Artificial Intelligence and Robotics Commons^™

Research On Control Strategy Of Isolated Ac-Dc Matrix Converter Under Input Unbalance, Wenlang Deng, Li Yong, Hongbin Pan

Journal of System Simulation

Abstract: To reduce the active power fluctuation of the isolated AC-DC matrix converter (IAMC) under the input unbalance and to suppress the resonance of the LC filter, a deadbeat direct power control strategy based on active damping is proposed. The strategy uses the grid-side current and its delay component to calculate the IAMC input side filter capacitor voltage, which eliminates the high-precision voltage sensor of traditional active damping control, reduces the hardware cost, without the rotation coordinate transformation and phase-locked loops, so the calculation cost is small. The simulation results show that the proposed control strategy can effectively …

Fast Maritime Simulator Scene Modeling Method Based On Aerial Images, Lijia Chen, Wang Kai, Shigang Li, Yanfei Tian

Journal of System Simulation

Abstract: Artificial Modeling using Computer Aid Design software are frequently applied in maritime simulator scene modeling,but with low accuracy, low efficiency and complicate workflow. It's not the ideal method of modeling a large-scale area. A modeling method is proposed based on aerial images in combination with tilt photography modeling, procedural modeling and image semantic segmentation method. It's a fast and highly automated modeling method specially for the maritime simulator. A water area near Tianxingzhou Yangtze River Bridge is chosen as the model. Experiment results show that the proposed method is of fast speed, high precision, high degree of automation, …

Networked Simulation And It's Development Trend, Duan Hong, Xiaogang Qiu

Journal of System Simulation

Abstract: Carrying out simulating whenever and wherever possible as well as the large-scale trend of simulation lead to the demand of networked simulation.. Through the changes of range, from model interconnection, experimental collaboration and domain simulation collaboration, three layers of meanings of networked simulation are analyzed; The characteristics of networked simulation are summarized from five aspects, pattern, application, target, focus, and morphology. The idea that the networked simulation will bring changes to the simulation application of the activity scope and method, resource construction, resource verification, organizational structure, and so on is proposed. By sorting out the development process …

Numerical Simulation Of Aerodynamic Characteristics For Double-All-Wing Unmanned Aerial Vehicle Based On Computational Fluid Dynamics Theory, Kuizhi Yue, Zhang Yuan, Liangliang Cheng, Dazhao Yu

Journal of System Simulation

Abstract: Follow the rapid progress of the nation's blue ocean strategic projection capability of sea power, an Unmanned Aerial Vehicle (UAV) with double-all-wing configuration is studied to match the trend. The UAV is conceptually designed by CATIA software, the aerodynamic characteristic of the model is numerically simulated by computational fluid dynamics (Computational Fluid Dynamics, CFD) theory, and the performance of the UAV to take off from the aircraft carrier is also analyzed. The lift coefficient, drag coefficient and lift-drag ratio of the double-all-wing UAV model with a angle of attack of 0 can reach 0.596 2, 0.037 6 and 15.824 …

Simulation Evaluation For Service Facilities Distribution And Flow Lines Optimization On Urban Rail Transfer Station, Liu Feng, Chunfu Shao, Hongfei Jia, Chunjiao Dong

Journal of System Simulation

Abstract: The rationality of passenger flow lines design and service facilities distribution inside urban rail transfer station directly affects the passenger's transfer efficiency. The Simulation technology are used to evaluate the station's transfer efficiency, and the optimal plan can be proposed, which can not only avoid the crowded passenger flow, but also improve the efficiency. The features of peak passenger flow are considered, the service level of the passenger's stay time is given, the change of congested area's passenger flow density is expressed in the form of time-varying chart. Combined with the overall chart of the average passenger flow density, …

Research On A Dynamically Perceptive Connectivity Restoration Method, Hua Xiang, Baohua Li, Hongjuan Yao, Wang Zhao, Jiaxian Zuo

Journal of System Simulation

Abstract: The cluster communication network has the characteristics of time-varying topology, strong node mobility, and unstable link status, etc. It is difficult to maintain a stable topology. The network is divided into disconnected subgroups. In order to solve the communication problem between different groups, the cooperative relay node is introduced and a dynamic-sensing connection recovery method is proposed. A cooperative communication model is built and the mathematical expression of link quality indicators is deduced, which designs a multi-objective link evaluation function and achieves the optimal relay location planning through a multi-objective connectivity recovery algorithm. Simulation results show that the …

