Operations Research, Systems Engineering and Industrial Engineering Commons^™

Research On Semi-Physical Simulation Model Of Special Vehicle Cockpit With Force Feedback, Liang Feng, Zhili Zhang, Xiangyang Li, Yihao Li, Wang Bei, Long Yong

Journal of System Simulation

Abstract: To improve the interaction and immersion of cockpit simulation training system, a semi-physical model of special vehicle cockpit with force feedback is studied. The important force feedback parts of the semi-physical simulation model are designed to provide more real operation experience for operators. The simulation training method of special vehicle cockpit based on dynamic model is studied to match the action input of operators with scene changes and provide a high fidelity visual experience. The driving simulation operation process of special vehicle is analyzed and verified. Simulation experiments show that the model has the characteristics of high precision, fast …

Simulation Research On U-Automated Container Terminal, Ding Yi, Tiantian Li

Journal of System Simulation

Abstract: To analyze the new U-layout effect on the handling operation efficiency in automated container terminal, the U-automated container terminal simulation model is established by FlexTerm software to visualize the handling operation process. The traditional automated terminal simulation model is established to compare the working efficiency under different working modes. The working ability of terminal handling equipment such as AGV（Automated Guided Vehicle）, yard cranes and rail-mounted gantry cranes are compared by simulation tests. Simulation results show that due to the creation of yard layout and handling technology, the superiority of U-layout is proved when the working efficiency of each handling …

Predictive Control Method Of Peak Hour Passenger Flow At Urban Rail Station, Xiaohe Li, Jianping Wu, Depin Peng

Journal of System Simulation

Abstract: With the rapid development of subway in China, the urban rail station, especially the transfer station, is prone to generate passenger congestion in the peak period. After analyzing the types of passenger flow in and out of the platform, a predictive control model of passenger flow is established based on the discrete linear quadratic optimal control theory. Taking Fuxingmen Station as an example, the simulation environment of the station is built by using the simulation software of Anylogic. The historical passenger flow data in peak period and the optimal passenger flow control sequence obtained by solving the passenger flow …

Study And Effect Evaluation On The Setting Of Contraflow Left-Turn At Intersections, Zhao Dan, Xuejun Niu, Shuhao Zhang, Jiaxu Wei

Journal of System Simulation

Abstract: Contraflow left-turn is one of the traffic organization ways at intersections. By analyzing the setting parameters of the contraflow left-turn, the length and the range of the contraflow left-turn lane, the constrained conditions of the contraflow left-turn are determined, and the applicable conditions are determined from the road, traffic and signal control. VISSIM software is used to analyze a road intersection, simulate and evaluate the indicators related to the intersection entrance, optimize the timing plan of contraflow left-turn lane, and validate the feasibility and advantages of contraflow left-turn lane. The results show that the intersection delays are reduced by …

Variety Recognition Based On Deep Learning And Double-Sided Characteristics Of Maize Kernel, Feng Xiao, Zhang Hui, Zhou Rui, Qiao Lu, Wei Dong, Dandan Li, Yuyao Zhang, Guoqing Zheng

Journal of System Simulation

Abstract: In order to construct a maize kernel variety recognition model with high recognition accuracy and suitable for mobile phone application, a mobile phone is used to obtain maize kernel double-sided (embryonic and non-embryonic) images. Based on the lightweight convolutional neural network MobileNetV2 and transfer learning, a maize kernel image variety recognition model is constructed. In view of the existing research methods are mainly for single-sided recognition of maize kernel variety, the performance of single-sided and double-sided characteristics modeling and recognition is compared. The results show that the double-sided recognition accuracy of maize kernel double-sided characteristics modeling is 99.83%, which …

Track Deception Modeling Method Based On Multi-Jammer Cooperation, Wang Rui, Xiangyang Li, Zhili Zhang, Hongguang Ma

Journal of System Simulation

Abstract: The false track generated by the jammer formation is an effective interference measure against networked radars. This paper starts from the basic principle of false track generation and presents the model of the measurement error of the radar for the false track and the control error of the jammer formation. It also presents an analysis of the effects of these errors on the false track. The effectiveness of the false track is further analyzed through the confrontation scenario between the rectangular front formation of a certain type of equipment and networked radars. The simulation results show that the false …

