Computer Engineering Commons^™

Causal Neuro-Symbolic Ai For Root Cause Analysis In Smart Manufacturing, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Root cause analysis is the process of investigating the cause of a failure and providing measures to prevent future failures. It is an active area of research due to the complexities in manufacturing production lines and the vast amount of data that requires manual inspection. We present a combined approach of causal neuro-symbolic AI for root cause analysis to identify failures in smart manufacturing production lines. We have used data from an industry-grade rocket assembly line and a simulation package to demonstrate the effectiveness and relevance of our approach.

Causal Knowledge Graph For Scene Understanding In Autonomous Driving, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The current approaches to autonomous driving focus on learning from observation or simulated data. These approaches are based on correlations rather than causation. For safety-critical applications, like autonomous driving, it’s important to represent causal dependencies among variables in addition to the domain knowledge expressed in a knowledge graph. This will allow for a better understanding of causation during scenarios that have not been observed, such as malfunctions or accidents. The causal knowledge graph, coupled with domain knowledge, demonstrates how autonomous driving scenes can be represented, learned, and explained using counterfactual and intervention reasoning to infer and understand the behavior of …

An Overview Of Ancillary Services Provided By Vehicle-To-Grid Systems, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

Vehicle-to-Grid (V2G) systems are emerging as a pivotal technology in modern power systems, offering a range of ancillary services that enhance the stability and reliability of power systems. This paper provides an overview of the key ancillary services provided by V2G systems, highlighting their role in grid modernization. Technical challenges, economic implications, and policy considerations associated with the deployment of V2G systems are explored to assess their potential impact on advancing a more resilient and sustainable energy infrastructure.

Bridging Human And Machine Intelligence: Reverse-Engineering Radiologist Intentions For Clinical Trust And Adoption, Akash Awasthi, Ngan Le, Zhigang Deng, Rishi Agrawal, Carol C. Hu, Hien Van Nguyen

Computer Science and Computer Engineering Faculty Publications and Presentations

In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the "black box" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation …

Analysis Of Multivariable Sensor Responses To Multi-Analyte Gas Samples In The Presence Of Interferents And Humidity, Sakin Sarwar Satter, Florian Bender, Nicholas Post, Antonio J. Ricco, Fabien Josse

Electrical and Computer Engineering Faculty Research and Publications

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg–Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the …

Resource Harvesting For Parallel Functions In Serverless Workflows, Peiman Fotouhi

LSU Master's Theses

In the rapidly evolving landscape of cloud computing, serverless architectures have gained attention for their scalability and cost-effectiveness. This thesis aims to introduce a novel approach to maximize resource utilization in serverless environments through the concept of harvesting idle resources within Directed Acyclic Graph (DAG)-based workloads. Our proposed solution targets resource harvesting at parallel stages by utilizing Machine Learning models to accurately harvest or accelerate serverless functions. Additionally, we present a scheduling algorithm specifically designed to address the unique requirements of DAG workloads in cloud environments.

The framework leverages dynamic resource allocation techniques to identify and exploit idle resources within …

Performance Analysis Of C++ Parallel Algorithms In Hpx, Srinivas Yadav Singanaboina

LSU Master's Theses

The exponential growth in computational power and the increasing demand for high-performance applications have driven the need for greater parallel efficiency. Over the years, the number of cores in consumer-level CPUs and high-performance computing (HPC) systems has grown significantly. In response, numerous parallel programming li- braries have been developed. Each of these libraries offers unique mechanisms to enhance parallel performance. In this paper, we investigate the performance of five such paral- lel programming backends: C++ std::execution::par, OpenMP, TBB, Taskflow, and HPX. We evaluate these libraries using two sets of benchmarks. The first set focuses on standard C++ STL algorithms, including …

Detecting Data Poisoning Attacks In Federated Learning For Healthcare Applications Using Deep Learning, Mohammed Aljanabi, Sahar Yousif Mohammed, Alaa Hamza Omran

Iraqi Journal for Computer Science and Mathematics

This work introduces a new approach to protecting the data in the healthcare applications of federated learning based on the classification of skin cancer. The recommended solution established and prevents the data poisoning attacks by using deep learning and CNN architectures namely VGG16. In a federated learning system which comprises of ten healthcare facilities, the approach enables the training of models in a collaborative way without compromising the medical data or the patients’ information. Data is meticulously prepared and preprocessed using the Skin Cancer MNIST: According to the HAM10000 dataset. As for the federated learning approach, VGG16’s feature extraction capability …

Prototyping Interactive Tactile Digital Logic Simulations: A Hybrid Approach, Logan Bateman

