Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Engineering (22)
- Social and Behavioral Sciences (17)
- Databases and Information Systems (13)
- Computer Engineering (11)
- Operations Research, Systems Engineering and Industrial Engineering (10)
-
- Theory and Algorithms (9)
- Education (8)
- Numerical Analysis and Scientific Computing (6)
- Other Computer Sciences (6)
- Psychology (6)
- Business (5)
- Programming Languages and Compilers (5)
- Software Engineering (5)
- Arts and Humanities (4)
- Cognition and Perception (4)
- Graphics and Human Computer Interfaces (4)
- Higher Education (4)
- Information Security (4)
- Life Sciences (4)
- Medicine and Health Sciences (4)
- Robotics (4)
- Technology and Innovation (4)
- Applied Mathematics (3)
- Computational Linguistics (3)
- Digital Communications and Networking (3)
- Library and Information Science (3)
- Linguistics (3)
- Institution
-
- Singapore Management University (38)
- Technological University Dublin (11)
- Florida International University (6)
- Bucknell University (5)
- University of Nebraska - Lincoln (5)
-
- Gettysburg College (3)
- Air Force Institute of Technology (2)
- Ateneo de Manila University (2)
- Central Washington University (2)
- College of Saint Benedict and Saint John's University (2)
- Edith Cowan University (2)
- University of Nevada, Las Vegas (2)
- University of New Mexico (2)
- Chapman University (1)
- City University of New York (CUNY) (1)
- Dartmouth College (1)
- Embry-Riddle Aeronautical University (1)
- Kennesaw State University (1)
- Loyola University Chicago (1)
- Maurer School of Law: Indiana University (1)
- Parkland College (1)
- Sacred Heart University (1)
- San Jose State University (1)
- Sheridan College (1)
- Southwestern Oklahoma State University (1)
- University of Kentucky (1)
- University of Tennessee, Knoxville (1)
- Western Michigan University (1)
- Western University (1)
- Western Washington University (1)
- Keyword
-
- Artificial intelligence (12)
- Artificial Intelligence (6)
- Machine Learning (6)
- Deep learning (5)
- Machine learning (5)
-
- ACT-R/Φ (3)
- Algorithms (3)
- Computer science (3)
- Education (3)
- Robotics (3)
- Automation (2)
- Click-through rate prediction (2)
- Cognitive Architecture (2)
- Computer Science Student Work (2)
- Convolutional neural networks (2)
- Data science (2)
- Domain knowledge (2)
- Energetics (2)
- Fashion (2)
- Generative adversarial networks (2)
- Multimodal dialogue (2)
- Neutrosophic logic (2)
- Operational research (2)
- Project Malmo (2)
- Reinforcement learning (2)
- Testing (2)
- [RSTDPub] (2)
- A.I.-augmented workers (1)
- ACT-R (1)
- AI (1)
- Publication
-
- Research Collection School Of Computing and Information Systems (34)
- Conference papers (7)
- FIU Electronic Theses and Dissertations (6)
- Faculty Conference Papers and Presentations (4)
- Faculty Publications (4)
-
- Articles (3)
- Computer Science Faculty Publications (3)
- Department of Computer Science and Engineering: Dissertations, Theses, and Student Research (3)
- All College Thesis Program, 2016-2019 (2)
- Branch Mathematics and Statistics Faculty and Staff Publications (2)
- Department of Information Systems & Computer Science Faculty Publications (2)
- MITB Thought Leadership Series (2)
- Research outputs 2014 to 2021 (2)
- A with Honors Projects (1)
- Articles by Maurer Faculty (1)
- Asian Management Insights (1)
- Computer Science Faculty Research (1)
- Computer Science Faculty Scholarship (1)
- Computer Science: Faculty Publications and Other Works (1)
- Dartmouth Scholarship (1)
- Electrical and Computer Engineering Publications (1)
- Faculty Journal Articles (1)
- Faculty Publications and Other Works -- EECS (1)
- Faculty and Research Publications (1)
- Information Science Faculty Publications (1)
- Languages Faculty Publications (1)
- Library Displays and Bibliographies (1)
- Library Scholarship (1)
- Life Sciences Faculty Research (1)
- Nebraska College Preparatory Academy: Senior Capstone Projects (1)
Articles 1 - 30 of 100
Full-Text Articles in Artificial Intelligence and Robotics
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
Articles
This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …
Leveraging Artificial Intelligence To Capture The Singapore Rideshare Market, Pradeep Varakantham
Leveraging Artificial Intelligence To Capture The Singapore Rideshare Market, Pradeep Varakantham
MITB Thought Leadership Series
BIKE-SHARING programmes face many of the issues encountered by their counterparts in the carsharing world. But in Singapore, there are a number of factors that have a unique impact on the industry. These include the regulatory structure and the significant fines for those companies who do not abide by these regulations. When this is combined with the competitive nature of the industry in one of the world's most dynamic cities, it becomes clear that first movers who leverage machine learning and prediction will come to dominate the industry
Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their collective influence'' on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference …
