Computer Engineering Commons^™

Efficient Hmm Map Matching Method Using R-Tree And Trajectory Segmentation, Yanjiao Song, Jiayue Zhou, Longhao Wang, Jing Wu, Rui Li, Xiaoping Rui

Journal of System Simulation

Abstract: In view of the incapability of traditional methods to efficiently process massive trajectory data, an improved HMM (hidden-Markov model) map matching algorithm is proposed. Spatial index for road networks is established through R-tree spatial index. GPS trajectory data are segmented based on the position change rates of trajectory points. R-tree index is used to quickly determine the candidate road section that sub-trajectories belong to, and the key points of the sub-trajectories instead of the entire sub-trajectories are selected to judge which road the sub-trajectories should be matched with. The map matching of each sub-trajectory is carried out on …

Research On Digital Twin Credibility Assessment Process And Index, Fan Yang, Ping Ma, Wei Li, Ming Yang

Journal of System Simulation

Abstract: With the application field expansion of digital twin technology, in order to meet the requirement of digital twin credibility and promote the digital twin credibility assessment, the credibility assessment process and indicators of digital twin are researched. The development process of digital twin is analyzed and the construction method of flexible multi-layer digital twin credibility assessment process model based on IDEF0 is proposed. Two index systems, process-stage-activity layers(P-S-A) and activity-element-feature layers(A-E-F),are proposed to solve the problems of defect backtracking and evaluation of complex objects. Several examples of index system are given.

Research On Real-Time Gesture Classification Algorithm Based On Imu And Semg Mixed Signals, Tao Wang, Yingnian Wu, Rui Yang, Yueying Sun

Journal of System Simulation

Abstract: In order to improve the gesture classification accuracy of surface electromyography (sEMG), the mixed signal of attitude and sEMG is collected by inertial measurement unit (IMU) and EMG sensor, and a GRU-BiLSTM double-layer network real-time gesture classification algorithm is proposed. The first layer of gated recurrent unit (GRU) detects the mutation point of the initial mixed signal though energy combination operator feature and locates the starting point of the dynamic data. The second layer Bi-directional long short term memory (BiLSTM) classifies the motion state mixed signal into 10 gestures in two directions though energy kernel phase map feature. …

Machine Learning-Based Simulation Research Of On-Line Subway Pedestrian Flow Control, Jiajie Shi, Peng Yang, Yannan Pi

Journal of System Simulation

Abstract: In recent years, a large number of digital experiments have been carried out in the field of space launch, such as digital design verification, digital joint training and simulation training, and rocket-ground joint simulation evaluation, all of which involve the space launch information visualization. Through virtual reality technology, system simulation technology, data visualization technology, etc., on the basis of multi-thread, multi-module architecture design idea, and message queue system interaction mode, the space launch visual simulation analysis technology platform with functions of data management, scenario management, calculation management, and script management is constructed. The application cases of visual simulation analysis …

Takeout Distribution Routes Optimization Considering Order Clustering Under Dynamic Demand, Houming Fan, Fushan Xian, Huaiqi Wang

Journal of System Simulation

Abstract: Takeout distribution optimization includes order allocation and route planning. Aiming at dynamic order and rider position change, with the goal of minimizing the overtime order proportion, average delivery time and average travel distance,a two-stage mathematical model is established based on the idea of pre-optimization and dynamic adjustment. In the pre-optimization stage, an improved variable neighborhood search algorithm is designed to obtain the initial distribution scheme. In the dynamic adjustment stage, a periodic optimization strategy is adopted to transform the problem into a virtual distribution center vehicle problem for solution. In each stage,different clustering methods are used to optimize the …

A Highly Efficient Broadband Multi-Functional Metaplate, Azhar Javed Satti, Muhammad Ashar Naveed, Isma Javed, Nasir Mahmood, Muhammad Zubair, Muhammad Qasim Mehmood, Yehia Massoud

Department of Electrical and Computer Engineering: Faculty Publications

Due to the considerable potential of ultra-compact and highly integrated meta-optics, multi-functional metasurfaces have attracted great attention. The mergence of nanoimprinting and holography is one of the fascinating study areas for image display and information masking in meta-devices. However, existing methods rely on layering and enclosing, where many resonators combine various functions effectively at the expense of efficiency, design complication, and complex fabrication. To overcome these limitations, a novel technique for a tri-operational metasurface has been suggested by merging PB phase-based helicity-multiplexing and Malus's law of intensity modulation. To the best of our knowledge, this technique resolves the extreme-mapping issue …

