92 research outputs found
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INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS)
Discrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can take randomness into account and address very detailed models. However, there are major challenges that are faced in simulating such systems, especially when they are used to support short-term decisions (e.g., operational decisions or maintenance and scheduling decisions considered in this research). First, a detailed simulation requires significant amounts of computation time. Second, given the enormous amount of dynamically-changing data that exists in the system, information needs to be updated wisely in the model in order to prevent unnecessary usage of computing and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement and address the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. To the best of our knowledge, the proposed DDDAMS methodology is one of the first efforts to present a coherent integrated decision making framework for timely planning and control of distributed manufacturing enterprises.To this end, comprehensive system architecture and methodologies are first proposed, where the components include 1) real time DDDAM-Simulation, 2) grid computing modules, 3) Web Service communication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are then developed and embedded into a real-time simulator for enabling its DDDAMS capabilities such as abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation. As part of the developed algorithms, improvements are made to the resampling techniques for sequential Bayesian inferencing, and their performance is benchmarked in terms of their resampling qualities and computational efficiencies. Grid computing and Web Services are used for computational resources management and inter-operable communications among distributed software components, respectively. A prototype of proposed DDDAM-Simulation was successfully implemented for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, where the results look quite promising
Recommended from our members
INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS)
Discrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can take randomness into account and address very detailed models. However, there are major challenges that are faced in simulating such systems, especially when they are used to support short-term decisions (e.g., operational decisions or maintenance and scheduling decisions considered in this research). First, a detailed simulation requires significant amounts of computation time. Second, given the enormous amount of dynamically-changing data that exists in the system, information needs to be updated wisely in the model in order to prevent unnecessary usage of computing and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement and address the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. To the best of our knowledge, the proposed DDDAMS methodology is one of the first efforts to present a coherent integrated decision making framework for timely planning and control of distributed manufacturing enterprises.To this end, comprehensive system architecture and methodologies are first proposed, where the components include 1) real time DDDAM-Simulation, 2) grid computing modules, 3) Web Service communication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are then developed and embedded into a real-time simulator for enabling its DDDAMS capabilities such as abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation. As part of the developed algorithms, improvements are made to the resampling techniques for sequential Bayesian inferencing, and their performance is benchmarked in terms of their resampling qualities and computational efficiencies. Grid computing and Web Services are used for computational resources management and inter-operable communications among distributed software components, respectively. A prototype of proposed DDDAM-Simulation was successfully implemented for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, where the results look quite promising
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Online State Estimation of a Microgrid using Particle Filtering
Recent technical advances in power systems on communications, computation and generation technologies have collectively lead to the development of microgrids. However, these microgrids are still heavily challenged by the state estimation problem which traditionally exists in power grids. State estimation in these systems is especially crucial due to the impact it has to the power flow control and the security of the system. In this work, we introduce a novel algorithm for online state estimation of microgrids using particle filtering. The proposed algorithm is fed by a database receiving data from electrical and environmental sensors in real time. The performance of the proposed algorithm is first validated through synthetic experiments. Then, the experiments are conducted using real data obtained from a benchmark low voltage microgrid. The experiments reveal that the proposed algorithm is able to achieve state estimations that are very close to the actual states (in terms of power injections). This way, significant improvement is premised in the functional performance of microgrids while savings are encountered in computational resource utilization. As part of its future venues, proposed particle filter-based state estimation algorithm will be embedded into a dynamic data driven adaptive simulation framework that is being designed for the power control and management of microgrids
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DDDAS-based Communication in Distributed Smartgrid Networks
The U.S. military spends over nine billion dollars annually to provide electrical power to its vast array of installations throughout the U.S. In an effort to reduce those costs and improve the security of their installations, the military in conjunction with the Department of Energy has started to install renewable energy infrastructure at various department owned facilities. One method to integrate these resources is by installing micro grids. Micro grids, however, are still reliant on a local utility for the vast majority of its electricity, and necessitate design of powerful decision-making units to function at its full utility allowing for isolation/desolation processes when an emergency occurs. In this work, we introduce a two-stage real-time decision-making and communications structure for integrating micro grids into a larger smart grid network using dynamic data driven adaptive simulation techniques. Through its demand prioritization scheme, the proposed framework has the capability to adjust to various micro grid types that have the potential for deployment in various municipalities throughout the country. Preliminary experiments have shown that different micro grid enabling parties may achieve significant cost savings by integrating micro grids into a larger regional or national smart grid across the U.S
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Automatic Partitioning of Large Scale Simulation in Grid Computing for Run Time Reduction
Tailoring information systems development to operations management and business strategy is an important way to bolster the effectiveness and efficiency of any institution’s managerial success. Innovations in Information Systems for Business Functionality and Operations Management offers a vital compendium of the latest research in IS/IT applications to business and operations management. With experts from around the world giving contributions in the form of case studies, methodologies, best practices, frameworks, and research, this critical collection of cutting-edge and state-of-the-art technological references will serve practitioners and academics alike.
