Computer Sciences Commons^™

Airport Flight Transit Support Time Collaborative Planning Modeling Based On Stn, Bin Chen, Yue Liu, Yalei Yang

Journal of System Simulation

Abstract: Under the constraint of resources, the collaborative planning of airport flight transit support time is one of the effective methods to improve airport operation efficiency. Based on Simple Temporal Network (STN), a planning model of flight transit support time is established. Based on the temporal decoupling, the shortest path matrix simplification, and the distance graph solving of STN task model considering resources, the method of collaborative planning of flight transit support time for airport considering resources is obtained. The comparison results of the simulation and the actual data show that STN task model considering resources can optimize the airport …

Vehicle Detection Method Based On Multi Scale Feature Fusion, Yin Wang, Feixiang Wang, Qianlai Sun

Journal of System Simulation

Abstract: Vehicle detection is the important research content and hotspot in the intelligent transportation. Aiming at the low detection accuracy and poor small-scale recognition effect of the traditional vehicle detection algorithm, an improved detection method based on YOLOv4(you only look once v4) is proposed to improve the detection performance of small target vehicles in traffic scenes. By redesigning the YOLOv4 network, the MobileNetv2 deep separable convolution module is used to replace the traditional convolution, and the convolutional block attention module (CBAM) attention module is integrated into the feature extraction network to ensure the detection accuracy of the model and reduce …

A Hybrid Algorithm Based On Seeker Optimization Algorithm And Salp Swarm Algorithm For Pid Parameters Optimization, Shaomi Duan, Huilong Luo, Haipeng Liu

Journal of System Simulation

Abstract: Aiming at the premature convergence of seeker optimization algorithm(SOA) during optimizing the global problems, a new SOA-SSA hybrid algorithm based on seeker optimization algorithm and salp swarm algorithm (SSA) is proposed.The SOA-SSA algorithm is based on a double population evolution strategy, in which some individuals of the population are evolved by seeker optimization algorithm and the rest are evolved from salp swarm algorithm. The individuals in SOA and SSA both employ an information sharing mechanism to realize the coevolution. These strategies increase the diversity of the population and avoid the premature convergence. The experimental results show that …

Application Of Improved Q Learning Algorithm In Job Shop Scheduling Problem, Yejian Zhao, Yanhong Wang, Jun Zhang, Hongxia Yu, Zhongda Tian

Journal of System Simulation

Abstract: Aiming at the job shop scheduling in a dynamic environment, a dynamic scheduling algorithm based on an improved Q learning algorithm and dispatching rules is proposed. The state space of the dynamic scheduling algorithm is described with the concept of "the urgency of remaining tasks" and a reward function with the purpose of "the higher the slack, the higher the penalty" is disigned. In view of the problem that the greedy strategy will select the sub-optimal actions in the later stage of learning, the traditional Q learning algorithm is improved by introducing an action selection strategy based on the …

Visual Inspection Model Of Uav Cluster Based On Improved Pigeon Flock Hierarchy, Qi Chen, Haoyang Cui

Journal of System Simulation

Abstract: Aim at UAV being vulnerable to the environmental interference and the low efficiency of the traditional single-person-UAV model in the transmission line inspection, a visual inspection model for the power line by UAV is proposed based on the improved pigeon flock hierarchy. The initial landmark point of the UAV is generated based on GPS coordinates of the aircraft-carrying vehicle and the tower to be inspected, and the movement trajectory is planned. The return point of the UAV is used to update the initial landmark of onward UAV, which realizes the dynamic handover between the work-exchanging UAV, and the landmark …

Design Of Interactive Simulated Water Gun Fire Fighting Training System Based On Steam Vr, Cheng Lu, Xuesheng Jin

Journal of System Simulation

Abstract: In order to save the fire fighting training resources and increase the immersion and experience of VR training, an interactive simulated water gun fire fighting training system based on Steam VR is designed. By using the Hall sensors and signal conversion circuit boards to collect and transmit the signal of the simulated water gun, and by using the Unity3D engine combined with the VIVE head-mounted display to build and present VR fire scene. The gun is controlled through C# programming to complete the interaction with the virtual fire scene. The system is evaluated by a post-questionnaire survey …

Application Of Observability In Performance Evaluation Of Photosynthesis Model, Hongnai Gao, Lijiang Fu, Qian Xia, Ya Guo

