Systems Science Commons^™

Strategy Optimization Method Of Multi-Dimension Projection Based On Deep Reinforcement Learning, Jing An, Guangya Si, Lei Zhang

Journal of System Simulation

Abstract: Based on the perfect performance of deep reinforcement learning (DRL) in strategy optimization, this paper proposes a strategy optimization method of action taking the multi-dimension projection action as the main research object. The method combines the simulation experiment method with the DRL method. After analyzing the current situation of strategy optimization research, the deep learning framework is selected according to the research problems, and a DRL multi-dimension projection strategy model based on the asynchronous advantage actor-critic (A3C) algorithm is constructed. Through simulation experiments, the interactive learning between the DRL model and the simulation of "out of the loop" is …

A Simulation Method Based On Multi-Source Sensors For Aircraft Type Identification, Shaozhu Gu, Yuxin Ying, Huajie Zhang, Yiqi Tong

Journal of System Simulation

Abstract: Existing simulation methods for aircraft type identification mainly focus on a single sensor and a single target. They do not consider the joint acquisition of aircraft parameters by various sensor devices such as optoelectronics, radar, and electronic detection in real scenarios, leading to the simple simulation scenarios. This paper proposes a simulation platform based on multi-source sensors. Specifically, the platform includes an infrared image simulator that uses a cycleGAN network to generate infrared images of the aircraft, a flight simulator that adopts the three-degree-of-freedom flight control method to generate the movement trajectory of the aircraft, a radar simulator, that …

Experimental Data For: "Effects Of Network Connectivity And Functional Diversity Distribution On Human Collective Ideation", Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. Maclaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, Hiroki Sayama

Systems Science and Industrial Engineering Faculty Scholarship

This is the dataset collected from our online human-subject experiments described in the following manuscript:

Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, and Hiroki Sayama:
"Effects of Network Connectivity and Functional Diversity Distribution on Human Collective Ideation"
https://arxiv.org/abs/2307.04284

The Hazard Prediction Problem, Mary E. Helander, Brendan Smith, Sylvia Charchut, Erika Swiatowy, Calvin Nau, Gregory Cavaretta, Timothy Schuler, Adam Schunk, Héctor Ortiz-Peña

Social Science - All Scholarship

This work formulates the hazard prediction problem while addressing the research question: Can machine learning create a model to automatically recognize patterns that correspond to hazard state conditions during a mission-critical operation? Supervised learning models were trained and tested on data observed from mission simulators, which allowed for safe observation of dynamic system states and undesirable casualty events. The prediction task was formulated as a binary classification problem, producing the probability of being in a hazard state at time t and providing situational awareness of a possible imminent loss. Several modeling architectures were investigated: neural networks, logistic regression, a support …

Cybersecurity Awareness And Risks Among Students, Staff & Faculty At The University Of North Alabama, Jerry Lwamba

Theses

As individuals and organizations become significantly connected to the internet, cybercriminals find an increasing number of ways to exploit their targets (Broadhurst, 2018). This study explores the world of cybersecurity awareness of risks associated with online activities within the University of North Alabama (UNA). This research aims to assess the level of cybersecurity awareness of risks university stakeholders face, and to also understand behaviors that expose them to cybercrime and emphasize the urge for comprehensive cybersecurity best practices. When cybercriminals employ tactics such as spam, phishing, or spear phishing in their attempts to break into network systems, distribute malware, or …

Interpersonal Skills In A Sociotechnical System: A Training Gap In Flight Decks, Kimberly Perkins, Sourojit Ghosh, Crystal Hall

Journal of Aviation/Aerospace Education & Research

This research analyzed the perceptions of interpersonal skills on established aviation safety models, Crew Resource Management (CRM), and Threat and Error Management (TEM) using feedback from industry pilots. The flight deck is a sociotechnical system where much research has focused on the technical aspect, whereas we spotlight its socio aspect. The aviation industry must invest in training pilots on interpersonal skills to enhance safety through increased efficacy of safety models integrated throughout existing training programs. A 34-question survey was disseminated across both commercial and business aviation pilots (N=822). We explored three research questions regarding pilots’ perceived training on interpersonal skills …

Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Austin T. Walden, Paul J. Thomas

Journal of Aviation/Aerospace Education & Research

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) developing an embedded machine learning framework. Data cleanup and …