Data Center Holistic Demand Response Algorithm To Smooth Microgrid Tie-Line Power Fluctuation, Ting Yang, Yingjie Zhao, Haibo Pen, Zhaoxia Wang
Data Center Holistic Demand Response Algorithm To Smooth Microgrid Tie-Line Power Fluctuation, Ting Yang, Yingjie Zhao, Haibo Pen, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal …
Effectiveness Of Physical Robot Versus Robot Simulator In Teaching Introductory Programming, Oka Kurniawan, Norman Tiong Seng Lee, Subhajit Datta, Nachamma Sockalingam, Pey Lin Leong
Effectiveness Of Physical Robot Versus Robot Simulator In Teaching Introductory Programming, Oka Kurniawan, Norman Tiong Seng Lee, Subhajit Datta, Nachamma Sockalingam, Pey Lin Leong
Research Collection School Of Computing and Information Systems
This study reports the use of a physical robot and robot simulator in an introductory programming course in a university and measures students' programming background conceptual learning gain and learning experience. One group used physical robots in their lessons to complete programming assignments, while the other group used robot simulators. We are interested in finding out if there is any difference in the learning gain and experiences between those that use physical robots as compared to robot simulators. Our results suggest that there is no significant difference in terms of students' learning between the two approaches. However, the control group …
Sequence Pattern Mining With Variables, James S. Okolica, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila
Sequence Pattern Mining With Variables, James S. Okolica, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila
Faculty Publications
Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that all of the relevant differences between items are captured through creating distinct items, e.g., if color matters then the same item in two different colors would have two items created, one for each color. In some domains, that is unrealistic. This paper makes two contributions. The first extends SPM algorithms to allow item differentiation through attribute variables for domains with large numbers of items, e.g, by having one item with a variable with a color attribute rather than …
Validation Study Of Image Recognition Algorithms, Jacob Miller, Jeremy Evert
Validation Study Of Image Recognition Algorithms, Jacob Miller, Jeremy Evert
Student Research
Developments in machine learning in recent years have created opportunities that previously never existed. One such field with an explosion of opportunity is image recognition, also known as computer vision; the process in which a machine analyzes a digital image.
In order for a machine to ‘see’ as a human does, it must break down the image in a process called image segmentation. The way the machine goes about doing this is important, and many algorithms exist to determine just how a machine will decide to group the pixels in an image.
This research is a validation study of related …
A Model-Based Ai-Driven Test Generation System, Dionny Santiago
A Model-Based Ai-Driven Test Generation System, Dionny Santiago
FIU Electronic Theses and Dissertations
Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. …
Exploring Online Novelty Detection Using First Story Detection Models, Fei Wang, Robert J. Ross, John D. Kelleher
Exploring Online Novelty Detection Using First Story Detection Models, Fei Wang, Robert J. Ross, John D. Kelleher
Conference papers
Online novelty detection is an important technology in understanding and exploiting streaming data. One application of online novelty detection is First Story Detection (FSD) which attempts to find the very first story about a new topic, e.g. the first news report discussing the “Beast from the East” hitting Ireland. Although hundreds of FSD models have been developed, the vast majority of these only aim at improving the performance of the detection for some specific dataset, and very few focus on the insight of novelty itself. We believe that online novelty detection, framed as an unsupervised learning problem, always requires a …
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality …
Multi-Robot Coordination And Scheduling For Deactivation & Decommissioning, Sebastian A. Zanlongo
Multi-Robot Coordination And Scheduling For Deactivation & Decommissioning, Sebastian A. Zanlongo
FIU Electronic Theses and Dissertations
Large quantities of high-level radioactive waste were generated during WWII. This waste is being stored in facilities such as double-shell tanks in Washington, and the Waste Isolation Pilot Plant in New Mexico. Due to the dangerous nature of radioactive waste, these facilities must undergo periodic inspections to ensure that leaks are detected quickly. In this work, we provide a set of methodologies to aid in the monitoring and inspection of these hazardous facilities. This allows inspection of dangerous regions without a human operator, and for the inspection of locations where a person would not be physically able to enter.
