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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 1 - 13 of 13
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
State Prediction Of Poverty Alleviation Objects Based On Hmm And Multidimensional Data, Jun He, Sunyan Hong, Yifang Zhou, Shikai Shen, Muquan Zou
State Prediction Of Poverty Alleviation Objects Based On Hmm And Multidimensional Data, Jun He, Sunyan Hong, Yifang Zhou, Shikai Shen, Muquan Zou
Journal of System Simulation
Abstract: In order to solve the problems of inaccurate prediction of poverty, poverty reduction and poverty returen, and the difficulty in identifying the key factors affecting the state transition, 8 key features and 22 observed states are extracted from the poverty reduction basic data and multi-industry data. The relationship between observed state and implied state is constructed, and the hidden markov model (HMM) of poverty alleviation is established. Data of a deep poverty county for three consecutive years are used as samples for parameter training, test experiment and result verification. The results show that the method has a strong …
A Machine Learning Approach To Understanding Emerging Markets, Namita Balani
A Machine Learning Approach To Understanding Emerging Markets, Namita Balani
Graduate Theses and Dissertations
Logistic providers have learned to efficiently serve their existing customer bases with optimized routes and transportation resource allocation. The problem arises when there is potential for logistics growth in an emerging market with no previous data. The purpose of this work is to use industry data for previously known and well-documented markets to apply data analytic techniques such as machine learning to investigate the uncertainty in a new market. The thesis looks into machine learning techniques to predict miles per stop given historical data. It mainly focuses on Random Forest Regression Analysis, but concludes that additional techniques, such as Polynomial …
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Journal of System Simulation
Abstract: For the parameter selection of support vector machine in modeling, a particle swarm optimization algorithm based on second-order oscillation and repulsion factor was proposed to optimize the parameter of SVM. The algorithm employed the nonlinear decreasing weight to balance the global and local search ability. Second-order oscillation factor could maintain the population diversity. The repulsion factor was introduced to make the swarm even distribution in search space, which could avoid local optimum. For the complex characteristics of nonlinearity, time-varying and multifactorial of electric power load, a support vector machine forecasting model based on data was proposed, and the influence …
Reconstitution Efficiency On Elution Process, Brandon Winter, Landon Beachy, Samantha Stejskal, Zachary Rice
Reconstitution Efficiency On Elution Process, Brandon Winter, Landon Beachy, Samantha Stejskal, Zachary Rice
Scholar Week 2016 - present
Our group is presenting on the Reconstitution Efficiency on Elution Process for MilliporeSigma. The process that we are working with is the mixing of BAAP (Bovine Albumin Acetone Powder) into reverse osmosis water. This process is the first step of many in the making of medical test kits, of which include HIV/AID, strep throat, and pregnancy tests. Our initial task was to reduce the mixing time by 50%, however after further exploration of the problem, a more stable and consistent process turned into our primary goal and a significant reduction in time became a secondary goal. Initially, we were using …
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Theses and Dissertations
A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Engineering Management & Systems Engineering Faculty Publications
Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …
Evaluation And Validation Of Distraction Detection Algorithms On Multiple Data Sources, Shashank Mehrotra
Evaluation And Validation Of Distraction Detection Algorithms On Multiple Data Sources, Shashank Mehrotra
Masters Theses
This study aims to evaluate algorithms designed to detect distracted driving. This includes the comparison of how efficiently they detect the state of distraction and likelihood of a crash. Four algorithms that utilize measures of cumulative glance, past glance behavior, and glance eccentricity were used to understand the distracted state of the driver and were validated on two separate data sources (i.e., simulator and naturalistic data). Additionally, an independent method for distraction detection was designed using data mining methods. This approach utilized measures like steering degree, lane offset, lateral and longitudinal velocity, and acceleration. The results showed a higher likelihood …
Data Analysis And Processing Techniques For Remaining Useful Life Estimations, John Scott Bucknam
Data Analysis And Processing Techniques For Remaining Useful Life Estimations, John Scott Bucknam
Theses and Dissertations
In the field of engineering, it is important to understand different engineering systems and components, not only in how they currently perform, but also how their performance degrades over time. This extends to the field of prognostics, which attempts to predict the future of a system or component based on its past and present states. A common problem in this field is the estimation of remaining useful life, or how long a system or component functionality will last. The well-known datasets for this problem are the PHM and C-MAPSS datasets. These datasets contain simulated sensor data for different turbofan engines …
Feature Knowledge Based Fault Detection Of Induction Motors Through The Analysis Of Stator Current Data, Ting Yang, Haibo Pen, Zhaoxia Wang, Che Sau Chang
Feature Knowledge Based Fault Detection Of Induction Motors Through The Analysis Of Stator Current Data, Ting Yang, Haibo Pen, Zhaoxia Wang, Che Sau Chang
Research Collection School Of Computing and Information Systems
The fault detection of electrical or mechanical anomalies in induction motors has been a challenging problem for researchers over decades to ensure the safety and economic operations of industrial processes. To address this issue, this paper studies the stator current data obtained from inverter-fed laboratory induction motors and investigates the unique signatures of the healthy and faulty motors with the aim of developing knowledge based fault detection method for performing online detection of motor fault problems, such as broken-rotor-bar and bearing faults. Stator current data collected from induction motors were analyzed by leveraging fast Fourier transform (FFT), and the FFT …
Empirical Analysis Of Volunteer Convergence Following The 2011 Tornado Disaster In Tuscaloosa, Alabama, Emmett Lodree, Lauren B. Davis
Empirical Analysis Of Volunteer Convergence Following The 2011 Tornado Disaster In Tuscaloosa, Alabama, Emmett Lodree, Lauren B. Davis
Dr. Lauren B Davis
Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu
Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu
Research Collection School Of Computing and Information Systems
As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …
Use On Multinomial Logistic Regression In Work Zone Crash Analysis For Missouri Work Zones, Paul Robin
Use On Multinomial Logistic Regression In Work Zone Crash Analysis For Missouri Work Zones, Paul Robin
Masters Theses
"This study focuses on the use of statistical data analysis procedures in identifying factors which affect the severity of crashes in work zones. Work zones are unsafe for the traffic passing through as well as the workers. Multinomial Logistic Regression has been used to analyse Missouri work zone crash data to identify significant factors which affect the severity of crashes. This particular type of regression analysis was used due to the mixed nature of data. Multinomial regression was used to compare crashes with severity Property Damage Only against crashes with Minor Injuries and Disabling Injuries/ Fatal. The factors considered were …
Evaluation System Design And Academic Performance Analysis Using Clustering And Simulation, Volkan Cakir
Evaluation System Design And Academic Performance Analysis Using Clustering And Simulation, Volkan Cakir
Engineering Management & Systems Engineering Theses & Dissertations
The starting point of this study was to understand the possible causes of evaluation system change in a military academic environment. With that motivation the objectives of this study were defined as examining student profiles in a military academy, establishing the nature of academic performance, comparing student groups that were identified by course scores, analyzing student performance changes over time and developing a manageable evaluation system and curriculum by comparing different scenarios.
An analysis was performed on the literature on academic performance prediction, cluster analysis methodologies and their development, and specifically summarized cluster analytic academic performance studies where these two …