Simulation And Optimization Of Supersonic Drag Characteristics Of Blunt Cone Ascender, Xiazhen Liu, Yuan Wu, Li Qi, Zhao Rui, Zhang Jian, Zhonghua Lu

Journal of System Simulation

Abstract: Reducing shock drag is an important objective of the aerodynamic optimization of Mars ascenders. The CFD(Computational Fluid Dynamics) method is used to study the supersonic drag characteristics of a typical blunt cone ascender. Under the design constraints of slenderness ratio of 0.42, 13 groups busbar shape set of zero-lift drag are compared, and the longitudinal static and dynamic stability are discussed. The research results show that, under the design constraints, the blunt shape with large bulbous and a gently sloped conical surface has low drag characteristics. The reason is that, after passing through the bulbous, the supersonic airflow …

Optimization Of Urban Metro-Based Underground Logistics System Network With Hub-And-Spoke Layout, Ren Rui, Wanjie Hu, Jianjun Dong, Zhilong Chen

Journal of System Simulation

Abstract: Dual application of passenger commuting and underground logistics based on urban metro (M-ULS) provides a solution for traffic congestion. A three-stage optimization approach for the hub-and-spoke M-ULS network is proposed. An Entropy-TOPSIS model for underground flow screening was established based on indicators of freight flows unity, order priority and regional accessibility, considering facility capacity constraints, a location-allocation-routing combinatorial model with construction and operation costs objectives is proposed, Set Coverage Exact Algorithm, Adaptive Immunity Clone Selection Algorithm and Floyd-Warshall Algorithm are developed. Geographic information statistics of Nanjing is applied as the benchmark for real-world simulation. Results show that the modeling …

Unsupervised Anomaly Instance Segmentation For Baggage Threat Recognition, Taimur Hassan, Samet Akçay, Mohammed Bennamoun, Salman Khan, Naoufel Werghi

Computer Vision Faculty Publications

Identifying potential threats concealed within the baggage is of prime concern for the security staff. Many researchers have developed frameworks that can automatically detect baggage threats from security X-ray scans. However, to the best of our knowledge, all of these frameworks require extensive training efforts on large-scale and well-annotated datasets, which are hard to procure in the real world, especially for the rarely seen contraband items. This paper presents a novel unsupervised anomaly instance segmentation framework that recognizes baggage threats, in X-ray scans, as anomalies without requiring any ground truth labels. Furthermore, thanks to its stylization capacity, the framework is …

Multi-Modal Self-Supervised Representation Learning For Earth Observation, Pallavi Jain, Bianca Schoen Phelan, Robert J. Ross

Conference papers

Self-Supervised learning (SSL) has reduced the performance gap between supervised and unsupervised learning, due to its ability to learn invariant representations. This is a boon to the domains like Earth Observation (EO), where labelled data availability is scarce but unlabelled data is freely available. While Transfer Learning from generic RGB pre-trained models is still common-place in EO, we argue that, it is essential to have good EO domain specific pre-trained model in order to use with downstream tasks with limited labelled data. Hence, we explored the applicability of SSL with multi-modal satellite imagery for downstream tasks. For this we utilised …

Ai Output: A Human Condition That Should Not Be Protected Now, Or Maybe Ever, Xiao Wang

Chicago-Kent Journal of Intellectual Property

AI is usually considered to be a form of automatic and autonomous work, but when applied to the creation of literary and artistic works, challenges arise in deciding whether the AI is the de facto author of its output and whether AI outputs or AI-generated products should be protected under the copyright system. This article argues that these outputs should be human creations because the working principles of AI determine that AI functions merely as a mathematical tool applied by humans to not only conceive of but also to execute the creation of AI outputs. The creativity reflected in these …

A Deep Learning Approach For Forecasting Global Commodities Prices, Ahmed Saied Elberawi, Mohamed Belal Prof.

Future Computing and Informatics Journal

Forecasting future values of time-series data is a critical task in many disciplines including financial planning and decision-making. Researchers and practitioners in statistics apply traditional statistical methods (such as ARMA, ARIMA, ES, and GARCH) for a long time with varying accuracies. Deep learning provides more sophisticated and non-linear approximation that supersede traditional statistical methods in most cases. Deep learning methods require minimal features engineering compared to other methods; it adopts an end-to-end learning methodology. In addition, it can handle a huge amount of data and variables. Financial time series forecasting poses a challenge due to its high volatility and non-stationarity …

Diagnostic Accuracy Of Machine Learning Models To Identify Congenital Heart Disease: A Meta-Analysis, Zahra Hoodbhoy, Uswa Jiwani, Saima Sattar, Rehana A. Salam, Babar Hasan, Jai K. Das

