215,410 research outputs found

    Systems analysis and design for accelerating process and cell line development

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    This presentation will highlight our work on integrating genomic science with systems analysis for cell culture engineering. Advances in computational and analytical tools in the past fifteen years have altered the landscape of process engineering. The traditional experimentation based process development is greatly augmented by data and model driven approaches. The availability of vast amount of bioprocess manufacturing data allowed us to gain valuable process insight through data mining. Those insights have led to genomic exploration and mathematical model development that provided mechanistic understanding of pivotal process features and aided in devising a better control of the process and product consistency. The data and model driven approach will also play a key role in a design based cell engineering for the development of production cell lines. A scenario of integrating data on genome stability and accessibility with model assisted cell engineering for cell line development will be presented and the potential and limitation of such an approach will be discussed with technical and regulatory considerations

    Predictive design analytics for optimal system design

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    “Predictive Design Analytics” proposed by this dissertation is a new paradigm to enable design engineers to extract important patterns from large-scale data characterized by four dimensions (volume, variety, velocity and veracity), and combine the extracted knowledge and its trend with complex systems optimization for various design decision making problems such as economical life cycle design, product family design and sustainable design. The goal of this research is the development of predictive design analytics methods for optimal systems design: Demand Trend Mining, Continuous Preference Trend Mining, Predictive Data-Driven Product Family Design, and Predictive Usage Mining for Life Cycle Assessment. To the best of the author’s knowledge, this is one of the first attempts to provide a systematic framework of predictive analytics for design, which comprises data preprocessing, data representation, predictive analytics algorithms, mathematical formulation of design problems, and design decision making. Demand trend mining (DTM) is developed to link pre-life (design and manufacturing) and end-of-life (remanufacturing and recycling) stages of a product for the improvement of initial product design. In order to capture hidden and upcoming trends of product demand, the algorithm combines three different models: decision tree for large-scale data, discrete choice analysis for demand modeling, and automatic time series forecasting for trend analysis. DTM dynamically reveals design attribute patterns that affect demands. A new design framework, Predictive Life Cycle Design (PLCD), is formulated, which connects DTM and optimal product design. The DTM algorithm interacts with the optimization-based model to maximize the total profit of a product through its life. For illustration, the developed model is applied to an example of smart-phone design, assuming that used phones are taken back for remanufacturing after one year. The result shows that the PLCD framework with the DTM algorithm identifies a more profitable product design over a product’s life cycle when compared to traditional design approaches that focus on the pre-life stage only. Continuous Preference Trend Mining (CPTM) is developed to generate multiple profit cycles of product design while addressing some fundamental challenges in previous studies. The CPTM algorithm captures a hidden trend of customer purchase patterns from accumulated transactional data. Unlike traditional, static data mining algorithms, the CPTM does not assume stationarity, but dynamically extracts valuable knowledge from customers over time. By generating trend embedded future data, the CPTM algorithm not only shows higher prediction accuracy in comparison with well-known static models, but also provides essential properties that could not be achieved with previously proposed models: utilizing historical data selectively, avoiding an over-fitting problem, identifying performance information of a constructed model, and allowing a numeric prediction. Furthermore, the formulation of the initial design problem is proposed, which can reveal an opportunity for multiple profit cycles. This mathematical formulation enables design engineers to optimize product design over multiple life cycles while reflecting customer preferences and technological obsolescence using the CPTM algorithm. For illustration, the developed framework is applied to an example of tablet PC design in the leasing market, and the result shows that the determination of optimal design is achieved over multiple life cycles. Predictive, data-driven product family design (PDPFD) is proposed as one of the predictive design analytics methods to address the challenge of determining optimal product family architectures with large-scale customer preference data. The proposed model expands clustering based data-driven approaches to incorporate a market-driven approach. The market-driven approach provides a profit model in the near future to determine the optimal position and number of product architectures among product architecture candidates generated by the k-means clustering algorithm. Unlike discrete choice analysis models which were used in previous market-driven approaches, a market value prediction method is proposed as a dynamic model which can capture and reflect the trend of customer preferences. Prediction intervals provide market uncertainties of the dynamic profit model for product family architecture design. A universal electric motors design example is used to demonstrate the implementation of the proposed framework with large-scale data. The comparative study shows that the PDPFD algorithm can generate more profit than pure clustering based data-driven models, which shows the necessity of combining data-driven and market-driven approaches. Predictive usage mining for life cycle assessment (PUMLCA) is developed to provide the usage modeling in life cycle assessment (LCA) which has been rarely discussed despite the magnitude of environmental impact from the usage stage. The PUMLCA algorithm can serve as an alternative of the conventional constant rate method. By modeling usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon, which can provide more accurate estimation of environmental impact. Large-scale sensor data of product operation is suggested as a source of data for the proposed method to mine usage patterns and build a usage model for LCA. The PUMLCA algorithm can provide a similar level of prediction accuracy to the constant rate method when data is constant, and the higher prediction accuracy when data has complex patterns. In order to mine important usage patterns more effectively, a new automatic segmentation algorithm is developed based on the change point analysis. The PUMLCA algorithm can also handle missing and abnormal values from large-scale sensor data, identify seasonality, formulate a predictive LCA for existing and new machines. Finally, the LCA of agricultural machinery demonstrates the proposed approach and highlights its benefits and limitations