Journal of System Simulation

Abstract: The photochemical reaction of photosynthesis involves a variety of physiological substances that cannot be directly measured. By modeling the control system, the state of these physiological substances can be es-timated based on the chlorophyll fluorescence, but the reliability of the state estimation is not given in all the reference documents. In response to this problem, based on the photochemical reaction kinetic model, the observability of the nonlinear system is introduced to evaluate the reliability of the state estimation. Aiming at the existing observability methods lacking the direct comparability due to the different dimensions of the components of different states, …

Fuzzy Super-Twisting Second Order Sliding Mode Trajectory Tracking Control For Robotic Manipulator, Baolin Du, Dachang Zhu, Yihua Pan

Journal of System Simulation

Abstract: A fuzzy super-twisting second order sliding mode control method is proposed for the uncertainties of the model error and external disturbance on the trajectory tracking accuracy of robotic manipulator. Based on the dynamic model of the robotic, a new non-singular terminal sliding mode manifold is designed, and an improved super-twisting algorithm is used to design the second order sliding mode controller. In order to solve the problem that the matching disturbance can only be compensated under the condition of the known disturbance boundary in the sliding mode control, the fuzzy logic algorithm is used to carryout the online compensation …

Research On Fire Emergency Evacuation Simulation Based On Cooperative Deep Reinforcement Learning, Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang

Journal of System Simulation

Abstract: The fire accident is a major threat to the public safety, in which the high temperature, toxic and harmful gases seriously interfer the selection of the evacuation routes. Deep reinforcement learning is introduced into the research of emergency evacuation simulation, and a cooperative double deep Q network algorithm is proposed for the multi-agent environment. A fire scene model that changes dynamically over time is established to provide the real-time information on the distribution of the dangerous areas for the evacuation. The independent agent neural networks are integrated and the multi-agent unified deep neural network is established to realize the …

Steps Towards Digital Transformation In The Pharmaceutical Manufacturing Landscape Knowledge-Enabled Technology Transfer, Anne Greene Professor, Martin Lipa Dr

Level 3

No abstract provided.

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

Securing Critical Cyber Infrastructures And Functionalities Via Machine Learning Empowered Strategies, Tao Hou

USF Tampa Graduate Theses and Dissertations

Machine learning plays a vital role in understanding threats, vulnerabilities, and security policies. In this dissertation, two machine learning empowered approaches on improving the security of critical cyber infrastructures and functionalities will be discussed.

The first work focuses on preventing attacks that use adversarial, active end-to-end topology inference to obtain the topology information of a target network. The topology of a network is fundamental for building network infrastructure functionalities. In many scenarios, enterprise networks may have no desire to disclose their topology information. To this end, we propose a Proactive Topology Obfuscation (ProTO) system that adopts a detect-then-obfuscate framework: (i) …

From Equal-Mass To Extreme-Mass-Ratio Binary Inspirals: Simulation Tools For Next Generation Gravitational Wave Detectors, Samuel Douglas Cupp

LSU Doctoral Dissertations

Current numerical codes can successfully evolve similar-mass binary black holes systems, and these numerical waveforms contributed to the success of the LIGO Collaboration's detection of gravitational waves. LIGO requires high resolution numerical waveforms for detection and parameter estimation of the source. Great effort was expended over several decades to produce the numerical methods used today. However, future detectors will require further improvements to numerical techniques to take full advantage of their detection capabilities. For example, the Laser Interferometer Space Antenna (LISA) will require higher resolution simulations of similar-mass-ratio systems than LIGO. LISA will also be able to detect extreme-mass-ratio inspiral …

Space-Efficient Algorithms And Verification Schemes For Graph Streams, Prantar Ghosh

Dartmouth College Ph.D Dissertations

Structured data-sets are often easy to represent using graphs. The prevalence of massive data-sets in the modern world gives rise to big graphs such as web graphs, social networks, biological networks, and citation graphs. Most of these graphs keep growing continuously and pose two major challenges in their processing: (a) it is infeasible to store them entirely in the memory of a regular server, and (b) even if stored entirely, it is incredibly inefficient to reread the whole graph every time a new query appears. Thus, a natural approach for efficiently processing and analyzing such graphs is reading them as …