Low Power Visual Odometry Technology Based On Monocular Depth Estimation, Ma Rong, Qiurui Chen, Zhang Han, Mei Zheng, Wang Rui, Wei Wei

Journal of System Simulation

Abstract: With the development of artificial intelligence, precision machinery and computing technology, micro-unmanned system will play an important role in the future battlefield. To solve the lack of monocular visual odometry scale, micro robot power consumption and load limits, the monocular depth estimation technology is introduced and a low view dataset is collected. A convolutional neural network to predict depth information from a single image is built, and the structure of neural network model is optimized. The depth estimation with monocular visual odometry are combined and deployed on JetsonNano. Experiments show that the combined monocular visual odometry can recover scale …

Research On Supply Chain Recovery Strategy Under Disruption Risks, Yang Yi, Peng Chen, Yujiu Yang, Yulong Wang

Journal of System Simulation

Abstract: Aiming at supply chain disruptions, two effective supply chain recovery strategies are proposed, namely, a supplier’s preset emergency inventory strategy and a manufacturer’s product change strategy, meanwhile, emergency inventory storage cost and products return rate after change are also taken into consideration. In order to maximize the manufacturer’s profit and customer satisfaction, a single-objective model and a bi-objective model are established, respectively. The optimization problems in the two models are transformed into mixed integer programming problems, moreover, corresponding solution algorithms are presented based on ILOG CPLEX. The simulation results show that the disruption recovery strategies proposed can not only …

Radar Emitter Signal Identification Via Distance Features, Yingkun Huang, Weidong Jin, Yan Kang, Jiehao Zhu

Journal of System Simulation

Abstract: Aiming at the problem that traditional recognition methods of radar emitter signal have low accuracy in low signal to noise ratio (SNR) environment, and are usually suitable for only several specific radar signals, an identification approach of radar signal based on distance features is proposed. Several cluster centers are extracted via the k-means algorithm, and the Dynamic Time Warping (DTW) values between the radar signal and the cluster center are calculated respectively, which are combined as the input features of k-Nearest Neighbor (k-NN) algorithm. The simulation results show that when the SNR is 3 dB, the identification rate …

Study On Prediction Of Crystal Properties Based On Deep Learning, Buwei Wang, Wang Min, Fan Qian, Ya'nan Wang, Hanwen Zhang, Yunliang Yue

Journal of System Simulation

Abstract: Predicting crystal properties using traditional machine learning methods requires complex feature engineering. In order to bypass time-consuming feature engineering, element network (ElemNet), representation learning from stoichiometry (Roost), compositionally-restricted attention-based network (CrabNet) and crystal graph convolution neural network (CGCNN) based on deep learning technology are used to simulate the formation energy, total energy per atom, band gap, and Fermi energy of crystal. The residual learning is introduced into CGCNN, and a crystal graph convolution residual neural network (CGCRN) is proposed. In the CGCRN, the number of hidden layers and the number of nodes in the hidden layers are increased, …

Improved Ant Colony Optimization Algorithm For Jamming Resource Allocation, Qingyun Wang, Dezhong Jiao, Shi Shuo, Genyan Peng, Junhua Sun, Yuxin Duan

Journal of System Simulation

Abstract: Ant Colony Optimization (ACO) is a new intelligence optimization algorithm. When applied to jamming resource allocation, the velocity of convergence in optimization process is slow and the probability of obtaining the global optimal solution is low. In order to raise the efficiency of jamming resource allocation and the probability of getting global optimal solution, the attenuation factor is improved to a variable that changes according to the exponential function in optimization process. The attenuation factor is taken as a relatively small value in the initial search phase, and increases monotonically and exponentially as the number of iterations increases. Simulation …

Brief Review On Applying Reinforcement Learning To Job Shop Scheduling Problems, Xiaohan Wang, Zhang Lin, Ren Lei, Kunyu Xie, Kunyu Wang, Ye Fei, Chen Zhen