Tailoring information systems development to operations management and business strategy is an important way to bolster the effectiveness and efficiency of any institution’s managerial success. Innovations in Information Systems for Business Functionality and Operations Management offers a vital compendium of the latest research in IS/IT applications to business and operations management. With experts from around the world giving contributions in the form of case studies, methodologies, best practices, frameworks, and research, this critical collection of cutting-edge and state-of-the-art technological references will serve practitioners and academics alike
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MR2: A Two-stage Feature Selection Algorithm in High-throughput Methylation Data for Max-relevance and Min-redundancy
Recent advances reveal that DNA methylation plays an important role in regulating different genome functions where anomalous methylation levels are associated with various cancer types. Feature selection algorithms are geared towards high-throughput analysis of DNA methylation to help identify idiosyncratic DNA methylation profiles associated with cancer types and subtypes. In high dimensional and highly correlated DNA methylation data, feature selection algorithms aim at selecting an efficient and comprehensive feature set to better capture characteristics of phenotypes. In this work, we introduce a two-stage feature selection algorithm (MR2) based on maximum relevance and minimum redundancy criteria. The features that satisfy the relevance conditions are filtered in the first stage, in the second stage, the final subset of loci is selected to reach minimal redundancy by using a k-medoids clustering algorithm that embeds a succinct uncertainty measure score. The performance of the proposed feature selection algorithm is benchmarked against those of the principal component analysis and four other commonly used filtering methods using lung and breast cancer datasets obtained from Gene Expression Omnibus in terms of their classification errors in support vector machine classifiers. Our MR2 algorithm outperforms these filtering based algorithms while at the same time providing more interpretable results
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Sequential Monte Carlo-based Radar Tracking in the Presence of Sea-Surface Multipath
In order to detect the location or track the trajectory of a target, the radar emits signals in predetermined directions and frequencies through its transmitter. However, there is always more than one path for signals' propagation where the ones return indirectly may cause interferences with the ones return directly. This interference may mislead the signal receiver in isolating the correct target echo and seriously degrade the performance of the radar system. In this work, we develop a tracking mechanism based on a sequential Monte Carlo sampling technique that addresses the multipath interference, hence promises improvements in the capacity and accuracy of the radar tracking systems. To the best of our knowledge, this work is the first to relax one of the major assumptions that has consistently been made in the literature of the sea surface multipath effects by considering both the specular and diffuse reflection aspects in an integrated manner. The accuracy and efficiency of the developed method for state estimation of targets (range, elevation and velocity) are tested in 2-D and 3-D space using synthetic experiments. The proposed work premises significant reduction in the conventional radar's performance degradation and provide highly precise electronic support for its naval applications
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Effect of Communication Network Properties on Data-driven Decision-making in Large-Scale Interconnected Microgrids
Microgrids have emerged as a viable solution to incorporate renewable distributed energy sources into conventional power systems, leading to the decarbonization and decentralization of the electricity supply. Microgrids and their extended adaptation are considered promising options to advance today's electricity grid. However, the electricity supplied by renewable sources is not stable enough to be depended on to maintain optimal operating conditions for the microgrid. Thus, microgrids employ auxiliary electricity generation and storage units to mitigate the drawbacks due to the intermittence in electricity supply from renewable energy sources. These units are preferred because of their ability to adjust electricity output to keep the operational efficiency of microgrids as high as possible. Due to the stochastic nature of the microgrids, real-time data collected via measurement units hold critical importance to be able to timely react to the changes that occurred in the environment. Considering the massive amount of data needed to be processed for efficient planning of the microgrid operations, the existence of a communication link that can provide reliable and fast service is essential. In this study, we highlight the need for capable communication links to enable large-scale applications of multiple microgrids and develop a scenario-based mixed-integer linear programming (SB-MILP) model to solve the energy management problem, then we simulate the operation of multiple microgrids to examine the effect of communication network properties on the cost-efficiency of microgrids operating collaboratively. We conclude by providing insights on handling a massive amount of data using envisioned communication technologies within multiple interconnected microgrids
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Intra-Cluster Distance Minimization in DNA Methylation Analysis Using an Advanced Tabu-Based Iterative kk-Medoids Clustering Algorithm (T-CLUST)
Recent advances in DNA methylation profiling have paved the way for understanding the underlying epigenetic mechanisms of various diseases such as cancer. While conventional distance-based clustering algorithms (e.g., hierarchical and k k -means clustering) have been heavily used in such profiling owing to their speed in conduct of high-throughput analysis, these methods commonly converge to suboptimal solutions and/or trivial clusters due to their greedy search nature. Hence, methodologies are needed to improve the quality of clusters formed by these algorithms without sacrificing from their speed. In this study, we introduce three related algorithms for a complete high-throughput methylation analysis: a variance-based dimension reduction algorithm to handle high-dimensionality in data, an outlier detection algorithm to identify the outliers of data, and an advanced Tabu-based iterative k k -medoids clustering algorithm (T-CLUST) to reduce the impact of initial solutions on the performance of conventional k k -medoids algorithm. The performance of the proposed algorithms is demonstrated on nine different real DNA methylation datasets obtained from the Gene Expression Omnibus DataSets database. The accuracy of the cluster identification obtained by our proposed algorithms is higher than those of hierarchical and k k -means clustering, as well as the conventional methods. The algorithms are implemented in MATLAB, and available at: http://www.coe.miami.edu/simlab/tclust.html
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