Co-Evolving Multi-Agent Transfer Reinforcement Learning Via Scenario Independent Representation, Ayesha Siddiqua

Graduate Theses/Dissertations

Multi-Agent Reinforcement Learning (MARL) addresses complex tasks involving cooperation and competition among agents, training them to develop optimal policies for collective goals. However, facilitating simultaneous learning for multiple agents is challenging because the complexity increases rapidly with the number of agents. Current methods encompass a variety of centralized, decentralized, semi-centralized, and hybrid approaches to balance the trade-offs between computational efficiency, scalability, and coordination in MARL. In this study, I employed a centralized training with semi-centralized execution (CTSCE) framework, utilize both local observations from agents and abstracted global observations to effectively train agents in cooperative environments. Additionally, earning complex, domain-specific tasks …

Mouralherwaqh Coastal Wetland Road Crossing Da'luk, Romel Robinson Ii

Cal Poly Humboldt theses and projects

The integration of Indigenous and Western science plays an essential role in Tribally led collaborations for land management. This process of woven sciences is rooted in reciprocal relations and partnerships guided by Tribal Nations. Our cohort was invited by Wiyot Tribal Representatives to investigate a culvert located within the wetlands of Mouralherwaqh— a parcel of land reacquired by the Wiyot Tribe in 2022. This document seeks to share our experience and analysis as part of the Wiyot Tribe’s broader journey in navigating ecocultural restoration projects within Mouralherwaqh. The four community interests we investigated for the wetland crossing included a resized …

A Human-Centered Framework For Oncological Healthcare System Modeling Applied To Mobile Radiation Delivery, Alex Price

Doctoral Dissertations

"Healthcare systems engineering is challenging due to the complexity, geographic expansiveness, inertial properties, and the heterogeneous sub-systems, with varying vested interests. Additionally, humans comprise many of the forms and functions, and are independent, with resistance and uncertainty in their decision-making that adds complexity. Human-centered systems modeling approaches are therefore needed to capture this unique aspect of a healthcare system and to enhance understanding of emergent healthcare properties. This dissertation focuses on integrating systems modeling within healthcare and emphasizes the importance of incorporating human decision-making. Moreover, the incorporation of a human-centered approach yields particularly valuable insights into the decision-making processes that …

Leveraging Redundancy As A Link Between Spreading Dynamics On And Of Networks, Felipe Xavier Costa

Electronic Theses & Dissertations (2024 - present)

A constant quest in network science has been in the development of methods to identify the most relevant components in a dynamical system solely via the interaction structure amongst its subsystems. This information allows the development of control and intervention strategies in biochemical signaling and epidemic spreading. We highlight the relevant components in heterogeneous dynamical system by their patterns of redundancy, which can connect how dynamics affect network topology and which pathways are necessary to spreading phenomena on networks. In order to measure the redundancies in a large class of empirical systems, we develop the backbone of directed networks methodology, …

Research On Digital Twin Data Modeling And Evaluation Method Of Automated Container Terminal, Guoxuan Xu, Daofang Chang, Jiaqi Li, Qiang Ling

Journal of System Simulation

Abstract: To make full use of the massive operation data of automated container terminals and further realize the digital and intelligent transformation of terminals driven by digital twin, a method for digital twin data modeling and effect verification and evaluation of automated container terminals is proposed. The application framework and operation mechanism based on digital twin are studied. Based on the data processing logic of digital twin framework, a method of terminal operation process evolution and dynamic data modeling based on digital twin is proposed. To verify whether the data could meet the effective operation of the digital twin, a …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Development Of Several Typical Virtual Reality Fusion Technologies, Qiqi Feng, Zhiming Dong, Wencheng Peng, Yi Dai, Bingshan Si

Journal of System Simulation

Abstract: Virtual reality fusion can realize the two-way interaction, mapping and linkage between virtual world and physical world, which attracts the attention of countries in the world. In order to sort out and make statistics on concept connotation, academic status and application of the related new technologies, digital twin, cyber-physical systems, metaverse and live-virtual-constructive simulation are taken as representatives. The comparison on the development process, functional characteristics, target trends, etc. is carried out.