On-Line Process Physics Tests Via Lyapunov-Based Economic Model Predictive Control And Simulation-Based Testing Of Image-Based Process Control, Henrique Oyama, A. F. Leonard, Minhazur Rahman, Govanni Gjonaj, Michael Williamson, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Next-generation manufacturing involves increasing use of automation and data to enhance process efficiency. An important question for the chemical process industries, as new process systems (e.g., intensified processes) and new data modalities (e.g., images) are integrated with traditional plant automation concepts, will be how to best evaluate alternative strategies for data-driven modeling and synthesizing process data. Two methods which could be used to aid in this are those which aid in testing data-based techniques on-line, and those which enable various data-based techniques to be assessed in simulation. In this work, we discuss two techniques in this domain which can be …

Machine Learning With Kay, Lasith Niroshan, James Carswell

Conference Papers

Computational power is very important when training Deep Learning (DL) models with large amounts of data (Wooldridge, 2021). Hence, High-Performance Computing (HPC) can be leveraged to reduce computational cost, and the Irish Centre for High-End Computing (ICHEC) provides significant infrastructure and services for research and development to both academia and industry. A portion of ICHEC's HPC system has been allocated for institutional access, and this paper presents a case study of how to use Kay (Ireland's national supercomputer) in the remote sensing domain. Specifically, this study uses clusters of Kay Graphics Processing Units (GPUs) for training DL models to extract …

Editorial, Michael E. Whitman, Herbert J. Mattord, Hossain Shahriar

Journal of Cybersecurity Education, Research and Practice

Since 2016, it has been the mission of the Journal of Cybersecurity Education, Research, and Practice (JCERP) to be a premier outlet for high-quality information security and cybersecurity-related articles of interest to teaching faculty and students. This is the 12^th edition of the (JCERP) and, as ever, we are seeking authors who produce high-quality research and practice-oriented articles focused on the development and delivery of information security and cybersecurity curriculum, innovation in applied scholarship, and industry best practices in information security and cybersecurity in the enterprise for double-blind review and publication. The journal invites submissions on Information Security, Cybersecurity, …

Osm-Gan: Using Generative Adversarial Networks For Detecting Change In High-Resolution Spatial Images, Lasith Niroshan, James Carswell

Articles

Detecting changes to built environment objects such as buildings/roads/etc. in aerial/satellite (spatial) imagery is necessary to keep online maps and various value-added LBS applications up-to-date. However, recognising such changes automatically is not a trivial task, and there are many different approaches to this problem in the literature. This paper proposes an automated end-to-end workflow to address this problem by combining OpenStreetMap (OSM) vectors of building footprints with a machine learning Generative Adversarial Network (GAN) model - where two neural networks compete to become more accurate at predicting changes to building objects in spatial imagery. Notably, our proposed OSM-GAN architecture achieved …

Learning To Generalize Dispatching Rules On The Job Shop Scheduling, Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takac

Machine Learning Faculty Publications

This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem sizes. To address this issue, we introduce a novel Adversarial Curriculum Learning (ACL) strategy, …

An Empirical Study On Sampling Approaches For 3d Image Classification Using Deep Learning, Nicholas Michelette

Theses and Dissertations

A 3D classification method requires more training data than a 2D image classification method to achieve good performance. These training data usually come in the form of multiple 2D images (e.g., slices in a CT scan) or point clouds (e.g., 3D CAD modeling) for volumetric object representation. The amount of data required to complete this higher dimension problem comes with the cost of requiring more processing time and space. This problem can be mitigated with data size reduction (i.e., sampling). In this thesis, we empirically study and compare the classification performance and deep learning training time of PointNet utilizing uniform …

Determining American Sign Language Joint Trajectory Similarity Using Dynamic Time Warping (Dtw), Rohith Mandavilli

Computer Science Senior Theses

As American Sign Language (ASL), the language used by Deaf/Hard of Hearing (D/HH) Americans has grown in popularity in recent years, an unprecedented number of schools and organizations now offer ASL classes. Many hold misconceptions about ASL, assuming it is easily learned; however due to its rich, complex grammatical construction, it’s not mastered easily beyond a basic level. Therefore, it becomes ever more important to improve upon existing techniques to teach ASL. The Dartmouth Applied Learning Initiative (DALI) at Dartmouth college in coordination with the Robotics and Reality Lab developed an application on the Oculus Quest that helps D/HH individuals …

A Complete Process Of Text Classification System Using State-Of-The-Art Nlp Models, Varun Dogra, Sahil Verma, Kavita, Pushpita Chatterjee, Jana Shafi, Jaeyoung Choi, Muhammad Fazal Ijaz

