Department of Civil and Environmental Engineering Publications

Prediction of wind farm power ramp rates: A data-mining approach

Haiyang Zheng et al. — Tue, 13 Sep 2011 15:58:47 PDT

In this paper, multivariate time series models were built to predict the power ramp ratesof a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithmswere tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10-60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy ofthe power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided. 2009 by ASME.

Modeling manufacturing dependability

Armen Zakarian et al. — Tue, 13 Sep 2011 15:58:42 PDT

A new approach in evaluating the availability of cellular manufacturing systems is presented. The approach divides the system into two subsystems without constructing a system level Markov chain. The approach incorporates imperfect coverage and imperfect repair factors in the Markovian models. These factors have great impact on the availability of the whole system. The approach was used in the evaluation of transients and steady-state performances of three alternative designs based on industrial samples. It was shown that imperfections that constitutes even a small percentage of the systems' faults reduces the availability of the whole system at large.

Forming teams: an analytical approach

Armen Zakarian et al. — Tue, 13 Sep 2011 15:58:35 PDT

The selection of multi-functional teams is a key issue in problem solving. Currently there are no papers in the literature that discuss analytical approaches to forming teams. Furthermore, no comprehensive model exists to prioritize team membership based on customer requirements or product characteristics. To deal with the underlying complexities of the team selection process, a methodology for team formation is developed. The methodology is based on the Analytical Hierarchy Process (AHP) approach and the Quality Function Deployment (QFD) method. A QFD planning matrix is used to organize the factors considered in the team selection. The importance measure for each team member is determined with the AHP approach. A mathematical programming model is developed to determine the composition of a team. The methodology developed in this paper is tested by the selection of teams in concurrent engineering. A detailed discussion of the model implementation and how to reduce the number of comparisons in the AHP process is presented. Possible modifications of the model to include 'soft factors', i.e., leadership, morale, personalities of group members, group values and so on are also discussed.

Analysis of process models

Armen Zakarian et al. — Tue, 13 Sep 2011 15:58:29 PDT

Process modeling tools, such as the integrated Definition (IDEF) methodology, allow for a systematic representation of processes in manufacturing, product development, and service applications. Most of the process modeling methodologies are based on informal notation, lack mathematical rigor, and are static and qualitative, thus difficult to be used for analysis. In this paper, a new analysis approach for process models based on signed directed graphs (SDG's) and fuzzy sets is presented. A membership function of fuzzy sets quantifies and transforms incomplete and ambiguous information of process variables into an SDG qualitative model. The effectiveness of the approach is illustrated with an industrial example. The architecture of an intelligent system for qualitative/quantitative analysis of process models is presented.

Process analysis and reengineering

Armen Zakarian et al. — Tue, 13 Sep 2011 15:58:23 PDT

To achieve meaningful improvements of the process performance measures such as quality, speed, service, and cost, fundamental rethinking and redesign of the underlying process is required. Numerous corporations have been forced to change their processes in order to survive in a highly competitive market. To perform analysis and reengineering of processes, a structured and unified approach is required. In this paper, a framework based on the IDEF methodology, stream analysis approach, and dynamic simulation for process analysis and reengineering is presented. The stream analysis approach is used for analysis, diagnosis, and management of process changes represented with an IDEF model. To evaluate the impact of changes considered, support the process analysis, and to model performance of the proposed process, a dynamic simulation is used. This study extends the IDEF methodology by including quantitative information. The latter improves IDEF process analysis and reengineering capability, and facilitates the formulation of a dynamic simulation model. The significance of the results presented in the paper arises from the fact that many companies, e.g. Lockheed-Martin, General Motors, Rockwell International, are using IDEF for representing their processes. 2001 Elsevier Science Ltd. All rights reserved.

2010 2nd International Conference on Information Technology Convergence and Services, ITCS 2010: Message from the general chairs

Sang-Soo Yeo et al. — Tue, 13 Sep 2011 15:58:17 PDT

Scheduling with Alternative Operations

Jaekyoung Ahn Weihua He et al. — Tue, 13 Sep 2011 15:58:10 PDT

Considerable investments in the installation of an automated manufacturing system requires scheduling approaches that highly utilize its resources. The incorporation of alternative operations into a scheduling system increases the utilization rate of resources and reduces the makespan of manufacturing products. In the paper, a heuristic algorithm is developed for a scheduling problem with and without alternative operations. The effect of alternative operations on the performance of schedules generated are studied with five dispatching rules. The testing effort involves 240 scheduling problems obtained for randomly generated data. The computational results show that the most dissimilar resources (MDRR) dispatching rule for the case with alternative operations performs best among the dispatching rules tested. The quality of schedules (makespan, utilization rate of resources) generated with any dispatching rule improves when alternative operations are used.

Data base requirements for concurrent design systems

Ranko Vujosevic et al. — Tue, 13 Sep 2011 15:58:03 PDT

The data base requirements for concurrent design systems are discussed. An object-oriented data base, which allows for definition of complex objects, specification of relationships between objects, and modular expandability without affecting the existing information is defined. The data base is developed based on the object-oriented data model implemented in Smalltalk-80. An assumption-based truth maintenance system for maintaining the dependency relationships between design and manufacturing information is described.

