Artificial Intelligence and Robotics Commons^™

Design And Research On Simulation Model Of Limestone-Wet Flue Gas Desulfurization Absorption Tower, Zhang Yue, Han Pu, Zhang Xiang

Journal of System Simulation

Abstract: The mass transfer and chemical reaction processes of wet flue gas desulfurization (FGD) are very complex. It is helpful to improve the operation level of desulfurization site by studying the internal mechanism and establishing a high precision mechanism model. The absorption tower is divided into some regions, and the modeling targets in different regions are determined. The sulfur dioxide absorption region is divided into different heights of the "cell" by the slurry droplet falling distance and exposure time. According to the motion characteristics and mass transfer process, the desulfurization efficiency and pressure drop are calculated, and the absorption efficiency …

Indoor Scene Generation Based On Case-Based Reasoning And Collaborative Filtering, Peihua Song, Jinyuan Jia

Journal of System Simulation

Abstract: To solve the problem of time-consuming and single scene generation in indoor scene generation, we propose an indoor scene generation algorithm based on case-based reasoning and collaborative filtering techniques. The algorithm of functional area division is performed on the two-dimensional room floor plan. Thecase-based reasoning is used to generate 3D scenes; and the collaborative filtering is used to generate diverse indoor scenes. A scene evaluation method based on user feedback information is proposed. Experiments were carried out on the living room and bedroom to generate scenes. The experimental results show that the proposed algorithm is effective. The running time …

Simulation Algorithm Of Streak Tube Multi-Spectral Laser 3d Imaging System, Jingya Cao, Shaokun Han, Wenze Xia, Wang Liang

Journal of System Simulation

Abstract: The streak tube laser three-dimensional imaging system is one of the most promising practical laser imaging systems, because of its high precision, large field of view and high frame frequency. A multispectral imaging simulation algorithm for multi-slit streak tube is proposed based on the research of the multi-spectral imaging. The derivation of the main structural parameters is given. The influence of the number of different detectors on the time resolution and spatial resolution of the system is analyzed. In the model of the simulation system, the laser echo signals of multiple wavelength bands are fed into the photocathode of …

Visualization Platform Of Lightweight Toxic Gases Dispersion, Jinyuan Jia, Xinzhi Zhao, Panjing Li

Journal of System Simulation

Abstract: The sudden accidents usually emit large quantities of toxic gases and harmful pollutants in a short time. It is very necessary and meaningful to predict and visualize the dispersing trend of those toxic gases over Internet as accurate and fast as possible for people to evacuate safely and immediately. We propose a lightweight simulation for air pollution dispersion based on the Gaussian puff model. With this method, we can visualize the procedure of toxic gas dispersing on a webpage in real-time. we can also estimate the pollution of toxic gas dispersion trend and analyze their further pollution effects on …

Hardware-In-The-Loop Simulation Method For Laser Active Imaging Guidance, Zhang Qing, Yijie Li

Journal of System Simulation

Abstract: The system of laser active imaging guidance hardware-in-the-loop simulation is a significant system for testing and validating the capability of laser active imaging guidance and control, it also means a lot in practical applications. The composition of guidance and control system for the laser active imaging aerocraft is analyzed, and the purpose and test flow for the hardware-in-the-loop simulation system are introduced. The integrated project for laser active imaging guidance hardware-in-the-loop simulation system is designed. The functions and elements for the crucial simulation equipment of laser active imaging echo simulator are analyzed; the elements of model composition and …

Improved Dv-Hop Localization Scheme Based On The Best Path Matching Strategy, Qinqin Shi, Xu Qiang, Jianping Zhang

Journal of System Simulation

Abstract: Improvement strategies are provided in view of the fact that the original DV-Hop (Distance, Vector-Hop) method has great error in distance estimation due to the network anisotropy in practical applications and the sensitivity of its location derivation algorithm to the range error. In the distance estimation step, a strategy of computing path similarity parameter is proposed to find out the most similar beacon-beacon path to beacon-node path; in the position calculation step, a modified PSO (Particle Swarm Optimization) algorithm is added after the initial position of an unknown node being derived using Lateration algorithm. Simulation shows that compared with …

Traffic Dynamic Regulation And Control Technology In The Central Area Of Nanning City, Yiman Du, Hongfan Su, Jianping Wu, Chen Yu, Huang Lin, Longfei Liu

Journal of System Simulation

Abstract: As the transportation develops rapidly, the traffic burden increases in downtown areas, especially in the areas of convention center, government building, and business & financial center, which exacerbates traffic congestion nearby. Conventional traffic control measures taken include stagger shifts, congestion charging, and tail number limit. Interests of multi aspects are involved in these measures, which has broad effect. The traffic managers now begin to ameliorate traffic burden by technical methods. A feedback-gate control based regional traffic volume control method was proposed and a microscopic simulation was adopted. This reasearch aims to ameliorate traffic pressure and balance traffic flow through …

