Ship-Gan: Generative Modeling Based Maritime Traffic Simulator,
2021
Singapore Management University
Ship-Gan: Generative Modeling Based Maritime Traffic Simulator, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar
Research Collection School Of Computing and Information Systems
Modeling vessel movement in a maritime environment is an extremely challenging task given the complex nature of vessel behavior. Several existing multiagent maritime decision making frameworks require access to an accurate traffic simulator. We develop a system using electronic navigation charts to generate realistic and high fidelity vessel traffic data using Generative Adversarial Networks (GANs). Our proposed Ship-GAN uses a conditional Wasserstein GAN to model a vessel's behavior. The generator can simulate the travel time of vessels across different maritime zones conditioned on vessels' speeds and traffic intensity. Furthermore, it can be used as an accurate simulator for prior decision …
Edgeduet: Tiling Small Object Detection For Edge Assisted Autonomous Mobile Vision,
2021
Singapore Management University
Edgeduet: Tiling Small Object Detection For Edge Assisted Autonomous Mobile Vision, Xu Wang, Zheng Yang, Jiahang Wu, Yi Zhao, Zimu Zhou
Research Collection School Of Computing and Information Systems
Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while …
Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos,
2021
University of Arkansas, Fayetteville
Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos, Luke Vincent
Graduate Theses and Dissertations
Machine learning is a rapidly accelerating tool and technology used for countless applications in the modern world. There are many digital algorithms to deploy a machine learning program, but the most advanced and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is proposed, developed, and tested to demonstrate equivalent learning abilities with fractional power consumption compared to its digital adversary.
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning,
2021
Singapore Management University
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem ofmultiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains - air-traffic control and cooperative navigation, shows better solution quality than previous approaches.
Machine Learning Models For Deciphering Regulatory Mechanisms And Morphological Variations In Cancer,
2021
University of Massachusetts Boston
Machine Learning Models For Deciphering Regulatory Mechanisms And Morphological Variations In Cancer, Saman Farahmand
Graduate Doctoral Dissertations
The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances …
Working With Smart Machines: Insights On The Future Of Work,
2021
Babson College
Working With Smart Machines: Insights On The Future Of Work, Thomas H. Davenport, Steven M. Miller
Research Collection School Of Computing and Information Systems
In this article, we share our observations on how and why AI-based systems are being deployed. We look at how these systems have been integrated into existing and new work processes, especially the implications for the changing nature of work and how it will be conducted in future with AI-based smart machines. This will help companies that are in the earlier stages of considering, planning, or deploying these systems to know what to expect from recent developments in practice. We draw our analysis from 24 case studies that we have recently completed on AI system usage in actual operational settings.
Adaptive Operating Hours For Improved Performance Of Taxi Fleets,
2021
Singapore Management University
Adaptive Operating Hours For Improved Performance Of Taxi Fleets, Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Taxi fleets and car aggregation systems are an important component of the urban public transportation system. Taxis and cars in taxi fleets and car aggregation systems (e.g., Uber) are dependent on a large number of self-controlled and profit-driven taxi drivers, which introduces inefficiencies in the system. There are two ways in which taxi fleet performance can be optimized: (i) Operational decision making: improve assignment of taxis/cars to customers, while accounting for future demand; (ii) strategic decision making: optimize operating hours of (taxi and car) drivers. Existing research has primarily focused on the operational decisions in (i) and we focus on …
A Differential Testing Approach For Evaluating Abstract Syntax Tree Mapping Algorithms,
2021
Singapore Management University
A Differential Testing Approach For Evaluating Abstract Syntax Tree Mapping Algorithms, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan, Yuan Wang, Shanping Li
Research Collection School Of Computing and Information Systems
Abstract syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e., the extent to which an algorithm captures the evolution of code. We observe that a program element often has only one best-mapped program element. Based on this observation, we propose a hierarchical approach to automatically compare the similarity of mapped statements and tokens by different algorithms. By performing the comparison, we determine if eachof the compared algorithms generates inaccurate mappings for a statement …
Unveiling The Mystery Of Api Evolution In Deep Learning Frameworks: A Case Study Of Tensorflow 2,
2021
Singapore Management University
Unveiling The Mystery Of Api Evolution In Deep Learning Frameworks: A Case Study Of Tensorflow 2, Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, John C. Grundy
