Computer Sciences Commons^™

Corner Localization In Digital Images Based On Radon Transform, Xuguang Wang, Zhang Qin, Su Jie

Journal of System Simulation

Abstract: Location accuracy is one of the key indexes of measuring the performance of feature point.. High location accuracy feature point detection has important implications for the applications such as 3D reconstruction, motion estimation, target tracking and recognition, image registration, etc. A corner location algorithm based on Radon transform is proposed.Edge binary images are obtained with traditional method first, ideal straight lines and curves are extracted then, potential corner locations are calculated next, fake corners are rejected finally. Experimental results on both simulation and real images show that, this method performs better than the existing methods on interest point localization …

A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma

Master's Projects

Text summarization has been a long studied topic in the field of natural language processing. There have been various approaches for both extractive text summarization as well as abstractive text summarization. Summarizing texts for a single document is a methodical task. But summarizing multiple documents poses as a greater challenge. This thesis explores the application of Latent Semantic Analysis, Text-Rank, Lex-Rank and Reduction algorithms for single document text summarization and compares it with the proposed approach of creating a hybrid system combining each of the above algorithms, individually, with Restricted Boltzmann Machines for multi-document text summarization and analyzing how all …

3d Shape Prediction On Convolutional Deep Belief Networks, Gregory Y. Enriquez

Master's Projects

The field of image recognition software has grown immensely in recent years with the emergence of new deep learning techniques. Deep belief networks inspired by Hinton [11] were one of the earliest methodologies of deep learning in the late 2000s. More recently, convolutional neural networks have been used in deep learning techniques, architecture, and software to identify patterns in imagery in order to make predictions such as classification, image segmentation, etc. Traditional two-dimensional, or 2D, images stored as picture files, typically contain red, green, and blue color data for each individual pixel in the picture. However, more recent commercial 2.5D …

Music Retrieval System Using Query-By-Humming, Parth Patel

Master's Projects

Music Information Retrieval (MIR) is a particular research area of great interest because there are various strategies to retrieve music. To retrieve music, it is important to find a similarity between the input query and the matching music. Several solutions have been proposed that are currently being used in the application domain(s) such as Query- by-Example (QBE) which takes a sample of an audio recording playing in the background and retrieves the result. However, there is no efficient approach to solve this problem in a Query-by-Humming (QBH) application. In a Query-by-Humming application, the aim is to retrieve music that is …

Generating Contextual Text Embeddings For Emergency Department Chief Complaints Using Bert, David Chang

Yale Day of Data

We applied BERT, a state-of-the-art natural language processing model, on chief complaint data from the Yale Emergency Department to map free-text notes to structured chief complaint categories.

Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King

Computational and Data Sciences (PhD) Dissertations

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …

Employing Earth Observations And Artificial Intelligence To Address Key Global Environmental Challenges In Service Of The Sdgs, Wenzhao Li

Computational and Data Sciences (PhD) Dissertations

Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge …

Incorporating Word Order Explicitly In Glove Word Embedding, Brandon Cox

Computer Science and Computer Engineering Undergraduate Honors Theses

Word embedding is the process of representing words from a corpus of text as real number vectors. These vectors are often derived from frequency statistics from the source corpus. In the GloVe model as proposed by Pennington et al., these vectors are generated using a word-word cooccurrence matrix. However, the GloVe model fails to explicitly take into account the order in which words appear within the contexts of other words. In this paper, multiple methods of incorporating word order in GloVe word embeddings are proposed. The most successful method involves directly concatenating several word vector matrices for each position in …

Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed

Dissertations

Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project …

Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis (FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …

Deep Reinforcement Learning Pairs Trading, Andrew Brim

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

This research applies a deep reinforcement learning technique, Deep Q-network, to a stock market pairs trading strategy for profit. Artificial intelligent methods have long since been applied to optimize trading strategies. This work trains and tests a DQN to trade co-integrated stock market prices, in a pairs trading strategy. The results demonstrate the DQN is able to consistently produce positive returns when executing a pairs trading strategy.