Journal of System Simulation

Abstract: Reinforcement Learning (RL) achieves lower time response and better model generalization in Job Shop Scheduling Problem (JSSP). To explain the current overall research status of JSSP based on RL, summarize the current scheduling framework based on RL, and lay the foundation for follow-up research, the backgrounds of JSSP and RL are introduced. Two simulation techniques commonly used in JSSP are analyzed and two commonly used frameworks for RL to solve JSSP are given. In addition, some existing challenges are pointed out, and related research progress is introduced from three aspects: direct scheduling, feature representation-based scheduling, and parameter search-based scheduling.

Hyperspectral Rx Anomaly Detection Method Based On The Fusion Of Spatial And Spectral Feature, Liu Xuan, Xiangyang Li, He Fang, Jianwei Zhao, Fenggan Zhang

Journal of System Simulation

Abstract: To address the problem that the hyperspectral anomaly detection algorithm does not make full use of the spatial information of the hyperspectral image and the detection accuracy is limited, a FSSRX (Fusing Spatial and Spectral Reed-Xiaol) anomaly detection algorithm that fuses spatial and spectrum information is proposed to improve the accuracy of hyperspectral anomaly detection. In FSSRX algorithm, the spatial feature of hyperspectral images is firstly extracted by the EMAP(Extended Multi-attribute Profile) method and the abnormal score of each pixel in spatial features is then calculated with RX detector. Meanwhile, RX anomaly detection is carried out directly on the …

Research On Model Reuse Technology Based On Semantic Matching And Composition, Xingyu Tian, Guangxun Zeng, Yunbo Gao, Lili Ye, Guanghong Gong, Li Ni

Journal of System Simulation

Abstract: In order to solve the data barriers between the conceptual model and the simulation scenario of the combat system, the intelligent mapping and model reuse technology of the simulation scenario is researched. The conceptual model is analyzed using DOM technology. Based on the ontology theory, the knowledge base of the combat domain is constructed and the web crawler is customized to build the domain thesaurus. Through the SWRL rule library, the reasoning engine is called to realize the relational reasoning at the semantic level. An intelligent matching algorithm is designed to map the semantic relationship to the combination relationship …

The Product Test Scheduling Problem, Megan Wydick Martin, Cliff Ragsdale, John Fico, Carlos G. Cajica-Sierra, Richard M. Fetcenko

International Journal of Applied Management and Technology

This research focused on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Often, these products must receive a certification from prescribed regulatory agencies at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the makespan while ensuring that technicians are available when needed to complete in-process and at-completion inspections. We refer to this as the product test scheduling problem. …

Choosing Wearable Internet Of Things Devices For Managing Safety In Construction Using Fuzzy Analytic Hierarchy Process As A Decision Support System, Sharique Khalid

Theses, Dissertations and Capstones

Many safety and health risks are faced daily by workers in the field of construction. There is unpredictability and risk embedded in the job and work environment. When compared with other industries, the construction industry has one of the highest numbers of worker injuries, illnesses, fatalities, and near-misses. To eliminate these risky events and make worker performance more predictable, new safety technologies such as the Internet of Things (IoT) and Wearable Sensing Devices (WSD) have been highlighted as effective safety systems. Some of these Wearable Internet of Things (WIoT) and sensory devices are already being used in other industries to …

Clarity, Organization, Precision, Economy: A Technical Writing Guide For Engineers, David J. Adams, University Of New Haven

Civil Engineering Faculty Book Series

This fourth edition of COPE was sparked by my involvement with PITCH (Project to Integrate Technical Communication Habits) at the Tagliatela College of Engineering at the University of New Haven. This new edition contains additional material on data displays, as well as some additional material on writing about data—including how to avoid rhetorical shifts that undermine the precision of a technical report.