Unrelated Parallel Machine Scheduling With Additional Resource And Learning Effect, Youlian Zheng, Deming Lei

Journal of System Simulation

Abstract: To solve unrelated parallel machine scheduling problem(UPMSP) with additional resource and learning effect, a dynamical artificial bee colony(DABC) algorithm is proposed to minimize the makespan. A new representation and decoding process is given and two initial bee swarms are constructed. A swarm evaluation method is applied to dynamically decide employed bee swarms and onlooker bee swarms. Employed bee phase and onlooker bee phase are implemented in different ways to increase exploration ability. The experimental results show that the new strategies of DABC are effective and reasonable, and can obtain results with better convergence, average value and stability, which d …

Research And Design Of Etc Simulation Platform For Expressway, Fumin Zou, Feng Guo, Sijie Luo, Lüchao Liao, Nan Li, Yue Xing

Journal of System Simulation

Abstract: It is difficult to quantitatively calculate and display the real-time traffic situation of expressway ETC system, and there is no simulation system for ETC to optimize the operating situation. A simulation system based on ETC data in proposed, in witch there are three key algorithms. ETC data feature extraction algorithm provides the feature of generating simulation data for the simulation platform. The improved multitask scheduling algorithm has the computing ability of multitasks in simulation environment. The algorithm of expressway traffic flow control strategy provides the decision index for traffic flow control on the way. The experimental results show that …

Research And Development Of Simulation Training Platform For Multi-Agent Collaborative Decision-Making, Cheng Cheng, Zhijie Chen, Ziming Guo, Ni Li

Journal of System Simulation

Abstract: Reinforcement learning simulation platform can be an interactive and training environment for reinforcement learning. In order to make the simulation platform compatible with the multi-agent reinforcement learning algorithms and meet the needs of simulation in military field, the similar processes in multi-agent reinforcement learning algorithms are refined and a unified interface is designed to embed and verify different types of deep reinforcement learning algorithms on the simulation platform and to optimize the back-end service of the simulation platform to accelerate the training process of the algorithm model. The experimental results show that, by unifing the interface, the simulation platform …

Reliable Emergency Rescue Model Of Uavs Based On Blockchain, Mengyao Du, Kai Xu, Miao Zhang, Xiang Fu, Quanjun Yin

Journal of System Simulation

Abstract: Natural disasters may unpredictably disrupt ground communication infrastructure and transportation systems, and UAVs emergency response can deal with such uncertainties and highly dynamic scenarios. Aiming at the robustness requirements of decentralized rescue systems. UAV emergency rescue chain (UERChain) based on blockchain technology is proposed. By deploying UAV backbone nodes within a layered local network, the smart contracts for managing reputation considering UAV social relationships are designed. The blockchain is employed as a trust mechanism to realize the trustworthy interactions among distributed UAVs. Experimental results show that, UERChain has higher robustness, and within controllable resource constraints, the reputation management and …

Optimized Scheduling Of Distribution Network With Distributed Generation Based On Coronavirus Herd Immunity Optimizer Algorithm, Xiaomeng Wu, Rongze Yuan, Yingliang Li, Qi Zhu

Journal of System Simulation

Abstract: Following the large-scale entry of distributed new energy into the network, the uncertainty factor of the distribution network increases significantly, and the difficulty of reactive power optimization scheduling increases accordingly. Traditional optimization solutions have many limitations and shortcomings, and a dynamic reactive power optimization scheme for active distribution networks based on a multi-scenario approach is proposed. The mathematical modeling is carried out separately for the uncertainty of new energy and load, and the multi-scenario method is used to transform the uncertainty problem into a deterministic problem. A mathematical model is constructed on the distribution network side to pursue the …

Data Simulation Testing Framework For Complex Process Equipment Software, Jinkun Zhang, Longfei Shi, Chi Hu, Hao Zhang, Yonghui Yang

Journal of System Simulation

Abstract: Due to the complex task, tight coupling, strict timing, and a large amount of interchange data, the technical threshold of automated testing of bus communication equipment software is high, and the implementation is difficult. The ideas of data-driven testing and keyword-driven testing are introduced, and a data simulation testing framework is proposed. Configuration rules are formulated and implemented in the framework. Testers can simulate peripheral data for complex process equipment software and implement automated testing by only focusing on the task analysis, and configuring interchange data and keywords. There is no need to develop test scripts, which reduces the …