Engineering Commons^™

Indicator Transfer Learning Based On Cloud Model And Maximum Mean Discrepancy, Lixia Xu, Jilong Zhong, Shaoshi Wu, Yishan Ding, Xiaoyu Zhai, Shizhao Chen, Yizhe Wang, Xue Wen, Juanfang Zeng, Xinwen Hou

Journal of System Simulation

Abstract: In response to the problem of rare data samples in application experiment scenarios, this paper proposes an indicator transfer learning method based on cloud models and Maximum Mean Discrepancy (MMD), which transfers the indicator calculation model from typical simulation experiment scenarios to application experiment scenarios to meet the needs across platform and domain simulation evaluation. Using the maximum mean difference method to align the indicator distribution in the typical simulation experiment scenario to the indicator distribution in the application experiment scenario, thereby achieves indicator transfer, and by using cloud models based on a small number of examples for modeling …

Adversarial Simulation Testing Algorithm For Svm Based On Multi-Objective Evolutionary Optimization, Feixing Li, Lining Xing, Yu Zhou

Journal of System Simulation

Abstract: Machine learning typically mines underlying patterns and rules from data, making it susceptible to phenomena such as overfitting and underfitting, which in turn affects the generalization and robustness of learning models. This paper explores the potential fragility and instability of SVM from the perspective of adversarial simulation testing. The adversarial simulation strategy employed involves selectively contaminating training sample labels to simulate an attack on the SVM classifier, thereby degrading its performance and testing its dependency on training samples. To explore the ceiling of performance degradation of an SVM classifier under the combination attack of different samples, the contradictory objectives …

Simulation Study Of Personnel Evacuation In Fire Scenarios Of Old School Buildings, Qiankun Zhu, Jiwu Li, Yongfeng Du

Journal of System Simulation

Abstract: In order to improve the emergency evacuation capability of an old school building under fire scenarios, a fire evacuation model of an old school building is developed. The PyroSim software is used to build a fire dispersion model to simulate and analyse the changes of smoke visibility, temperature and CO at the safety exit of the fire floor in the school building under the condition of mechanical smoke exhaust, automatic sprinkler and whether the windows of the fire room are open or not. The simulation of the exit status and evacuation of people has been carried out in conjunction …

Research On Orb-Slam Algorithm Based On Windowed Matching Estimation, Wanye Yao, Zewei Pang, Peijie Sun, Zhu Wang

Journal of System Simulation

Abstract: To address unstability of location accuracy of ORB-SLAM system caused by randomness of camera pose solution method, an improved pose solution method based on feature point windowed matching and analytical ICP is proposed, and the mobile robot ORB-SLAM system is constructed. The extracted feature points are windowed to improve matching efficiency while ensuring good feature point matching, the analytical ICP algorithm is used to solve the camera pose for avoiding iteration, and the windowed pose solution with the smallest error is selected for bundle adjustment to reduce the pose errors caused by local information loss or mismatching. The results …

Carbon Footprint Analysis And Low-Carbon Optimization Method Simulation Study Of Power Transformer Based On Digital Twin Technology, Dongxue Li, Yan Liu, Boyao Shen, Yongteng Jing, Qiang Ma, Ran Liu

Journal of System Simulation

Abstract: Power transformers are the main energy-consuming equipment for substations. According to the goal of “carbon peak, carbon neutralization” in China, it is of great significance to accurately calculate the carbon footprint of transformers and seek low-carbon optimization methods. A method for constructing a digital twin model of power transformer magnetic characteristics is proposed. Based on the three-dimensional electromagnetic time-harmonic field finite element analysis method, a threedimensional model of SZ11-31.5MVA/66kV power transformer is established. The transformer loss map is obtained under fluctuating load condition, and the transformer digital twin model is constructed. The carbon footprint of the transformer is analyzed, …

Uav Online Track Planning Based On Dmoea-Aptc Algorithm, Erchao Li, Shenghui Zhang

Journal of System Simulation

Abstract: In order to solve the dynamic multi-objective optimization problem with time correlation, this paper introduces the concept of time correlation feature and establishes the model of UAV timecorrelation dynamic multi-objective optimization problem moedl on the basis of UAV online track planning problem, and proposes a dynamic multi-objective double-layer optimization algorithm using adaptive predictive response mechanism and time-correlation optimization mechanism (DMOEA-APTC). The intensity of environmental change was judged according to the correlation of environmental change and different response mechanisms were used to quickly adapt to environmental change. In the optimization process, the least square method was used to learn the …

Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo

Journal of System Simulation

Abstract: For the resource limitation of intensive surveillance tasks in edge computing, a surveillance task offloading and resource allocation algorithm based on DRL is proposed. With the optimization objectives of surveillance task delay and recognition accuracy, the joint decision objective optimization solution of task offloading, wireless channel allocation, and image compression rate was modeled as a Markov decision process. To address the problem of slow and unstable algorithm convergence due to the high volatility of training samples caused by the dynamic nature of wireless channels and the randomness of surveillance tasks, an attention mechanism is used to jointly encode channel …

Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel, Chao Yuan, Yao Zhang, Yadong Zhao, Dawei Xu, Jing Yuan, Yongjie Zhai

Journal of System Simulation

Abstract: Aiming at the problem that the inner space of underground pipeline cable is narrow and closed, which cannot be inspected by humans, and the existing pipeline robot cannot adapt to the special environment of pipeline cable, a miniaturized, compact pipeline cable inspection robot is designed. This robot is capable of operating within the underground pipeline where cables have already been laid to inspect the inner wall of the pipeline and the working condition of the cables. According to the requirements of the working conditions, the whole three-dimensional model of the robot has been established. The mapping relationship between the …

A New Model Predictive Current Controller Forac-Dc Matrix Converter In Unbalanced Grids, Wenlang Deng, Minghai Wu, Haipeng Xie, Yingjie Hu

Journal of System Simulation

Abstract: To reduce the fluctuation of active power on the grid side of AC-DC matrix converters under unbalanced input conditions and to address the issue of variable switching frequency in discrete model predictive control, this paper proposes a novel model predictive control method. This method selects effective vectors based on the phase angle of the grid current, thus avoiding the computational burden of evaluating the value function in traditional model predictive control. Additionally, a second-order extended complex Kalman filter is introduced, which achieves the computation accuracy of the secondorder term of the Taylor series expansion and enables the application of …

Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds, Hanlin Huo, Xiangjun Zou, Yan Chen, Xinzhao Zhou, Mingyou Chen, Chengen Li, Yaoqiang Pan, Yunchao Tang

Journal of System Simulation

Abstract: In response to the large and complex data volume and high redundancy of visual point cloud obstacle recognition in complex unstructured orchard environments, which severely impacts the real-time performance and efficiency of harvesting operations, a point cloud compression algorithm is proposed based on point cloud segmentation to enhance the efficiency of point cloud obstacle recognition and environmental adaptability. An Informed RRT* based approach is used combined with an inverse projection algorithm, mapping-based informed RRT*(M-Informed RRT*) to solve the harvesting path problem. By constructing a highly real-time and robust integrated robot system for sampling, perception, and obstacle avoidance, efficient obstacle …

An Improved Path Planning Algorithm For Mobile Robots, Haijie Sun, Hongjun San, Le Xiao, Dexin Yao, Jiupeng Chen, Xiaoyuan Yang

Journal of System Simulation

Abstract: To solve the problems of invalid sampling and non-optimal paths of the RRT, the quasi-stream avoidance algorithm is proposed. The RRT algorithm is introduced to specify the sampling interval to limit the sampling points and enhance the goal-oriented nature of sampling. The quasi-stream avoidance algorithm incorporating the A* algorithm (QSA*) is used to quickly bypass the obstacle when it is encountered. A path optimization algorithm is used to smooth the searched path. The simulation results show that compared with the RRT algorithm, the computation time of the RRT-QSA* algorithm is reduced by 96.83%~99.88%, the number of search nodes is …

Research On System-Of-Systems Confrontation Simulation Method Based On Operation Loops, Shan Zhong, Yesheng Zhu, Menglu Zhou

Journal of System Simulation

Abstract: In the field of modeling and analyzing capabilities for operation system-of-systems (SoS), traditional structured capability assessment models lack the analysis of the interaction between both rivals and armies in different roles. The system model based on operation loop theory can be combined with the relationship between sensor, decision-making, influence, and target nodes for system capability calculation, but the existing model is usually only suitable for static analysis and cannot be used for dynamic simulation of SoS confrontation. In order to solve the problems above, a SoS confrontation simulation method based on operation loops is proposed. It abstracts both rivals’ …