Research Collection School Of Computing and Information Systems
API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract …
Guest Editorial: Non-Iid Outlier Detection In Complex Contexts,
2021
Singapore Management University
Guest Editorial: Non-Iid Outlier Detection In Complex Contexts, Guansong Pang, Fabrizio Angiulli, Mihai Cucuringu, Huan Liu
Research Collection School Of Computing and Information Systems
Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. Due to its significance in many critical domains like cybersecurity, fintech, healthcare, public security, and AI safety, outlier detection has been one of the most active research areas in various communities, such as machine learning, data mining, computer vision, and statistics. Traditional outlier-detection techniques generally assume that data are independent and identically distributed (IID), which are significantly challenged in complex contexts where data are actually non-IID. These contexts are ubiquitous in not only graph data, sequence data, …
A Hyperelastic Porous Media Framework For Ionic Polymer-Metal Composites And Characterization Of Transduction Phenomena Via Dimensional Analysis And Nonlinear Regression,
2021
University of Nevada, Las Vegas
A Hyperelastic Porous Media Framework For Ionic Polymer-Metal Composites And Characterization Of Transduction Phenomena Via Dimensional Analysis And Nonlinear Regression, Zakai J. Olsen
UNLV Theses, Dissertations, Professional Papers, and Capstones
Ionic polymer-metal composites (IPMC) are smart materials that exhibit large deformation in response to small applied voltages, and conversely generate detectable electrical signals in response to mechanical deformations. The study of IPMC materials is a rich field of research, and an interesting intersection of material science, electrochemistry, continuum mechanics, and thermodynamics. Due to their electromechanical and mechanoelectrical transduction capabilities, IPMCs find many applications in robotics, soft robotics, artificial muscles, and biomimetics. This study aims to investigate the dominating physical phenomena that underly the actuation and sensing behavior of IPMC materials. This analysis is made possible by developing a new, hyperelastic …
Exploring Ai And Multiplayer In Java,
2021
Minnesota State University Moorhead
Exploring Ai And Multiplayer In Java, Ronni Kurtzhals
Student Academic Conference
I conducted research into three topics: artificial intelligence, package deployment, and multiplayer servers in Java. This research came together to form my project presentation on the implementation of these topics, which I felt accurately demonstrated the various things I have learned from my courses at Moorhead State University. Several resources were consulted throughout the project, including the work of W3Schools and StackOverflow as well as relevant assignments and textbooks from previous classes. I found this project relevant to computer science and information systems for several reasons, such as the AI component and use of SQL data tables; but it was …
Exploring Complementary Strengths Of Invariant And Equivariant Representations For Few-Shot Learning,
2021
Center for Research in Computer Vision
Exploring Complementary Strengths Of Invariant And Equivariant Representations For Few-Shot Learning, Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah
Computer Vision Faculty Publications
In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning (FSL) is the dominant approach to address this issue, where the objective is to quickly adapt to novel categories in presence of a limited number of samples. FSL tasks have been predominantly solved by leveraging the ideas from gradient-based meta-learning and metric learning approaches. However, recent works have demonstrated the significance of powerful feature representations with a simple embedding network that can outperform existing sophisticated FSL algorithms. In this work, we build on this insight and propose a novel training mechanism that simultaneously enforces equivariance …
Mesoscopic Traffic Simulation Model And Calibration Considering Stretching-Segment Design,
2021
1. Research Center of Intelligent Transport System, Sun Yat-Sen University, Guangzhou 510006, China; ;2. Guangdong Provincial Key Laboratory of Intelligent Transport System, Guangzhou 510006, China; ;
Mesoscopic Traffic Simulation Model And Calibration Considering Stretching-Segment Design, Zhaocheng He, Xuanhua Lin, Peilin Nie, Ronghui Zhang
Journal of System Simulation
Abstract: In order to make the simulation model fit the characteristics of urban traffic, both high accuracy and high performance, a lightweight mesoscopic traffic simulation system and the process of calibration are established. The speed-density model and vertical queue model are equivalent to the vehicle movement processes, the simulation accuracy and calibration efficiency are improved by the stretching-segment design at urban intersections of vertical queuing model, and the real individual vehicle information is used as the calibration data source. The application of the model in Xuancheng urban road network shows that, compared with the vertical queuing model, it can …
Modeling And Simulation Of Electric Vehicle Industry Development Based On System Dynamics,
2021
The Center of Collaboration and Innovation, Jiangxi University of Technology, Nanchang 330098, China;
Modeling And Simulation Of Electric Vehicle Industry Development Based On System Dynamics, Yueqiang Fu, Tiantian Xia