MS in Computer Science Project Reports

Tactile exhibits are common in museums and on the walls of university halls. However, few (if any) tools exist for creating tactile exhibits for teaching digital logic or computing concepts. This project implemented a framework for creating tactile digital logic simulation exhibits, with a focus on rapid prototyping and distributed architecture. Prototyping allows for fast iteration, with the ability to simulate unlimited hardware components such as buttons, light emitting diodes (LEDs), and other input or output devices. Through the abstraction of implementations and a distributed communication protocol, switching to real hardware is seamless and works in tandem with simulated hardware. …

Enhanced Shoulder-Surfing Cued Recall Graphical Password System: Sequential Passpoint, Titus D. Fofung

Cybersecurity Graduate Research Symposium

During the past two decades, many graphical passwords have been used widely as an alternative to text-based passwords. However, most graphical password systems are plagued by shoulder-surfing problems, usability, and remembering capability. This study proposed a new graphical password called SPP (Sequential PassPoint), allowing users to remember three click-points on two images in specified order and image order. When the image order changes, the click order is reversed. Two decoy images for three random clicks were introduced to enhance the security of SPP. The proposed SPP system was validated both theoretically and empirically

Experimental Study To Assess The Role Of Environment And Device Type On The Success Of Social Engineering Attacks: The Case Of Judgment Errors, Tommy Pollock

Cybersecurity Graduate Research Symposium

No abstract provided.

Assessing Organizational Investments In Cybersecurity And Financial Performance Before And After Data Breach Incidents Of Cloud Saas Platforms, Munther B. Ghazawneh

Cybersecurity Graduate Research Symposium

No abstract provided.

Examining Consumers' Selective Information Privacy Disclosure Behaviors In An Organization's Secure E-Commerce Systems, Patrick I. Offor

Cybersecurity Graduate Research Symposium

No abstract provided.

Dcai: The 4th International Workshop On Data-Centric Ai, Yanjie Fu, Kunpeng Liu, Dongjie Wang

Computer Science Faculty Publications and Presentations

Machine learning traditionally emphasizes developing models for given datasets, but real-world data is often messy, making model improvement insufficient for enhancing performance. Data-Centric AI (DCAI) is an emerging field that systematically improves datasets, leading to significant practical ML advancements. While experienced data scientists have manually refined datasets through trial-and-error and intuition, DCAI approaches data enhancement as a systematic engineering discipline. DCAI represents a shift from focusing on models to the underlying data used for training and evaluation. Despite the dominance of common model architectures and predictable scaling rules, building and using datasets remain labor-intensive and costly, lacking infrastructure and best …

Interpreting Black-Box Time Series Classifiers Using Parameterised Event Primitives, Ephrem Tibebe Mekonnen, Luca Longo, Pierpaolo Dondio

Conference papers

Amidst the remarkable performance of deep learning models in time series classification, there is a pressing demand for methods that unveil their prediction rationale. Existing feature importance techniques often neglect the temporal nature of time series data, focusing solely on segment importance. Addressing this gap, this paper introduces a local model-agnostic method akin to LIME, which generates neighbouring samples by randomly perturbing segments of the original instance. Subsequently, weights are computed for each neighbouring instance based on its distance from the original, elucidating its influence. Parameterised event primitives (PEPs) are then extracted from these perturbed samples, encompassing increasing and decreasing …

Three-Dimensional Outdoor Object Detection In Quadrupedal Robots For Surveillance Navigations, Muhammad Hassan Tanveer, Zainab Fatima, Hira Mariam, Tanazzah Rehman, Razvan Cristian Voicu

Faculty Articles

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects …

A Plugin-Based Unreal Engine Adapter For Hla-Based Distributed Simulation, Mei Yang, Peng Wang

Journal of System Simulation

Abstract: With the wide application of game engine-based simulation in transportation, military and other fields, the demand for interoperability between game engine and traditional simulations is becoming increasingly strong. For the HLA-based integration of Unreal Engine and the traditional simulations, a plugin-based Unreal Engine adapter for distributed simulation is designed, which enables the rapid development of Unreal Engine federate and the efficient integration. The simulation shows the feasibility of the plugin-based Unreal Engine adapter.