A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja

Research Collection School Of Computing and Information Systems

A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal …

An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop

Sim Kee Boon Institute for Financial Economics

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ" role="presentation" style="box-sizing: border-box; display: inline; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">λλ) of a linear quantile lasso regression. The FRM is calculated by taking the average …

Seer: An Explainable Deep Learning Midi-Based Hybrid Song Recommender System, Khalil Damak, Olfa Nasraoui

Faculty and Staff Scholarship

State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning …

Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele

Research Collection School Of Computing and Information Systems

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for …

Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen

Research Collection School Of Computing and Information Systems

Vehicle Routing Problem (VRP) is a combinatorial problem where a certain set of nodes must be visited within a certain amount of time as well as the vehicle’s capacity. There are numerous variants of VRP such as VRP with time windows, where each node has opening and closing time, therefore, the visiting time must be during that interval. Another variant takes time-dependent constraint into account. This variant fits real-world scenarios, where at different period of time, the speed on the road varies depending on the traffic congestion. In this study, three objectives – total traveling time, total traveling distance, and …

Falcon: Framework For Anomaly Detection In Industrial Control Systems, Subin Sapkota

Boise State University Theses and Dissertations

Industrial Control Systems (ICS) are used to control physical processes in the nation's critical infrastructures. They are composed of subsystems that control physical processes by analyzing the information received from the sensors. Based on the state of the process, the controller issues control commands to the actuators. These systems are utilized in a wide variety of operations such as water treatment plants, power, and manufacturing, etc. While the safety and security of these systems are of high concern, recent reports have shown an increase in targeted attacks that are aimed at manipulating the physical processes to cause catastrophic consequences. This …

An Iot-Driven Smart Cafe Solution For Human Traffic Management, Maruthi Prithivirajan, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In this study, we present an IoT-driven solution for human traffic management in a corporate cafe. Using IoT sensors, our system monitors human traffic in a physical cafe located at a large international corporation located in Singapore. The backend system analyzes the streaming data from the sensors and provides insights useful to the cafe visitors as well as the cafe manager.

3d-Printing And Machine Learning Control Of Soft Ionic Polymer-Metal Composite Actuators, James D. Carrico, Tucker Hermans, Kwang J. Kim, Kam K. Leang

Mechanical Engineering Faculty Research

This paper presents a new manufacturing and control paradigm for developing soft ionic polymer-metal composite (IPMC) actuators for soft robotics applications. First, an additive manufacturing method that exploits the fused-filament (3D printing) process is described to overcome challenges with existing methods of creating custom-shaped IPMC actuators. By working with ionomeric precursor material, the 3D-printing process enables the creation of 3D monolithic IPMC devices where ultimately integrated sensors and actuators can be achieved. Second, Bayesian optimization is used as a learning-based control approach to help mitigate complex time-varying dynamic effects in 3D-printed actuators. This approach overcomes the challenges with existing methods …

Augmenting Education: Ethical Considerations For Incorporating Artificial Intelligence In Education, Dana Remian

Instructional Design Capstones Collection

Artificial intelligence (AI) has existed in theory and practice for decades, but applications have been relatively limited in most domains. Recent developments in AI and computing have placed AI-enhanced applications in various industries and a growing number of consumer products. AI platforms and services aimed at enhancing educational outcomes and taking over administrative tasks are becoming more prevalent and appearing in more and more classrooms and offices. Conversations about the disruption and ethical concerns created by AI are occurring in many fields. The development of the technology threatens to outpace academic discussion of its utility and pitfalls in education, however. …

“Where’S The I-O?” Artificial Intelligence And Machine Learning In Talent Management Systems, Manuel F. Gonzalez, John F. Capman, Frederick L. Oswald, Evan R. Theys, David L. Tomczak

Personnel Assessment and Decisions

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; …

Robot Simulation Analysis, Jacob Miller, Jeremy Evert

Student Research

• Simulate virtual robot for test and analysis

• Analyze SLAM solutions using ROS

• Assemble a functional Turtlebot

• Emphasize projects related to current research trajectories for NASA, and general robotics applications

Robust And Self-Synchronous Steganography For Voice-Over-Ip Based On Ldpc Codes, Zhanzhan Gao, Guangming Tang, Haitao Song

Journal of System Simulation

Abstract: VoIP (Voice over IP) is a kind of voice communication technology based on UDP/IP protocols, so packet loss will inevitably happen when the channel environment deteriorates. Besides that, stego VoIP data flows are real-time and have no head or tail, which pose more challenges to accurately extract the secret messages. This paper proposes a robust and self-synchronous VoIP steganography method, which solves the above two problems under the premise of maintaining good imperceptibility. In this method, low density parity check (LDPC) codes are introduced to preprocess the secret data, and the encoded data are embedded into voice …

No-Collision Curling Trajectory Simulation System, Tian Yu, Xu Ming

Journal of System Simulation

Abstract: In just a few decades, China’s curling accomplished splendid work. But it’s scientific research falling behind a lot. Applying computer simulation technology in sports training can effectively help the athletes to understand the problems that exist in the training. When curling stone slides on the ice, it can be regarded as two-dimensional motion of rigid body, which can be decomposed into two parts: translation of the center of mass and rotation of a fixed axis around the center of mass. Using computer simulation technology to simulate the curling trajectory, and with the help of this system, the athletes can …