Neuro-Symbolic Ai For Deep Analysis Of Social Media Big Data, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, Amit P. Sheth

Faculty Publications

This tutorial introduces a neuro-symbolic AI framework to analyze big data from social media platforms. Integrating human-curated knowledge through symbolic AI with the pattern recognition capabilities of neural networks enhances the adaptability and efficiency of traditional neural network approaches. Knowledge-guided zero-shot learning techniques enable swift adaption to new linguistic contexts and emerging events [6]. Participants will explore how to design, develop, and utilize these models in specific domains, such as public health surveillance, that require dynamic adaptation to new terminologies. This session The tutorial aims to equip attendees with practical skills and a deep understanding of how to apply neuro-symbolic …

Evaluating Ai Language Models For Patient Queries On Total Knee Replacement (Tkr), Brianna Guillen, Anesu Karen Murambadoro, Victoria Elizondo, Matthew Hnatow, Michael Sander

Research Colloquium

Introduction: Within the past few years, large language models (LLMs) (ChatGPT, LLaMa 3, Microsoft Copilot) have increasingly become a resource that patients engage with to learn about health care procedures, including total knee replacement (TKR). Previous studies have analyzed the efficacy of large language models in providing accurate and relevant responses to questions about various procedures. Our study aims to evaluate the clarity, validity, and understandability of LLMs to patient questions about total knee replacement and assess the consistency of these models and their effectiveness in providing accurate, valid, and guideline-adherent information to patients.

Methods: We selected 30 frequently asked …

A Bert-Based Model For Classifying Customers In The Financial Sector: A Case Of Zb Bank, Fungai Jacqueline Kiwa, Martin Muduva

African Conference on Information Systems and Technology

This study explores the implementation of a BERT-based model in ZB Bank, Zimbabwe, to improve complaint management and enhance customer satisfaction. Traditional manual handling of client complaints leads to slow responses and unresolved issues. The study aims to develop an accurate complaint classification model using advanced Natural Language Processing (NLP) and machine learning techniques, specifically the BERT model, and assess its real-world performance. The methodology involved collecting a dataset of customer complaints from ZB Bank, pre-processing the text data, and applying the BERT model within the Team Data Science Process (TDSP). The dataset was split into training (80%) and testing …

Anomalous Transaction Detection In Bank Credit Card Data Using Machine Learning, Lerdinia Varaidzo Mapepa, Jerremiah Musariwa, Lucia Makwasha, Samuel Mugijima

African Conference on Information Systems and Technology

Illegal money changers pose a number of risks to the financial system, including but not limited to money laundering, fraud, and other under-the-carpet dealings intended to frustrate regulatory efforts for financial integrity. The efficiency and accuracy of anti-money laundering (AML) measures using machine learning (ML) models in the detection of suspicious patterns in bank card transactions are investigated in this paper. The key focus will be to develop an efficient machine learning framework that should be proficient in underlining main transactions dealing with illegal money changers and other similar fraudulent activities. The features indicative of illicit behaviour are determined by …

Ai Bioelectricity Management System, Fungai Jacqueline Kiwa, Tawanda Bundukutu, Thoko Matnell Mawoyo, Batsiranai Linda Chiduku, Martin Muduva, Belinda Ndlovu

African Conference on Information Systems and Technology

This document emphasizes on the generation of electricity from trees and its usability in all the sectors of Zimbabwe. The research focused on positively changing the lives of citizens through the provision of uninterrupted and reliable bioelectricity. The literature review was completely and accurately performed through finding out the current news associated with the use of trees in producing electricity and the use of AI to manage the flow. The Scrum’s development model was adopted and followed during the research project to address issues like transparency, early mitigation of risks and constant feedback. The Scrum-model is one of the best …

Understanding And Enhancing Linux Kernel-Based Packet Switching On Wifi Access Points, Shiqi Zhang

Computer Science and Engineering Master's Theses

As the number of WiFi devices and their traffic demands continue to rise, the need for a scalable and highperformance wireless infrastructure becomes increasingly essential. Central to this infrastructure are WiFi Access Points (APs), which facilitate packet switching between Ethernet and WiFi interfaces. Despite APs’ reliance on the Linux kernel’s data plane for packet switching, the detailed operations and complexities of switching packets between Ethernet and WiFi interfaces have not been investigated in existing works. This paper makes the following contributions towards filling this research gap. Through macro and micro-analysis of empirical experiments, our study reveals insights in two distinct …