Launch Editorial, Jindong Qin, Xiaofang Chen, Lida Xu

Information Technology & Decision Sciences Faculty Publications

The digital economy is first and foremost a data economy, and data is the first element of the digital economy. Management System Engineering (MSE) is dedicated to the methodology of System Engineering (SE) and the practice of Management Decision Making. The digital economy is a network economy, and the Internet is the basic carrier of the digital economy. [Extracted from the article]

Data-Driven Semantic Modeling For Welding Assemblies, Fahim Ahmed

Wayne State University Dissertations

A significant information gap is prevalent between the design domain and the manufacturing domain. The designers lack manufacturing awareness as they have little knowledge regarding the manufacturability of their designs. The welding domain falls prey to this issue as designers lack manufacturing awareness and welding engineers lack weldability of the product assembly. Data-driven techniques have shown promising results in the analysis and understanding of complex welding processes. Data analytics play a significant role to turn data into valuable insights to assist in the weldability certification decision-making or weldability prediction for Resistance Spot Welding (RSW) as well. We have used machine …

Optimization-Based Uav Fleet Routing And Safety Assurance – Models, Algorithms, And Prototyping, Zhenyu Zhou

Wayne State University Dissertations

Unmanned aerial vehicles (UAVs), especially multi-rotor drones, have been increasingly used in various scenarios in the last decade. With the reduced hardware costs, improved battery life, and enhanced processor performance, we can eventually allow all kinds of drones to automatically travel through the low-altitude airspace. The large-scale application of drones will extend the basic transportation facilities from the ground to the air and form 3D transportation networks for the future. Compared to current ground-vehicle and aircraft traffic systems, multi-UAV systems are far from well-developed. Most current multi-UAV systems are human-operated or pre-programmed to perform specific tasks. The current application of …

Automotive Product Assortment Planning With Consideration Of Distribution Channel Dynamics, Madagedara Maduka Rushanjalee Balasooriya

Wayne State University Dissertations

Automotive original equipment manufacturers (OEMs) are putting a lot of effort into maintaining an efficient order catalog to offer better products to their customers in maketo-stock (MTS) markets. While product “assortment planning" research grows to more effectively identify the best assortments for OEMs, the existing configurable assortment planning literature ignores a significant dimension: the impact of distribution channels, especially dealer franchise networks. Dealers face unique challenges in trying to best satisfy the choice preferences of their local consumers by balancing their limited product configuration inventory with profitability. In many predominantly MTS automotive markets such as the U.S., the reality is …

Improving Or Operations Using Machine Learning Techniques, Tannaz Khaleghi

Wayne State University Dissertations

Recently, health care related studies are being widely conducted by researchers using unique and efficient techniques to increase system profitability, quality of care, and patient satisfaction. Surgery department is considered as the hospital's engine, and cost of surgical services has a huge impact on the overall profitability of the hospital. This thesis proposes novel approaches to improve the efficiency of surgical services by using machine learning concepts.

In the first part, this research investigates the prediction of the surgery durations and Current Procedural Terminology (CPT) Codes. Accurate prediction of the surgery duration will improve the utilization of indispensable surgical resources …

Blockchain-Based Digital Trust Mechanism: A Use Case Of Cloud Manufacturing Of Lds Syringes For Covid-19 Vaccination, Trupti Rane, Jingwei Huang

Engineering Management & Systems Engineering Faculty Publications

Trust is essential in the digital world. It is a critical task to build digital trust for the ongoing digital engineering transformation. Aiming at developing a blockchain-based digital trust mechanism for Cloud Manufacturing or Manufacturing-as-a-Service (MaaS), in this paper, we use the manufacturing of low dead space (LDS) medical syringes through Cloud Manufacturing as a motivating scenario to develop a basic framework. To meet the need of optimally saving COVID-19 vaccine doses to save more lives, the medical device manufacturing community needs to make a swift move to meet the surged need for LDS syringes. Cloud Manufacturing is a form …

A Primer On The Human Readiness Level Scale (Ansi/Hfes 400-2021), Kelly Steelman, Holly Handley, Katie Plant (Ed.), Gesa Praetorius (Ed.)

