Engineering Commons^™

A Novel Interpretation Of Average Run Length For Assessing The Performance Of Control Charts, Gonçalo Sousa

Masters Theses

The Average Run Length (ARL) is a performance measure of Control Charts widely used within Statistical Process Control. In this study we propose a new approach for the computation of the ARL that is based on a novel interpretation of out-of-control signals produced by a Control Chart. Specifically, out-of-control signals used to calculate traditional ARLs may correspond to Type I errors and may not reflect a Control Chart’s true performance. To compensate for this issue, for Shewhart and EWMA charts, constraints are applied to the calculation of ARLs so that only out-of-control signals that occur when the corresponding statistic is …

A Talking Cart, Abdullah Bin Naeem

LSU New Orleans Theses and Dissertations

This research investigates the development of a robust AI-powered detection and tracking engine aimed at revolutionizing the retail checkout experience. The foundation of this work is a comprehensive exploration of state-of-the-art Computer Vision methodologies, particularly focusing on object detection, segmentation, and tracking. The study employs a modular pipeline that integrates advanced visual recognition algorithms with a robust data processing framework.

Key to this work is the construction of a synthetic dataset using Unity3D, enabling the generation of high-quality annotated data that mirrors real-world retail scenarios. This approach addresses the challenge of insufficient labeled datasets by simulating diverse and cluttered shopping …

Model Reference Adaptive Control For Mobile Manipulators And Beyond, Srivatsan Srinivasan

All Dissertations

In recent years, robotics has expanded into various sectors, including manufacturing, transportation, and household services, making the integration of autonomy a critical area of research. This shift aims to ensure safety and enhance the utility of autonomous systems. Traditionally, robotic applications focused separately on mobility, like automated guided vehicles, and manipulation, such as serial-chain arms in manufacturing. Today, however, we see a merging of these capabilities in the growing field of mobile manipulator robots that combine movement with purposeful interactive functionalities.

A typical mobile manipulator is a robotic arm mounted on a wheeled base. This thesis focuses on advancing control …

Accessibility And Usability Of Medical Devices For Users With Disabilities: Insights From A Bibliometric And Thematic Analysis, Karen Daniela Gonzalez Silva

Open Access Theses & Dissertations

No abstract provided.

Sensitive Grip Mounting Surfaces For Physical Human-Robot Interaction., Branden Shay

Electronic Theses and Dissertations

As the development of robotics becomes ever increasingly present in our society, the need for studying how humans and robots interact with each other becomes more of a necessity to be properly integrated in the changing world around us. In this thesis, we undertake the study and development of how to create a tactile human-robot operator interface to guide a robot perform a physical task. The implementation of tactile interfaces such as SkinCell sensor arrays emulating skin, and of a robotic handlebar fitted with tactile sensors are critical examples of the challenges that come with mounting and packaging such sensors …

Learning To Accelerate Globally Optimal Solutions: Applications In The Ac Optimal Power Flow Problem, Muhammet Fatih Cengil

Graduate Theses and Dissertations

The Alternating Current Optimal Power Flow (AC-OPF) problem is a fundamental optimization challenge critical to ensuring the economical and reliable operation of power grids. While fast heuristic methods provide upper-bound solutions, assessing their quality requires lower bounds obtained from relaxations of the AC-OPF problem. This dissertation focuses on finding globally optimal solutions to the AC-OPF problem by enhancing the effectiveness and efficiency of Quadratic Convex (QC) relaxations. Leveraging machine learning techniques, we aim to achieve tighter relaxations faster and improve computational performance, enabling practical scalability for real-time applications.

In Chapter 2, we propose a machine learning-based method to accelerate the …

Recovery Resiliency Of Interdependent Power Systems Infrastructure Subject To Extreme Events, Partha P. Sarker

Graduate Theses and Dissertations (2019 - present)

When Hurricane Maria struck the island of Puerto Rico on September 20, 2017, it devastated the island’s aging power systems infrastructure and inflicted an island-wide power outage that left Puerto Rico in total darkness for an entire week before the system slowly started to recover. This unprecedented failure of the critical power systems infrastructure exacerbated the failure of other critical infrastructures or lifeline systems of the island. This research explores and quantifies the relationships or interdependencies that exist between the power systems and other critical infrastructure systems by investigating the post-hurricane recovery data of these lifeline systems. Subsequently, the research …

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Interaction-Sensitive Tree-Based Statistical Models, Xiaotong Sun

