Engineering Commons^™

Construction Of A Digital Twin-Based Ship Manufacturing Workshop Monitoring System, Tianxiang Hu, Hui Ye, Xiaofei Yang

Journal of System Simulation

Abstract: In order to address issues of opacity in production information and difficulties in collecting equipment data in shipbuilding workshops, a digital twin ship manufacturing workshop monitoring system is designed based on the Unity physics platform. The essential steps in building a virtual reality platform are outlined, encompassing the creation of a virtual ship workshop, the development of data transmission methods for multi-source heterogeneous data acquisition, implementation of data-driven methods for achieving virtual-real synchronization, and enhancement of data visualization capabilities. By practically designing a real-time monitoring system for the welding assembly line production process in shipbuilding, the 3D scene reproduction …

Improved Target Detection Algorithm For Aerial Images Based On Yolov5, Yecai Guo, Jingdong Sun, Saha Amitave

Journal of System Simulation

Abstract: In order to improve the existing small target detection methods, which suffer from low detection accuracy, high false detection rate and high leakage rate, the FSD-YOLOv5 algorithm is proposed, which has three improvements based on the YOLOv5 algorithm. The Focal EIoU is used instead of the original CIoU to improve the model convergence speed and regression accuracy. To cope with the deficiencies in CNN architecture, we adopt a new CNN building block called SPD-Conv is adopted. To address the problem of the reduced or lost information of small objects in feature maps caused by downsampling in convolutional neural networks, …

Collaborative Optimization Problem Of Dynamic Pre-Maintenance And Green Scheduling, Yuyan Jiang, Ning Ma, Yan Li, Rumeijiang Gan, Fuyu Wang

Journal of System Simulation

Abstract: For the traditional flexible job shop scheduling problem, a joint optimization of machine dynamic pre-maintenance and green scheduling is considered to establish an integrated optimization model with the optimization objectives of minimizing maximum completion time, total carbon emissions, and total cost. An improved NSGA-II algorithm is proposed to solve the model. A three-layer encoding method based on process, machine, and pre maintenance is adopted to design a one-step decoding scheme that considers process allocation, machine selection, and machine pre-maintenance strategies. The algorithm improves the elitist retention strategy, designs an adaptive crossover mutation function with algebraic changes, and a mutation …

Trajectory Optimization Of Robotic Arm Based On Improved Simulated Annealing Genetic Algorithm, Qiang Xu, Jianlei Xu, Yanhai Hu, Haihui Chen, Xing Zhang, Zhaohui Xing

Journal of System Simulation

Abstract: To optimize the working trajectory of the robotic arm, a modified simulated annealing genetic algorithm is proposed. Comprehensively considering the operating requirements and performance characteristics of the robotic arm, the five-order polynomial interpolation method is used to plan a smooth motion trajectory in the joint space. The penalty function method is used to handle the individuals that do not meet the constraint conditions, and the fitness function is recalibrated by the dynamic linear calibration method. An adaptive adjustment mechanism for crossover probability and variation probability is set to modify the genetic algorithm. The cooling idea of the simulated annealing …

Research On Flexible Integrated Scheduling Under Stochastic Processing Times Based On Improved D3qn Algorithm, Xiang Li, Xiaoyu Ren, Yongbing Zhou, Jian Zhang

Journal of System Simulation

Abstract: Aiming at the problem of time uncertainty in discrete manufacturing workshops, we construct an integrated scheduling mathematical model with the optimization objective of minimizing the maximum completion time based on the consideration of equipment and process constraints, and propose an improved dual-competitive deep Q-network algorithm (ID3QN) to solve the flexible integrated scheduling problem under stochastic working hours. The levels of process, machine, and overall scheduling are designed as features. Eight composite scheduling rules are formed as the action space by combining process rules based on processing times, processing sequences, and process structure tree, along with machine rules relevant to …

Combat Effectiveness Evaluation Of Air Defense Missile Weapon System Based On Rbf Neural Network, Peng Zhang, Ke Feng, Jiancheng Gong, Xiaoqiang Yang, Jinxing Shen

