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Full-Text Articles in Engineering

A Multi-Stage Optimization Model For Flexibility In Engineering Design, Ramin Giahi, Cameron A. Mackenzie, Chao Hu Oct 2019

A Multi-Stage Optimization Model For Flexibility In Engineering Design, Ramin Giahi, Cameron A. Mackenzie, Chao Hu

Cameron A. MacKenzie

Engineered systems often operate in uncertain environments. Understanding different environments under which a system will operate is important in engineering design. Thus, there is a need to design systems with the capability to respond to future changes. This research explores designing a hybrid renewable energy system while taking into account long-range uncertainties of 20 years. The objective is to minimize the expected cost of the hybrid renewable energy system over the next 20 years. A design solution may be flexible, which means that the design can be adapted or modified to meet different scenarios in the future. The value of ...


Vaccine Distribution Strategies Against Polio: An Analysis Of Turkey Scenario, Elif Elçin Günay, Kijung Park, Sena Aydoğan, Gül E. Okudan Kremer Sep 2019

Vaccine Distribution Strategies Against Polio: An Analysis Of Turkey Scenario, Elif Elçin Günay, Kijung Park, Sena Aydoğan, Gül E. Okudan Kremer

Gül Okudan-Kremer

As a result of the ongoing Syrian civil war, almost 3 million refugees moved to Turkey since 2011 because of security reasons. However, the government operated refugee camps have been largely inadequate to accommodate this huge number of refugees. Therefore, almost 91% of the Syrian refugees live out of government-operated camps. According to a Turkish Disaster and Emergency Management Agency (AFAD) report, 45.4% of the children under 5 years old who live out of camps are not vaccinated against polio. This presents a serious health threat to the local population and the whole region. In order to deal with ...


Assessing Values-Based Sourcing Strategies In Regional Food Supply Networks: An Agent-Based Approach, Caroline C. Krejci, Michael C. Dorneich, Richard T. Stone Sep 2019

Assessing Values-Based Sourcing Strategies In Regional Food Supply Networks: An Agent-Based Approach, Caroline C. Krejci, Michael C. Dorneich, Richard T. Stone

Richard T. Stone

The recent increase in demand for regionally produced food has resulted in a need for more efficient distribution methods. To connect regional food producers and consumers, intermediated regional food supply networks have developed. The intermediary, known as a regional food hub, serves as an aggregation point for products and information. It may also act as a filter to ensure that the requirements of both producers and consumers are consistently met. This paper describes an empirically based agent-based model of a regional food network in central Iowa that is intermediated by a food hub. The model was used to test a ...


The Use Of Sacrificial Support Structures In A Rapid Machining Process, Wutthigrai Boonsuk, Matthew C. Frank Sep 2019

The Use Of Sacrificial Support Structures In A Rapid Machining Process, Wutthigrai Boonsuk, Matthew C. Frank

Wutthigrai Boonsuk

Rapid prototyping techniques for CNC machining have been developed in an effort to produce functional prototypes in appropriate materials. One of the major challenges is to develop an automatic fixturing system for the part during the milling process. The current proposed method, sacrificial support fixturing, is similar to the support structures used in existing rapid processes, such as Stereolithography. During the machining process, the sacrificial supports emerge incrementally and, at the end of the process, are the only entities connecting the part to the stock material. In this paper, we propose methodologies for the design of sacrificial support structures for ...


Effects Of Three Dry Powder Inhalers On Deposition Of Aerosolized Medicine In The Human Oral-Pharyngeal-Laryngeal Regions, Mohammed Ali Sep 2019

Effects Of Three Dry Powder Inhalers On Deposition Of Aerosolized Medicine In The Human Oral-Pharyngeal-Laryngeal Regions, Mohammed Ali

Mohammed Ali

The dry powder inhaler (DPI) is a popular, effective and convenient drug delivery device for inhalation therapy to treat asthma. However, a large quantity (approximately 54%) of inhaled aerosols deposit in the oropharyngeal region. Deposition in this region is undesirable because it provides minimum therapeutic benefits and has adverse localized or systemic side effects. This study reports a method of examining electrostatic charge effects on deposition of three DPI aerosols (Spiriva Handihaler, Advair Diskus, and Pulmicort Turbohaler) in a cadaver-based cast of the human oral-pharyngeal-laryngeal (OPL) regions. Experimental aerosols were generated from the three commercially available DPIs by means of ...