Computer Science Faculty Research

With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using …

What-Is And How-To For Fairness In Machine Learning: A Survey, Reflection, And Perspective, Zeyu Tang, Jiji Zhang, Kun Zhang

Machine Learning Faculty Publications

Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We also consider fairness inquiries from a dynamic perspective, and further consider the long-term impact that is induced by current prediction and decision. In light of the differences in the characterized fairness, we present a flowchart …

Video Anomaly Detection: Practical Challenges For Learning Algorithms, Keval Doshi

USF Tampa Graduate Theses and Dissertations

Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of several existing methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, real-time decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Furthermore, several critical tasks such as continual learning, model interpretability and cross-domain adaptability are completely neglected in existing works. Motivated by these research gaps, in this dissertation we discuss our …

A Digital Application For Assessment Of Neurocognitive Disabilities, Thomas H. Auriemma

Theses and Dissertations

Background: Neuropsychological assessment is designed to identify neurocognitive impairment and has traditionally relied on pen-and-paper tests. The behavior collected from these tests is usually expressed as a total summary score or a score that reflects a restricted number of features that assess errors. There is now interest in coupling traditional paper and pencil tests with digital assessment technology. In this context traditional metrics such as summary scores are still available. However, using digital technology, a host of time-based parameters can now be obtained. These time-based parameters include the total time to complete the task or total time to completion, as …

Using Graph Theoretical Methods And Traceroute To Visually Represent Hidden Networks, Jordan M. Sahs

UNO Student Research and Creative Activity Fair

Within the scope of a Wide Area Network (WAN), a large geographical communication network in which a collection of networking devices communicate data to each other, an example being the spanning communication network, known as the Internet, around continents. Within WANs exists a collection of Routers that transfer network packets to other devices. An issue pertinent to WANs is their immeasurable size and density, as we are not sure of the amount, or the scope, of all the devices that exists within the network. By tracing the routes and transits of data that traverses within the WAN, we can identify …

Analysis Of Federated Scheduling For Integer-Valued Workloads, Marion Sudvarg, Chris Gill

Computer Science Faculty Research & Creative Works

In federated scheduling of parallel real-time tasks on multiprocessor systems, high-utilization tasks are allocated dedicated processors on which they execute exclusively. Several methods exist for allocating a sufficient number of processors to guarantee that each task meets its deadline. In this paper, we propose two new strategies for allocating unit-speed cores to tasks with integer workload and deadline values. The first method can be performed in constant time for each high-utilization task, given the task's total workload, critical-path length, and deadline. The second method exploits the DAG structure of high-utilization tasks, providing a potentially better schedule in pseudo-polynomial time. We …

Learning To Control Under Time-Varying Environment, Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takac, Bin Gu

Machine Learning Faculty Publications

This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a challenging task. At a cost of NP-hard offline planning, prior works have introduced online convex optimization algorithms, although they suffer from nonparametric rate of regret. In this paper, we propose the first computationally tractable online algorithm with regret guarantees that avoids offline planning over the state linear feedback policies. Our algorithm is based on the optimism in the face of uncertainty (OFU) principle in which we optimistically …

Flecs: A Federated Learning Second-Order Framework Via Compression And Sketching, Artem Agafonov, Dmitry Kamzolov, Rachael Tappenden, Alexander Gasnikov, Martin Takac

Machine Learning Faculty Publications

Inspired by the recent work FedNL (Safaryan et al, FedNL: Making Newton-Type Methods Applicable to Federated Learning), we propose a new communication efficient second-order framework for Federated learning, namely FLECS. The proposed method reduces the high-memory requirements of FedNL by the usage of an L-SR1 type update for the Hessian approximation which is stored on the central server. A low dimensional 'sketch' of the Hessian is all that is needed by each device to generate an update, so that memory costs as well as number of Hessian-vector products for the agent are low. Biased and unbiased compressions are utilized to …

Offline Reinforcement Learning With Causal Structured World Models, Zheng-Mao Zhu, Xiong-Hui Chen, Hong-Long Tian, Kun Zhang, Yang Yu

Machine Learning Faculty Publications

Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment. Previous model-based offline RL methods learn fully connected nets as world-models to map the states and actions to the next-step states. However, it is sensible that a world-model should adhere to the underlying causal effect such that it will support learning an effective policy generalizing well in unseen states. In this paper, We first provide theoretical results that causal world-models can outperform plain world-models for offline RL by incorporating the causal structure into the generalization error …