Gesture Recognition Method Based On Multi-Feature Fusion, Yuanming Wang, Zhang Jun, Yuanhui Qin, Xiujuan Chai

Journal of System Simulation

Abstract: Aiming at the specific application requirements of command gesture for flight deck, a gesture recognition method based on multi-feature fusion is proposed. The 3D trajectory feature vector and hand sparse representation are established from two aspects of the trajectory and posture based on the visual information collected by depth camera. On the one hand, the gesture is recognized through normalization resampling and alignment based on the trajectory feature. On the other hand, the gesture is recognized through sparse representation alignment based on the HOG feature. The recognition results are fused effectively. The experimental results indicate that our …

Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.

SDSU Data Science Symposium

Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component …

Cs04all: Machine Learning Module, Hunter R. Johnson

Open Educational Resources

These are materials that may be used in a CS0 course as a light introduction to machine learning.

The materials are mostly Jupyter notebooks which contain a combination of labwork and lecture notes. There are notebooks on Classification, An Introduction to Numpy, and An Introduction to Pandas.

There are also two assessments that could be assigned to students. One is an essay assignment in which students are asked to read and respond to an article on machine bias. The other is a lab-like exercise in which students use pandas and numpy to extract useful information about subway ridership in NYC. …

Cs04all: Natural Language Processing Project, Hunter R. Johnson

Open Educational Resources

In this archive there are two activities/assignments suitable for use in a CS0 or Intro course which uses Python.

In the first activity, students are asked to "fill in the code" in a series of short programs that compute a similarity metric (cosine similarity) for text documents. This involves string tokenization, and frequency counting using Python string methods and datatypes.

https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/Proj1?viewer=share/

In the second activity (taken directly from Think Python 2e) students use a pronunciation dictionary to solve a riddle involving homophones.

https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/Dicts2?viewer=share/

This OER material was produced as a result of the CS04ALL CUNY OER project

Bots In Libraries: They're Coming For Your Jobs (Or Is It?), Salihin Mohammed Ali

Research Collection Library

With advancements in Artificial Intelligence (AI) and Machine Learning (ML), we have seen a rise in the use of bots, specifically chatbots, to deliver information services. Motivated by the Smart Nation programme, these chatbots have sprung up in sectors as transport, healthcare, banking and education in Singapore. What are these chatbots? How do they work? Will they take our jobs? SMU Libraries tries to answer these questions by delving into the mechanics of creating chatbots. The proof-of-concept aims to find out and understand use cases where these bots can be useful to delivering library information services to its campus community. …

Weakly-Admissible Semantics And The Propagation Of Ambiguity In Abstract Argumentation Semantics, Pierpaolo Dondio

Other

The concept of ambiguous literals of defeasible logics is mapped to the set of undecided arguments identified by an argumentation semantics. It follows that Dung’s complete semantics are all ambiguity propagating, since the undecided status of an attacking argument is always propagated to the attacked argument, unless the latter is defeated by another accepted argument. In this paper we investigate a novel family of abstract argumentation semantics, called weakly-admissible semantics, where we do not require an acceptable argument to be necessarily defended from the attacks of undecided arguments. Weakly-admissible semantics are conflict-free, ambiguity blocking, non-admissible (in Dung’s sense), but employing …

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …

Dish: Democracy In State Houses, Nicholas A. Russo

Master's Theses

In our current political climate, state level legislators have become increasingly impor- tant. Due to cuts in funding and growing focus at the national level, public oversight for these legislators has drastically decreased. This makes it difficult for citizens and activists to understand the relationships and commonalities between legislators. This thesis provides three contributions to address this issue. First, we created a data set containing over 1200 features focused on a legislator’s activity on bills. Second, we created embeddings that represented a legislator’s level of activity and engagement for a given bill using a custom model called Democracy2Vec. Third, we …

Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van

Research Collection School Of Computing and Information Systems

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to …

Transnfcm: Translation-Based Neural Fashion Compatibility Modeling, Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multi-relational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items …

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …

Culture Clubs: Processing Speech By Deriving And Exploiting Linguistic Subcultures, David Guy Brizan

Dissertations, Theses, and Capstone Projects

Spoken language understanding systems are error-prone for several reasons, including individual speech variability. This is manifested in many ways, among which are differences in pronunciation, lexical inventory, grammar and disfluencies. There is, however, a lot of evidence pointing to stable language usage within subgroups of a language population. We call these subgroups linguistic subcultures.