Journal of System Simulation

Abstract: A combat effectiveness evaluation method based on RBF neural network is proposed to address the problems of high dimensionality, high complexity, and subjective evaluation methods in current air defense missile weapon systems. A combat effectiveness index system for air defense missile weapon systems has been constructed by analyzing the OODA environmental combat theory. The RBF neural network model simulation is implemented using MATLAB, and several methods such as BP, PCABP, and Elman neural network are compared and verified through simulation. The simulation results show that the predicted evaluation results of the RBF neural network model are closer to the …

Dynamic Loading Simulation Method For Large-Scale Spiking Neural Network, Jiawei Shen, Daye Cai, Guoqing Yang, Pan Lü, Hong Li

Journal of System Simulation

Abstract: To address the problem of high GPU memory requirements in large-scale spiking neural network simulation, a dynamic loading simulation method for large-scale spiking neural networks is proposed. This method uses data movement at the sub-network granularity and utilizes the host memory as a larger memory pool to reduce the limitation of GPU memory on the model simulation scale, enabling large-scale spiking neural network simulation on a single GPU computer. The pipeline acceleration technique is adopted to reduce the impact of data movement on simulation speed. The simulation of a million-scale neural network is achieved in a single GPU experimental …

Quadrotor Uav Path Planning Based On Rapidly-Exploration Directional Tree Algorithm, Shijun Hu, Hailiang Liu, Binglei Wang, Wenke Su

Journal of System Simulation

Abstract: Aiming at the problems of low planning success rate, slow convergence speed, and suboptimal paths in the RRT algorithm for quadrotor UAV path planning of in complex environments, a directional exploration tree algorithm is proposed, which uses a directional sampling strategy to improve the directionality of the tree expansion, and by introducing an adaptive target adjustment strategy and a branch expansion strategy, the tree can expand quickly towards the target point while avoiding obstacles. The redundant points in the initial path are removed by the pruning process, and then the trajectory correction and smoothing process are performed on the …

An Empirical Analysis Of New Perspectives For Strategy Solving In Intelligent Game-Theoretic Decision-Making, Jiongming Su, Junren Luo, Shaofei Chen

Journal of System Simulation

Abstract: With the development of artificial intelligence technology, especially the promotion of largescale pre-training model theory, some new perspectives of strategy solving for intelligent game-theoretic decision-making have gradually been widely concerned and discussed. This paper combines the development of artificial intelligence technology and the transformation of strategy solving paradigm for intelligent game-theoretic decision-making, takes Chess (two-player zero-sum perfect information game), diplomacy (multi-player general-sum imperfect information game), and StarCraft Multi-Agent Challenge (multi-agent Markov game) as the research object for empirical analysis on sequential decision-making, the new paradigm and new way of strategy solving are analyzed according to the new perspective of …

Point Cloud Registration Method Based On Improved Grey Wolf Algorithm And Adaptive Splitting Kd-Tree, Yuanhao Du, Xiuli Geng, Chengzhi Xu, Yinhua Liu

Journal of System Simulation

Abstract: Traditional GWO algorithms suffer from limitations such as insufficient search efficiency and susceptibility to local optima. A novel method for the registration of point clouds of complex industrial components is proposed based on an improved GWO algorithm and ICP. To address the problem of uneven population distribution caused by random initialization in GWO, chaotic mapping is employed to initialize the gray wolf population, ensuring a more uniform distribution of individuals within the search space. A non-linear control parameter strategy is introduced to strike a balance between the algorithm's local search and global search capabilities. Elite reverse learning is integrated …

Gaussian Chaotic Fire Hawk Optimization Algorithm For Solving Dynamic Optimization Problems, Yongzhang Chen, Yuanbin Mo

Journal of System Simulation

Abstract: There are many important chemical processes in the chemical industry rely on dynamic optimization with factors such as nonlinearity and discontinuity. In order to find a more efficient solution algorithm, Gaussian Chaotic fire hawk optimization algorithm is proposed based on the fire hawk optimization algorithm, which is used to solve such problems after parameterizing the control variables. The original way of initializing the populations is replaced using tent chaotic mapping in order to make more sense of the initial distribution of the algorithm; a more targeted update method has been proposed in the analysis of fire hawk location updates …

Exploring Generative Artificial Intelligence (Ai) Applications In Fashion: Ethical Concerns In Human-Like Technology, Melissa Kathryn Gonzalez

Faculty Scholarship and Creative Works

No abstract provided.