Assessing Values-Based Sourcing Strategies In Regional Food Supply Networks: An Agent-Based Approach, Caroline C. Krejci, Michael C. Dorneich, Richard T. Stone Sep 2019

Assessing Values-Based Sourcing Strategies In Regional Food Supply Networks: An Agent-Based Approach, Caroline C. Krejci, Michael C. Dorneich, Richard T. Stone

Michael C. Dorneich

The recent increase in demand for regionally produced food has resulted in a need for more efficient distribution methods. To connect regional food producers and consumers, intermediated regional food supply networks have developed. The intermediary, known as a regional food hub, serves as an aggregation point for products and information. It may also act as a filter to ensure that the requirements of both producers and consumers are consistently met. This paper describes an empirically based agent-based model of a regional food network in central Iowa that is intermediated by a food hub. The model was used to test a ...


Maize Yield And Nitrate Loss Prediction With Machine Learning Algorithms, Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios Archontoulis Aug 2019

Maize Yield And Nitrate Loss Prediction With Machine Learning Algorithms, Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios Archontoulis

Mohsen Shahhosseini

Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scenario planning, but their use is limited because of data requirements and long run times. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of five machine learning (ML) algorithms as meta-models for a cropping systems simulator (APSIM) to inform future decision-support tool development. We asked: 1) How well do ML meta-models predict maize yield and N losses using pre-season information? 2) How many data ...


Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Aug 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Mohsen Shahhosseini

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...


Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Aug 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Guiping Hu

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...


Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li Aug 2019

Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li

Zeyi Sun

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential ...


Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo Aug 2019

Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo

Ruwen Qin

In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans' actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators' actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on ...


Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke Aug 2019

Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke

David Lee Enke

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication ...


Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke Aug 2019

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke

David Lee Enke

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P ...


Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns Aug 2019

Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns

Steven Corns

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify ...


Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli Aug 2019

Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli

Cihan H. Dagli

Automated segmentation in brain magnetic resonance image (MRI) plays an important role in the analysis of many diseases and conditions. In this paper, we present a new architecture to perform MR image brain segmentation (MRI) into a number of classes based on type of tissue. Recent work has shown that convolutional neural networks (DenseNet) can be substantially more accurate with less number of parameters if each layer in the network is connected with every other layer in a feed forward fashion. We embrace this idea and generate new architecture that can assign each pixel/voxel in an MR image of ...


Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli Aug 2019

Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli

Cihan H. Dagli

In this paper, we investigate the diagnostic data from patients suffering with Parkinson's disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson's research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson's Disease diagnosis ...


Supporting Shrinkage: Better Planning And Decision-Making For Legacy Cities, Michael P. Johnson Jr., Justin B. Hollander, Eliza W. Kinsey, George Chichirau, Charla Burnett Aug 2019

Supporting Shrinkage: Better Planning And Decision-Making For Legacy Cities, Michael P. Johnson Jr., Justin B. Hollander, Eliza W. Kinsey, George Chichirau, Charla Burnett

Michael P. Johnson

Planning and policy design for shrinking and distressed regions is challenging. Traditionally, planners use
a variety of tools and incentives to encourage land uses that accommodate changes in populations,
infrastructure and activities to maximize quality of life and social and environmental sustainability.
These are generally designed for growing cities and politically and socially active communities. Since
many regions face significant disparities in social supports, financial resources and quality of life, use of
these tools is thus problematic.

Information technology and the Internet have transformed the production of goods and services. The
‘big data’, ‘smart cities’ and ‘e-government’ movements make it ...


Design Thinking: An Approach With Various Perceptions, Sanne Bouwman, Jesper Voorendt, Boris Eisenbart, Seda Mckilligan Aug 2019

Design Thinking: An Approach With Various Perceptions, Sanne Bouwman, Jesper Voorendt, Boris Eisenbart, Seda Mckilligan

Seda McKilligan

Design Thinking has become increasingly popular across different disciplines. However, what it exactly entails is becoming more and more vague, leading to the term being used for many different approaches and applications. This paper presents an interview study with experts on the application and training of Design Thinking in academia and industry. We find a divide with some seeing Design Thinking as a mere toolbox of methods, while others see it as an umbrella term for the mindset that determines how designers think and act. Subjects unanimously attest the approach large potential to support certain types of businesses, when applied ...