Graduate Theses and Dissertations

This dissertation introduces a tree-based framework to improve the interpretability and modeling of interaction effects among variables, essential in fields like biostatistics, healthcare, science and engineering. Traditional regression methods often fail to clearly capture complex interactions, while tree-based approaches, despite their interpretability, face performance limitations and overfitting concerns. Our proposed interaction-sensitive tree-based method, designed for seamless integration, combines various statistical techniques tailored to different data types, leveraging ensemble learning methods to enhance accuracy and mitigate overfitting. We present methods for regression, survival analysis, and classification, validated with case studies and benchmarked against traditional models using metrics like BIC and R-squared. …

A Data-Driven Approach For Automated Multi-Site Competitive Facility Location, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper addresses the challenge of optimal retail expansion in competitive urban environments through a novel approach to the Competitive Facility Location (CFL) problem. Traditional methods for solving CFL problems often struggle with large-scale scenarios, relying on manual pre-selection of candidate sites and imposing limitations on the number of new locations. Our approach leverages Adaptive Large Neighborhood Search (ALNS) enhanced with data enrichment techniques, including community detection on road networks and population weighting based on mobility data. We developed two ALNS variants: Community Geometric Centroid (CGC-ALNS) and Population Weighted Centroid (PWC-ALNS). These methods automate site selection, eliminating manual pre-selection while …

The Practical Adoption And Application Of Blockchain Technology Within The Beverage Industry, Alexander Adams Jr

Electronic Theses, Projects, and Dissertations

Abstract

The beverage industry is facing heightened scrutiny as the demand for transparency and accountability reaches new heights. In the age of information technology, companies must prioritize enhanced traceability to ensure product safety, comply with government regulations, maintain customer trust, and protect brand integrity. This thesis explores the potential of blockchain technology as a solution to these challenges, focusing on its ability to decentralize data, improve traceability, and expedite response times during safety recalls. The research provides an overview of the evolution of food safety regulations, beginning with the first establishment by Upland Sinclair, and examines current traceability practices and …

Data-Driven Quality Improvement For Sustainability In Automotive Packaging Systems, Tyler Mcknight

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Tyler McKnight on November 25, 2024.

Motivating Sustainability Through The State Of Biologically Inspired Design, Bryan Watson

Sustainability Conference

There are multiple arguements for sustainability, but one that resonates with environmentalists and the public alike is the need for preservation to all us to discovery natural solutions to our problems. Common examples often given include medical discoveries, unique mechanisms, and new materials. This presentation focuses on two ideas to motivate sustainability. First, what is the current state of biologically inspired design? Is there more to learn from nature? To answer these questions, recent research is presented which examined 660 Biologically Inspired Design samples from three data sources: Google Scholar, Google News, and the Asknature.org “Innovations” database. The data were …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

Effect Of Workload And Trust On Automation Levels In Human-Robot Collaboration, Abhiram Maddula

LSU Master's Theses

Automation is becoming increasingly common in manufacturing and assembly plants. The future lies in hybrid workspaces where the strengths of humans and robots complement each other, with robots excelling in precision, speed, and strength, and humans excelling in creativity, emotional intelligence, and complex decision-making. Collaborative robots can foster a more efficient and productive work environment by bridging the gap between human and machine capabilities. This study examines how semi-automated and automated modes impact human-robot collaboration, focusing on mental workload, trust, and task performance.

In this experiment, 58 participants performed a primary task alongside a collaborative robot assembling a miniature lamppost …

A Method For Key Node Identification In Operational Target System Based On War Gaming, Yongfu Zhang, Yang Liu, He Yuan

Journal of System Simulation

Abstract: The identification of key nodes in an operational target system is an important basis for combat command decision-making. Due to the lack of experimental verification of key node identification in the current operational target system in a campaign-level dynamic confrontation environment, a complex network model of operational target system with large-scale entities and complex interaction relationship was constructed by taking integrated air defense network as an example, with the help of the data derived from the large joint war gaming; the characteristics of wargame data were considered, and the value characteristics of combat targets and network structure characteristics were …

Platform Path Optimization Method Based On Cumulative Detection Probability Of Sonar Search, Xiang Wei, Xingxuan Liu, Dianzheng Fu, Tianji Yang, Jiaxuan Yang

Journal of System Simulation

Abstract: To address the lack of research on the optimal path of mobile search platform to search for moving targets, this paper proposes a path optimization method of mobile search platform based on cumulative search probability theory. Based on the cumulative detection probability (CDP), one of the important criteria of sensor performance evaluation, a single-peak CDP calculation formula is constructed by using a time series correlation model, namely the (λ, σ) process model. A set of target motion scenarios are constructed, and the trajectory probability of target scenarios and their CDP at different time are corrected by Bayesian posterior probability. …

Modeling For Decision Support Of Flight Ground Support Process, Zhiwei Xing, Ruiwen Yu, Biao Li, Zhaoxin Chen