Leveraging Network Science For Customer Segmentation And Product Recommendation, Ali Nasirzonouzi

Northeast Journal of Complex Systems (NEJCS)

The rapid growth in e-commerce has forced the development and implementation of enhanced customer segmentation and recommendation systems, improving business results and improving customer experience. Traditional approaches, such as RFM analysis and clustering algorithms like K-means, are very helpful in many situations but usually fail to catch complex interdependencies among customers and products. This paper proposes a new approach using network science methodologies, a bipartite graph model, toward the advancement of customer segmentation and product recommendation. It implements a bipartite graph of customers and products using the "Online Retail II" dataset and proceeds with community detection, segmenting customers into unique …

Volatility Modelling In Garch Frameworks: A Comparative Analysis Of Non-Gaussian Error Distributions With Skewed Parameters., Olatunbosun Adewale Akanbi, Timothy Olabisi Olatayo, Abass Ishola Taiwo

Al-Bahir

Forecasting volatility in financial time series remains challenging due to their asymmetric nature and excess kurtosis. This study evaluates and compares the performance of four variant of GARCH models incorporating skewed non-Gaussian error innovation distribution. The performances of these GARCH family of models under the skewed error innovation distributions were evaluated for three different unique data sets to have a more robust assessment of the performance of these skewed error innovation distributions. This study leverage on daily closing prices of Bitcoin, Naira to Dollar Exchange rates and daily Nigeria All Share Index between January 1, 2015 and January 26, 2024. …

Stochastic Generalization Models Learn To Comprehensively Detect Volatile Organic Compounds Associated With Foodborne Pathogens Via Raman Spectroscopy, Bohong Zhang, Anand K. Nambisan, Abhishek Prakash Hungund, Xavier Jones, Qingbo Yang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Ensuring food safety requires continuous innovation, especially in the detection of foodborne pathogens and chemical contaminants. In this study, we present a system that combines Raman spectroscopy with machine learning (ML) algorithms for the precise detection and analysis of VOCs linked to foodborne pathogens in complex liquid mixtures. A remote fiber-optic Raman probe was developed to collect spectral data from 42 distinct VOC mixtures, representing contamination scenarios with dilution levels ranging from undiluted to highly diluted states. A dataset comprising 1445 Raman spectra was analyzed using classification and regression ML models, including multi-layer perceptron (MLP), random forest, and extreme gradient …

Viscoelastic Protective Armor Liners And Protective Armor, Fatih Dogan, Cody Thomas, Catherine Johnson

Materials Science and Engineering Faculty Research & Creative Works

No abstract provided.

Retracted: Deep Learning-Based Beamforming Optimization For Reconfigurable Intelligent Surface-Assisted Wireless Communication Systems, Mohammed Firas Jassim, Alhamzah Taher Mohammed, Osamah Abdullah

Iraqi Journal for Computer Science and Mathematics

This research investigates how deep learning might be used to optimize beamforming in wireless communication systems that are helped by Reconfigurable Intelligent Surfaces (RIS). Our goal is to increase the possible data rates by dynamically forecasting the best phase shifts for RIS elements by utilizing Convolutional Neural Networks (CNN) and hybrid CNN-Long Short-Term Memory (CNN-LSTM) models. We assess the performance of these deep learning models against conventional genie-aided techniques by simulating real-world wireless settings using the DeepMIMO dataset. The findings demonstrate that beamforming based on deep learning can reach near-optimal performance, greatly lowering the overhead associated with channel estimation while …

The Permutation Annihilator Ideals In Commutative Permutation Bck–Algebras With Their Applications, Shuker Khalil, Ali Abbas Asmae

Iraqi Journal for Computer Science and Mathematics

This paper introduces new concepts such as permutation BCK--algebra, permutation involutory ideal, commutative permutation BCK--algebra, and prime permutation ideal. Additionally, their attributes are examined. This paper elucidates a method for determining a relationship between the chemical structure of atoms for the chemical element Cadmium, and some of our suggestions are given here. In this work, the structure of the sets 𝒜 and λ_n^β∗𝒜 are defined. Next, we show that if 𝒜 is a permutation ideal, then λ_n^β∗𝒜 is a permutation ideal that contains 𝒜. Also, in any commutative permutation BCK--algebra the …