All You Need Is Unary: End-To-End Bit-Stream Processing In Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi

Faculty Scholarship

Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm introduced to achieve energy efficiency with a lightweight and single-pass training model. Hypervectors (HVs) at the heart of the HDC systems play a fundamental role in elevating the accuracy and obtaining the desired performance. Image-based HV encoding requires two types of HVs: Position and Level HVs. State-of-the-art approaches utilize pseudo-random methods for generating these HVs, which might degrade system performance and cause higher power consumption due to poor randomness in HV generation. These conventional methods require iteratively calculating orthogonal Positional HVs for acceptable accuracy. This work proposes a fast, ultra-lightweight, and high-quality …

Prompt Engineering Principles For Generative Ai Use In Extension, Paul A. Hill, Lendel K. Narine, Aubree L. Miller

Journal of Extension

The prevalence of Generative AI (GenAI) and Large Language Models (LLMs) is increasing rapidly. For Extension professionals, the utilization of prompt engineering is key to leveraging GenAI and LLMs effectively. Prompt engineering involves crafting prompts that elicit desired LLM responses. This article discusses prompt engineering principles, providing examples and guidance. The application of prompt engineering in Extension is explored, showcasing its potential to enhance programs, deliver personalized advice, engage audiences, and disseminate research-based information. By learning prompt engineering skills, Extension professionals can harness the power of GenAI and LLMs, enhancing their ability to address complex challenges in the 21st century.

Cyber Threat Intelligence Sharing In Nigeria, Muhammad Abubakar Nainna, Julian Bass, Lee Speakman

Communications of the IIMA

Cybersecurity challenges are common in Nigeria. Sharing cyber threat intelligence is essential in addressing the extensive challenges posed by cyber threats. It also helps in meeting regulatory compliance. There are a range of impediments that prevent cyber threat intelligence sharing. We hypothesise that we want to maximise this cyber threat intelligence sharing to resist malicious attackers. Therefore, this research investigates factors influencing threat intelligence sharing in Nigeria's cyber security practitioners. To achieve this aim, we conducted research interviews with 14 cyber security practitioners using a semi-structured, open-ended interview guide, which was recorded and transcribed. We analysed the data using an …

An International Consensus Panel On The Potential Value Of Digital Surgery, Jamie Erskine, Payam Abrishami, Jean Christophe Bernhard, Richard Charter, Richard Culbertson, Jo Carol Hiatt, Ataru Igarashi, Gretchen Purcell Jackson, Matthew Lien, Guy Maddern, Joseph Soon Yau Ng, Anita Patel, Koon Ho Rha, Prasanna Sooriakumaran, Scott Tackett, Giuseppe Turchetti, Anastasia Chalkidou

School of Public Health Faculty Publications

OBJECTIVES: The use of digital technology in surgery is increasing rapidly, with a wide array of new applications from presurgical planning to postsurgical performance assessment. Understanding the clinical and economic value of these technologies is vital for making appropriate health policy and purchasing decisions. We explore the potential value of digital technologies in surgery and produce expert consensus on how to assess this value. DESIGN: A modified Delphi and consensus conference approach was adopted. Delphi rounds were used to generate priority topics and consensus statements for discussion. SETTING AND PARTICIPANTS: An international panel of 14 experts was assembled, representing relevant …

Utilizing Deep Learning In Smart Glass System To Assist The Blind And Visually Impaired, Asmaa A. Hekal, Mohamed S. Sharaf, Ahmed A. Sayed, Ibrahim R. Abdelrahman, Ahmed A. Salem, Ahmed M. Elhussieny, Saeed Y. Kouta, Eman S. Abass

Future Engineering Journal

This paper presents a groundbreaking assistive technology designed to empower visually impaired individuals in their daily lives. With an estimated global population of 2.2 billion facing visual impairments, addressing the challenges they encounter is of paramount importance. The research introduces a comprehensive electronic device integrating advanced computer vision and deep learning techniques. The system incorporates real-time object detection, robust facial recognition, and precise currency denomination identification. Powered by a Raspberry Pi 4 Model B+ and an ESP32-CAM Development Board, the device offers users unparalleled environmental awareness. Utilizing YOLOv4-tiny for object detection and a hybrid face recognition model combining HaarCascades, Histogram …