Hangprinter For Large Scale Additive Manufacturing Using Fused Particle Fabrication With Recycled Plastic And Continuous Feeding, Ravneet S. Rattan, Nathan Nauta, Alessia Romani, Joshua Pearce

Electrical and Computer Engineering Publications

The life cycle of plastic is a key source of carbon emissions. Yet, global plastics production has quadrupled in 40 years and only 9 % has been recycled. If these trends continue, carbon emissions from plastic wastes would reach 15 % of global carbon budgets by 2050. An approach to reducing plastic waste is to use distributed recycling for additive manufacturing (DRAM) where virgin plastic products are replaced by locally manufactured recycled plastic products that have no transportation-related carbon emissions. Unfortunately, the design of most 3-D printers forces an increase in the machine cost to expand for recycling plastic at …

Machine Learning Applications In Malware Classification: A Metaanalysis Literature Review, Tjada Nelson, Austin O'Brien, Cherie Noteboom

Research & Publications

With a text mining and bibliometrics approach, this study reviews the literature on the evolution of malware classification using machine learning. This work takes literature from 2008 to 2022 on the subject of using machine learning for malware classification to understand the impact of this technology on malware classification. Throughout this study, we seek to answer three main research questions: RQ1: Is the application of machine learning for malware classification growing? RQ2: What is the most common machine-learning application for malware classification? RQ3: What are the outcomes of the most common machine learning applications? The analysis of 2186 articles resulting …

Ads-B Classification Using Multivariate Long Short-Term Memory–Fully Convolutional Networks And Data Reduction Techniques, Sarah Bolton, Richard Dill, Michael R. Grimaila, Douglas Hodson

Faculty Publications

Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of …

Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha

Faculty Publications

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). …

Towards Machine Learning-Based Fpga Backend Flow: Challenges And Opportunities, Imran Taj, Umer Farooq

All Works

Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus …

Stand-Up Comedy Visualized, Berna Yenidogan

Dissertations, Theses, and Capstone Projects

Stand-up comedy has become an increasingly popular form of comedy in the recent years and comedians reach audiences beyond the halls they are performing through streaming services, podcasts and social media. While comedic performances are typically judged by how 'funny' they are, which could be proxied by the frequency and intensity of laughs through the performance, comedians also explore untapped social issues and provoke conversation, especially in this age where interaction with artists goes beyond their act. It is easy to see commonalities in the topics addressed in comedians’ work such as relationships, race and politics.This project provides an interactive …

Resource Management In Mobile Edge Computing For Compute-Intensive Application, Xiaojie Zhang

Dissertations, Theses, and Capstone Projects

With current and future mobile applications (e.g., healthcare, connected vehicles, and smart grids) becoming increasingly compute-intensive for many mission-critical use cases, the energy and computing capacities of embedded mobile devices are proving to be insufficient to handle all in-device computation. To address the energy and computing shortages of mobile devices, mobile edge computing (MEC) has emerged as a major distributed computing paradigm. Compared to traditional cloud-based computing, MEC integrates network control, distributed computing, and storage to customizable, fast, reliable, and secure edge services that are closer to the user and data sites. However, the diversity of applications and a variety …

Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …

Skeleton-Based Hand Gesture Recognition Using Data-Level Fusion, Oluwaleke Yusuf

Theses and Dissertations

Hand Gesture Recognition (HGR) is a form of perceptual computing that allows artificial systems to capture and interpret human gestures. HGR has applications in human-machine interaction, virtual reality, augmented reality, and human behavior analysis. The human hand can assume a near-infinite number of poses and orientations to form myriad gestures, thus increasing the difficulty of the HGR task.

The hand skeleton of connected joints effectively describes the hand’s geometric shape and thus contains richer semantic gesture information while eliminating noise from individual differences in physical hand characteristics. The efficacy and computational efficiency of skeleton-based HGR frameworks can be significantly enhanced …

A Novel Insect And Pest Identification Model Based On A Weighted Multipath Convolutional Neural Network And Generative Adversarial Network, Vinita Abhishek Gupta, M.V. Padmavati, Ravi R. Saxena, Raunak Kumar Tamrakar

Karbala International Journal of Modern Science

Timely identification of insects and their management play a significant role in sustainable agriculture development. The proposed hybrid model integrates a weighted multipath convolutional neural network and generative adversarial network to identify insects efficiently. To address the shortcomings of single-path networks, this novel model takes input from numerous iterations of the same image to learn more specific features. To avoid redundancy produced due to multipath, weights have been assigned to each path. For Xie2 dataset, the model shows 3.75%, 2.74%, 1.54%, 1.76%, 1.76%, 2.74 %, and 2.14% performance improvement from AlexNet, ResNet50, ResNet101, GoogleNet, VGG-16, VGG-19, and simple CNN respectively. …

Completeness Of Nominal Props, Samuel Balco, Alexander Kurz

Engineering Faculty Articles and Research

We introduce nominal string diagrams as string diagrams internal in the category of nominal sets. This leads us to define nominal PROPs and nominal monoidal theories. We show that the categories of ordinary PROPs and nominal PROPs are equivalent. This equivalence is then extended to symmetric monoidal theories and nominal monoidal theories, which allows us to transfer completeness results between ordinary and nominal calculi for string diagrams.

Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden

National Training Aircraft Symposium (NTAS)

An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has …

Artificial Intelligence For Automated Detection Of Large Mammals Creates Path To Upscale Drone Surveys, Javier Lenzi, Andrew Barnas, Abdelrahman A. Elsaid, Travis Desell, Robert F. Rockwell, Susan N. Ellis-Felege

Biology Faculty Publications

Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: “adult …

Research On Mixed Flow Line Balancing And Scheduling Optimization With Multiple Constraints, Zhenping Li, Ying Shi, Lingyun Wu

Journal of System Simulation

Abstract: Aiming at the phenomena of unbalanced load between stations and product accumulation caused by unreasonable design of mixed flow line in G enterprise, based on the matching relationship between processes and stations, cycle time, process priority and other constraint, with the objectives of reducing the number of stations, balancing the workload between stations, and reducing the products waiting time, a multi-objective mixed integer programming model for mixed flow line balance and product scheduling problem is established. A hierarchical algorithm and a hybrid heuristic algorithm are designed respectively; the accuracy of the hierarchical algorithm is verified by small-scale …

Simulation-Based Adaptive Dynamic Scheduling For Bi-Objective Parallel Multi-Processor Open Shop, Yarong Chen, Shuchen Guan, Chengjun Huang, Lixia Zhu, Fuhder Chou

Journal of System Simulation

Abstract: Aiming at the parallel multi-processor open shop scheduling problem with uncertain job's release time,processing time and urgent jobs, an adaptive dynamic method integrating FlexSim simulation model and NSGA-Ⅱ algorithm is designed to optimize the bi-objectives of TWC(total weighted completion time) and TWT(total weighted tardiness). By using the FlexSim simulation model, this method determines the adaptive scheduling cycle according to the dynamic workload of the open shop, and conducts right-shift rescheduling to the urgent jobs. NSGA-Ⅱ algorithm is used to generate the bi-objective optimization scheduling scheme. Experimental results of a grain sorting shop show that compared with the rule-based real-time …

Uniform Experimental Design With Constrained Region Based On Fruit Fly Algorithm, Jiawei Zhou, Xin Du, Youcong Ni, Hu Zhang, Hao Zhang, Haoran Ni, Feng Wang

Journal of System Simulation

Abstract: To solve the problems that existing two-phase differential evolutionary algorithms still have poor diversity of population distribution and weak local search ability in solving uniform designs in constrained experimental region, a new two-phase fruit fly optimization algorithm (ToPFOA) based on uniform experimental design is proposed. In the first stage, fruit fly search strategy combined with differential operator, K-means clustering and external document updating the centers of clusters is used todynamically improve distribution diversity of population in constrained region. In the second stage, a new fruit fly operator is designed to improve local search ability in constrained region. …

Modulation Recognition Method Of Mixed Signal Based On Intelligent Analysis Of Cyclic Spectrum Section, Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan

Journal of System Simulation

Abstract: Aiming at the problems of low intelligence and poor adaptability for the existing mixed signal recognition methods, an intelligent recognition method based on cyclic spectral cross section and deep learning is proposed. For common mixed communication signals, the characteristics of zero frequency cross section of cyclic spectrum are theoretically deduced and analyzed. Two new pre-processing methods, nonlinear segmental mapping and directional pseudo-clustering are proposed, which can effectively improve the adaptability and consistency of cross section features. The pre-processed feature graph is combined with the residual network (ResNet), and the deep learning network is used to mine and analyze the …

Research On Modeling And Simulation Of Application Efficiency Of Tactical Medical Equipment, Guowei Lu, Xueqiang Tao, Deguang Duan, Hao Li, Zerui Zhang, En Chen

Journal of System Simulation

Abstract: In view of the lack of effective modeling and simulation means for the current research on the application efficiency of tactical medical treatment equipment in our army, a modeling and simulation research framework for the application effectiveness of equipment through the wounded model, equipment model and evaluation model is constructed. Based on the multi-agent method in Anylogic8.7.0 modeling and simulation platform, the casualty generation and its circulation process among medical treatment equipment are simulated. In the context of a tactical medical exercis, the overall support capability of medical treatment equipment is evaluated scientifically and quantitatively, and the key equipment …