Engineering Management & Systems Engineering Faculty Publications

"The Human Readiness Level (HRL) Scale is a simple 9-level scale for evaluating, tracking, and communicating the readiness of a technology for safe and effective human use. It is modeled after the well-established Technology Readiness Level (TRL) framework that is used throughout the government and industry to communicate the maturity of a technology and to support decision making about technology acquisition. Here we (1) introduce the ANSI/HFES 400-2021 Standard that defines the HRL scale and (2) provide concrete examples of evaluation activities to support the application of HRLs in the development of automated driving systems."

Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …

Theorizing The Initial Response Of Countries In Bringing Covid-19 Pandemic Under Control: The Effect Of Change Readiness Of Countries, M. Mahdi Moeini Gharagozloo, Farinaz Sabz Ali Pour, Chen Chen, Mozhgan Moeini Gharagozloo

Engineering Management & Systems Engineering Faculty Publications

Pandemic crises can bring the biggest and deepest shocks to countries around the world. In the first quarter of 2020, a global pandemic named “COVID-19” spread all over the world and not only took so many lives and created so much fear but also brought a tremendous financial pain as a result of shutting down economies to fight with this unknown contagious virus. This paper examines how countries’ change readiness enables them to bring the spread of an international crisis under control. We propose that higher levels of change readiness would help countries to cope with risks and uncertainties generated …

Acute Coronary Syndrome Prediction: A Data-Driven Machine Learning Modeling Approach In Emergency Care, Joshua Oluwatobiloba Emakhu

Wayne State University Dissertations

Healthcare facilities are faced with significant challenges all year round, with patients presenting to the emergency department (ED) with different health issues. Of these challenges, heart disease seems to be an outlier. With heart disease being the primary cause of mortality and morbidity in both developed and developing countries, clinical concerns for acute coronary syndrome (ACS) are one of emergency medicine’s most common patient encounters. Of the three sub-categories of ACS, non-ST-segment elevation myocardial infarction (NSTEMI) has a long-term impact on the well-being of patients if left untreated. Previous efforts in hospital management have applied machine learning algorithms in differentiating …

Mixed-Integer Programming Methods For Modeling And Optimization Of Cascading Processes In Complex Networked Systems, Cheng-Lung Chen

Electronic Theses and Dissertations, 2020-2023

Dynamics and growth of many natural and man-made systems can be represented by large-scale complex networks. Entity interactions and community interconnections within complex networks increase the level of difficulty for the investigation on structural network properties such as robustness, vulnerability and resilience. In this dissertation, we develop methodologies based on mixed-integer programming techniques to solve challenging optimization problems that model cascading processes in complex networked systems. In particular, we seek to provide decision making recommendations for problems related to different types of cascading processes in networks commonly considered in a variety of applications: interdependent infrastructure networks and social networks. In …

Assessing Patient Safety Culture In United States' Hospitals, Abdulmajeed Azyabi

Electronic Theses and Dissertations, 2020-2023

Patient safety is founded on continuous learning because there is an urgent need to report and learn from errors, accidents, near misses, and adverse events. The traditional approach to patient safety, based on forming mortality committees and investigating accidents, will no longer be effective. Frameworks, surveys, and assessment tools have been developed over the last decade to assist organizations in measuring and understanding their culture. This a retrospective cross-sectional study included 67,010 respondents from Agency for Health care Research and Quality (AHRQ) 2018 comparative database was analyzed using partial least squares structural equation modeling (PLS-SEM). This research explored whether the …

Air Force Pilot's Recognition About The Effectiveness Of Active Noise Cancellation On Hearing Health, Performance, And Aviation Safety, Kyungtaek Hwang

Electronic Theses and Dissertations, 2020-2023

The purpose of this thesis is to suggest the application of Active Noise Cancellation in Air Force pilot headset and helmet not only to reduce noise-induced hearing damage, but also to enhance pilot performance and aviation safety. Despite the recent advances of sound treatment technology, the interior sounds of a military aircraft exceed the damage risk criterion of 8 hrs/day exposure. Air Force pilots are flying under the extreme condition where noise is severe and prevalent. The exposure to noise can lead to permanent hearing loss, stress and fatigue, unintelligible communication, and deterioration of speech perception and recall. With all …