Journal of System Simulation

Abstract: Aiming at the problems of insufficient decision-making ability and low operational efficiency of the flight ground support process, a decision support model of the flight ground support process based on the department of defense architecture framework (DoDAF) is proposed. Starting from the support operation, support resources, and the relationship between them, the quantitative description of the flight ground support process is performed. DoDAF and the model-based systems engineering (MBSE) modeling method are combined to establish a decision support model of the flight ground support process. The decision utility function is established to analyze the utility value of the comprehensive …

End-To-End Motion Planning Of Unmanned Vehicles Based On Multimodal Deep Reinforcement Learning, Kaiyuan Ding, Askar Hamdulla, Bin Zhu, Eksan Firkat, Zhengtang Ma

Journal of System Simulation

Abstract: Since the agent cannot sense the surrounding environment and cannot successfully avoid obstacles, reinforcement learning fails to be generalized to robot motion planning in difficult terrain. Therefore, a solution based on multimodal deep reinforcement learning, which learns to blend proprioceptive states with high-dimensional depth sensor inputs, is proposed for the motion planning of unmanned vehicles. To be specific, proprioceptive states offer contact measurement for immediate reaction, and the unmanned vehicle can learn and forecast environmental changes with its attached visual sensors, proactively navigating around obstacles and uneven terrains numerous time steps ahead. TransProAct (transformer-based proactive action), a unique end-to-end …

Flexible Job Shop Scheduling Method Based On Collaborative Agent Reinforcement Learning Algorithm, Jian Li, Huankun Li, Pengbo He, Huabei Wang, Liping Xu, Kui He

Journal of System Simulation

Abstract: To enhance the efficiency of flexible job shop scheduling, this paper develops a Markov decision process with specific constraints tailored to the scheduling problem. A cooperative agent reinforcement learning method is proposed to solve the problem of concurrent selection of workpieces and machines. During the construction of the Markov decision process, a disjunctive graph is introduced to represent the state characteristics. Two agents are introduced to select the workpieces and machines. The reward parameters governing the entire scheduling process are established by predicting variations in the minimum-maximum completion time across different time points. A GIN(graph isomorphic network) graph neural …

Dual-Resource Constrained Distributed Flexible Scheduling For Aerospace Structural Components, Yufang Wang, Dianqing Zhang, Xiaolin Hua, Binbin Yao, Fan Chen

Journal of System Simulation

Abstract: A dual-resource constrained distributed flexible job-shop scheduling model was established by taking into account the worker constraints of the finishing process and the requirements of distributed multi-factory collaboration in the production of aerospace structural components. A hybrid grey wolf optimization algorithm based on the critical factory was proposed to solve this problem. The model contained four subproblems: factory selection, operation sequencing, machine selection, and worker selection. In view of these four sub-problems, a four-layer coding and a new decoding method were designed to avoid the use conflict of machines and workers. In addition, a new mechanism for hunting and …

Automatic Detection Algorithm For Typical Defects Of Substation Based On Improved Yolov5, Zhongkai Xu, Yanling Liu, Xiaojuan Sheng, Chao Wang, Wenjun Ke

Journal of System Simulation

Abstract: In response to the challenges present in the context of defect recognition in substations, such as complex substation defects and sample imbalance, an improved YOLOv5 algorithm was proposed. The Transformer model was introduced into the YOLOv5 network structure, leveraging the self-attention mechanism to capture long-range dependencies among features. A focal loss-based optimization was employed to improve the loss function, as well as the detection accuracy and robustness of defects of small sample substations. To meet the requirements of substation defect recognition, a dedicated dataset was constructed. A clustering algorithm was applied to the real annotation boxes to generate more …

Object Detection Of Lightweight Transformer Based On Knowledge Distillation, Gaihua Wang, Kehong Li, Qian Long, Jingxuan Yao, Bolun Zhu, Zhengshu Zhou, Xuran Pan

Journal of System Simulation

Abstract: In autonomous driving, the efficiency and accuracy of object detection are significant. Object detection based on Transformer structure has gradually become the mainstream method, eliminating the complex anchor generation and non-maximum suppression (NMS). It has problems of high computing cost and slow convergence. An object detection model of the based lightweight pooling transformer (LPT) is designed, which contains a pooling backbone network and dual pooling attention mechanism. A general knowledge distillation method is intended for the DETR (detection transformer) model, which transfers prediction results, query vector, and features extracted by the teacher as knowledge to the LPT model to …

A Hybrid Genetic Search Algorithm For Capacitated Electric Vehicle Routing Problem, Dongyao Jin, Mi Liu, Yena Zhu, Yijiang Zhao

