Numerical Analysis and Scientific Computing Commons^™

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 …

---

Go to article

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 …

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 …

Simulation Of Real-Time Path Planning And Formation Control For Unmanned Surface Vessel, Dalei Song, Wenhao Gan, Yingzhi Xu, Xiuqing Qu, Jiangli Cao

Journal of System Simulation

Abstract: Safety and collision-free navigation are the basis of normal navigation of an unmanned surface vessel. The high-fidelity virtual ocean is constructed by using Unity3D.On the basis of the vessel modeling, a real-time path planning and formation control method for unknown complex environments is proposed. Firstly, the local environment information is obtained by the laser sensor. Then the real-time local path planning is completed by combining A-star and route-thinning methods under the replanning strategy. In addition, formation control is carried out based on the leader-follower strategy and consistency method, and the artificial potential field …

Research On Optimal Scheduling Of Microgrid Based On Nbbo Algorithm, Lisheng Wei, Benben Yang, Ruixia Sun

Journal of System Simulation

Abstract: In view of the economic and environmental collaborative optimization of a microgrid with a micro gas turbine, power to gas(P2G) system with the ability to abandon wind and light for consumption and capture carbon is introduced, and a microgrid optimal scheduling model with P2G system based on novel biogeography-based optimization(NBBO) algorithm is proposed. The microgrid model with the P2G system is constructed, and the working principle of the main equipment is analyzed. The reserve capacity of a wind turbine is introduced to reduce the influence of the randomness of wind power …

Deep Reinforcement Learning-Based Control Strategy For Boost Converter, Yuxuan Dai, Chenggang Cui

Journal of System Simulation

Abstract: In view of the problems of model uncertainty and nonlinearity in bus voltage stability control of Boost converter, an intelligent control strategy based on model-free deep reinforcement learning(RL) is proposed. RL double DQN(DDQN) algorithm and deep deterministic policy gradient(DDPG) algorithm are used, and the Boost converter controller is designed. The state, action space, reward function, and neural network are also designed to improve the dynamic performance of the controller. The joint simulation of the Boost converter model and RL agent is realized by RL modelica(RLM …

Point Cloud Registration Method Based On Improved Covariance Matrix Descriptor, Yuan Zhang, Haoyu Han, Xie Han, Jiaxu Fu

Journal of System Simulation

Abstract: Point cloud registration is a key part of the digital protection of cultural relics. Improving registration accuracy and noise resistance is the main goal of point cloud registration for cultural relics. In order to solve this problem, a three-dimensional (3D) point cloud registration method based on a covariance matrix descriptor is proposed. The tensor voting method is used to eliminate the noise points, and the internal shape signature method is used to extract the key points from the point cloud after removing the noise. Then, the neighborhood information is constructed for the extracted key points, …

Outlier Detection During Thermal Processes Based On Improved Gaussian Mixture Model, Zheng Wu, Yue Zhang, Ze Dong

Journal of System Simulation

Abstract: Abnormal data detection during thermal processes is the basis for performing system modeling, control, and optimization and constitutes an important part of data processing. In this paper, an unsupervised outlier detection algorithm during thermal processes based on an improved Gaussian mixture model is proposed. The algorithm captures a class of data clusters under specific working conditions by using Gaussian components in each dimension, modifies the posterior probability density of the traditional model by adding penalty constraint factors to penalize the false detection and missed detection items, and identifies abnormal data according to the correlation differences with the …

Improved Social Force Model Based On Enhancing Psych Behavioral Heterogeneity, Yandong Liu, Gaoxiang Huang, Wen Chen

Journal of System Simulation

Abstract: Simulating the evacuation behavior of people under anxiety is of great significance for solving the kinematic problems such as escape. At present, most at home and abroad studies consider the anxiety factors as the only medium of population evacuation without considering how external key factors affect anxiety factors in such emergency environments. The improved social force model is proposed, combined with Agent-based stampede risk assessment, the influence of key environmental variables on the anxiety factor is quantified. The psychological force parameters are introduced, and the impact of the anxiety factor on the actual evacuation process is applied to the …

