Numerical Analysis and Scientific Computing Commons^™

Dynamic Simulation Of Urban Agglomeration Passenger Transport Network Vulnerability Based On Multi-Agent, Chengbing Li, Yunfei Li, Peng Wu

Journal of System Simulation

Research on vulnerability of comprehensive passenger transportation network in urban agglomerations helps to ensure the transportation efficiency of intercity travel.A comprehensive passenger transport network model of urban agglomeration is built based on multi-layer complex network theory. Urban transportation transfer factors are considered, actual passenger flow is used to calibrate the station load and capacity and a dynamic model of network cascading failure is constructed. Multi-agents are used to simulate the actual passenger flow, Dijkstra algorithm is used to find the shortest path, and two time-dimensional vulnerability evaluation indicators are proposed.MATLAB is used to carry out the dynamic simulation …

Secure State Estimation Of Distribution Network Based On Kalman Filter Decomposition, Xinghua Liu, Siwen Dong, Jiaqiang Tian

Journal of System Simulation

A new state estimation algorithm is proposed to improve the accuracy to obtain the optimal state estimation of distribution network against FDI attack. In the case of phasor measurement units being attacked and the measurement results being altered,the optimal Kalman estimate can be decomposed into a weighted sum of local state estimates. Focusing on the insecurity of the weighted sum method,a convex optimization based on local estimation is proposed to replace the method and combine the local estimation into a secure state estimation. The simulation results show that the proposed estimator is consistent with the Kalman …

Joint Optimization Strategy Of Computing Offloading And Edge Caching For Intelligent Connected Vehicles, Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang

Journal of System Simulation

To guarantee the low-delay communication of intelligent connected vehicles, the V2X channel model and the multi-access edge computing (MEC) technology, are used to carry out the research of the joint optimization strategy of computing offloading and edge caching.An intelligent connected vehicle with task offloading and edge caching model least-deep deterministic policy gradient(L-DDPG) is developed.By integrating the vehicular local and edge computing resources, the classification processing of different computing tasks in V2X scenarios is supported.The vehicular computing request is prejudged by edge platform to ensure the rapid response of continuous homogeneous computing tasks. Combining with the least recently …

Research On Energy Coordination Control Strategy In Dc Microgrid, Zibao Lu, Hao Ding, Fangyun Sun, Ziqiong Ding, Li Gong, Rui Zheng

Journal of System Simulation

Aiming of the bus voltage stability and energy flow balance in DC microgrids,a switching control strategy based on the energy balance relationship of DC microgrid is proposed. The bus voltage balance of DC microgrid is transformed into energy balance, and DC microgrid is modeled as a linear switching system with five modes. A controller is designed for each mode to stabilize the bus voltage. To achieve the seamless and smooth switching between modes, on the basis of maintaining the stability of any switching, the corresponding mode switching rules are given based on the energy flow characteristics of microgrid.Theoretical …

An Approach To Solving The Incoming Target Based On Uncertain Time Series, Jing Yang, Minghua Lu, Xingchen Hu, Jinping Wu

Journal of System Simulation

The traditional solution method for the incoming attacking target in water lacks the time series characteristics mining on multi-dimensional and uncertain observation data. Aiming at the time series prediction with high complexity and missing data, a method based on adaptive window interpolation and deep variable weight long-term and short-term memory network model for missing time series observation data with multiple sampling frequencies is proposed, which is compared and verified on the simulation data and public test data set.Aiming at the random missing problem caused by the inconsistency of sampling frequency of multi-source observation information, an adaptive window imputation method …

Golden Eagle Optimizer Algorithm Combining Levy Flight And Brownian Motion, Jiaxin Deng, Damin Zhang, Qing He, Jianping Zhao

Journal of System Simulation

Aiming at the slow attenuation and low convergence precision of golden eagle optimization algorithm, a new algorithm combining Levy fight and Brownian motion is proposed.In order to increase the diversity, Fuch chaotic map is introduced to initialize the golden eagle individuals. Levy flight mechanism and Brownian motion mechanism are introduced into the position update formula of golden eagle individual to improve the search accuracy and help to the jump out of local optimum. The reduction factor is introduced into the overall position update formula of the golden eagle individual to improve the convergence speed. Compared with 9 original …