Journal of System Simulation
Abstract: New energy electric vehicle are the main trend of automobile industry upgrading. It plays an important role in ensuring the energy security and improving the ecological environment. It is of theoretical and practical significance to carry out research on the development of new energy electric vehicles. The affecting factors are systematically analyzed, the causal relationship model and stock flow model are established, and the dynamic equation of the model are determined and the parameter assignments are made. The model is verified and the system simulation and analysis are performed. The development trend and main influencing factors of …
Small Fault Detection Based On Cumulative Sum Of Neighbor Statistic,
2021
Information Engineering School, Shenyang University of Chemical Technology, Shenyang 110142, China;
Small Fault Detection Based On Cumulative Sum Of Neighbor Statistic, Xiaoping Guo, Jiajun Gao, Jianbin Guo, Li Yuan
Journal of System Simulation
Abstract: Aiming at the small faults and the common data non-linear problems of industrial process, a fault detection method based oncumulative sum of neighbor statistic (CUSUM-NS) is proposed. Mutual information principal component analysis (MIPCA) is used to reduce the dimension of training data, and the principal components based on mutual information are extracted to construct a new sample space. For the new sample space after dimensionality reduction, the nonlinear features of the process data can be fully extracted through the distance square sum statistics of k nearest neighbors. Cumulative summation(CUSUM) method is used to accumulate the sum of squares of …
Visual Simulation Platform For Visible Light Reconnaissance Load Of Unmanned Aerial Vehicle,
2021
1. Beijing Institute of Technology, School of Automation, Beijing 100081, China; ;
Visual Simulation Platform For Visible Light Reconnaissance Load Of Unmanned Aerial Vehicle, Yuzhou Chen, Li Yuan, Qinglin Wang, Zhang Qing, Jinyuan Zhang
Journal of System Simulation
Abstract: In view of the simulation and evaluation requirement of the visual system parameters on the performance of video imaging during the operation and reconnaissance of unmanned aerial vehicle, a visual simulation platform for the reconnaissance load is designed and constructed. The collected video is processed according to the visual system parameters and the flight parameters, and the support for the evaluation and the index design of the unmanned aerial vehicle reconnaissance load system is provided, and the guidance is provided for the flight parameters and the flight track setting when the unmanned aerial vehicle reconnaissance and operation …
Research On Some Questions Of Simulation Body Of Knowledge,
2021
1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; ;2. The PLA's 31002 Unit, Beijing 100094, China;
Research On Some Questions Of Simulation Body Of Knowledge, Xiaogang Qiu, Duan Hong, Xie Xu, Bin Chen
Journal of System Simulation
Abstract: Simulation body of knowledge (BOK) is the knowledge required to conduct Modeling and Simulation (M&S) activities, which is a logic system consisting of concepts, propositions, and inferences that are tightly related to each other. The simulation BOK organizes the M&S knowledge in a hierarchical way, reflects the composition and structure of the knowledge in the M&S domain. The establishment of the simulation BOK is crucial to advance the M&S research and education. The requirements for establishing the simulation BOK are summarized, three basic features of the simulation knowledge, practical, systematical, and epochal are discussed, the challenges of establishing the …
Study On Composition Of Simulation Discipline Knowledge Areas,
2021
1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; ;
Study On Composition Of Simulation Discipline Knowledge Areas, Duan Hong, Xiaogang Qiu, Xie Xu, Rusheng Ju
Journal of System Simulation
Abstract: Many disciplines, such as Software Engineering, Automation, have sorted out and formed their own knowledge areas and constructed their Body of Knowledge to steer teaching and study efforts. Describing the composition of Modeling and Simulation body of knowledge from the perspective of knowledge area plays an important role in the simulation engineering education and the development of simulation technology. According to the needs of simulation teaching, the body of knowledge of simulation discipline is divided into three levels, knowledge area, knowledge unit and knowledge topic. On the basis of reviewing the current status of the research and the role …
Research On Combat Simulation Body Of Knowledge,
2021
1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; ;
Research On Combat Simulation Body Of Knowledge, Xie Xu, Xiaogang Qiu, Duan Hong, Kedi Huang
Journal of System Simulation
Abstract: Combat simulation is an important research method in modern military domain, since it provides a virtual battlespace for entities of various types that are involved in a battle to interact with each other. Over last several decades, combat simulation has been widely applied in different applications, and as a result the body of knowledge of combat simulation has been extended a lot. The relevant articles and textbooks in combat simulation are extensively investigated, and a three-layer structure to organize the body of knowledge of combat simulation is proposed. Eleven knowledge areas that should be included in the combat simulation …