Simulation And Optimization Of Integrated Production Logistics System Of Underground Coal Mining, Dressing, And Backfilling, Xiangqian Wang, Puhao Guo, Xiangrui Meng

Journal of System Simulation

Abstract: In order to study the green mining mode of coal, the bottleneck problem in the integrated production logistics system of underground coal mining, dressing, and backfilling under the goal of achieving the basic production capacity target is explored, so as to promote the coordinated and efficient logistics transportation of underground coal and gangue. A mine in Shanxi is selected as the prototype, and the queuing theory is adopted to analyze the operation process of the integrated coal production logistics system. A discrete event model is established with the help of Anylogic simulation software for related experimental optimization …

Research On Collaborative Task Allocation Method Of Multiple Uavs Based On Blockchain, Shuangcheng Niu, Yuqiang Jin, Kunhu Kou

Journal of System Simulation

Abstract: The autonomous collaborative control of a multi-unmanned aerial vehicle (UAV) system lacks a unified underlying technology platform and faces single point failure and information security threats. In order to solve these problems, an idea to build collaborative task planning platforms based on blockchain technology is proposed. With thecollaborative task allocation of multiple UAVs as research objects, an online, safe, high-efficiency, and real-time task allocation method is designed. The contract network task allocation algorithm is described as a smart contract, and system consensus is reached based on the blockchain consensus algorithm. In addition, …

Auxiliary Decision-Making Method For Flight Support Force Allocation Based On Dbn, Junyang Liu, Shisong Zhu

Journal of System Simulation

Abstract: In order to make up for the deficiency of decision-making methods of full deployment and proportional allocation in airport flight support activities, an auxiliary decision-making model for airport flight support force allocation based on a dynamic Bayesian network (DBN) is constructed.On the basis of quantifying the strength of the support force, thehidden Markov model and Hausdorff distance algorithm are introduced to make auxiliary decisions for flight support activities under different conditions and define relevant decision evaluation indexes as the basis for model verification. Simulation tests are carried out on airport flight support activities under two …

Two-Stage Distributed Robust Optimal Dispatching For A Combined Heat And Power Virtual Power Plant, Yaqian Fan, Songyuan Yu, Fang Fang

Journal of System Simulation

Abstract: Combined heat and power virtual power plant (CHP-VPP) aggregates various electrical and thermal output units and takes into account the uncertainty of wind and solar output, dynamic electricity prices, thermal comfort of users, and other influences to achieve optimal dispatching of overall output. A two-stage distributed robust optimal dispatching method is proposed. In the first stage, planned dispatching is considered, so as to maximize the benefit of CHP-VPP. In the second stage, a fuzzy set of wind and solar output uncertainties is constructed based on the distributed robust method of moment uncertainty, and …

Multi-Camera Vehicle Recognition Method Based On Feature Robustness Enhancement, Huicheng Luo, Shujuan Wang

Journal of System Simulation

Abstract: Due to factors such as viewpoint changes, complex environments, and pose differences under multiple cameras, the images of the same vehicle in different scenes show huge appearance ambiguity, which brings challenges to vehicle identity matching. In order to solve this problem, a feature robustness enhancement method for vehicle recognition is proposed under the transformer framework. Based on the fact that the structural information of the vehicle is invariant under multiple cameras, a module for enhancing structural information guided by contour features is designed, and a structural feature perception loss is proposed to promote the fusion …

Picking Path Planning Of Container Robots Based On Improved Genetic Algorithm, Yuwen Wu, Zhiyue Niu, Zhenping Li

