Artificial Intelligence and Robotics Commons^™

Human Characteristics Impact On Strategic Decisions In A Human-In-The-Loop Simulation, Andrew J. Collins, Shieda Etemadidavan

Engineering Management & Systems Engineering Faculty Publications

In this paper, a hybrid simulation model of the agent-based model and cooperative game theory is used in a human-in-the-loop experiment to study the effect of human demographic characteristics in situations where they make strategic coalition decisions. Agent-based modeling (ABM) is a computational method that can reveal emergent phenomenon from interactions between agents in an environment. It has been suggested in organizational psychology that ABM could model human behavior more holistically than other modeling methods. Cooperative game theory is a method that models strategic coalitions formation. Three characteristics (age, education, and gender) were considered in the experiment to see if …

Computational Intelligent Impact Force Modeling And Monitoring In Hislo Conditions For Maximizing Surface Mining Efficiency, Safety, And Health, Danish Ali

Doctoral Dissertations

"Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the “Extremely Uncomfortable Limits”. This study was a novel effort in developing deep learning-based solution to the HISLO problem.

This research study developed a rigorous mathematical model and a 3D virtual simulation model to …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Android has been the most popular smartphone system with multiple platform versions active in the market. To manage the application’s compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we conduct a systematic study of this modern software mechanism. Our objective is to measure the current practice of declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the (in)consistency between DSDK versions and their host apps’ API calls. To successfully analyze a modern dataset of 22,687 popular apps (with an …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri

Electronic Theses and Dissertations, 2020-2023

The advancements on the Internet have enabled connecting more devices into this technology every day. This great connectivity has led to the introduction of the internet of things (IoTs) that is a great bed for engagement of all new technologies for computing devices and systems. Nowadays, the IoT devices and systems have applications in many sensitive areas including military systems. These challenges target hardware and software elements of IoT devices and systems. Integration of hardware and software elements leads to hardware systems and software systems in the IoT platforms, respectively. A recent trend for the hardware systems is making them …

Artificial Intelligence And Soft Computing In Smart Structural Systems, Sajad Javadinasab Hormozabad

Theses and Dissertations--Civil Engineering

Next-generation smart cities are the key feature in the next chapter of human life. Cities that employ innovative and technology-driven solutions to improve the sustainability, resilience, prosperity, and amenity of the community are considered smart cities. Development of smart cities requires fundamental innovations in many technical and technological aspects including those contributing to smart structures. Smart technologies improve the structural performance against natural disasters like earthquakes, hurricanes, tornados, and promote the sustainability of structural systems. Next-generation smart structures encompass a variety of technologies including Structural Control (SC) and Structural Health Monitoring (SHM). SC covers methodologies and technologies that modify the …

Light Field Compression And Manipulation Via Residual Convolutional Neural Network, Eisa Hedayati

Dissertations, Master's Theses and Master's Reports

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF's, using simulation, it is possible to explore a high-quality captured LF's properties. The immediate concerns following the LF capture are its storage and processing …

Converting Optical Videos To Infrared Videos Using Attention Gan And Its Impact On Target Detection And Classification Performance, Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li

Electrical & Computer Engineering Faculty Publications

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. …

Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Model continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main …

Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya

Honors Theses

Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …

Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided …

Weakly Supervised Learning For Multi-Image Synthesis, Muhammad Usman Rafique

Theses and Dissertations--Electrical and Computer Engineering

Machine learning-based approaches have been achieving state-of-the-art results on many computer vision tasks. While deep learning and convolutional networks have been incredibly popular, these approaches come at the expense of huge amounts of labeled data required for training. Manually annotating large amounts of data, often millions of images in a single dataset, is costly and time consuming. To deal with the problem of data annotation, the research community has been exploring approaches that require less amount of labelled data.

The central problem that we consider in this research is image synthesis without any manual labeling. Image synthesis is a classic …

The Enlightening Role Of Explainable Artificial Intelligence In Chronic Wound Classification, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies …

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as …

Cybersecurity Leaders: Knowledge Driving Human Capital Development, Sharon L. Burton

Publications

Cybersecurity leaders must be able to use critical reading and thinking skills, exercise judgment when policies are not distinct and precise, and have the knowledge, skills, and abilities to tailor technical and planning data to diverse customers’ levels of understanding. Ninety-three percent of cybersecurity leaders do not report directly to the chief operating officer. While status differences influence interactions amid groups, attackers are smarter. With the aim of protecting organizations and reducing risk, knowledge about security must increase. Understanding voids are costly and increased breach chances are imminent. Burning questions exist. What are needed technological learnings for cybersecurity leaders to …

Development Of A Real Time Human Face Recognition Software System, Askar Boranbayev, Seilkhan Boranbayev, Mukhamedzhan Amirtaev, Malik Baimukhamedov, Askar Nurbekov