Selection of machinable volumes. An object-oriented approach

Ranko Vujosevic et al. — Tue, 13 Sep 2011 15:57:58 PDT

An object-oriented approach for selection of machinable volumes to be included in a process plan is presented. The approach uses a feature-based part representation. Volumes of the material to be removed are generated in parallel with the definition of form features of the part. Geometric reasoning is used for grouping these volumes into a set of machinable volumes. The selection of machinable volumes with the minimum corresponding machining cost, fixture and tool-utilization costs is performed by a heuristic algorithm. Precedence constraints among machinable volumes are established and used to generate a sequence of machinable volumes. The concepts introduced in the paper have been implemented in Smalltalk-80 object-oriented programming environment.

Reason Maintenance in Product Modeling

R. Vujosevic et al. — Tue, 13 Sep 2011 15:57:51 PDT

Constraint-based control of boiler efficiency: A data-mining approach

Zhe Song et al. — Tue, 13 Sep 2011 15:57:46 PDT

In this paper, a data-mining approach is used to develop a model for optimizing the efficiency of an electric-utility boiler subject to operating constraints. Selection of process variables to optimize combustion efficiency is discussed. The selected variables are critical for control of combustion efficiency of a coal-fired boiler in the presence of operating constraints. Two schemes of generating control settings and updating control variables are evaluated. One scheme is based on the controllable and noncontrollable variables. The second one incorporates response variables into the clustering process. The process control scheme based on the response variables produces the smallest variance of the target variable due to reduced coupling among the process variables. An industrial case study and its implementation illustrate the control approach developed in this paper. 2007 IEEE.

Optimization of temporal processes: A model predictive control approach

Zhe Song et al. — Tue, 13 Sep 2011 15:57:39 PDT

A dynamic predictive-control model of a nonlinear and temporal process is considered. Evolutionary computation and data mining algorithms are integrated for solving the model. Data-mining algorithms learn dynamic equations from process data. Evolutionary algorithms are then applied to solve the optimization problem guided by the knowledge extracted by data-mining algorithms. Several properties of the optimization model are shown in detail, in particular, a selection of regressors, time delays, prediction and control horizons, and weights. The concepts proposed in this paper are illustrated with an industrial case study in combustion process. 2008 IEEE.

Mining Pareto-optimal modules for delayed product differentiation

Zhe Song et al. — Tue, 13 Sep 2011 15:57:32 PDT

This paper presents a framework for finding optimal modules in a delayed product differentiation scenario. Historical product sales data is utilized to estimate demand probability and customer preferences. Then this information is used by a multiple-objective optimization model to form modules. An evolutionary computation approach is applied to solve the optimization model and find the Pareto-optimal solutions. An industrial case study illustrates the ideas presented in the paper. The mean number of assembly operations and expected pre-assembly costs are the two competing objectives that are optimized in the case study. The mean number of assembly operations can be significantly reduced while incurring relatively small increases in the expected pre-assembly cost. 2009 Elsevier B.V. All rights reserved.

Multiobjective optimization of temporal processes

Zhe Song et al. — Tue, 13 Sep 2011 15:57:26 PDT

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework. 2006 IEEE.

Mining Markov chain transition matrix from wind speed time series data

Zhe Song et al. — Tue, 13 Sep 2011 15:57:20 PDT

Extracting important statistical patterns from wind speed time series at different time scales is of interest to wind energy industry in terms of wind turbine optimal control, wind energy dispatch/scheduling, wind energy project design and assessment, and so on. In this paper, a systematic way is presented to estimate the first order (one step) Markov chain transition matrix from wind speed time series by two steps. Wind speed time series data is used first to generate basic estimators of transition matrices (i.e. first order, second order, third order, etc.) based on counting techniques. Then an evolutionary algorithm (EA), specifically double-objective evolutionary strategy algorithm (ES), is proposed to search for the first order Markov chain transition matrix which can best match these basic estimators after transforming the first order transition matrix into its higher order counterparts. The evolutionary search for the first order transition matrix is guided by a predefined cost function which measures the difference between the basic estimators and the first order transition matrix, and its high order transformations. To deal with the potential high dimensional optimization problem (i.e. large transition matrices), an enhanced offspring generation procedure is proposed to help the ES algorithm converge efficiently and find better Pareto frontiers through generations. The proposed method is illustrated with wind speed time series data collected from individual 1.5 MW wind turbines at different time scales. 2011 Elsevier Ltd.