Integrating Fuzzy Set Theory With Association Rule Mining For Advanced E-Commerce Recommendations, Hind Raad Ibraheem, Murtadha Mohammed Hamad

Iraqi Journal for Computer Science and Mathematics

The dynamic nature of e-commerce necessitates the adoption of cutting-edge technologies to improve the online shopping experience. Our research introduces a groundbreaking methodology called Fuzzy Association Rule Mining (FARM), combining fuzzy set theory with traditional Association Rule Mining (ARM). Unlike conventional ARM, which focuses solely on the frequency of jointly purchased items, FARM also considers the sold quantities, leveraging the Apriori algorithm to discern customer preferences from historical sales data across the UCI Online Retail II, Market Basket, and Movielens datasets. This hybrid of fuzzy set theory with ARM enables a better understanding of complicated consumer behaviors and associations between …

Liu-Type Estimator In Inverse Gaussian Regression Model Based On (R-(K-D)) Class Estimator, Zeina Ameer Hadied, Oday Esam Al-Saqal, Zakariya Yahya Algamal

Iraqi Journal for Computer Science and Mathematics

When multicollinearity arises in the inverse Gaussian regression (IGR), there is a substantially unstable variance in the maximum likelihood estimator. Based on the (r-(k-d)) class estimation method, we present a novel Liu-type estimator in the IGR model in this study. The study examines the e ectiveness of the suggested estimator and draws comparisons with alternative estimators. Based on simulation and real data results, the suggested estimate performs better than the other estimators in terms of mean squared error.

The Efficacy Of Utilizing Artificial Intelligence Techniques In Developing Critical Thinking In Mathematics Among Secondary School Students And Their Attitudes Toward It, Mohammad A. Tashtoush, Aida B. Qasimi, Nawal H. Shirawia, Lubna A. Hussein

Iraqi Journal for Computer Science and Mathematics

The aim of this study is to investigate the efficacy of Artificial Intelligence (AI) techniques and programs in developing Critical Thinking Skills (CTSs) in mathematics among secondary school students, as well as their attitudes towards it. This study employed an experimental methodology, which was applied to a sample of 91 students. A critical thinking test and a scale to measure students' Attitudes Towards Mathematics (ATM) were also utilized. This study revealed significant improvements in the mean scores of critical thinking skills among secondary students who were exposed to Artificial Intelligence Techniques (AITs), particularly in deduction, interpretation, inference, and evaluation. Additionally, …

Quality Evaluation For Colored Point Clouds Produced By Autonomous Vehicle Sensor Fusion Systems, Colin Schaefer, Zeid Kootbally, Vinh Nguyen

Michigan Tech Publications

Perception systems for autonomous vehicles (AVs) require various types of sensors, including light detection and ranging (LiDAR) and cameras, to ensure their robustness in driving scenarios and weather conditions. The data from these sensors are fused together to generate maps of the surrounding environment and provide information for the detection and tracking of objects. Hence, evaluation methods are necessary to compare existing and future sensor systems through quantifiable measurements given the wide range of sensor models and design choices. This paper presents an evaluation method to compare colored point clouds, a common fused data type, among two LiDAR-camera fusion systems …

Design Thinking Process Leads To Prototype, Rouri Takahashi

CAFE Symposium 2025

This project follows the Design Thinking Process and involves the development of an innovative, zippered small pouch designed with unique interlocking. The idea and design are entirely original, inspired by the principles of the Interlock system. I would like to present my steps through the current prototype.

Microstructural, Oxidation, And Mechanical Behavior Of Nbti-Based Refractory Alloys With 5 To 10 Pct Co, Cr, And Ni Additions, Tae Kyong John Kim, Jennifer Carter

Faculty Scholarship

NbTi-based refractory alloys with additions of Co, Cr, and Ni represent an interesting medium-entropy alloy system with potential for protective oxide film formation, high strength, and ductility. This study investigates the microstructural evolution, oxidation behavior, and mechanical properties of NbTi-based alloys containing 5 to 10 at. pct Co, Cr, and Ni. CALPHAD predictions suggest that this composition range can be heat treated to obtain a predominantly body-centered cubic matrix phase. Mechanical properties, including microhardness, yield strength, maximum strength, and specific strength are evaluated through isothermal compression tests conducted between room temperature and 800 °C. The oxidation kinetics of these alloys …

Anxiety Reduction In Autism Spectrum Disorder Using Novel Deep-Pressure Cutaneous Stimulation Therapy, Aaron Andrews, Jordyn Huecker, Kennedy Madrid, Lorissa Thorpe, James Fini, Josilyn Zamora, Victoria Oyeniyi, Maura Hart, Amanda Page, James Barber, Chase Taylor, Halah Khan, Spencer Willardson, Kyle Bills Phd, Christina Small

Annual Research Symposium

No abstract provided.