Monitoring Activity And Climate Impact In Market Hogs With Activity Ball, Brad Aronson, Eli Sents, Kyle Wenck, Derek Yegge, Joseph R. Vanstrom, Jacek A. Koziel, Steven Hoff, Brett Ramirez Aug 2019

Monitoring Activity And Climate Impact In Market Hogs With Activity Ball, Brad Aronson, Eli Sents, Kyle Wenck, Derek Yegge, Joseph R. Vanstrom, Jacek A. Koziel, Steven Hoff, Brett Ramirez

Brett Ramirez

Prairie Systems is a web-based data management provider, primarily servicing the swine industry. Their products include the Feed Allocation System, which helps producers manage feed inputs, Smart Order, which helps feed manufacturers and distributors manage feed order fulfillment, and LeeO, which tracks individual animals through RFID technologies. Prairie Systems’ line of products is implementing precision agriculture practices into the livestock industry.

To help producers better manage their operations, Prairie Systems is seeking to gain a better understanding of the daily activity of market hogs. To do this, we were tasked with designing and fabricating a container to collect activity data ...


Automated Finishing Pig Feeder Adjustment, Ryan Godfredsen, Kelley Mabeus, Michael Meyer, Brett C. Ramirez, Shweta Chopra, Jacek A. Koziel Aug 2019

Automated Finishing Pig Feeder Adjustment, Ryan Godfredsen, Kelley Mabeus, Michael Meyer, Brett C. Ramirez, Shweta Chopra, Jacek A. Koziel

Brett Ramirez

Feed costs in the swine industry are typically 60-70% of the total swine production costs. Feeder adjustment is used to decrease feed costs. When feeders are properly adjusted, the growth rate can be improved while the wasted feed is minimized. Currently, feeders are manually monitored and adjusted by daily caretaker inspection, which is very laborious. Automated adjustment would reduce labor need and allow for the integration of precision management in the future.


Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Jul 2019

Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Guiping Hu

Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge especially in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known ...


Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Jul 2019

Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Mohsen Shahhosseini

Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge especially in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known ...


Transportation Safety Performance Of Us Bus Transit Agencies And Population Density: A Cross-Sectional Analysis (2008-2014), Ilker Karaca, Peter T. Savolainen Jul 2019

Transportation Safety Performance Of Us Bus Transit Agencies And Population Density: A Cross-Sectional Analysis (2008-2014), Ilker Karaca, Peter T. Savolainen

Ilker Karaca

The paper examines the transportation safety performance of transit agencies providing public bus service in the US by using data from the National Transit Database (NTD)

Uses NTD data for a seven-year period from 2008 to 2014 • 3,853 observations for 651 public transportation agencies in 50 states

Seven types of bus transit fatalities and injuries (including passengers, operators, pedestrians, bicyclists)

Main explanatory variable: urban density obtained from the US Census figures

Other explanatory variables: total agency revenue miles, unlinked passenger trips, agency fleet size, and urban population


Seven Hci Grand Challenges, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie Y. C. Chen, Jianming Dong, Vincent G. Duffy, Xiaowen Fang, Cali Fidopiastis, Gino Fragomeni, Limin Paul Fu, Yinni Guo, Don Harris, Andri Ioannou, Kyeong-Ah (Kate) Jeong, Shin'ichi Konomi, Heidi Kromker, Masaaki Kurosu, James R. Lewis, Aaron Marcus, Gabriele Meiselwitz, Abbas Moallem, Hirohiko Mori, Fiona Fui-Hoon Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Dylan Schmorrow, Keng Siau, Norbert Streitz, Wentao Wang, Sakae Yamamoto, Panayiotis Zaphiris, Jia Zhou Jun 2019