Development Opportunities And Application Prospects Of Aero-Engine Simulation Technology Under Digital Transformation, Jianguo Cao

Journal of System Simulation

Abstract: The development of China's social economy and the improvement of its national defense capability in the new era put forward higher requirements for the development of aero-engines. It is urgent to promote the digital transformation of aero-engines in order to achieve coordinated, agile and efficient aero-engine development. Based on the current research and development of aero-engine in China, this paper clarifies the new connotation of "speediness and efficiency, accurate mapping, comprehensive coverage, and dynamic prediction" given by the development of emerging cutting-edge technologies to aero-engine simulation technology, as well as the new technical features of "spatio-temporal ubiquity, data driven, …

Knn Fault Detection Based On Reconstruction Error And Multi-Block Modeling Strategy, Jing Zheng, Weili Xiong, Xiaodong Wu

Journal of System Simulation

Abstract: For the fault monitoring algorithm based on k-nearest neighbor (kNN), the abnormal information that caused the fault is easy to be overwhelmed by the normal operating condition information, which leads to the problem of untimely fault detection and low alarm rate. A kNN fault monitoring method based on reconstruction error is proposed using auto-encoder and multi-block modeling strategy. The method uses the normal working condition data set to train the auto-encoder model, and extracts the reconstruction error based on the model to solve the problem that abnormal information is easy to be overwhelmed. Further considering the fault characteristics such …

Chameleon Swarm Algorithm For Segmental Variation Learning Of Population And S-Type Weight, Damin Zhang, Yi Wang, Linna Zhang

Journal of System Simulation

Abstract: It is the best choice for intelligent algorithms to be applied to specific fields to explore strong searching ability, good reliability and stability.In this paper, aiming at the defects of chameleon swarm algorithm, such as unstable solution, low convergence accuracy and unbalanced search and development, a chameleon swarm algorithm (RMSCSA) based on population diversity segmental mutation learning and S-type weight is proposed. The refraction mirror learning strategy (RML) is introduced to make the chameleon more consistent with the observation in nature and enhance its diversity. The introduction of segmental variation of population diversity can keep the individuals with poor …

Large-Scale Multi-Objective Natural Computation Based On Dimensionality Reduction And Clustering, Weidong Ji, Yuqi Yue, Xu Wang, Ping Lin

Journal of System Simulation

Abstract: In multi-objective optimization problems, as the number of decision variables increases, the optimization ability decreases significantly. To solve "dimension disaster", a large-scale multi-objective natural computation method based on dimensionality reduction and clustering is proposed. The decision variables are optimized by locally linear embedding(LLE) to obtain the representation of high-dimensional variables in the low-dimensional space, then the individuals are grouped through K-means to select the appropriate guide individuals for the population to strengthen the convergence and diversity. To verify the effectiveness, the method is applied to the multi-objective particle swarm optimization algorithm and the non-dominated sorting genetic algorithm. The convergence …

Research On Multiple Filter Signal Compensation For Washout Algorithm Optimization Of Flight Simulator, Weichao Liu, Hui Wang

Journal of System Simulation

Abstract: Aiming at the defects of signal loss and poor adaptability of the classical washout algorithm when applied to flight simulator, an optimization scheme of washing algorithm based on multiple filtering signal compensation is proposed. Analyzing the lost signal in classical washout algorithm, intercepting the lost signals to the depth filter with depth filtering strategy, basing on human perception errors and platform movement margin, after multiple filtering signal to certain proportion respectively compensation to the three channel of washout algorithm to achieve the maximum reduction of signal loss, thus reducing human perception error. The classical washing algorithm and the improved …

Scheduling Optimization Of Aluminum Extrusion Production Line Based On Timed Petri Net And Bso Algorithm, Yali Wu, Shuting He, Yanxi Yang, Lianqiang Feng, Fuqiang Wang, Yulu Chen

Journal of System Simulation

Abstract: For the problems of long production period and low efficiency caused by the complicated processes and large scheduling capacity of aluminum extrusion production line in industrial production, a timed Petri net (TdPN) scheduling model of aluminum extrusion production line is proposed and analyzed for reasonableness. The brain storm optimization (BSO) algorithm is introduced into the model, and an optimized scheduling algorithm for aluminum extrusion scheduling problems is proposed based on the individual encoding and decoding methods. The simulated annealing local search mechanism is used to improve the performance of BSO algorithm in the later stage, which can achieve the …