Particle Swarm Optimization For New Energy Truck Scheduling In Network Environment, Chuanchao Zhao, Rui Zheng, Li Gong, Xiaolu Ma

Journal of System Simulation

In V2X intelligent network environment, the dispatching system of new energy trucks needs real-time dynamic information.The system under traditional particle swarm scheduling method is prone to fall into local optimum and low solution efficienty.An improved particle swarm scheduling method for new energy trucks is proposed on the basis of multi-objective optiminaztion research. The inertia weight update method is improvedso that the inertia weight decreases non-linearly, andthe risk of the system falling into local optimum is reduced.A priori path encoding method is designed and optimized,the solution efficiency of the algorithm is improved, …

Research On Application Of Monarch Butterfly Optimization Particle Filter In Slam, Zhiqiang Chen, Menglong Cao, Wenbin Zhao

Journal of System Simulation

In traditional particle filter resampling, weight degradation and loss of particle diversity are prone to occur, which leads to the decrease in filtering accuracy and result in inaccurate robot positioning and inaccurate mapping. An optimized particle filter algorithm based on the improved monarch butterfly algorithm is proposed.The algorithm replaces the particle individual with the monarch butterfly individual, and integrates the migration operator and adjustment operator in the monarch butterfly algorithm into the particle filter algorithm. The adaptive genetic parameters are introduced to the iterative update process of the monarch butterfly, and the linear combination optimization resampling method is used …

A Uav Target Tracking And Control Algorithm Based On Siamrpn, Songming Jiao, Hui Ding, Yufei Zhong, Xin Yao, Jiahao Jiahao Zheng

Journal of System Simulation

Aiming at the requirement of autonomously tracking land moving targets of rotary-wing UAVs, an autonomous and stable UAV tracking and control system that can adapt to the common interference environments such as scale changes, occlusions, and attitude changes is constructed.The system extracts the imaging position of the target in airborne camera through the twin network based on deep learning, and obtains the relative pose of the target. The image processing algorithm is designed to process the icons in the tracking frame, and the yaw angle of UAV relative to the tracking target is obtained, Kalman filter is introduced to …

Predicting Location And Training Effectiveness (Plate), Erik Rolf Bruenner

Master's Theses

Abstract Predicting Location and Training Effectiveness (PLATE)
Erik Bruenner

Physical activity and exercise have been shown to have an enormous impact on many areas of human health and can reduce the risk of many chronic diseases. In order to better understand how exercise may affect the body, current kinesiology studies are designed to track human movements over large intervals of time. Procedures used in these studies provide a way for researchers to quantify an individual’s activity level over time, along with tracking various types of activities that individuals may engage in. Movement data of research subjects is often collected through …

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Accelerating Parameter Identifiability Of Differential Models With Applications To Parameter Estimation, Ilia Ilmer

Dissertations, Theses, and Capstone Projects

The task of mathematical modeling involves working with real world phenomena described via parametric ordinary differential equations (ODE). Typically, an ODE model consists of states, parameters, inputs, and outputs. The states represent quantities whose dynamics the model describes, the parameters are quantities that are specific to the phenomenon being studied. Finally, inputs and outputs represent functions that are being added and measured from experiments, respectively. One of the questions that arises in studies of such models, is whether for given input-output setup one can efficiently and correctly estimate the values of parameters or initial conditions. This property of parameters or …

Imitating Opponent To Win: Adversarial Policy Imitation Learning In Two-Player Competitive Games, The Viet Bui, Tien Mai, Thanh H. Nguyen

Research Collection School Of Computing and Information Systems

Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim agent) to perform poorly in a multi-agent environment. In existing studies, adversarial policies are directly trained based on experiences of interacting with the victim agent. There is a key shortcoming of this approach --- knowledge derived from historical interactions may not be properly generalized to unexplored policy regions of the victim agent, making the trained adversarial policy significantly less effective. In this work, we design a new effective adversarial policy learning algorithm that overcomes …

