Artificial Intelligence and Robotics Commons^™

Deep One-Class Classification Via Interpolated Gaussian Descriptor, Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their …

Do-Gan: A Double Oracle Framework For Generative Adversarial Networks, Aye Phyu Phye Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, Xiaoli Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a new approach to train Gen-erative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discrim-inator oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. Training GANs is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend the double oracle framework to GANs. We first generalize the players' strategies as the trained models of generator and discriminator from the best response or-acles. We then compute the …

Wildfire Risk Assessment Using Convolutional Neural Networks And Modis Climate Data, Sean F. Nesbit

Master's Theses

Wildfires burn millions of acres of land each year leading to the destruction of homes and wildland ecosystems while costing governments billions in funding. As climate change intensifies drought volatility across the Western United States, wildfires are likely to become increasingly severe. Wildfire risk assessment and hazard maps are currently employed by fire services, but can often be outdated. This paper introduces an image-based dataset using climate and wildfire data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). The dataset consists of 32 climate and topographical layers captured across 0.1 deg by 0.1 deg tiled regions in California and Nevada between …

A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz

Faculty Publications

The term human digital twin has recently been applied in many domains, including medical and manufacturing. This term extends the digital twin concept, which has been illustrated to provide enhanced system performance as it combines system models and analyses with real-time measurements for an individual system to improve system maintenance. Human digital twins have the potential to change the practice of human system integration as these systems employ real-time sensing and feedback to tightly couple measurements of human performance, behavior, and environmental influences throughout a product’s life cycle to human models to improve system design and performance. However, as this …

One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin

Dissertations

Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder …

A Self-Learning Intersection Control System For Connected And Automated Vehicles, Ardeshir Mirbakhsh

Dissertations

This study proposes a Decentralized Sparse Coordination Learning System (DSCLS) based on Deep Reinforcement Learning (DRL) to control intersections under the Connected and Automated Vehicles (CAVs) environment. In this approach, roadway sections are divided into small areas; vehicles try to reserve their desired area ahead of time, based on having a common desired area with other CAVs; the vehicles would be in an independent or coordinated state. Individual CAVs are set accountable for decision-making at each step in both coordinated and independent states. In the training process, CAVs learn to minimize the overall delay at the intersection. Due to the …

Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld

Dissertations

This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.

This work …

Optimization Opportunities In Human In The Loop Computational Paradigm, Dong Wei

Dissertations

An emerging trend is to leverage human capabilities in the computational loop at different capacities, ranging from tapping knowledge from a richly heterogeneous pool of knowledge resident in the general population to soliciting expert opinions. These practices are, in general, termed human-in-the-loop (HITL) computations.

A HITL process requires holistic treatment and optimization from multiple standpoints considering all stakeholders: a. applications, b. platforms, c. humans. In application-centric optimization, the factors of interest usually are latency (how long it takes for a set of tasks to finish), cost (the monetary or computational expenses incurred in the process), and quality of the completed …

Representation Learning In Finance, Ajim Uddin

Dissertations

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing.

Financial analysts’ earnings forecast …

Nusax: Multilingual Parallel Sentiment Dataset For 10 Indonesian Local Languages, Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Rahmad Mahendra, Fajri Koto, Ade Romadhony, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Pascale Fung, Timothy Baldwin, Jey Han Lau

Natural Language Processing Faculty Publications

Natural language processing (NLP) has a significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only widely available for high-resource languages such as English and Chinese, while it remains inaccessible to many languages due to the unavailability of data resources and benchmarks. In this work, we focus on developing resources for languages in Indonesia. Despite being the second most linguistically diverse country, most languages in Indonesia are categorized as endangered and some are even extinct. We develop the first-ever parallel resource for 10 low-resource languages in Indonesia. Our resource includes …

Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth

Theses

Machine learning models have been shown to be vulnerable against various backdoor and data poisoning attacks that adversely affect model behavior. Additionally, these attacks have been shown to make unfair predictions with respect to certain protected features. In federated learning, multiple local models contribute to a single global model communicating only using local gradients, the issue of attacks become more prevalent and complex. Previously published works revolve around solving these issues both individually and jointly. However, there has been little study on the effects of attacks against model fairness. Demonstrated in this work, a flexible attack, which we call Un-Fair …

Collaborative Design And Simulation Integrated Method Of Civil Aircraft Take-Off Scenarios Based On X Language, Pengfei Gu, Lin Zhang, Zhen Chen, Junjie Ye

Journal of System Simulation

Abstract: For the large and complex products, the current traditional model-based systems engineering (MBSE) method of the integrated implementation of multiple modeling and simulation languages and platforms for system design and simulation verification can not ensure the efficient and accurate feedback of system design to realize the quick design optimization. X language, a new generation of integrated modeling and simulation language based on complex systems and supporting MBSE, is used to realize the integrated modeling and simulation on cross-domain subsystems of civil aircraft for the take-off scenarios. From the demand analysis of the take-off process of civil aircraft, the system-level …

Cross Level Switching Technology For Multi-Resolution Model Of Complex Products, Wei Li, Wenjia Zhang, Heming Zhang

