71 research outputs found

    Spatial-temporal data modelling and processing for personalised decision support

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    The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

    Spatial-temporal data modelling and processing for personalised decision support

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    The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

    Assessing the factors of green computing adoption among manufacturing employees: an analysis of the electrical and electronic sector

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    Recent trends and heavy uses of IT products and electronic gadgets have led to a proliferation of green computing studies because these wastes are not biodegradable. A significant amount of previous studies has been performed on green computing at the organizational level with most studies tend to focus on developed countries. The present study examined numerically the most influential factor towards the employees’ intention to adopt green computing and measure the intention level of employees in green computing adoption. The current study explored five adoption factors with five hypotheses have been established. These hypotheses were theorized from Theory of Planned Behaviour with emphasis on environmental concern. Previous studies from Malaysia context have primarily concentrated on green computing in education sector compared to manufacturing sector. Hence the study was conducted at electrical and electronic industries located at southern Malaysia. Questionnaires were purposely distributed to 250 respondents, however only 110 responses were valid that yielded response rate of 56%. Respondents are among the employees in IT and administration department that equipped with ICT application. The finding verifies the most influential factor affecting green computing adoption is environmental concern with 43.8%. All factors were proved to have positive correlation to the green computing intention. This correlation is related to the high intention level of employees in practicing green computing due to headquarters initiatives and the conditions set by the importing countries. Social norms have less impact towards behavioral intention to practice green computing that manifested by the low correlation percentage. The current study contributes to our knowledge on the green computing intention among manufacturing employees in Malaysia besides the implications of the results and future research directions. This study will help the policy makers in promoting environmental awareness among users of computing devices

    Effects of spent garnet on the compressive and flexural strengths of concrete

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    Sand is the non-renewable resource which has been over-exploited from rivers in sync with the rapid development of construction industries to produce concrete. This affected the morphology of rivers and interrupted the functionality of riverine ecosystems by pollution. Meanwhile, the unrecyclable spent garnets were disposed of on a large scale and led to waste pollution. Therefore, this study aimed to determine the compressive and flexural strengths of concrete consisting of spent garnet as sand replacement. The specimens were prepared with consisting of spent garnet as sand replacement by weight in 0%, 10%, 20%, 30% and 40%. They were tested under compressive strength test at the age of 7 and 28 days while flexural strength test was conducted on the 28days. The findings revealed that the workability of fresh concrete was enhanced by an incremental amount of spent garnet. However, the compressive and flexural strengths of concrete consisting of spent garnet were discerned to be lower than control samples at all levels of replacement. Overall, the replacement with 20% spent garnet showed the optimum compressive and flexural strengths. It is concluded that the usage of spent garnet is considered as a promising resource for reducing consumption of sand and thus, improving the environmental problems

    An Iterated Greedy Algorithm for a Parallel Machine Scheduling Problem with Re-entrant and Group Processing Features

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    This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry

    Overview on: sequencing in mixed model flowshop production line with static and dynamic context

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    In the present work a literature overview was given on solution techniques considering basic as well as more advanced and consequently more complex arrangements of mixed model flowshops. We first analyzed the occurrence of setup time/cost; existing solution techniques are mainly focused on permutation sequences. Thereafter we discussed objectives resulting in the introduction of variety of methods allowing resequencing of jobs within the line. The possibility of resequencing within the line ranges from 1) offline or intermittent buffers, 2) parallel stations, namely flexible, hybrid or compound flowshops, 3) merging and splitting of parallel lines, 4) re-entrant flowshops, to 5) change job attributes without physically interchanging the position. In continuation the differences in the consideration of static and dynamic demand was studied. Also intermittent setups are possible, depending on the horizon and including the possibility of resequencing, four problem cases were highlighted: static, semi dynamic, nearly dynamic and dynamic case. Finally a general overview was given on existing solution methods, including exact and approximation methods. The approximation methods are furthermore divided in two cases, know as heuristics and methaheuristic

    Makespan Minimization in Re-entrant Permutation Flow Shops

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    Re-entrant permutation flow shop problems occur in practical applications such as wafer manufacturing, paint shops, mold and die processes and textile industry. A re-entrant material flow means that the production jobs need to visit at least one working station multiple times. A comprehensive review gives an overview of the literature on re-entrant scheduling. The influence of missing operations received just little attention so far and splitting the jobs into sublots was not examined in re-entrant permutation flow shops before. The computational complexity of makespan minimization in re-entrant permutation flow shop problems requires heuristic solution approaches for large problem sizes. The problem provides promising structural properties for the application of a variable neighborhood search because of the repeated processing of jobs on several machines. Furthermore the different characteristics of lot streaming and their impact on the makespan of a schedule are examined in this thesis and the heuristic solution methods are adjusted to manage the problem’s extension

    Extended classification for flowshops with resequencing

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    Este trabajo presenta una clasificación extendida de líneas de flujo no-permutación. Se consideran las múltiples opciones que se presentan al incluir la posibilidad de resecuenciar piezas en la línea. Se ha visto que en la literatura actual no se ha clasificado con profundidad este tipo de producción. Abstract This paper presents an extended cassification for non-permutation flowshops. The versatile options which occur with the possibility of resequencing jobs within the line are considered. The literature review shows that no classification exists which considers extensively this type of flowshop
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