Research On Sequential Design Methods For Satellite Combat Simulation Tests, Yanlin Wang, Zhijun Cheng, Zichen Wang, Jian Zhong

Journal of System Simulation

Abstract: Aiming at the problem that satellite monitoring mission simulation tests cannot take into account the number of sample points and model accuracy in the complex test space, a hybrid sequential test design method for satellite simulation tests based on sample density and local nonlinearity is proposed. Voronoi division is used to describe the density of discrete point distribution, and the nonlinearity is measured with the help of Taylor expansion and sample point neighborhood gradient information. The two are combined to calculate the hybrid metrics, and the sample points are ranked and new ones are added until the stopping criterion …

Improving Nsga-Iii Algorithm For Solving High-Dimensional Many-Objective Green Flexible Job Shop Scheduling Problem, Yigang Xu, Yong Chen, Chen Wang, Yunxian Peng

Journal of System Simulation

Abstract: Aiming at the poor initial solution quality and low local search efficiency of NSGA-III in solving the many-objective flexible job shop scheduling model, an improved NSGA-III (NSGA-III-TV) is proposed. Based on MSOS encoding, the different mixed initialization strategies are adopted for OS and MS chromosomes to improve the quality of initial solutions. Based on the critical path, an improved N6 neighborhood structure is used for neighborhood search, which effectively reduce the completion time and reducing search randomness. Three effective mutation operators are employed to expand the search space and improve the convergence capability in the later stages. Test results …

Multi-Objective Energy-Efficient No-Wait Flow Shop Scheduling Based On Hybrid Discrete State Transition Algorithm, Cong Wang, Jiaying Yu, Hongli Zhang

Journal of System Simulation

Abstract: A hybrid discrete state transition algorithm (HDSTA) is designed to solve the energyefficient no-wait flow shop scheduling problem (EENWFSP) minimizing makespan and total energy consumption. According to the characteristics of the problem, the coding method of job sequence and speed matrix is designed, and the heuristic algorithm is used to obtain the high-quality initial solution. According to the properties of EENWFSP, solving and allocating four discrete operators. The swap, shift and symmetry operators are embedded in secondary state transition are used for job sequence optimization, and the substitute operators are used for machine speed optimization. The speed substitute strategy …

Adaptive Recognition Method Of Capability Boundary Parameters For Unmanned Autonomous Systems, Jinwen Li, Peng Wang, Youmei Pan, Xinyao Hui

Journal of System Simulation

Abstract: To effectively cope with the dimension curse in simulation testing and reduce the number of simulations times needed in the traditional full-space parameter traversal, it is necessary to obtain specific simulation data to accurately reflect the modeling characteristics of the test data to obtain the informative and representative samples of the original data with a smaller number of simulations. A digital simulation test model for adaptive recognition ;/of capability boundary parameters for UAS is proposed. The model is initially constructed with a good point set with a multi-weight structure; In combination with an adaptive kernel function boundary point recognition, …

Operational Effectiveness Analysis Method Based On Spherical Fibonacci Lattice, Weiran Guo, Jiahao Zhou, Xiang Huang, Xin Zhao

Journal of System Simulation

Abstract: An operational effectiveness modeling and computing method based on spherical Fibonacci lattice is proposed to address the low simulation accuracy and computational efficiency of large-scale sampling for traditional latitude and longitude grids. The formal description of operational effectiveness in command information systems and the generation method of spherical Fibonacci grid points are provided. A command information systems capability analysis framework is constructed, which focuses on ensuring the continuous mission support and uses the responsibility area of combat tasks as a reference. Through a top-down system design approach, a layered and decoupled combat effectiveness computing framework is proposed which studies …

Ground Robot Relocation Method Based On Uav Point Cloud Map, Hongzhi Huang, Kai Yan, Changfeng Liu, Jianwen Wang, Bin Luo

Journal of System Simulation

Abstract: In response to the challenge of relocalization in air-ground collaborative systems without the support of Global Navigation Satellite System (GNSS), and the associated issues of insufficient accuracy, a coarse-to-fine relocalization algorithm based on a three-dimensional point cloud map is proposed. The algorithm eliminates the influence of invalid point clouds from the sky and ground through index filtering, performs coarse localization by extracting global features from the point cloud and applying truncated least squares estimation, and then employs voxel-based iterative closest point (ICP) for precise optimization to obtain the more accurate localization results. A ground robot localization and autonomous navigation …

Research On Digital Twin Technology Of Coal Mine Tunneling Machine System, Xingwang Cai, Yaoqiang Pei, Jihua Yang, Ruifeng Pang

Journal of System Simulation

Abstract: Aiming at the problems in coal mine tunneling operations, including complex geological structures, harsh on-site conditions, low automation of excavation equipment, and frequent safety incidents, a digital twin solution is proposed. PLC as hardware core, MQTT protocol, and EMQX as foundation, through the integration of IoT, internet, digital twins, and virtual reality technologies, the mapping rules for parameter matrices and a fault cause analysis table are established, which realize the functions such as data collection, processing during excavation, data mapping between equipment and twin models, data storage, process simulation, remote control, intelligent fault detection, and early warning, and significantly …