Physics & Astronomy Faculty Publications

In this study, a system for real-time face recognition was built using the Open Face tools of the Open CV library. The article describes the methodology for creating the system and the results of its testing. The Open CV library has various modules that perform many tasks. In this paper, Open CV modules were used for face recognition in images and face identification in real time. In addition, the HOG method was used to detect a person by the front of his face. After performing the HOG method, 128 face measurements were obtained using the image encoding method. A convolutional …

A Deep Machine Learning Approach For Predicting Freeway Work Zone Delay Using Big Data, Abdullah Shabarek

Dissertations

The introduction of deep learning and big data analytics may significantly elevate the performance of traffic speed prediction. Work zones become one of the most critical factors causing congestion impact, which reduces the mobility as well as traffic safety. A comprehensive literature review on existing work zone delay prediction models (i.e., parametric, simulation and non-parametric models) is conducted in this research. The research shows the limitations of each model. Moreover, most previous modeling approaches did not consider user delay for connected freeways when predicting traffic speed under work zone conditions. This research proposes Deep Artificial Neural Network (Deep ANN) and …

Analyzing Dispatching Wave Policies For E-Commerce Logistics Based On The Multi-Agent-Based Simulation, Zhiqiang Niu, Chaoyang Li, Hongyu Dong, Zhang Feng, Lingyun Meng, Tong Lu, Shengnan Wu

Journal of System Simulation

Abstract: To provide superior on-line shopping experiences and maintain sustained profitability,e-commerce logistics companies need design an effective dispatching wave policy strategy to handle the tradeoff between the advantage of economies of scale and fast-pace delivery services.A multi-agent-based simulation framework is proposed,where business processes in logistics are built as different nodes in the simulation network.Case studies are conducted to test the order delivery process with various wave strategies in the Beijing metropolitan area.Experimental results show that situation-dependent wave strategies are sensitive to different patterns of online shopping demands.

Optimization Algorithm For Planar Led Distribution And Connection, Fei Yue, Zhiqiang Gui, Yuyou Yao, Benzhu Xu, Liping Zheng

Journal of System Simulation

Abstract: The distribution and grouping of planar LED can be modeled as a multi constraint optimization problem.A novel algorithm based on the centroidal capacity-constrained power diagram for LED distribution is proposed,to achieve the goal of uniform illumination of planar LED.An optimization algorithm of LED combination and connection based on the greedy strategy is proposed to save materials.The experiment results show that the proposed algorithms are simple,effective in layout and grouping with rapid convergence,and can be used in practical applications.

An Online Evaluation Framework Of Complex Simulation System Based On Acceptability Criteria, Zhenglin Sun, Weiqiang Yuan, Weiqing Li

Journal of System Simulation

Abstract: An online simulation evaluation method based on Acceptability Criteria (AC) for the lag of current complex simulation systems is proposed.A qualitative and quantitative AC to index mapping model is used to establish an evaluation index system.Based on index sets and evaluation functions,a seven-tuple model of a simulation process finite automaton is proposed,and a mapping of the simulation process to automata and a data-driven state transfer mechanism are given.Based on the above results,an online evaluation tool is designed and the effectiveness is verified through a case.The result that the method can effectively solve lag in the evaluation …

Amorphous Sio2/Si Interface Defects And Mechanism Of Passivation/Depassivation Reaction, Zhuocheng Hong, Zuo Xu

Journal of System Simulation

Abstract: The amorphous silicon dioxide/silicon (a-SiO₂/Si) interface is an important part of semiconductor devices.The passivation and depassivation process of silicon dangling bond defects (P_b-type defects) at the SiO₂/Si interface has a significant impact on semiconductor devices.Based on molecular dynamics and first-principles calculation methods,a-SiO₂/Si(111) interface model is constructed based on a-SiO₂ and crystalline Si.The CI-NEB (Climbing Image-Nudged Elastic Band) method is used to study the passivation and depassivation reactions of H₂ and H atoms of P_b defects at the a-SiO₂/Si(111) interface. The curves,barriers,and transition state structures of …

Non-Cooperative Target Feature Point Cloud Registration Optimization Based On Icp Algorithm, Wei Liang, Muyao Xue, Huo Ju, Jinjie Zhang

Journal of System Simulation

Abstract: Aiming at the pose measurement caused by non-cooperative targets in visual measurement that cannot provide cooperation information,the ICP(Iterative Closest Point) algorithm is used to register the point cloud down-sampling data acquired at different times to complete the relative pose measurement of the target.The point cloud data of the target at the current moment is obtained using the structure from motion algorithm and the feature point matching algorithms are compared based on threshold matching and optical flow matching method.The extracted feature points are reconstructed by triangulation.The relative pose changes of the object at different times are calculated by using …

Human-Computer Interaction Speech Emotion Recognition Based On Random Forest And Convolution Feature Learning, Wang Jing, Hongyan Liu, Fangfang Liu, Qingqing Wang