Integrating Neural Networks For Predictive Torque Control And Obstacle Avoidance In Autonomous Robot, Viswanath Kodali, Harsha Vardhan Borra, Kiran P

Northeast Journal of Complex Systems (NEJCS)

In the field of robotics, precise motion control and accurate computation of joint forces are critical for ensuring optimal performance. Traditional methods, such as using the Jacobian matrix for joint angle determination and Euler-Lagrange equations for torque computation, are reliable but computationally intensive, making them less suitable for real-time applications. This paper presents an advanced approach to improving the productivity and efficiency of a 3-Degree of Freedom (DOF) robotic arm by utilizing Artificial Neural Network (ANN). The proposed system dynamically predicts joint angles and torque, enabling faster and more efficient motion control.

To address the challenge of obstacle avoidance in …

Grnn-Fa Hybrid Model For Predicting The Useful Life Of Dump Truck Tyres, Festus Kunkyin-Saadaari, Alfred Kesseh, Victor Kwaku Agadzie

Journal of Sustainable Mining

The useful life of dump truck tyres is crucial for optimising economic efficiency and safety in mining operations. This study employs advanced hybrid models, including a generalised regression neural network (GRNN) combined with an artificial bee colony (ABC), ant colony optimisation (ACO), and firefly algorithm (FA), to predict tyre life at Perseus Mining Ghana Limited to enhance prediction accuracy. Sensitivity analysis using simple linear regression (SLR) identified tread depth as the most influential factor, with a correlation coefficient of 0.9638. Among the models, GRNN-FA demonstrated superior performance, achieving the highest correlation coefficient (r = 0.9586) and lowest mean squared error …

Email Spam Classification Based On Deep Learning Methods: A Review, Ekramul Haque Tusher, Mohd Arfian Ismail, Anis Farihan Mat Raffei

Iraqi Journal for Computer Science and Mathematics

Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. A thorough literature evaluation is required to have a comprehensive overview of the current research on utilizing deep learning methods for email spam classification. This review aims to identify the various deep learning techniques used for email spam, their effectiveness, and areas for future research. By synthesizing the outcomes …

Experimental Investigation And Operating Characteristics Of Lhr Engine Using Bio-Fuel Blends With Additives, Suresh R

Theses and Dissertations

The huge energy demand and environmental anxiety are the reasons for the focus on the interest in alternative fuels for diesel engines. This has resulted in a worldwide search for renewable, less pollutant and agricultural-based alternate fuels. Also, attention is given to increasing the efficiency of a conventional diesel engine when operating on alternative fuels. The bio-fuel derived from non-edible oil such as Pongamia oil and Neem seed oil are suggested alternative fuels for this LHR engine.

Diesel engine combustion components viz., the piston crown and liner were coated with Aluminium Titanate thermal barrier coating material. The objective of this …

Trpv4 Dominates High Shear-Induced Initial Traction Response And Long-Term Relaxation Over Piezo1, Mohanish Chandurkar, Manli Yang, Majid Rostami, Sangyoon J. Han

Michigan Tech Publications

Modulation of endothelial traction is critical for the responses of endothelial cells to fluid shear stress (FSS), which has profound implications for vascular health and atherosclerosis. Previously, we demonstrated that under high FSS, endothelial cells rapidly increase traction forces, followed by relaxation, with traction aligning in the flow direction. In contrast, low shear preconditioning induces a modest short-term increase in traction (min), followed by a secondary long-term (>14 hr) rise, with traction/cells aligning perpendicular to the flow. The upstream mechanosensors driving these responses, however, remain unknown. Here, we sought the roles of Piezo1 and TRPV4 ion channels in shear-induced …