First, …
Automatic Extraction Of Narrative Structure From Long Form Text, Joshua Daniel Eisenberg
Automatic Extraction Of Narrative Structure From Long Form Text, Joshua Daniel Eisenberg
FIU Electronic Theses and Dissertations
Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives.
My work has given computers awareness of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a …
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
FIU Electronic Theses and Dissertations
The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, …
Improving Knowledge Tracing Model By Integrating Problem Difficulty, Sein Minn, Feida Zhu, Michel C. Desmarais
Improving Knowledge Tracing Model By Integrating Problem Difficulty, Sein Minn, Feida Zhu, Michel C. Desmarais
Research Collection School Of Computing and Information Systems
Intelligent Tutoring Systems (ITS) are designed for providing personalized instructions to students with the needs of their skills. Assessment of student knowledge acquisition dynamically is nontrivial during her learning process with ITS. Knowledge tracing, a popular student modeling technique for student knowledge assessment in adaptive tutoring, which is used for tracing student's knowledge state and detecting student's knowledge acquisition by using decomposed individual skill or problems with a single skill per problem. Unfortunately, recent KT models fail to deal with practices of complex skill composition and variety of concepts included in a problem simultaneously. Our goal is to investigate a …
Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang
Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang
Research Collection School Of Computing and Information Systems
Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good …
Gesture Recognition With Transparent Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Gesture Recognition With Transparent Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Research Collection School Of Computing and Information Systems
Transparent solar cell is an emerging solar energy harvesting technology that allows us to see through these cells. This revolutionary discovery is creating unique opportunities to turn any mobile device screen into solar energy harvester. In this paper, we consider the possibility of using such energy harvesting screens as a sensor to detect hand gestures. As different gestures impact the incident light on the screen in a different way, they are expected to create unique energy generation patterns for the transparent solar cell. Our goal is to recognize gestures by detecting these solar energy patterns. A key uncertainty we face …
Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, Chittayong Surakitbanharn,, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal
Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, Chittayong Surakitbanharn,, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal
Computer Science: Faculty Publications and Other Works
Physical media (like surveillance cameras) and social media (like Instagram and Twitter) may both be useful in attaining on-the-ground information during an emergency or disaster situation. However, the intersection and reliability of both surveillance cameras and social media during a natural disaster are not fully understood. To address this gap, we tested whether social media is of utility when physical surveillance cameras went off-line during Hurricane Irma in 2017. Specifically, we collected and compared geo-tagged Instagram and Twitter posts in the state of Florida during times and in areas where public surveillance cameras went off-line. We report social media content …
Linking Gait Dynamics To Mechanical Cost Of Legged Locomotion, David V. Lee, Sarah L. Harris
Linking Gait Dynamics To Mechanical Cost Of Legged Locomotion, David V. Lee, Sarah L. Harris
Life Sciences Faculty Research
For millenia, legged locomotion has been of central importance to humans for hunting, agriculture, transportation, sport, and warfare. Today, the same principal considerations of locomotor performance and economy apply to legged systems designed to serve, assist, or be worn by humans in urban and natural environments. Energy comes at a premium not only for animals, wherein suitably fast and economical gaits are selected through organic evolution, but also for legged robots that must carry sufficient energy in their batteries. Although a robot's energy is spent at many levels, from control systems to actuators, we suggest that the mechanical cost of …
From Rankings To Ratings: Rank Scoring Via Active Learning, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
From Rankings To Ratings: Rank Scoring Via Active Learning, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Conference papers
In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the state-of-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons.
Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua
Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user's intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the …
Knowledge-Aware Multimodal Fashion Chatbot, Lizi Liao, You Zhou, Yunshan Ma, Richang Hong, Tat-Seng Chua
Knowledge-Aware Multimodal Fashion Chatbot, Lizi Liao, You Zhou, Yunshan Ma, Richang Hong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Multimodal fashion chatbot provides a natural and informative way to fulfill customers’ fashion needs. However, making it ‘smart’ in generating substantive responses remains a challenging problem. In this paper, we present a multimodal domain knowledge enriched fashion chatbot. It forms a taxonomy-based learning module to capture the fine-grained semantics in images and leverages an endto-end neural conversational model to generate responses based on the conversation history, visual semantics, and domain knowledge. To avoid inconsistent dialogues, deep reinforcement learning method is used to further optimize the model.