    A Data Scientific Approach Towards Predictive Maintenance Application in Manufacturing Industry

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    Most industries have recently started to harness the power of data to assess their performance and improve their production systems for future competitiveness and sustainability. Therefore, utilization of data for obtaining insights through data-driven approaches is invading every domain of industrial applications. Predictive maintenance (PdM) is one of the highest impacted industrial use cases in data-driven applications due to its ability to predict machine failures by implementing machine learning algorithms. This study aims to propose a systematic data scientific approach to provide valuable insights by analysing industrial alarm and event log data, which might further be used for investigation in root cause understanding and planning of necessary maintenance activities. To do that, a Cross-Industry Standard Process for Data Mining (CRISP-DM) is followed as a reference model in this study. The results are presented by first understanding the relationship between alarms and product types being processed in the selected machines by using exploratory data analysis (EDA). Along with this, the behavior of problematic alarms is identified. Afterward, a predictive analysis formulated as a multi-class classification problem is performed using various Machine Learning (ML) models to predict the category of alarm and generate rules to be used for further investigation in maintenance planning. The performance of the developed models is evaluated based on the different metrics and the decision tree model is selected with the higher accuracy score among them. As a theoretical contribution, this study presents an implementation of predictive modeling in a structured way, which uses a systematic data scientific approach based on industrial alarm and event log data. On the other hand, as a practical contribution, this study provides a set of decision rules that can act as decision support for further exploration of possible in-depth root causes through the other contextual data, and hence it gives an initial foundation towards PdM application in the case company

    Automatic domain ontology extraction for context-sensitive opinion mining

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    Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

    Model-driven Enterprise Systems Configuration

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    Enterprise Systems potentially lead to significant efficiency gains but require a well-conducted configuration process. A promising idea to manage and simplify the configuration process is based on the premise of using reference models for this task. Our paper continues along this idea and delivers a two-fold contribution: first, we present a generic process for the task of model-driven Enterprise Systems configuration including the steps of (a) Specification of configurable reference models, (b) Configuration of configurable reference models, (c) Transformation of configured reference models to regular build time models, (d) Deployment of the generated build time models, (e) Controlling of implementation models to provide input to the configuration, and (f) Consolidation of implementation models to provide input to reference model specification. We discuss inputs and outputs as well as the involvement of different roles and validation mechanisms. Second, we present an instantiation case of this generic process for Enterprise Systems configuration based on Configurable EPCs

    Consumer-driven innovation networks and e-business management systems

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    This paper examines the use of consumer-driven innovation networks within the UK food-retailing industry using qualitative interview-based research analysed within an economic framework. This perspective revealed that, by exploiting information gathered directly from their customers at point-of-sale and data mining, supermarkets are able to identify consumer preferences and co-ordinate new product development via innovation networks. This has been made possible through their information control of the supply-chain established through the use of transparent inventory management systems. As a result, supermarkets’ e-business systems have established new competitive processes in the UK food-processing and retailing industry and are an example of consumer-driven innovation networks. The informant-based qualitative approach also revealed that trust-based transacting relationships operated differently from those previously described in the literature
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