) framework and the technological, organization and environmental (ST.O.E.TOE) theoretical framework to identify the skills and capabilities urgently needed by accounting professionals at the organizational level in the context of big data analytics. The authors conducted comprehensive content analysis of academic literature issues from professional accounting bodies, and reports of accounting practitioners to gain insights of competencies that accounting professionals at organizational level need to possess to be prominent in the big data era. Findings – The findings show that in the big data era accounting professionals should acquire the seven competences to be competitive. The competencies are (1) skills in data analytic techniques and the use of statistical models; (2) knowledge of the business; (3) skills to control the risk from the emerging technologies; (4) skills and knowledge in accounting profession; (5) skills to understand the insights from big data analytics; (6) skills to test the quality, veracity and integrity of data; and (7) skills to communicate with others. The study also found that these competences do not exist alone. These competences are closely related to each other and complement each other, which means that accounting professionals cannot expect to stand out in the future competition by mastering only one or two of these seven competences. Research limitations/implications – The application of big data analytics is still in its infancy and its effects have yet to be confirmed so that there is no reliable case to collect and analyse. One limitation is the absence of practical case in this paper. The findings will help accounting professionals to understand the future of work. Practical implications – The findings will help accounting professionals to acquire new skills and knowledge in related areas and remain competitive in the post big data era. Social implications – This study highlights skills necessary for accountants to thrive in a big data era, emphasizing the shift from traditional accounting functions to roles that integrate advanced data capabilities and strategic insight. Originality/value – This is one of the few papers that seek to explore the competencies required by accounting professional at organizational level in the big data era using the ACCA’s research of accountant’s professional quotients and ST.O.E.TOE theoretical framework. This paper seeks to contribute to the literature on accounting professionals’ competencies at organizational level in the big data era. Big data analytics in accounting is influencing the evolution of accounting professionals’ competencies and is a key interest in accounting change literature. The nature of accounting work will undergo fundamental changes due to big data. Yet we know little about this due to big data being in its infancy stage in the accounting literature.

text: Big data analytics role in shaping the work of accounting function and accounting professionals Purpose – This paper aims to examine how big data can be applied in accounting and identifies the competences future accountants at organizational level need to possess to remain competitive. Design/methodology/approach – This study used the association of chartered certified accountants (ACCA’s) framework and the technological, organization and environmental (ST.O.E.TOE) theoretical framework to identify the skills and capabilities urgently needed by accounting professionals at the organizational level in the context of big data analytics. The authors conducted comprehensive content analysis of academic literature issues from professional accounting bodies, and reports of accounting practitioners to gain insights of competencies that accounting professionals at organizational level need to possess to be prominent in the big data era. Findings – The findings show that in the big data era accounting professionals should acquire the seven competences to be competitive. The competencies are (1) skills in data analytic techniques and the use of statistical models; (2) knowledge of the business; (3) skills to control the risk from the emerging technologies; (4) skills and knowledge in accounting profession; (5) skills to understand the insights from big data analytics; (6) skills to test the quality, veracity and integrity of data; and (7) skills to communicate with others. The study also found that these competences do not exist alone. These competences are closely related to each other and complement each other, which means that accounting professionals cannot expect to stand out in the future competition by mastering only one or two of these seven competences. Research limitations/implications – The application of big data analytics is still in its infancy and its effects have yet to be confirmed so that there is no reliable case to collect and analyse. One limitation is the absence of practical case in this paper. The findings will help accounting professionals to understand the future of work. Practical implications – The findings will help accounting professionals to acquire new skills and knowledge in related areas and remain competitive in the post big data era. Social implications – This study highlights skills necessary for accountants to thrive in a big data era, emphasizing the shift from traditional accounting functions to roles that integrate advanced data capabilities and strategic insight. Originality/value – This is one of the few papers that seek to explore the competencies required by accounting professional at organizational level in the big data era using the ACCA’s research of accountant’s professional quotients and ST.O.E.TOE theoretical framework. This paper seeks to contribute to the literature on accounting professionals’ competencies at organizational level in the big data era. Big data analytics in accounting is influencing the evolution of accounting professionals’ competencies and is a key interest in accounting change literature. The nature of accounting work will undergo fundamental changes due to big data. Yet we know little about this due to big data being in its infancy stage in the accounting literature.

year: 2025

title: Weaving kaupapa Māori and e-Learning

authors: Neal, Terry

subjects: New Zealand

abstract: This paper describes the creation and design of an online professional development course that aims to help educators in New Zealand to weave kaupapa Māori and e-Learning approaches together. The team sought to model weaving of kaupapa Māori and e-Learning approaches in how they worked together and the educational design for the material. The authors propose that educators with these skills are crucial if we are to effectively meet the needs of Māori learners, more than that, of all New Zealand learners, in the 21st century.

text: Weaving kaupapa Māori and e-Learning This paper describes the creation and design of an online professional development course that aims to help educators in New Zealand to weave kaupapa Māori and e-Learning approaches together. The team sought to model weaving of kaupapa Māori and e-Learning approaches in how they worked together and the educational design for the material. The authors propose that educators with these skills are crucial if we are to effectively meet the needs of Māori learners, more than that, of all New Zealand learners, in the 21st century.

year: 2006-09