Research On Application Of Intelligent Indication Signs In Improving Efficiency Of Crowd Evacuation, Heng Niu, Yanbin Han, Liang Li, Weilin Chen, Sijie Niu, Qingtao Hou

Journal of System Simulation

Abstract: Aiming at the low emergency evacuation efficiency of dense crowds in confined spaces, a crowd evacuation guidance model based on intelligent signs is constructed in this paper. A balanced congestion strategy to optimize the signs layout and a social network search-based sign guidance weights optimization method are proposed to improve evacuation efficiency. Simulation experiments show that the optimized intelligent sign layout can effectively alleviate the local congestion during evacuation and reduce the time consumption of pedestrians caused by congestion. The optimized intelligent sign guidance weights can further balance the utilization rate of exits, which is conducive to making full …

Garage Agv Path Planning And Simulation Based On Improved Dwa, Zongfang Ma, Linxuan Zhang, Lin Song, Jia Wang

Journal of System Simulation

Abstract: Aiming at the path planning and real-time obstacle avoidance of AGV in complex path environment of intelligent garage, an improved hybrid algorithm combining ant colony algorithm and dynamic window method is proposed. In the global planning, the adaptive adjustment of pheromone volatilization coefficient and the fusion of angle parameters are introduced to establish the garage direction pheromone matrix to increase the guidance ability of target points, expand the direction selectivity of ants. In the local planning, the improved DWA of the obstacle distance evaluation subfunction based on elliptic equation is designed. By extracting the global path node of the …

Adaptive Tracking Control For Omnidirectional Vehicle Based On Characteristic Modeling, Lei Cheng, Haoyou Wang, Xinjie Chen

Journal of System Simulation

Abstract: Aiming at the stable and high-precision tracking control of omnidirectional mobile vehicles affected by their own characteristics and external disturbances, an adaptive tracking control approach for omnidirectional vehicles based on characteristic modeling is designed. The characteristic model is established by integrating the model properties into the characteristic parameters, and the characteristic parameters are estimated online by using the projected gradient method. A full coefficient adaptive control law based on the characteristic model is designed, and the stability of the proposed control method is analyzed by using Lyapunov theory. The effectiveness and rationality of the proposed adaptive control scheme are …

Modeling Method Of Blue Army Warship Formation Air Defense Command And Control For System-Of-Systems Simulation, Shikang Chen, Zhimin Wang, Biao Liang, Kunren Gu, Keman Song, Yuan Gao

Journal of System Simulation

Abstract: The command and control of warship formation air defense is a key link for the blue army's sea to air combat tasks and the modeling of air defense for warship formation is an important component of simulation modeling of blue army's maritime combat system. Based on the background of system-of-systems simulation, the air defense command and control modeling for blue army is designed. Through a modular modeling method, functional module including asset management and plan, unified situation generation, command decision are designed. According to blue army's command and control logic, these modules are integrated and the air defense command …

Search Technology For Aircraft Debris Integrating Data Augmentation And Deep Learning Algorithm, Zhe Yang, Yinghan Cui, Lingxi Guo, Jiaxin Li, Xusheng Wu

Journal of System Simulation

Abstract: The reliable recovery of aircraft debris is of great significance for the complete acquisition of flight test data and the subsequent research and development of models. To ensure the safety of flight tests,the landing area of aircraft experiments is generally an unmanned area,and the actual landing point of the aircraft often deviates from the theoretical landing point. The characteristics of the debris target are complex and the dispersion area is large, making it difficult to search for aircraft debris solely by manpower. Aiming at the difficult problem of aircraft debris recovery in the landing area, through on UAV platforms …

Two-Level Optimal Dispatch Of Power System Based On Load-Storage Carbon Flow Model, Yang Yu, Yuxing Xia, Wentao Lu, Mai Liu, Shixu Gao, Dongyang Chen

Journal of System Simulation

Abstract: In order to reduce the output of high energy consuming units on the power generation side, increase the absorption capacity of wind power, and consider the flexible resource allocation such as load and energy storage, a two-level economic low-carbon optimal scheduling method for power systems based on a carbon storage and discharge model is proposed. Based on the carbon emission flow theory of power system, a model for load and energy storage equipment is established; A demand response model based on the electricity carbon coupling price is established on the load side, and in view of the limitation on …