scholarly journals Judgement Theorems and an Approach for Solving the Constellation-to-Ground Coverage Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiming Song ◽  
Xiangyun Hu ◽  
Maocai Wang ◽  
Guangming Dai
Keyword(s):  
The State ◽  
Coverage Problem ◽  
Satellite Constellation ◽  
Novel Approach ◽  
Satellite Applications ◽  
Ground Coverage ◽  
Multiple Coverage ◽  
Coverage Problems

The satellite constellation-to-ground coverage problem is a basic and important problem in satellite applications. A group of judgement theorems is given, and a novel approach based on these judgement theorems for judging whether a constellation can offer complete single or multiple coverage of a ground region is proposed. From the point view of mathematics, the constellation-to-ground coverage problem can be regarded as a problem entailing the intersection of spherical regions. Four judgement theorems that can translate the coverage problem into a judgement about the state of a group of ground points are proposed, thus allowing the problem to be efficiently solved. Single- and multiple-coverage problems are simulated, and the results show that this approach is correct and effective.

scholarly journals Cell Area-Based Method for Analyzing the Coverage Capacity of Satellite Constellations

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhiming Song ◽  
Haidong Liu ◽  
Guangming Dai ◽  
Maocai Wang ◽  
Xiaoyu Chen
Keyword(s):  
High Efficiency ◽  
Cell Area ◽  
Satellite Constellations ◽  
Coverage Area ◽  
Computation Efficiency ◽  
Coverage Analysis ◽  
Satellite Applications ◽  
A Cell ◽  
Ground Coverage ◽  
Coverage Problems

Constellation-to-ground coverage analysis is an important problem in practical satellite applications. The classical net point method is one of the most commonly used algorithms in resolving this problem, indicating that the computation efficiency significantly depends on the high-precision requirement. On this basis, an improved cell area-based method is proposed in this paper, in which a cell is used as the basic analytical unit. By calculating the accuracy area of a cell that is partly contained by the ground region or partly covered by the constellation, the accurate coverage area can be obtained accordingly. Experiments simulating different types of coverage problems are conducted, and the results reveal the correctness and high efficiency of the proposed analytical method.

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

scholarly journals Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

2018 ◽  
Vol 7 (2.16) ◽  
pp. 29
Author(s):  
Gaurav Makwana ◽  
Lalita Gupta
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Breast Cancer Diagnosis ◽  
The State ◽  
Svm Classifier ◽  
Common Disease ◽  
Test Technique ◽  
Novel Approach ◽  
Breast Calcification

Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature. 

scholarly journals Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network

2020 ◽  
Author(s):  
Zeyun Tang ◽  
Yongliang Shen ◽  
Xinyin Ma ◽  
Wei Xu ◽  
Jiale Yu ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Human Performance ◽  
The State ◽  
Ensemble Model ◽  
Human Reasoning ◽  
Convolutional Network ◽  
Gating Mechanism ◽  
Novel Approach ◽  
Multiple Documents ◽  
State Of Art

Multi-hop reading comprehension across multiple documents attracts much attentions recently. In this paper, we propose a novel approach to tackle this multi-hop reading comprehension problem. Inspired by the human reasoning processing, we introduce a path-based graph with reasoning paths which extracted from supporting documents. The path-based graph can combine both the idea of the graph-based and path-based approaches, so it is better for multi-hop reasoning. Meanwhile, we propose Gated-GCN to accumulate evidences on the path-based graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning. We evaluate our approach on WikiHop dataset, and our approach achieves the the-state-of-art accuracy against previous published approaches. Especially, our ensemble model surpasses the human performance by 4.2%.

scholarly journals Learning to Transfer Relational Representations through Analogy

2019 ◽  
Vol 33 ◽  
pp. 10015-10016
Author(s):  
Gaetano Rossiello ◽  
Alfio Gliozzo ◽  
Michael Glass
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
The State ◽  
Large Set ◽  
Relational Information ◽  
Siamese Network ◽  
Distant Supervision ◽  
Novel Approach ◽  
Art Methods

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

scholarly journals Flexible Wi-Fi Communication among Mobile Robots in Indoor Industrial Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Jetmir Haxhibeqiri ◽  
Elnaz Alizadeh Jarchlo ◽  
Ingrid Moerman ◽  
Jeroen Hoebeke
Keyword(s):  
Mobile Robots ◽  
Ad Hoc ◽  
Mobile Node ◽  
Mesh Network ◽  
Coverage Problem ◽  
Problem Detection ◽  
Speed Up ◽  
Coverage Problems ◽  
The Impact ◽  
Configurable Architecture

In order to speed up industrial processes and to improve logistics, mobile robots are getting important in industry. In this paper, we propose a flexible and configurable architecture for the mobile node that is able to operate in different network topology scenarios. The proposed solution is able to operate in presence of network infrastructure, in ad hoc mode only, or to use both possibilities. In case of mixed architecture, mesh capabilities will enable coverage problem detection and overcoming. The solution is based on real requirements from an automated guided vehicle producer. First, we evaluate the overhead introduced by our solution. Since the mobile robot communication relies in broadcast traffic, the broadcast scalability in mesh network is evaluated too. Finally, through experiments on a wireless testbed for a variety of scenarios, we analyze the impact of roaming, mobility and traffic separation, and demonstrate the advantage of our approach in handling coverage problems.