Ldptrace: Locally Differentially Private Trajectory Synthesis, Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios …

Gnnlens: A Visual Analytics Approach For Prediction Error Diagnosis Of Graph Neural Networks., Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis …

Improving The Efficiency Of Exponential Ratio-Type Estimator For Population Median: A Calibration Weight Adjustment Approach, Mathew J. Iseh, Kufre J. Bassey

CBN Journal of Applied Statistics (JAS)

This paper modifies the Bahl and Tuteja exponential ratio-type estimator for population median under simple random and stratified sampling schemes using calibration weight adjustment technique with supplementary information to vary the stratum weights. The bias and mean square error of the modified estimator were obtained up to the second-order approximation, which satisfies the necessary conditions for efficiency. The findings show that the new estimator surpasses existing estimators in efficiency gain. This suggests the appropriateness of calibration weight modification in boosting the efficiency of a population parameter estimator under stratified random sampling especially where the population parameter of the auxiliary variable …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

Domain Decomposition Methods For Linear And Non-Linear Elliptic Problems, Tadanaga Takahashi

Dissertations

The primary purpose of this dissertation is to expand upon the circle of domain decomposition methods (DDM) which are algorithms that reformulate a boundary value problem in terms of multiple localized problems on subdomains. The first project involves expanding upon DDMs in a relatively mature field: the Helmholtz equation for wave scattering applications. The proposed method is an adaptation of a continuous cross-point Finite Element Non-overlapping DDM algorithm. The usual unbounded computational domain is truncated and then the near-field wave pattern is solved with a parallelized finite element method. Several improvements over the standard transmission operator are discussed in this …

Covid-19 In Casinos: Analysis Of Covid-19 Contamination And Spread With Economic Impact Assessment, Anastasia (Stasi) D. Baran, Jason D. Fiege

International Conference on Gambling & Risk Taking

Abstract:

The COVID-19 pandemic caused tremendous disruption for casinos, with the virus causing various lengths of shutdowns, capacity restrictions, and social distancing strategies such as machine removals or section closures. Although most of the world has now eased off these measures, it is important to review lessons learned to understand, and better prepare for similar circumstances in the future. We present Monte Carlo slot floor simulation software customized to simulate players spreading COVID-19 on the slot floor. We simulate the amount of touch surface contamination; the number of potential surface contact exposure events per day, and a proximity exposures statistic …

Statistical Methods To Generate Artificial Slot Floor Data For The Advancement Of Casino Related Research, Courtney Bonner, Anastasia (Stasi) D. Baran, Jason D. Fiege, Saman Muthukumarana

International Conference on Gambling & Risk Taking

Abstract:

A common difficulty when researching gambling topics is the availability of high-quality data sets for development and testing. Due to the high level of secrecy within the gambling industry, if data is obtained for research purposes it is often prohibitively obfuscated, incomplete, or aggregated. Although these data have allowed for advancement in academic work, it leaves both the researchers and readers left wondering about what would be possible if more detailed data sets were available. To mitigate the paucity of data available to researchers, we present a Markov chain-based statistical process for producing artificial event data for a simulated …

The Locals Casino As A Social Network – Can An Interconnected Community Of Players Detect Differences In Hold?, Jason D. Fiege, Anastasia (Stasi) D. Baran

International Conference on Gambling & Risk Taking

Abstract

It is difficult for individual players to detect differences in theoretical hold between slot machines without playing an unrealistically large number of games. This difficulty occurs because the fractional loss incurred by a player converges only slowly to the theoretical hold in the presence of volatility designed into slot pay tables. Nevertheless, many operators believe that players can detect changes in hold or differences compared to competition, especially in a locals casino market, and therefore resist increasing holds. Instead of investigating whether individual players can detect differences in hold, we ask whether a population of casino regulars who share …