Journal of System Simulation

Abstract: Under the new "container-to-person" picking mode in intelligent warehouses, a new optimization model and its improved genetic algorithm are proposed to solve the picking path planning problem of multiple container robots. According to the picking mode and characteristics of container robots, the picking path planning problem is transformed into an asymmetric vehicle routing problem, and a mixed integer programming model is established with bi-objectives of the shortest total picking path and the least completion time. A hybrid genetic algorithm is designed to solve this model, and the effectiveness and stability of the algorithm are verified …

Simulation Platform For Optimization And Decision Making Of Flexible Manufacturing Process Of Automatic Production Lines, Chao Fu, Dongyue Wang, Xinxin Li, Min Xue

Journal of System Simulation

Abstract: In order to simulate a flexible manufacturing process to verify the correctness of theoretical computation and analysis and find other important factors for optimization and decision making in the manufacturing process, a simulation platform for the optimization and decision making of the flexible manufacturing process of automatic production lines is designed and developed. The framework of the simulation platform is constructed with four hierarchies, including the enterprise production hierarchy, optimization and decision making hierarchy, data resource hierarchy, and function module hierarchy. On this basis, the functional implementation of the modules of production allocation …

Modeling And Simulation Of Spaceborne, Near-Spaceborne, And Airborne Integrated Collaborative Remote Sensing System Based On Dodaf, Lili An, Tian Xia, Wenbin Yang, Xinbo Wu

Journal of System Simulation

Abstract: Spaceborne, near-spaceborne, and airborne integrated collaborative remote sensing system (SNA-ICRSS) makes comprehensive use of modern information technology to aggregate multiple and heterogeneous data in spaceborne, near-spaceborne, and airborne domains, so as to realize accurate emergency service and command decision-making. SNA-ICRSS is huge and complex, and there is a lack of research on its architecture modeling and simulation. According to the architecture characteristics of the SNA-ICRSS, the minimum prototype of the SNA-ICRSS is constructed; through the department of defense architecture framework (DoDAF) and activity based methodology (ABM) methods, the minimal prototype of the SNA-ICRSS is modeled, and the operational resource …

An Algorithm For Obtaining Interception Guidance Routes Weighted By Threat Indexes, Shuyuan Liu

Journal of System Simulation

Abstract: Interception guidance and threat assessment are two inseparable parts of command and control. The former implements specific command and guidance through guidance solutions, and the latter provides basis for threat assessment and situation advantage to command and control. Taking threat indexes as the weight, a kind of optimal interception route index is proposed to obtain the guidance solution with the least threat. Simulation results show that the proposed algorithm can effectively provide routing solutions for interception with the least threat, and is reliable under different situations, or with different weights of threat indexes. The algorithm provides a new idea …

Partial Task Offloading Strategy Of Cloud Robots Based On Game Theory Under Cloud-Edge Coordination, Chunmao Jiang, Zhenxing Yang

Journal of System Simulation

Abstract: How to rationally utilize the resources of central and edge clouds to reduce energy consumption of system equipment and shorten average task completion time is a fundamental challenge for computational task offloading of cloud robots. In this paper, we transform the computational task offloading problem of multiple cloud robots into a multi-actor game model by using the computational task completion time and energy consumption of cloud robots as cost measurement indicators and setting different cost weights according to actual needs. We also develop a game theory-based partial task offloading algorithm (GT-PTO). With the Nash equilibrium state …

Fixed-Wing Uav Detection Based On Simulated Data Transfer Learning, Yu Fu, Yao Zhang, Meng Zhao, Mianzhao Wang, Jiangpeng Zheng, Chen Jia, Shengyong Chen

Journal of System Simulation

Abstract: Data play an important role in visual inspection tasks, but it is difficult to obtain a sufficient amount of real fixed-wing UAV data. Therefore, a data set containing a large number of simulated fixed-wing UAV data and a small number of real fixed-wing UAV data is constructed, and the real fixed-wing UAV data are detected by training simulated fixed-wing UAV data based on the idea of weight transfer. On this basis, a two-stage learning strategy is proposed to further reduce the missed detection rate of UAVs by using multi-scale feature fusion.The simulation results show that …