Journal of System Simulation

Abstract: Focus on the different speech features of different types of people in the automatic speech emotion recognition of emotional robots,a random forest for speech emotion recognition is proposed,and a preliminary simulation experiment of emotional social robot system based on convolution feature learning is carried out.The results show that the emotional robot can track in real time,the seven basic emotions of excitement,anger,sadness,happiness,surprise,fear and neutrality.By using non personalized speech emotion features,the original personalized speech emotion features are supplemented,and the general emotion and special emotion are extracted.For emotional robot,using these indicators has a certain application prospect in the simulation experiment …

Research On Fuzzy Control And Optimization For Traffic Lights At Single Intersection, Jiajia Liu, Xingquan Zuo

Journal of System Simulation

Abstract: Aiming at the traffic signal control at urban single intersection,a fuzzy control method for traffic lights is presented.The method is based on a four-phase phasing sequence to control the traffic lights at a single intersection.Inputs of the fuzzy controller are the number of vehicles in line and the arrival rate of vehicles,and the output is the green light extension time of the current green light phase.A genetic algorithm (GA) is used to optimize fuzzy rules and membership functions of the fuzzy control system to improve the performance of the fuzzy controller.The fuzzy control method is realized by using …

Reliability Evaluation Method For Reuse Model Of Complex Simulation System, Ziheng Ye, Fuzhen Zhang, Yaoqin Zhu, Weiqing Li

Journal of System Simulation

Abstract: Model reuse can enhance the flexibility and scalability of simulation applications,and is necessary in the construction of complex simulation systems.Assessing credibility in multi-model combination simulation is the basic question of whether the model can be effectively reused.Aiming at the two simulation model reuse,in different application environments of physical model-oriented and numerical settlement-oriented,the credibility evaluation method for simulation reuse models based on bias propagation is proposed,and the respective modeling methods and evaluation methods are introduced in detail. Taking UAV as an example,the comparison result with the classic credibility evaluation method shows that the method can reduce the difficulty of …

Establishment And Development Of Simulation-Based Aero Engine Acquisition On, Caiyun Liang, Hongxin Li, Yanfeng Sui, Luan Xu, Shi Feng

Journal of System Simulation

Abstract: Follow the increasing demand of aircraft for aero engine‘s capabilities and because of the increase of engine‘s own technical difficulty,the risks,cycles and costs of the engine development is rising,and the high demand of traditional acquisition model is urgently needed.The idea of digitalized aero engine acquisition is presented,which starts from joint analysis,applies multi-dimensional scaling technology,carries out integrated simulation based on the models of each dimension of virtual prototype,and realizes the evaluation of technical scheme.The mapping relationship among technical solutions,schedules and costs etc.are established,and a basic framework for simulation based acquisition of engines is constructed by using the work …

Automatic Discovery Method Of Dynamic Job Shop Dispatching Rules Based On Hyper-Heuristic Genetic Programming, Suyu Zhang, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: The dynamic job shop has the uncertainty of resource state and the randomness of tasks,so it is difficult to find the common dispatching rules applicable to a variety of complex production scenarios.A method for automatic discovery of dynamic shop dispatching rules based on Hyper-Heuristic genetic programming is proposed,with makespan and average weighted tardiness as the optimization goals,is improved by using the automatic discovery of machine sequencing rules and the dynamic adaptability of workshop scheduling under different production scenarios.Through the semantic analysis of dispatching rules,the function of terminators on different optimization objectives is analyzed.The experiment result shows that …

A Survey On Underwater Bionic Electric Perception, Guangming Xie, Junzheng Zheng, Wang Chen

Journal of System Simulation

Abstract: Sensing and detection technologies for underwater robots in complex underwater environments have been urgently needed.Weakly electric fields-based underwater bionic electric perception is a promising technical route.A kind of fish in nature,called weakly electric fish,can perceive their surrounding environment and other creatures through the varied electric field generated by themselves.Inspired by the electric fish,researchers have focused on the principle and methods of underwater perception based on weakly electric fields and the applications for intelligent underwater robots.The biological mechanism of weakly electric fish,the modeling and perception theory of underwater electric field,the underwater electric perception technology,and their applications are reviewed,and the …

Research On Recovering Of Complex Networks Based On Boundary Nodes Of Giant Connected Component, Zhe Wang, Jianhua Li, Kang Dong

Journal of System Simulation

Abstract: Network recovery is an important way to solve the inevitable failure,and the reasonable recovery strategy can reduce the cost of resource and improve the network robustness.In order to study the dynamic behavior of recovery process and the relationship between recovery and network robustness,a Recovery Model of Boundary Nodes (RMBN) based on boundary of giant connected component is proposed,and two network Recovery strategies,Average Recovery of Boundary Nodes (ARBN) strategy and Priority Recovery of Boundary Nodes (PRBN) strategy are designed.The simulation results of different recovery strategies on three network models show that with the increase of recovery ratio,the …