Optimising product configurations with a data-mining approach

Z. Song et al. — Tue, 13 Sep 2011 15:57:13 PDT

Customers benefit from the ability to select their desired options to configure final products. Manufacturing companies, however, struggle with the dilemma of product diversity and manufacturing complexity. It is important, therefore, for them to capture correlations among the options provided to the customers. In this paper, a data mining approach is applied to manage product diversity and complexity. Rules are extracted from historical sales data and used to form sub-assemblies as well as product configurations. Methods for discovering frequently ordered product sub-assemblies and product configurations from 'if-then' rules are discussed separately. The development of the sub-assemblies and configurations allows for effective management of enterprise resources, contributes to the innovative design of new products, and streamlines manufacturing and supply chain processes. The ideas introduced in this paper are illustrated with examples and an industrial case study. [ABSTRACT FROM AUTHOR]; Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data Mining in Predicting Survival of Kidney Dialysis Patients

Shital Shah et al. — Tue, 13 Sep 2011 15:57:07 PDT

The number of dialysis patients due to end stage kidney disease is increasing. Finding ways to improve patient outcomes and reduce the cost of dialysis is a challenging task. Dialysis care is complex and multiple factors may influence patient survival. More than 50 parameters may be monitored while providing a kidney dialysis treatment. Understanding the collective role of these parameters in determining outcomes for an individual patient and administering individualized treatments is of importance. Individual patient survival may depend on a complex interrelationship between multiple demographic and clinical variables, medications, and medical interventions. In this research, a data mining approach is used to elicit knowledge about the interaction between these variables and patient survival. Two different data mining algorithms are employed for extracting knowledge in the form of decision rules. Data mining is performed on the individual visits of the "most invariant" patients as they form "signatures" for their decision categories. The concepts introduced in this research have been applied and tested using a data collected at four dialysis sites. The computational results are reported.

Cancer gene search with data-mining and genetic algorithms

Shital Shah et al. — Tue, 13 Sep 2011 15:57:02 PDT

Cancer leads to approximately 25% of all mortalities, making it the second leading cause of death in the United States. Early and accurate detection of cancer is critical to the well being of patients. Analysis of gene expression data leads to cancer identification and classification, which will facilitate proper treatment selection and drug development. Gene expression data sets for ovarian, prostate, and lung cancer were analyzed in this research. An integrated gene-search algorithm for genetic expression data analysis was proposed. This integrated algorithm involves a genetic algorithm and correlation-based heuristics for data preprocessing (on partitioned data sets) and data mining (decision tree and support vector machines algorithms) for making predictions. Knowledge derived by the proposed algorithm has high classification accuracy with the ability to identify the most significant genes. Bagging and stacking algorithms were applied to further enhance the classification accuracy. The results were compared with that reported in the literature. Mapping of genotype information to the phenotype parameters will ultimately reduce the cost and complexity of cancer detection and classification. 2006 Elsevier Ltd. All rights reserved.

Relabeling algorithm for retrieval of noisy instances and improving prediction quality

Shital Shah et al. — Tue, 13 Sep 2011 15:56:57 PDT

A relabeling algorithm for retrieval of noisy instances with binary outcomes is presented. The relabeling algorithm iteratively retrieves, selects, and re-labels data instances (i.e., transforms a decision space) to improve prediction quality. It emphasizes knowledge generalization and confidence rather than classification accuracy. A confidence index incorporating classification accuracy, prediction error, impurities in the relabeled dataset, and cluster purities was designed. The proposed approach is illustrated with a binary outcome dataset and was successfully tested on the standard benchmark four UCI repository dataset as well as bladder cancer immunotherapy data. A subset of the most stable instances (i.e., 7% to 51% of the sample) with high confidence (i.e., between 64%-99.44%) was identified for each application along with most noisy instances. The domain experts and the extracted knowledge validated the relabeled instances and corresponding confidence indexes. The relabeling algorithm with some modifications can be applied to other medical, industrial, and service domains. 2009 Elsevier Ltd. All rights reserved.

Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment

Shital C. Shah et al. — Tue, 13 Sep 2011 15:56:52 PDT

Bladder cancer is the fifth most common malignant disease in the United States with an annual incidence of around 63,210 new cases and 13,180 deaths. The cost for providing care for patients with bladder cancer disease is high. Bladder cancer treatment options such as immunotherapy, chemotherapy, radiation therapy, transurethral resection, and cystectomy, are used with varying success rates. In this research, data from a nationwide bacillus Calmette-Guerin (BCG) plus interferon-alpha (IFN- ) immunotherapy clinical trial was considered. Data mining algorithms were used to analyze the effectiveness of immunotherapy treatment and to understand the prominent parameters and their interactions. The extracted knowledge was used to build a patient recognition model for prediction of treatment outcomes. The data was analyzed to understand the impact of various parameters on the treatment outcome. A list of significant parameters such as cumulative tumor size, presence of residual disease, stages of prior bladder cancer, current state of bladder cancer, and the presence of current bladder cancer (T1) is provided. The decision-making approach outlined in the paper supplemented with additional knowledge bases will lead to a comprehensive analytical road map of the BCG/IFN- immunotherapy treatment. It will provide individualized guidelines for each stage of the treatment as well as measure the success of the treatment. 2005 Elsevier Ltd. All rights reserved.