March Of The Silent Bots, Paul Robert Griffin
March Of The Silent Bots, Paul Robert Griffin
MITB Thought Leadership Series
Self-intelligent software robots, or ‘bots’ are everywhere. These small pieces of code run automated tasks when you order a taxi, search for a restaurant or check the weather. Quietly beavering away, it is unknown how many bots exist, but undoubtedly this number is set to surge over time. Already, bots comprise roughly half of all internet traffic.
Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Huang, Tat-Seng Chua
Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Huang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user’s intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the …
Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua
Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …
Evaluating Prose Style Transfer With The Bible, Keith Carlson, Allen Riddell, Daniel Rockmore
Evaluating Prose Style Transfer With The Bible, Keith Carlson, Allen Riddell, Daniel Rockmore
Dartmouth Scholarship
In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few publicly available corpora for this task. In this work, we identify a high-quality source of aligned, stylistically distinct text in different versions of the Bible. We provide a standardized split, into training, development and testing data, of the public domain versions in our corpus. This corpus is …
Higher-Level Consistencies: Where, When, And How Much, Robert J. Woodward
Higher-Level Consistencies: Where, When, And How Much, Robert J. Woodward
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Determining whether or not a Constraint Satisfaction Problem (CSP) has a solution is NP-complete. CSPs are solved by inference (i.e., enforcing consistency), conditioning (i.e., doing search), or, more commonly, by interleaving the two mechanisms. The most common consistency property enforced during search is Generalized Arc Consistency (GAC). In recent years, new algorithms that enforce consistency properties stronger than GAC have been proposed and shown to be necessary to solve difficult problem instances.
We frame the question of balancing the cost and the pruning effectiveness of consistency algorithms as the question of determining where, when, and how much of a higher-level …
A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson
A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson
Faculty Publications
Analyzing how agent interactions affect macro-level self-organized behaviors can yield a deeper understanding of how complex adaptive systems work. The dynamic nature of complex systems makes it difficult to determine if, or when, a system has reached a state of equilibrium or is about to undergo a major transition reflecting the appearance of self-organized states. Using the notion of local neighborhood entropy, this paper presents a metric for evaluating the macro-level order of a system. The metric is tested in two dissimilar complex adaptive systems with self-organizing properties: An autonomous swarm searching for multiple dynamic targets and Conway's Game of …
Secondary Frequency Stochastic Optimal Control In Independent Microgrids With Virtual Synchronous Generator-Controlled Energy Storage Systems, Ting Yang, Yajian Zhang, Zhaoxia Wang, Haibo Pen
Secondary Frequency Stochastic Optimal Control In Independent Microgrids With Virtual Synchronous Generator-Controlled Energy Storage Systems, Ting Yang, Yajian Zhang, Zhaoxia Wang, Haibo Pen
Research Collection School Of Computing and Information Systems
With the increasing proportion of renewable energy in microgrids (MGs), its stochastic fluctuation of output power has posed challenges to system safety and operation, especially frequency stability. Virtual synchronous generator (VSG) technology, as one effectivemethod, was used to smoothen frequency fluctuation and improve the system's dynamic performance,which can simulate the inertia and damping of the traditional synchronous generator. This study outlines the integration of VSG-controlled energy storage systems (ESSs) and traditional synchronous generators so they jointly participate in secondary frequency regulation in an independent MG. Firstly, a new uncertain state-space model for secondary frequency control is established, considering the measurement …
Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Online spatio-temporal matching of servers/services to customers is a problem that arises at a large scale in many domains associated with shared transportation (e.g., taxis, ride sharing, super shuttles, etc.) and delivery services (e.g., food, equipment, clothing, home fuel, etc.). A key characteristic of these problems is that the matching of servers/services to customers in one stage has a direct impact on the matching in the next stage. For instance, it is efficient for taxis to pick up customers closer to the drop off point of the customer from the first stage of matching. Traditionally, greedy/myopic approaches have been adopted …
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
Research Collection School Of Computing and Information Systems
Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service …