Journal of System Simulation

Abstract: The capacitated electric vehicle routing problem (CEVRP) is an NP-hard combinatorial optimization problem in logistics distribution, aiming to minimize the total delivery distance of electric vehicles while satisfying carrying capacity and battery charge constraints. A hybrid genetic search algorithm is proposed to solve CEVRP by decomposing it into two subproblems: capacitated vehicle routing problem (CVRP) and fixed-route vehicle charging problem (FRVCP). A coding scheme with a two-layer chromosome structure is designed to represent the decision variables of these two subproblems. A Split operation is employed to generate vehicle routes for solving CVRP, and five neighborhood search operators, including Relocate, …

Traffic Sign Recognition Model With Long-Tail Distribution Based On Yolox-Tiny, Yunpeng Wu, Yingxiong Fu, Lijun Shen, Feng Cui

Journal of System Simulation

Abstract: Accurate recognition of traffic signs plays an important role in the field of intelligent driving. Traffic sign training datasets with long-tail distribution increase the difficulty of traffic sign recognition. A traffic sign recognition model with long-tail distribution based on YOLOX-Tiny was proposed to improve the poor performance of the model trained on long-tail distribution datasets. A long-tail traffic sign dataset was created based on the TT100K_2021 (tsinghua-tencent 100K 2021) dataset. YOLOX-Tiny was chosen as the underlying model by considering picture numbers in datasets, sample distribution, and model size. Equalization loss v2 (EQL v2) was used as classification loss to …

Real-Time Lidar Slam Algorithm Based On Distribution Optimal Registration, Weigang Li, Chuxiang Yu, Yongqiang Wang, Shaofeng Zou

Journal of System Simulation

Abstract: When scanning the surrounding environment, a lidar will generate some cluttered and sparse point cloud, which will cause excessive distribution fitting errors and correlation distances in the registration process, thus affecting the accuracy of the registration algorithm and the effect of simultaneous localization and mapping (SLAM). To address this problem, a real-time lidar SLAM algorithm based on distribution optimal registration is proposed. An eigenspectrum filter is designed, which takes the normalized minimum eigenvalue as the filtering object to filter out the points that do not match the set distribution in order to reduce the distribution fitting error. Secondly, a …

Research On Green Job Shop Scheduling Based On Herd Immunity Optimizer, Xunde Ma, Li Bi, Junjie Wang

Journal of System Simulation

Abstract: In view of the green flexible job shop scheduling problem where machines have multiple speeds, a green flexible job shop scheduling model under multiple speeds was constructed to minimize the makespan and total energy consumption under different speeds. A discrete coronavirus herd immunity optimizer (DCHIO) was proposed for a solution. A discrete individual updating method was introduced for the relatively large solution space of the multi-speed problem, based on which a population updating mechanism with multi-scale joint search was proposed to search the solution space quickly and uniformly. A dynamic mutation operation was designed to enhance the population diversity …

Agv Scheduling Problem At Automated Terminals Based On Improved Dqn Algorithm, Chengji Liang, Shidong Zhang, Yu Wang, Bin Lu

Journal of System Simulation

Abstract: A future tasks considering deep Q-network (F-DQN) algorithm was proposed to output realtime scheduling results of automated guided vehicles (AGVs) at automated terminals. This algorithm combined the advantages of real-time scheduling and static scheduling, improving the system status by considering static future task information when making real-time decisions, so as to obtain a better scheduling solution. In this study, the actual layout and equipment conditions of the Yangshan phase IV automated terminal were considered, and a series of simulation experiments were conducted using the Plant Simulation software. The experimental results show that the F-DQN algorithm can effectively solve the …

Global-Local Fusion For Efficient 3d Object Detection, Bin Lu, Minghan Wang, Yang Sun, Zhenyu Yang

Journal of System Simulation

Abstract: As the 3D object detection based on point clouds shows an incapacity of feature extraction and incongruity between classification and regression, this research introduces a novel ResCST architecture based on the SECOND network. It incorporates residual connections into the 3D sparse convolutional layer, with the advantages of capturing long-distance dependent relation by SwinTransformer and obtaining local features by convolutional neural network integrated, proposing the CNN-SwinTransformer hybrid model for enhanced feature extraction. It introduces the RCIoU method for the joint optimization of classification and regression tasks. The experimental results show that the model achieves a 3D detection accuracy of 91.21%, …

Optimization Of Crucial Targets For Air Defense Based On Combined Weighting-Topsis Model, Peng Zhang, Ke Feng

Journal of System Simulation

Abstract: To optimize the selection of crucial defended targets in regional air defense operations and improve the selection accuracy, this paper constructs a factor model considering the importance of air defense targets from the perspectives of target value, defense urgency, target vulnerability, and target recovery. Under the optimization of the TOPSIS method through a combination of the ANP and entropy weight methods, tendentious opinions of commanders and the excessive reliance on objective data can be overcome to ensure the factor weighting is more reasonable and accurate; Super Decisions is used to calculate the weights of the ANP method, accelerating data …