Simulation And Optimization Of Supersonic Drag Characteristics Of Blunt Cone Ascender,
2021
1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;;2. University of Chinese Academy of Sciences, Beijing 100049, China;;
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 …
Uniform Experimental Design Of Constrained Region Based On Evolutionary Algorithm,
2021
1. Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing Electro-mechanical Engineering Institute, Beijing 100074, China;;
Uniform Experimental Design Of Constrained Region Based On Evolutionary Algorithm, Jianing Wei, Hao Hao, Qutong Chang, Lin Tao, Zhang Hu
Journal of System Simulation
Abstract: The parameters of a simulation system are usually generated by the experimental design. Aiming at high design difficulty and computational cost of the uniform experimental design, of the constraint region, a two-stage differential evolutionary algorithm is further improved. The design is modeled as a constrained optimization problem. A strategy combining distribution estimation algorithm (EDA) and differential evolution (DE) is adopted. A point-deletion method is proposed to reduce the time complexity of optimizing the population uniformity. To demonstrate the advantages, the test instances and engineering applications are used in experimental analysis. The experimental results show that the performance, stability, and …
Research On Agile Reentry Method For Maneuvering Vehicle,
2021
1. Beijing Institute of Control Engineering, Beijing 100094, China;;2. BUAA,7th Research Division, Beijing 100191, China;
Research On Agile Reentry Method For Maneuvering Vehicle, Lin Yue, Yingmin Jia, Songtao Fan
Journal of System Simulation
Abstract: Alarge-scale orbit maneuver method is proposed and analyzed for hypersonic space reentry vehicles, and the air assisted methods are adopted to conduct space orbit maneuvers by complying with the energy balance strategy of the aircraft. By the L/D ratio of aircraft, the efficiency of the propellant use during orbital maneuvers is elevated, and the maneuverability is also enhanced, while the risk of aircraft being tracked in space can be reduced. Aero-assisted guidance technology is applied based on full-coefficient adaptive prediction-correction technology which significantly simplifies the computation of the GNC computer, while such application ensures the convergence and enhances the …
Transmission Process Prediction Of Novel Coronavirus Based On System Dynamics,
2021
School of Information, Beijing Wuzi University, Beijing 101149, China;
Transmission Process Prediction Of Novel Coronavirus Based On System Dynamics, Xuepeng Lu, Shang Jiao, Junhui Zhao, Lulu Lü, Zhou Li
Journal of System Simulation
Abstract: The transmission characteristics of novel coronavirus is considered and a new SE4IR2 model based on the principle of system dynamics is proposed. The US epidemic data from June to November is used to set the isolation rate and other parameters, and the SE4IR2 model is used to fit, analyze and predict the development of the epidemic trend in the next stage. The empirical part uses the data from June to November in the United States to achieve the parameters of the SE4IR2 model, obtains the parameter values in December and January through the time series prediction model, and compares …
Optimization Of Urban Metro-Based Underground Logistics System Network With Hub-And-Spoke Layout,
2021
1. College of Defense Engineering, Army Engineering University of PLA, Nanjing 210001, China;;
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 …
Research On Simulation Experiment Support Technology For Complex Sos Of Live And Constructive Symbiosis,
2021
1. Joint Operations College, National Defense University, Beijing 100091, China;;
Research On Simulation Experiment Support Technology For Complex Sos Of Live And Constructive Symbiosis, Wu Xi, Ma Jun
Journal of System Simulation
Abstract: The technology of simulation experiment for System of Systems (SoS) is very important in research of complex SoS. The operation SoS is typical complex SoS.Based on the concept of parallel system, symbiotic simulation, digital twin, etc., the main characteristics of the technology are summarized. Focus on the architecture of complex system simulation experiment environment, the experiment environment architecture, hybrid simulation driving mechanism and virtual real data access mode are studied. A feasible method is provided to carry out the complex SoS experiment of multi domain joint, data fusion, resource integration and human-computer integration, and the comprehensive application of …
Research On Control Strategy Of Isolated Ac-Dc Matrix Converter Under Input Unbalance,
2021
College of Information Engineering, Xiangtan University, Xiangtan 411105, China;
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 …
Unsupervised Anomaly Instance Segmentation For Baggage Threat Recognition,
2021
Khalifa University of Science and Technology
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,
2021
Technological University Dublin
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,
2021
Chicago-Kent College of Law
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,
2021
Arab Academy for Science & Technology
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,
2021
Aga Khan University
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,
2021
College of Information Science and Electronic Engineering, Zhejiang University
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,
2021
University of Kansas
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,
2021
United Arab Emirates University
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,
2021
Bucknell University
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,
2021
University of Arkansas, Fayetteville
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 …
Meta-Inductive Node Classification Across Graphs,
2021
Singapore Management University
Meta-Inductive Node Classification Across Graphs, Zhihao Wen, Yuan Fang, Zemin Liu
Research Collection School Of Computing and Information Systems
Semi-supervised node classification on graphs is an important research problem, with many real-world applications in information retrieval such as content classification on a social network and query intent classification on an e-commerce query graph. While traditional approaches are largely transductive, recent graph neural networks (GNNs) integrate node features with network structures, thus enabling inductive node classification models that can be applied to new nodes or even new graphs in the same feature space. However, inter-graph differences still exist across graphs within the same domain. Thus, training just one global model (e.g., a state-of-the-art GNN) to handle all new graphs, whilst …
Improving Collection Understanding For Web Archives With Storytelling: Shining Light Into Dark And Stormy Archives,
2021
Old Dominion University
Improving Collection Understanding For Web Archives With Storytelling: Shining Light Into Dark And Stormy Archives, Shawn M. Jones
Computer Science Theses & Dissertations
Collections are the tools that people use to make sense of an ever-increasing number of archived web pages. As collections themselves grow, we need tools to make sense of them. Tools that work on the general web, like search engines, are not a good fit for these collections because search engines do not currently represent multiple document versions well. Web archive collections are vast, some containing hundreds of thousands of documents. Thousands of collections exist, many of which cover the same topic. Few collections include standardized metadata. Too many documents from too many collections with insufficient metadata makes collection understanding …
Deep Learning Approaches For Seagrass Detection In Multispectral Imagery,
2021
Old Dominion University
Deep Learning Approaches For Seagrass Detection In Multispectral Imagery, Kazi Aminul Islam
Electrical & Computer Engineering Theses & Dissertations
Seagrass forms the basis for critically important marine ecosystems. Seagrass is an important factor to balance marine ecological systems, and it is of great interest to monitor its distribution in different parts of the world. Remote sensing imagery is considered as an effective data modality based on which seagrass monitoring and quantification can be performed remotely. Traditionally, researchers utilized multispectral satellite images to map seagrass manually. Automatic machine learning techniques, especially deep learning algorithms, recently achieved state-of-the-art performances in many computer vision applications. This dissertation presents a set of deep learning models for seagrass detection in multispectral satellite images. It …