Evaluating Fine Tuned Deep Learning Models For Real-Time Earthquake Damage Assessment With Drone-Based Images, Furkan Kizilay, Mina R. Narman, Hwapyeong Song, Husnu S. Narman, Cumhur Cosgun, Ammar Alzarrad

Computer Sciences and Electrical Engineering Faculty Research

Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate …

Precision Medicine For Apical Lesions And Peri-Endo Combined Lesions Based On Transfer Learning Using Periapical Radiographs, Pei Yi Wu, Yi Cheng Mao, Yuan Jin Lin, Xin Hua Li, Li Tzu Ku, Kuo Chen Li, Chiung An Chen, Tsung Yi Chen, Shih Lun Chen, Wei Chen Tu, Patricia Angela R. Abu

Ateneo Laboratory for Intelligent Visual Environments

An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. …

Self-Replication Via Tile Self-Assembly, Andrew Alseth, Daniel Hader, Matthew J. Patitz

Computer Science and Computer Engineering Faculty Publications and Presentations

In this paper we present a model containing modifications to the Signal-passing Tile Assembly Model (STAM), a tile-based self-assembly model whose tiles are capable of activating and deactivating glues based on the binding of other glues. These modifications consist of an extension to 3D, the ability of tiles to form “flexible” bonds that allow bound tiles to rotate relative to each other, and allowing tiles of multiple shapes within the same system. We call this new model the STAM*, and we present a series of constructions within it that are capable of self-replicating behavior. Namely, the input seed assemblies to …

Evaluating Mixed Reality Technology For Tracking Hand Motion For Shoulder Rehabilitation Assessment*, Sergio A. Salinas, Katarina Grolinger, Marie-Eve Lebel, Ana Luisa Trejos

Electrical and Computer Engineering Publications

Shoulder injuries and conditions are common musculoskeletal complaints that can limit a patient’s range of motion and daily activities. Recently, serious games and mixed reality technologies, such as the HoloLens, have been proposed for shoulder rehabilitation. However, it is unclear if this technology accurately tracks 3D hand movementsfor reporting therapy-related kinematic metrics. This paper presents accuracy and repeatability tests of the HoloLens 2 in tracking hand movements, and its potential for shoulder rehabilitation assessment. Comparisons were made between index fingertip, palm, and wrist movements captured by the HoloLens 2 and an Aurora electromagnetic system, which was used as the ground …

Improving The Cooling Time Of Twisted Coiled Actuators In Soft Robotics, Alex Lizotte, Parisa Daemi, Brendan Difabio, Ana Luisa Trejos

Electrical and Computer Engineering Publications

Fabric cooling channels for twisted coiled actuators (TCAs) were recently proposed to achieve the required response times for motion assistance in a manner suitable for soft wearable robotic devices. While previous work demonstratedthat the fabric channel reduced the cooling time by 42% in comparison to the same TCA without the cooling channel, the TCAs were still not cooled quickly enough to support human motion. Therefore, in this paper, two variations to the channel are proposed to further reduce the cooling time of the TCAs. The variations include unsealing the inlet and adding vents along the length of the channel to …

Dynamic Maze Puzzle Navigation Using Deep Reinforcement Learning, Luisa Shu Yi Chiu

Master's Theses

The implementation of deep reinforcement learning in mobile robotics offers a great solution for the development of autonomous mobile robots to efficiently complete tasks and transport objects. Reinforcement learning continues to show impressive potential in robotics applications through self-learning and biological plausibility. Despite its advancements, challenges remain in applying these machine learning techniques in dynamic environments. This thesis explores the performance of Deep Q-Networks (DQN), using images as an input, for mobile robot navigation in dynamic maze puzzles and aims to contribute to advancements in deep reinforcement learning applications for simulated and real-life robotic systems. This project is a step …

Design And Implementation Of An Inverted Short Baseline Acoustic Positioning System, Jakob Frabosilio

Master's Theses

This document details the design, implementation, testing, and analysis of an inverted short baseline acoustic positioning system. The system presented here is an above-water, air-based prototype for an underwater acoustic positioning system; it is designed to determine the position of remotely-operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) in the global frame using a method that does not drift over time.

A ground-truth positioning system is constructed using a stacked hexapod platform actuator, which mimics the motion of an AUV and provides the true position of an ultrasonic microphone array. An ultrasonic transmitter sends a pulse of sound towards …