The two broad problems are defined and a survey of the work in this space is performed. The two broad problems are: linguistic subculture detection, commonly performed via Language Identification, Accent Identification or Dialect Identification approaches; and speech and language processing tasks taken which may see …

Deep Learning Based Medical Image Analysis With Limited Data, Jiaxing Tan

Dissertations, Theses, and Capstone Projects

Deep Learning Methods have shown its great effort in the area of Computer Vision. However, when solving the problems of medical imaging, deep learning’s power is confined by limited data available. We present a series of novel methodologies for solving medical imaging analysis problems with limited Computed tomography (CT) scans available. Our method, based on deep learning, with different strategies, including using Generative Adversar- ial Networks, two-stage training, infusing the expert knowledge, voting based or converting to other space, solves the data set limitation issue for the cur- rent medical imaging problems, specifically cancer detection and diagnosis, and shows very …

Artificial Intelligence Hits The Barrier Of Meaning, Melanie Mitchell

Computer Science Faculty Publications and Presentations

Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI’s “barrier of meaning”; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.

Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal

SMU Data Science Review

In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern, …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks

SMU Data Science Review

In this paper, we present an approach to reducing bottom hole plunger dwell time for artificial lift systems. Lift systems are used in a process to remove contaminants from a natural gas well. A plunger is a mechanical device used to deliquefy natural gas wells by removing contaminants in the form of water, oil, wax, and sand from the wellbore. These contaminants decrease bottom-hole pressure which in turn hampers gas production by forming a physical barrier within the well tubing. As the plunger descends through the well it emits sounds which are recorded at the surface by an echo-meter that …

Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard

Yale Day of Data

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim …

A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas

SMU Data Science Review

There has been increasing growth in deployment of recommender systems across Internet sites, with various models being used. These systems have been particularly valuable for review sites, as they seek to add value to the user experience to gain market share and to create new revenue streams through deals. Hotels are a prime target for this effort, as there is a large number for most destinations and a lot of differentiation between them. In this paper, we present an evaluation of two of the most popular methods for hotel review recommender systems: collaborative filtering and matrix factorization. The accuracy of …

Application Of Virtual Reality In Rehabilitation Of Special Populations, Tingting Liu, Liu Zhen, Ping'an Qian, Rongrong Xuan, Wang Jin, Yanjie Chai

Journal of System Simulation

Abstract: With the continuous development of urbanization and the trend of population aging, the number of people who need physical and mental rehabilitation continues to increase. Using information technology to assist the rehabilitation process has the important practical significance. The existing studies about the application of virtual reality (VR) technology in the rehabilitation, psychological problems and children with autism for stroke are reviewed. A framework for the intelligent rehabilitation system is proposed. Virtual agent, the technology of somatosensory and voice recognition are advised to be used. Based on the proposed framework, a prototype rehabilitation game for autism and an upper …

A Review Of Rare Event Simulation, Liping Wang, Wenhui Fan

Journal of System Simulation

Abstract: Rare-event has a great significance for its lower probability of occurrence and greater harmfulness, the traditional Monte Carlo method is difficult to analyze it effectively. Rare-event simulation technique which is developed quickly and applied in many areas can sample the rare-event effectively with variance reduction methods and can estimate the occurrence probability of rare-event. The research status of rare-event simulation technique is summarized firstly; the principles, problems and progress of the main methods are analyzed and concluded secondly; after that the application fields of the technique are elaborated and summarized; the future development tendency of rare event simulation technique …

Integrated Modeling For Time Delay Stability Analysis In Gcps, Jianbo Jiang, Li Peng, Aimin Miao, Cao Min, Songhai Zhang, Tan Lei, Kerun Li

Journal of System Simulation

Abstract: According to the separation problems of physical and cyber modeling in time delay stability analysis for grid cyber-physical system (GCPS), an integrated modeling method is presented and proved by single machine infinite bus system (SMIBS). The time delay and stability of GCPS are analyzed and discussed, and the physical and cyber models of SMIBS are established respectively. The dynamic linking library by EA and VS based on information model (CIM) are generated, and the united simulation platform is achieved by MATLAB. The results of the simulation show that the unified structure of information flow …

Parameter Estimation Of Lfmcw Signals Based On Improved Sm Method, Jiang Li, Junni Zhou, Runling Yang, Liu Li

Journal of System Simulation

Abstract: The parameter estimation of linear frequency modulation continuous wave (LFMCW) signal is a research hotspot in electronic reconnaissance systems. In this paper, the time frequency distribution feature of LFMCW signals is studied first. Based on the improved S-method (SM), an algorithm for parameter estimation of LFMCW signals is proposed. It uses short-time Fourier transform (STFT) to calculate the SM with adaptive window width, which can suppress the crossterm interferences and improve the time-frequency resolution. Theoretical analysis and simulation experiment results demonstrate the validity of the proposed algorithm under low SNR.