Journal of System Simulation

Abstract: In the R&D of complex products, different design stages have different design goals and different simulation tasks, which need different resolution complex products simulation models. The models and interfaces for the multi-resolution characteristics are defined and thus the resolution control mechanism is studied. The description mechanisms for the system structure status and model resolution state are established separately, the control mechanism for the system resolution is proposed, and a cross level switching technology is sorted out. The experimental results show that the method can effectively solve the problem of model resolution switching, which ensures the simulation accuracy, improves …

A Cyber-Physical Integrated Modeling Method Oriented For Motion Simulation Of Complex Systems, Wenzheng Liu, Heming Zhang

Journal of System Simulation

Abstract: The traditional virtual modeling of motion simulation lacks the dynamic modeling of cyber subsystems and physical subsystems in complex systems. The advantages of the traditional kinematic virtual modeling and cyber calculation are combined, and aiming at the problem that the accuracy and real-time property of motion simulation cannot meet the actual industrial manufacturing requirements, a cyber physical integrated modeling method for the motion simulation of complex systems is proposed. The inconsistency between real robotic driving and virtual robot motion is solved, which is verified by a case study of mechanical arm motion control. A virtuality-reality mapping platform for complex …

Uav Formation Recovery And Consistency Simulation Based On Improved Potential Field, Ning Wang, Jiyang Dai, Jin Ying

Journal of System Simulation

Abstract: Aiming at the problems of multi-UAV formation collision avoidance, formation recovery and the consistency of position and velocity convergence, a distributed cooperative formation control algorithm based on the improved potential field principle and consistency theory is proposed. The static obstacle model, UAV particle model and the second-order system dynamic model are established; the coordination potential field function with coordination factors and communication weights is defined, which can achieve the control objectives of collision avoidance and formation recovery; on the basic consistency protocol, the formation center reference vector, expected speed and speed stabilization items are introduced to achieve the convergence …

Multi-Objective Optimization Configuration Of Agv System Based On Response Surface And Nsga-Ii, Jianlin Fu, Guofu Ding, Jian Zhang, Haifan Jiang, Peipei Guo

Journal of System Simulation

Abstract: Automated guided vehicle(AGV) system plays an important role in the production flexibility and efficiency in manufacturing systems. Due to the dynamic and stochastic characteristics of AGV system with many variables, its optimal configuration is relatively complex. A method combining system simulation, mathematical analysis and multi-objective optimization is proposed to optimize the configuration of AGV system. The discrete event simulation is used to simulate the operation of AGV system, the sensitivity analysis is used to separate design variables, the factorial experiments and response surface methods are used to build the fitting multi-objective optimization mathematical model, and the non-dominated sorting genetic …

Study On The Scale Characteristics Of Permeability Of Tpms Porous Materials, Tong Wu, Qinghui Wang, Zhijia Xu

Journal of System Simulation

Abstract: Triply Periodic Minimal Surface (TPMS) has been widely used in the design of porous materials, however, there is insufficient research on the scale characteristics of permeability. Four commonly used TPMS units are chosen as the research object, based on the introduction of their mathematical models and porosity control methods, a numerical simulation model based on CFD (computational fluid dynamics) is established; TPMS units and cubic porous structures with different porosities are analyzed, the quantitative correlation between their scales and permeability is clarified, i.e., within the selected scale range, the permeability of various TPMS units and cubic porous structures is …

Triangular Mesh Boolean Operation Method For Finite Element Analysis, Yufei Guo, Kang Zhao, Yongqing Hai

Journal of System Simulation

Abstract: To shorten the cycle of finite element analysis (FEA), an adaptive triangular mesh Boolean operation method for finite element analysis is proposed. The ADT (alternating digital tree) data structure is applied to the intersection calculation of triangular meshes, which improves the efficiency of the intersection calculation of Boolean operations. A sphere packing algorithm and a node addition/deletion algorithm are used to remesh some remeshing regions, which ensures the efficiency of the method and the high-quality of remeshed meshes. An improved octree background grid is used to record and smooth the size field, which can generate size-adaptive meshes. The size …

Cellular Automata Model Of Mixed Traffic Flow Composed Of Intelligent Connected Vehicles’ Platoon, Yangsheng Jiang, Sichen Wang, Kuan Gao, Meng Liu, Zhihong Yao

Journal of System Simulation

Abstract: To solve the existing cellular automata model of automatic-manual driving that does not consider the behavior of vehicle platoon, a cellular automata model of mixed traffic flow with the intelligent connected vehicles platoon is proposed, and the characteristics of mixed traffic flow are analyzed. The existing car-following behaviors in mixed traffic flow are analyzed. Based on the characteristics of the car-following behaviors, the cellular automata rules of human-driven vehicles (HDV), adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC) are developed, respectively. Based on the numerical simulation experiments, the mixed traffic flow characteristics and congestion conditions are analyzed …

Water Body Extraction From High Resolution Remote Sensing Images Based On Fused Visual Word Bags, Xin Wang, Mingjun Xu, Jian Xiao, Lizhong Xu

Journal of System Simulation

Abstract: Aiming at the problem that water body extraction is easily influenced by shadow or light in high resolution remote sensing images, an improved algorithm based on fusion of visual word bags is proposed. Based on the deep analysis of the characteristics of remote sensing water body targets, a spectral feature extraction approach is designed. To enhance the description ability of water body targets, a novel visual word bag fusion model based on local binary pattern and spectral feature is constructed. Based on the proposed visual word bag fusion model, a water body target classifier is presented. …