Artificial Intelligence and Robotics Commons^™

Train Energy Saving Operation Based On Simulated Annealing Algorithm, Liu Wei, Jiaxuan Xu, Peipei Wang, Ruilong Liu, Jingkun Tang

Journal of System Simulation

Abstract: For the timing and energy saving algorithm of urban railway, the train operation iterative process was analyzed by dividing the operation intervals to meet the requirements of the subway trains timing energy saving operation. The differentiation was adopted to establish the train operation iterative model, and as a result the timing energy saving problem was transformed into the solution of train output using coefficient sequence. An optimization solving method based on simulated annealing algorithm was proposed and the algorithm was implemented using MFC program. Three intervals of Shanghai Metro Line 3 were selected and the solution of the train …

Event-Triggered Non-Fragile H∞ State Estimation For Fuzzy Time-Delay Neural Networks, Yanqin Wang, Weijian Ren

Journal of System Simulation

Abstract: For a class of fuzzy neural networks with randomly occurring time-varying delays and randomly data packet loss, an event-triggered non-fragile H_∞ state estimator is designed. The event-triggered condition is introduced to determine whether the signal is transmitted or not, so as to reduce the occupation rate of network resource. Random variables of Gaussian distribution and the multiplicative gain uncertainties are adopted to construct the non-fragile state estimator with randomly occurring gain variations. By constructing Lyapunov function, and via stochastic computation and linear matrix inequality technique, the sufficient conditions for the existence of non-fragile estimators are obtained, which guarantee …

Svm Prediction Of Performance Degradation Of Rolling Bearings With Fusion Of Kpca And Information Granulation, Jiya Xu, Wang Yan, Dahu Yan, Zhicheng Ji

Journal of System Simulation

Abstract: To effectively predict the performance degradation index and its fluctuation ranges of the rolling bearing, a prediction method based on kernel principal component analysis algorithm and fuzzy information granulation using support vector machine is proposed. The kernel principal component analysis is utilized to preprocess the data to acquire the main feature vector, construct T² and SPE statistics, and to analyze its trend. The statistical information is used as the performance degradation index. Theory of fuzzy information granulation is used to granulate the performance degradation index and extract the useful information. The granulated data are put to the support …

Detail-Preserving Shape Deformation In Sph Fluid Control, Feng Gang, Shiguang Liu

Journal of System Simulation

Abstract: It remains a challenging problem to drive particles-based fluid simulation to match target shape and deform fluid shape between different models smoothly in fluid control, especially when the natural fluid motion should be preserved. To achieve the desired behavior, the models are divided into source objects and target objects, and the control particles are generated from them. According to a space point correspondence between source control particles and target control particles, the control particles' movements are calculated which drive fluid to match different models. The control energy from source control particles is transferred to target control particles so as …

Test Data Compression Scheme For Fast Search Best Rational Approximate Fraction, Haifeng Wu, Wenfa Zhan, Yifei Cheng

Journal of System Simulation

Abstract: Rapid growth of test data volume becomes a major factor for test time and manufacturing cost increasing. To reduce test data volume, a code-based compression scheme with fast search best rational approximate fraction is presented. The run-length data is converted into floating point numbers; and the equal best rational approximate fractions of floating point numbers is searched quickly; the appearing law of run-length data in the form of integer numerator and integer denominator is stored instead of storing run-length data directly. This scheme is compatible with traditional code-based methods. It also has simple compression and decompression protocol, good compression …

Performance Analysis And Research Of Shark Fin Structure Based On Simulation, Liu Shuang, Lü Chao, Rao Yong, Shiming Wang

Journal of System Simulation

Abstract: The shark fin, the pectoral fin and the dorsal fin were studied based on the numerical simulation method; the pressure field, velocity field and flow field of the specimen structure were analyzed with three different flow velocities; the analysis and comparison of the mechanical properties of caudal fin, dorsal fin and pectoral fin were presented. The results show that the setting width of the tail fin can be set to 0.5 ~ 4.8 meters so as to improve the anti-karman vortex effect; the width of the tail fins should be relatively narrow in order to guarantee the base of …

Effect Of Interfacial Curvature On Drag Reduction Of Superhydrophobic Microchannels, Chunxi Li, Zhang Shuo, Xuemin Ye

Journal of System Simulation

Abstract: The two-dimensional fluid flow in superhydrophobic microchannels with transverse grooves was numerically simulated with Fluent to investigate the impact of the liquid-gas interface curvature on the effective slip behavior in the laminar regime. The effects of shear-free fraction, normalized periodic cell length and Reynolds number on the normalized slip length and pressure drop reduction are also examined. The results show that as protrusion angle increases, the normalized slip length and pressure drop reduction exhibit with single-hump variations. When θ=θ_opt, increments in the normalized slip length and pressure drop reduction tend to be greater as shear-free …

Imu Single-Axis Rotation Method And Error Analysis Of Modulation Inertial Navigation System, Sun Wei, Ruibao Li, Zhang Yuan, Yang Dan

Journal of System Simulation

Abstract: Aiming at the problem that inertial device bias has an influence on the improvement of system precision, a single axis error modulation scheme whose sensitive axis is misalignment with rotation axis which can continuously rotate in clockwise and counterclockwise is proposed. Based on the same level precision of inertial components in IMU, the IMU is installed on rotation mechanism with non-coincidence. The symmetric part of inertial device deviation can be compensated by the positive and negative cancellation of the device deviation in the rotation axis direction and the rotation modulation in the vertical plane of the rotary axis. The …

A Machine Learning Framework To Classify Mosquito Species From Smart-Phone Images, Mona Minakshi