Department of Paediatrics and Child Health

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.
Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to …

P2v-Rcnn: Point To Voxel Feature Learning For 3d Object Detection From Point Clouds, Jiale Li, Yu Sun, Shujie Luo, Ziqi Zhu, Hang Dai, Andrey S. Krylov, Yong Ding, Ling Shao

Computer Vision Faculty Publications

The most recent 3D object detectors for point clouds rely on the coarse voxel-based representation rather than the accurate point-based representation due to a higher box recall in the voxel-based Region Proposal Network (RPN). However, the detection accuracy is severely restricted by the information loss of pose details in the voxels. Different from considering the point cloud as voxel or point representation only, we propose a point-to-voxel feature learning approach to voxelize the point cloud with both the point-wise semantic and local spatial features, which maintains the voxel-wise features to build the high-recall voxel-based RPN and also provides the accurate …

On The Use Of Minimum Penalties In Statistical Learning, Ben Sherwood, Bradley S. Price

Faculty & Staff Scholarship

Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not scale to other types of models. In this article we proposed the MinPEN framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using mild assumptions. The MinPen framework utilizes a novel penalty based on the minimum function to exploit detected relationships between responses. An iterative algorithm that …

Edge Detail Analysis Of Wear Particles, Mohammad Shakeel Laghari, Ahmed Hassan, Mubashir Noman

Computer Vision Faculty Publications

Tribology is the study of wear particles that are generated in all machines with interacting mechanical parts. Particles are separated from the surfaces due to friction and relative motion. These microscopic particles vary in certain characteristics of size, quantity, composition, and morphology. Wear particles or wear debris are categorized by six morphological attributes of shape, edge details, texture, color, size, and thickness ratio. Particles can be identified with the help of some or all of these attributes however, only edge details analysis is considered in this paper. The objective is to classify these particles in a coherent way based on …

Examining The Effects Of Race On Human-Ai Cooperation, Akil A. Atkins, Christopher L. Dancy, Matthew S. Brown

Faculty Conference Papers and Presentations

Recent literature has shown that racism and implicit racial biases can affect one’s actions in major ways, from the time it takes police to decide whether they shoot an armed suspect, to a decision on whether to trust a stranger. Given that race is a social/power construct, artifacts can also be racialized, and these racialized agents have also been found to be treated differently based on their perceived race. We explored whether people’s decision to cooperate with an AI agent during a task (a modified version of the Stag hunt task) is affected by the knowledge that the AI agent …

Computational Frameworks For Multi-Robot Cooperative 3d Printing And Planning, Laxmi Prasad Poudel

Graduate Theses and Dissertations

This dissertation proposes a novel cooperative 3D printing (C3DP) approach for multi-robot additive manufacturing (AM) and presents scheduling and planning strategies that enable multi-robot cooperation in the manufacturing environment. C3DP is the first step towards achieving the overarching goal of swarm manufacturing (SM). SM is a paradigm for distributed manufacturing that envisions networks of micro-factories, each of which employs thousands of mobile robots that can manufacture different products on demand. SM breaks down the complicated supply chain used to deliver a product from a large production facility from one part of the world to another. Instead, it establishes a network …

How Important Is The Train-Validation Split In Meta-Learning?, Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, D. Jason Lee, Sham Kakade, Huan Wang, Caiming Xiong

Research Collection School Of Computing and Information Systems

Meta-learning aims to perform fast adaptation on a new task through learning a “prior” from multiple existing tasks. A common practice in meta-learning is to perform a train-validation split (train-val method) where the prior adapts to the task on one split of the data, and the resulting predictor is evaluated on another split. Despite its prevalence, the importance of the train-validation split is not well understood either in theory or in practice, particularly in comparison to the more direct train-train method, which uses all the pertask data for both training and evaluation. We provide a detailed theoretical study on whether …

Leveraging Big Data For Pattern Recognition Of Socio-Demographic And Climatic Factors In Correlation With Eye Disorders In Telangana State, India, Amna Alalawi, Les Sztandera, Parth Lalakia, Anthony Vipin Das, Sai Prashanthi Gumpili, Richard Derman

Kanbar College Faculty Papers

Purpose: Big data is the new gold, especially in health care. Advances in collecting and processing electronic medical records (EMR) coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Ophthalmology has been an area of focus where results have shown to be promising. The objective of this study was to determine whether the EMR at a multi-tier ophthalmology network in India can contribute to the management of patient care, through studying how climatic and socio-demographic factors relate to eye disorders and visual impairment in the State of Telangana.

Methods: …