Seven Hci Grand Challenges, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie Y. C. Chen, Jianming Dong, Vincent G. Duffy, Xiaowen Fang, Cali Fidopiastis, Gino Fragomeni, Limin Paul Fu, Yinni Guo, Don Harris, Andri Ioannou, Kyeong-Ah (Kate) Jeong, Shin'ichi Konomi, Heidi Kromker, Masaaki Kurosu, James R. Lewis, Aaron Marcus, Gabriele Meiselwitz, Abbas Moallem, Hirohiko Mori, Fiona Fui-Hoon Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Dylan Schmorrow, Keng Siau, Norbert Streitz, Wentao Wang, Sakae Yamamoto, Panayiotis Zaphiris, Jia Zhou

Abbas Moallem

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security ...


Design By Taking Perspectives: How Engineers Explore Problems, Jaclyn K. Murray, Jaryn A. Studer, Shanna R. Daly, Seda Mckilligan, Colleen M. Seifert Jun 2019

Design By Taking Perspectives: How Engineers Explore Problems, Jaclyn K. Murray, Jaryn A. Studer, Shanna R. Daly, Seda Mckilligan, Colleen M. Seifert

Seda McKilligan

Background: Problem exploration includes identifying, framing, and defining design problems and bounding problem spaces. Intentional and unintentional changes in problem understanding naturally occur as designers explore design problems to create solutions. Through problem exploration, new perspectives on the problem can emerge along with new and diverse ideas for solutions. By considering multiple problem perspectives varying in scope and focus, designers position themselves to increase their understandings of the “real” problem and engage in more diverse idea generation processes leading to an increasing variety of potential solutions.

Purpose/Hypothesis: The purpose of this study was to investigate systematic patterns in problem ...


Challenges Of Erau’S First Suborbital Flight Aboard Blue Origin’S New Shepard M7 For The Cell Research Experiment In Microgravity (Crexim), Pedro Llanos, Kristina Andrijauskaite, Vijay V. Duraisamy, Francisco F. Pastrana, Erik Seedhouse, Sathya Gangadharan, Leonid Bunegin, Mariel Rico Jun 2019

Challenges Of Erau’S First Suborbital Flight Aboard Blue Origin’S New Shepard M7 For The Cell Research Experiment In Microgravity (Crexim), Pedro Llanos, Kristina Andrijauskaite, Vijay V. Duraisamy, Francisco F. Pastrana, Erik Seedhouse, Sathya Gangadharan, Leonid Bunegin, Mariel Rico

Pedro J. Llanos (www.AstronauticsLlanos.com)

Cell Research Experiment In Microgravity (CRExIM) was launched aboard Blue Origin’s New Shepard suborbital vehicle on Tuesday, December 12, 2017, from the West Texas Launch Site in Van Horn, Texas. One of the aims of this science experiment was to assess the effects of microgravity on murine T-cells during suborbital flight. These cells were placed in a NanoLab with a data logger that sensed the acceleration, temperature, and relative humidity during preflight, flight, and postflight operations. Some discrepancies in sensor measurement were noticed, and these errors were attributed partly to the difference in sampling rates and partly to the ...


Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke Mar 2019

Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke

David Lee Enke

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing ...


Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos Mar 2019

Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos

David Lee Enke

Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper ...


Determination Of Rule Patterns In Complex Event Processing Using Machine Learning Techniques, Nijat Mehdiyev, Julian Krumeich, David Lee Enke, Dirk Werth, Peter Loos Mar 2019

Determination Of Rule Patterns In Complex Event Processing Using Machine Learning Techniques, Nijat Mehdiyev, Julian Krumeich, David Lee Enke, Dirk Werth, Peter Loos

David Lee Enke

Complex Event Processing (CEP) is a novel and promising methodology that enables the real-time analysis of stream event data. The main purpose of CEP is detection of the complex event patterns from the atomic and semantically low-level events such as sensor, log, or RFID data. Determination of the rule patterns for matching these simple events based on the temporal, semantic, or spatial correlations is the central task of CEP systems. In the current design of the CEP systems, experts provide event rule patterns. Having reached maturity, the Big Data Systems and Internet of Things (IoT) technology require the implementation of ...


Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke Mar 2019

Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke

David Lee Enke

Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose ...