Operations Research, Systems Engineering and Industrial Engineering Commons^™

A Hierarchical Integrated Soft Sensing Modeling Method For Gauss Process Regression, Zhao Shuai, Xudong Shi, Weili Xiong

Journal of System Simulation

Abstract: Chemical processes are often characterized by nonlinearity and multi-phase, a soft sensor model based on the hierarchical ensemble of Gaussian process regression is proposed. First, the Gaussian mixture model is used to divide the process data into different operation phases. Then, the principal component analysis of each stage is carried out, and the model data are divided into several subspaces, according to the contribution of each auxiliary variable in the principal component space, and the corresponding Gaussian process regression model is built. The subspace model output is fused by means to obtain the first level ensemble output. Finally, the …

Low-Complexity Signal Detection Algorithm Based On Pgqsm, Fatang Chen, Yang Kang, Yongli Fu, Mengjie Li

Journal of System Simulation

Abstract: Pre-coding aided Generalized Quadrature Spatial Modulation (PGQSM) uses the known channel state information to perform pre-coding processing on the in-phase and quadrature signals of the quadrature spatial modulation (QSM), so as to reduce the complexity of the receiver for signal detection. At the same time, an improved Pre-coding aided Generalized Quadrature Spatial Modulation low complexity detection algorithm is proposed to solve the problem of the proposed algorithm’s high complexity at the receiver. The results of analysis and simulation show that the proposed algorithm can achieve almost the same BER performance as the maximum likelihood (ML) algorithm with more than …

Optimized Linear Adrc Control For A Class Of Fractional-Order Chaotic Systems, Huang Yu, Xie Tian, Wu Rui

Journal of System Simulation

Abstract: Linear active disturbance rejection control has many parameters and it is difficult to obtain appropriate parameters. Therefore, a quantum-behaved particle swarm optimization algorithm based on cosine decreasing function is proposed in this article for searching optimal LADRC parameters. In this algorithm, cosine decreasing function and updated equation of quantum-behaved particle swarm optimization algorithm are combined. By using the variable characteristic of cosine decreasing function with the increase of iteration times, the original global optimization ability of quantum particle swarm optimization is retained and its poor local optimization ability is overcome. And the simulation results show that the LADRC optimized …

Gcps Adaptive Scheduling Model Based On Cooperative Executor, Zhang Jing, Chen Yao, Sun Jun, Hongbo Fan

Journal of System Simulation

Abstract: Aiming at the problem that the uncertainty of grid cyber physical systems leads to chain failure, an adaptive GCPS dispatching model based on co-actuator is established. First, the constraint conditions of the system are analyzed. A model constraints of GCPS system is presented to describe the constraint conditions of the power system, and it is proved that it meets the consistency of the measure and representing methods. Second, the optimal value of approximation error is solved by PILOT, and the framework of CA-SADM is described. Finally, the performance index, output power accuracy and the influence of fault on the …

Analysis On The Mission Reliability For Auv Based On Monte Carlo Method, Li Juan, Kunyu Zhang, Haibo Li, Lijuan Yang, Mengdi Wang

Journal of System Simulation

Abstract: The high reliability of AUV is the necessary condition for it to complete the task satisfactorily and efficiently within the specified time. Traditional fault tree is very convenient for describing the reliability model of the system, but for large and complex system, this will significantly increase the computation of reliability parameters, thus the calculation is difficult. Because it to calculate the reliability parameters of complex systems by the traditional fault tree, this paper adopts the method of combining the minimum cut set of fault tree and Monte Carlo method to establish the reliability model for Underwater Autonomous Unmanned Vehicle …

Dynamic Inventory Routing Optimization Based On Deep Reinforcement Learning, Jianpin Zhou, Shuliu Zhang

Journal of System Simulation

Abstract: Aiming at the dynamic stochastic inventory routing problem with periodic fluctuation of demand, a novel simulation optimization approach based on deep reinforcement learning is proposed to achieving periodic steady strategy. Firstly a dynamic combinatorial optimization model is constructed. Then, by deep reinforcement learning and setting heuristic rules, the replenishment nodes set selection and the replenishment batch allocation weights in each period are determined. The simulation experimental results show that the proposed method can improve the average profit of a cycle by about 2.7% and 3.9% in low fluctuating demand case and by about 8.2% and 7.1% in high fluctuating …

Artificial Bee Colony Algorithm For Solving Fuzzy Multi-Objective Bed Allocation Model, Abdulhakeem Luqman Hasan

Karbala International Journal of Modern Science

With the improvement of the medical services frameworks rivalry, hospitals face more and more challenges. In the interim, allotment of resource has a crucial influence on performing competitive benefits in a hospitals. To choose the suitable beds number is one of the most essential tasks in hospital administration. Anyway, in true condition, bed allotment choice is a multiple-side problem with weakness and haphazardness of the information available. It is so sophisticated. Therefore, the research about bed allotment difficulty is comparatively rare under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we …

Mazak Laser Optimization, Maylon Ellington, Terrilian E. Agbor Oji, William N. Palm

Senior Design Project For Engineers

Starflex Fabrication is a manufacturing company that thrives to provide custom, precision fabricated parts, and assemblies to their customers.The Mazak laser is Starflex Fabrication’s primary cutting machine and is critical to the overall process for each order. Team Flex will look to optimize the laser’s process and improve overall shop throughput.

Investigation Of Retrieved Cardiac Devices, Anmar Salih, Iosif Papadakis Ktistakis, Spyridon Manganas, Abdul Wase, Tarun Goswami

Biomedical, Industrial & Human Factors Engineering Faculty Publications

Damage assessment of lead and pulse generator with various exposure times is important in the development of cardiac devices. Approximately, 92.1 million patients in the US suffer from cardiovascular diseases with an estimated healthcare cost of over $300 billion and at least one million with implantable cardiac devices. These devices are complex and composed on multiple levels and present challenges while assessing the damage. However, the study on the analysis of cardiac devices may lend insight into common damage patterns and improve future cardiac devices design. The objective of this work is to perform a thorough in vivo damage assessment …

Space Optimization And Process Improvement, Harith Alshareef

Senior Design Project For Engineers

Siemens Energy warehouse located in Suwanee, GA runs out of space in the Precision Lab due to rapid growth, seasonal peaks, and poor space utilization. The objective of the project is to redesign the Precision Lab layout and improve the tool assessment process to increase efficiency, productivity, and profitability.

The first solution is to add shelves in the Precision lab as well as the Torque lab and reduce the honeycombing effect on the shelves, which results in a 120%-150% increase in the storage space. We propose adding Tablets that include barcode scanner features to reduce the time spent on the …

Investigation Of A Neuro-Stimulator Retrieved Posthumously, Megan Markl, Rodney J. Gutherie, Tarun Goswami

Biomedical, Industrial & Human Factors Engineering Faculty Publications

A neurostimulator was investigated in this paper posthumously. Device was presented to our anatomical gift program. Investigation was multi-fold and contained visual inspection, using an optical microscope, and mechanical and electrical testing of leads and its insulator. It was concluded that the device could have been damaged during implantation, in vivo, during removal, and/or during transportation to author’s laboratories. The damage observed on the lead insulation is similar to that which can occur due to anchoring of the lead and hardening due to oxidation. Insulation stiffness was determined to be 1/10 of new insulator. The results reported here on the …

Simulation Of Skull Fracture Due To Falls, Anthony Vicini, Tarun Goswami

Biomedical, Industrial & Human Factors Engineering Faculty Publications

This study presents novel predictive equations for von Mises stress values of bones in the frontal and lateral regions of the skull. The equations were developed based on results of a finite element model developed during this research. The model was validated for frontal and lateral loading conditions with input values mimetic to fall scenarios. Using neural network processing of the information derived from the model achieved R2 values of 0.9990 for both the stress and deflection. Based on the outcome of the fall victims, a threshold von Mises stress of 40.9 to 46.6 MPa was found to indicate skull …

Assessing The Impact Of Principal Component Analysis On Accurately Predicting Melanoma Diagnosis Applied On Different Classification Models, Juan Cristobal Olmedo Rivera

Industrial, Manufacturing, and Systems Theses - Archive

With huge amounts of data at our disposal in the medical field, mathematical models are built to diagnose diseases. This study focuses on melanoma because it’s the type of skin cancer that accounts for most deaths, up to 7,230 in 2019 according to the American Cancer Society. The study focuses on the effectiveness on diagnosing melanoma and how Principal Component Analysis (PCA) impacts the performance of four models being assessed, which are: K Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Each model evaluates the melanoma dataset before and after performing the PCA …