Research On No-Wait Flow Shop Scheduling Based On Discrete State Transition Algorithm, Jiaying Yu, Hongli Zhang, Yingchao Dong

Journal of System Simulation

Abstract: In view of the no-wait flow shop problem (NWFSP) widely existing in the manufacturing industry, an improved discrete state transition algorithm (IDSTA) is proposed to solve the problem. The coding mode of the workpiece is designed based on the characteristics of the flow shop scheduling problem (FSSP). The initial solution is constructed by the Nawaz-Enscore-Ham (NEH) method with the standard deviation of the processing time of the workpiece as the priority, and a multi-neighborhood combinatorial search strategy based on insertion and exchange is designed to improve the quality of the initial solution. A discrete state transition algorithm ( …

Action Recognition Method Based On Projection Subspace Views Under Single Viewing Angle, Benyue Su, Manzhen Sun, Qing Ma, Min Sheng

Journal of System Simulation

Abstract: In view of the self-occlusion problem of joint action tracking by a depth camera under a single viewing angle, a new human action recognition method based on projection subspace views is proposed. Without adding data acquisition equipment, the method projects the three-dimensional(3D) action sequences obtained under a single viewing angle into multiple two-dimensional subspacesand then seeks the maximum distance between classes in the two-dimensional subspaces, so as to increase the distance between 3D actions based on the fusion of multiple subspace views as much as possible. The recognition rate in the self-built AQNU …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

All Dissertations

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Generative Stresnet For Crime Prediction, Ba Phong Tran, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this work, we combine STResnet (Zhang et al., 2017) with VAE Kingma & Welling (2013) to generate crime distribution. The outputs can be used for downstream tasks such as patrol deployment planning Chase et al. (2021).

Exploring A Gradient-Based Explainable Ai Technique For Time-Series Data: A Case Study Of Assessing Stroke Rehabilitation Exercises, Min Hun Lee, Yi Jing Choy

Research Collection School Of Computing and Information Systems

Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications. However, there has been limited exploration of explainable AI techniques on time-series data, especially in the healthcare context. In this paper, we describe a threshold-based method that utilizes a weakly supervised model and a gradient-based explainable AI technique (i.e. saliency map) and explore its feasibility to identify salient frames of time-series data. Using the dataset from 15 post-stroke survivors performing three upper-limb exercises and labels on whether a compensatory motion is observed or …

Wearing Masks Implies Refuting Trump?: Towards Target-Specific User Stance Prediction Across Events In Covid-19 And Us Election 2020, Hong Zhang, Haewoon Kwak, Wei Gao, Jisun An

Research Collection School Of Computing and Information Systems

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people’s stance, which we consider as their online behavior in …

Msrl-Net: A Multi-Level Semantic Relation-Enhanced Learning Network For Aspect-Based Sentiment Analysis, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of a given text towards several specific aspects. For implementing the ABSA, one way is to convert the original problem into a sentence semantic matching task, using pre-trained language models, such as BERT. However, for such a task, the intra- and inter-semantic relations among input sentence pairs are often not considered. Specifically, the semantic information and guidance of relations revealed in the labels, such as positive, negative and neutral, have not been completely exploited. To address this issue, we introduce a self-supervised sentence pair relation classification task and propose a …

Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …

Quantification Of Various Types Of Biases In Large Language Models, Sudhashree Sayenju

Doctor of Data Science and Analytics Dissertations

Natural Language Processing (NLP) systems are included everywhere on the internet from search engines, language translations to more advanced systems like voice assistant and customer service. Since humans are always on the receiving end of NLP technologies, it is very important to analyze whether or not the Large Language Models (LLMs) in use have bias and are therefore unfair. The majority of the research in NLP bias has focused on societal stereotype biases embedded in LLMs. However, our research focuses on all types of biases, namely model class level bias, stereotype bias and domain bias present in LLMs. Model class …