Ground Coverage Stitching Simulation Algorithm For Remote Sensing Satellite, Shuhao Liu, Wang Tong, Zhang Yue

Journal of System Simulation

Abstract: Ground coverage stitching is the base of remote sensing satellite mission simulation. A coverage stitching simulation algorithm is proposed, considering big time step and attitude maneuver simulation. Transient coverage region is computed by light of view intersection and tangent searching methods. Transient coverage boundaries of different simulation times are stitched by two-dimension convex hull algorithm. Between points of the stitched region polygon edges, new vertexes are interpolated. With the method proposed, limb coverage problem and coverage fusion when time step is large can be solved. Computer simulation results show that the algorithm achieves a fine accuracy and …

Locally Weighted Learning Control For Dynamic Restricted Manipulators, Wang Gang, Tairen Sun, Shengpei Ding

Journal of System Simulation

Abstract: This paper proposes a locally weighted learning control law for a manipulator with state and input constraints and modeling uncertainties. By visualizing the control input as an extended state, the control problem is converted into control design for a state-constraint uncertain nonlinear system. Barrier Lyapunov functions are introduced into a backstepping procedure and a locally weighted learning control is designed, which ensures the exponential convergence of the barrier functions to a small neighborhood of zero and then guarantees satisfaction of system constraints and the tracking error convergence. The control feasibility and effectiveness is validated by theoretical analysis and …

Simulation Analysis Of Vehicle Electromagnetic Environment Under Excitation Of Vhf Antenna, Xiaoying Sun, Hanqing Wang, Jinpeng Yang, Zhao Yu

Journal of System Simulation

Abstract: Aiming at the issue of distribution regularity of field strength in vehicle cab under excitation of vehicle-borne high-power VHF antenna, a method of numerical simulation combining with extreme value theory to predict maximum field strength is proposed, which minimizes sample collecting difficulty and fitting deviation. CST simulation software is applied to get field strength sample in vehicle cab, and generalized extreme value (GEV) is used to fitting entire field strength distribution. peak over threshold (POT) model based on Generalized Pareto distribution is used to research high field strength value sample statistical characteristics. Resultsshow that under antenna excitation of 92.5 …

Ti-A* Based Multi-Robot Dynamic Planning And Coordination Method, Zhao Ping, Xinyu Lei, Bozhi Chen, Jiaoyun Yang

Journal of System Simulation

Abstract: Multiple robots path planning problem in dynamic environment is one of the significant and basic problems of cluster robot perception and coordination. The planning problem is decomposed into two modules: the path planning and the conflict coordination. By introducing time-cost function, the Time Integrated A* algorithm (TI-A*) is introduced. Variable speed is employed simultaneously to coordinate the motion and hereby to avoid the deadlock and conflict. By adjusting the timing when the coordination phase is involved, two different coordination TI-A* algorithms are presented, including the speed-changing algorithm and path-changing algorithm. The two algorithms are compared and the respective …

Travel Time Reliability Of Inside Area Of Roundabouts, Yuzhou Duan, Tingting Chai, Jian Jie

Journal of System Simulation

Abstract: Based on the travel time reliability, this paper analyzes the traffic states in the inside area of the roundabout. The travel time reliability model is established from three aspects: route path, entrance and system, and the practicability of the model is verified by the VISSIM simulation. On this basis, the correlation analysis is made on between the adjacent paths of the same entrance and adjacent paths of the adjacent entrances, and the fitting equations are obtained. Taking the number of lanes, traffic volumes, proportion of left-turns and the rate of heavy vehicles as variables, a sensitivity analysis …

Integrated Simulation Optimization Of Hard Shoulder Running Combined With Speed Harmonization, Ruimin Li, Ye Zhen, Bin Li

Journal of System Simulation

Abstract: We have a study on hard shoulder running (HSR) combined with speed harmonization. The breakdown events were screened out during the study period from massive detector data. The impact of hard shoulder volume on breakdown probabilities was analyzed. The nonparametric breakdown probabilities distribution model and logistic regression model were put forward based on highway mainline traffic volume, hard shoulder volume and ramp volume from the study case which was divided into three factors-basic segment, on-ramp merging segment and off-ramp diverging segment. The speed harmonization and HSR were optimized through reducing the traffic breakdown probabilities of a congested traffic network. …