Planning And Optimization Of A Stochastic Multi-Phase Multi- Criteria Multi-Echelon Humanitarian Logistics Network, A B M Mainul Bari

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Disasters, may that be anthropogenic or natural, cause much havoc to vast area and population. Property and infrastructures get destroyed. People are often in need of urgent relief like dry foods and water to survive. In a country which is in the underdeveloped part of the world, relief and evacuation activities are usually carried out by local government run aid agencies. Most of the time, the local decision makers do the coordination or planning of these humanitarian activities largely based on either past experience or sometimes just pure hunch, which is neither efficient nor economic. Proper planning and coordination in …

Shared Or Dedicated Infrastructures: On The Impact Of Reprovisioning Ability, Roch A. Guérin, Kartik Hosanagar, Xinxin Li, Soumya Sen

Computer Science and Engineering Faculty Research

New technologies, such as virtualization, are transforming the way in which software and services are deployed and delivered to their users. They are behind the emergence of IT offerings such as cloud computing and converged networks, and manifest themselves through two important trends: (1) lower the cost of sharing a common infrastructure across multiple services with disparate resource requirements, and (2) dynamic provi- sioning of capacity in response to demand. Conventional wisdom is that both of these capabilities are synergistic, with greater provisioning flexibility improving the benefits derived from sharing computing or network resources. Consequently, a service operator should now …

Multivariate Time Series Pattern Recognition Using Machine Learning And Deep Learning Methods, Sai Abhishek Devar

Industrial, Manufacturing, and Systems Theses - Archive

In this research work, we have implemented machine learning & deep-learning algorithms on real-time multivariate time series datasets in the manufacturing & health care fields. The research work is organized in two case-studies. The case study-1 is about rare event classification in multivariate time series in a pulp and paper manufacturing industry, data was collected of multiple sensors at each stage of production line, the data contains a rare event of paper break that commonly occurs in the industry. For preprocessing we have implemented sliding window approach for calculating first order difference method to capture the variation in the data …

Machine Learning Applications To Predict Road Crash And Soccer Game Outcomes, Lu Bai

Master's Theses

Machine learning has become a cutting-edge and widely studied data science field of study in recent years across many industries and disciplines. In this thesis, two problems (1- crash severity prediction, 2- soccer game outcome prediction.) were investigated by using a set of machine learning approaches, namely: Ridge regression, Lasso Regression, Support Vector Machine (SVM), Neural Network (NN), Random Forest (RF).

The first study is focused on investigating the critical factors affecting crash severity on a comprehensive time-series state-wide traffic crash data. The dataset covers crashes occurred in the state of Connecticut between 1995 and 2014. Traffic crashes are an …

Resource Allocation And Task Scheduling Optimization In Cloud-Based Content Delivery Networks With Edge Computing, Yang Peng

Operations Research and Engineering Management Theses and Dissertations

The extensive growth in adoption of mobile devices pushes global Internet protocol (IP) traffic to grow and content delivery network (CDN) will carry 72 percent of total Internet traffic by 2022, up from 56 percent in 2017. In this praxis, Interconnected Cache Edge (ICE) based on different public cloud infrastructures with multiple edge computing sites is considered to help CDN service providers (SPs) to maximize their operational profit. The problem of resource allocation and performance optimization is studied in order to maximize the cache hit ratio with available CDN capacity.

The considered problem is formulated as a multi-stage stochastic linear …

Resource-Constrained Project Scheduling With Autonomous Learning Effects, Jordan M. Ticktin

Master's Theses

It's commonly assumed that experience leads to efficiency, yet this is largely unaccounted for in resource-constrained project scheduling. This thesis considers the idea that learning effects could allow selected activities to be completed within reduced time, if they're scheduled after activities where workers learn relevant skills. This paper computationally explores the effect of this autonomous, intra-project learning on optimal makespan and problem difficulty. A learning extension is proposed to the standard RCPSP scheduling problem. Multiple parameters are considered, including project size, learning frequency, and learning intensity. A test instance generator is developed to adapt the popular PSPLIB library of scheduling …

Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders

Graduate Theses and Dissertations

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created …