USF Tampa Graduate Theses and Dissertations

Mosquito borne diseases have been a constant scourge across the globe resulting in numerous diseases with debilitating consequences, and also death. To derive trends on population of mosquitoes in an area, trained personnel lay traps, and after collecting trapped specimens, they spend hours under a microscope to inspect each specimen for identifying the actual species and logging it. This is vital, because multiple species of mosquitoes can reside in any area, and the vectors that some of them carry are not the same ones carried by others. The species identification process is naturally laborious, and imposes severe cognitive burden, since …

Deep Learning For Link Prediction In Dynamic Networks Using Weak Estimators, Carter Chiu, Justin Zhan

Computer Science Faculty Research

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization …

Augustana Invitational Robotics Challenge 2018, Forrest Stonedahl

Celebration of Learning

We will be hosting the 3rd Annual Augustana Invitational Robotics Challenge. This event will involve student teams from Augustana and potentially several other schools in the region bringing forth the robots that they have designed, built, and programmed, to compete against one another. This year's challenge task involves the careful relocation of soda pop cans.

Cuoricino Thermal Pulse Classification By Machine Learning Algorithms, Joshua Mann

Physics

Many of the various properties of neutrinos are still a mystery. One unknown is whether neutrinos are Majorana fermions or Dirac fermions. Cuoricino and CUORE are experiments that aim to solve this mystery. Noise reduction in these experiments hinges on the ability to discern among alpha, beta and gamma particle detections using the thermal pulses they create. In this paper, we look at Cuoricino data and attempt to classify pulses, not as alpha, beta or gamma particles, but rather as signal, noise or calibration data. We will use this preliminary testing ground to examine various machine learning algorithms' abilities in …

Extractive Text Summarization With Deep Learning, Garrett G. Chan

Computer Engineering

This project explores extractive text summarization using the capabilities of Deep Learning. The goal of this project is to create an application with a neural network to take in text as its input, and create a summary that is a shorter, condensed version of the input text. This has been implemented in Python by configuring and training a neural network that takes in a vector of features that are extracted from the text using various Natural Language Processing libraries. The implementation demonstrates that we can train simple deep neural networks to successfully summarize text.

The Effect Of Endgame Tablebases On Modern Chess Engines, Christopher D. Peterson

Computer Engineering

Modern chess engines have the ability to augment their evaluation by using massive tables containing billions of positions and their memorized solutions. This report examines the importance of these tables to better understand the circumstances under which they should be used. The analysis conducted in this paper empirically examines differences in size and speed of memorized positions and their impacts on engine strength. Using this technique, situations where memorized tables improve play (and situations where they do not) are discovered.

Analysing Multi-Point Multi-Frequency Machine Vibrations Using Optical Sampling, Dibyendu Roy, Avik Ghose, Tapas Chakravarty, Sushovan Mukherjee, Arpan Pal, Archan Misra

Research Collection School Of Computing and Information Systems

Vibration analysis is a key troubleshooting methodology for assessing the health of factory machinery. We propose an unobtrusive framework for at-a-distance visual estimation of such (possibly high frequency) vibrations, using a low fps (frames-per-second) camera that may, for example, be mounted on a worker's smart-glass. Our key innovation is to use an external stroboscopic light source (that, for example, may be provided by an assistive robot), to illuminate the machine with multiple mutually-prime strobing frequencies, and use the resulting aliased signals to efficiently estimate the different vibration frequencies via an enhanced version of the Chinese Remainder Theorem. Experimental results show …

Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Solving combinatorial optimization problems using a fixed set of operators has been known to produce poor quality solutions. Thus, adaptive operator selection (AOS) methods have been proposed. But, despite such effort, challenges such as the choice of suitable AOS method and configuring it correctly for given specific problem instances remain. To overcome these challenges, this work proposes a novel approach known as I-AOS-DOE to perform Instance-specific selection of AOS methods prior to evolutionary search. Furthermore, to configure the AOS methods for the respective problem instances, we apply a Design of Experiment (DOE) technique to determine promising regions of parameter values …

Reserved Optimisation: Handling Incident Priorities In Emergency Response Systems, Muralidhar Konda, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Emergency (medical, fire or criminal) Management Systems(EMSs) are crucial for ensuring public safety and security. Typically in many cities, less than 20% of the cases received by EMSs belong to the extremely serious category and require immediate help. Rest of the incidents typically are less serious and thereby allow more flexibility in response time. Therefore, for efficient management of EMS requests, several EMSs now categorise an incoming emergency request into apriority level based on well studied “triaging” methods. Leading research on optimising emergency response has either focussed on data-driven models for settings with homogenous incidents or on generic heuristics (that …

Disentangled Person Image Generation, Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc Van Gool, Bernt Schiele, Mario Fritz

Research Collection School Of Computing and Information Systems

Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of the aforementioned image factors and generates novel person images at the same time. First, a multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself. Second, three corresponding mapping functions are learned in an …

Natural And Effective Obfuscation By Head Inpainting, Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, Mario Fritz

Research Collection School Of Computing and Information Systems

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. …

Influencing Exploration In Actor-Critic Reinforcement Learning Algorithms, Andrew R. Gough

Master's Theses

Reinforcement Learning (RL) is a subset of machine learning primarily concerned with goal-directed learning and optimal decision making. RL agents learn based on a reward signal discovered from trial and error in complex, uncertain environments with the goal of maximizing positive reward signals. RL approaches need to scale up as they are applied to more complex environments with extremely large state spaces. Inefficient exploration methods cannot sufficiently explore complex environments in a reasonable amount of time, and optimal policies will be unrealized resulting in RL agents failing to solve an environment.

This thesis proposes a novel variant of the Actor-Advantage …