A Novel Approach for Buckling Analysis of Pretwisted Spatially Curved Beams by State Equations

2016 ◽  
Vol 16 (05) ◽  
pp. 1550011 ◽  
Author(s):  
S. R. Kuo ◽  
Judy P. Yang ◽  
Y. B. Yang
Keyword(s):  
Rigid Body ◽  
Buckling Analysis ◽  
The State ◽  
Curved Beams ◽  
State Equations ◽  
State Matrix ◽  
Current Configuration ◽  
Matrix Operations ◽  
Novel Approach ◽  
Force Equilibrium

Based on force equilibrium and rigid body considerations, a novel approach is proposed for deriving the state equations and then the buckling equations of pretwisted spatially curved beams. Based on statical consideration of an infinitesimal element from the last calculated configuration [Formula: see text] to the current configuration [Formula: see text], a set of condition equations for the state matrix is derived. Next, by enforcing the rigid body rule for the beam, another set of condition equations for the state matrix is derived. From these two sets of equations, the state matrix of the beam is derived that leads directly to the buckling differential equations. The merit of the proposed approach is that it only requires simple differential and matrix operations. No hidden errors are possible because no higher-order terms need to be treated. In addition, a direct link is established between the straight and curved beam theories. Finally, examples are provided to demonstrate the application of the theory to the buckling analysis of various curved beams, including the helical ones.

scholarly journals A Novel Approach to Data Driven Pandemic Recovery: The Pandemic Recovery Acceleration Model

2020 ◽  
Author(s):  
Jeffrey P Gold ◽  
Christopher Wichman ◽  
Kenneth Bayles ◽  
Ali S Khan ◽  
Christopher Kratochvil ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Real Time ◽  
Local Community ◽  
The State ◽  
Data Driven ◽  
Real Time Analysis ◽  
Novel Approach ◽  
Acceleration Model ◽  
Care Systems

A data driven approach to guide the global, regional and local pandemic recovery planning is key to the safety, efficacy and sustainability of all pandemic recovery efforts. The Pandemic Recovery Acceleration Model (PRAM) analytic tool was developed and implemented state wide in Nebraska to allow health officials, public officials, industry leaders and community leaders to capture a real time snapshot of how the COVID-19 pandemic is affecting their local community, a region or the state and use this novel lens to aid in making key mitigation and recovery decisions. This is done by using six commonly available metrics that are monitored daily across the state describing the pandemic impact: number of new cases, percent positive tests, deaths, occupied hospital beds, occupied intensive care beds and utilized ventilators, all directly related to confirmed COVID-19 patients. Nebraska is separated into six Health Care Coalitions based on geography, public health and medical care systems. The PRAM aggregates the data for each of these geographic regions based on disease prevalence acceleration and health care resource utilization acceleration, producing real time analysis of the acceleration of change for each metric individually and also combined into a single weighted index, the PRAM Recovery Index. These indices are then shared daily with the state leadership, coalition leaders and public health directors and also tracked over time, aiding in real time regional and statewide decisions of resource allocation and the extent of use of comprehensive non-pharmacologic interventions.

scholarly journals Using Machine Learning for Decreasing State Uncertainty in Planning

2020 ◽  
Vol 69 ◽  
pp. 765-806
Author(s):  
Senka Krivic ◽  
Michael Cashmore ◽  
Daniele Magazzeni ◽  
Sandor Szedmak ◽  
Justus Piater
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Process ◽  
The State ◽  
Machine Learning Techniques ◽  
Planning Problem ◽  
Planning Techniques ◽  
Novel Approach ◽  
Learning Techniques ◽  
State Uncertainty

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

A Sequential Sampling Algorithm for Multistage Static Coverage Problems

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Binbin Zhang ◽  
Jida Huang ◽  
Rahul Rai ◽  
Hemanth Manjunatha
Keyword(s):  
Optimization Problems ◽  
Sequential Sampling ◽  
Minimum Energy ◽  
Pattern Search ◽  
Coverage Problem ◽  
Sampling Algorithm ◽  
System Designer ◽  
Area Of Interest ◽  
Coverage Problems ◽  
Multiple Stages

In many system-engineering problems, such as surveillance, environmental monitoring, and cooperative task performance, it is critical to allocate limited resources within a restricted area optimally. Static coverage problem (SCP) is an important class of the resource allocation problem. SCP focuses on covering an area of interest so that the activities in that area can be detected with high probabilities. In many practical settings, primarily due to financial constraints, a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources, i.e., agents. In the multistage formulation, agent locations for the next stage are dependent on previous-stage agent locations. Such multistage static coverage problems are nontrivial to solve. In this paper, we propose an efficient sequential sampling algorithm to solve the multistage static coverage problem (MSCP) in the presence of resource intensity allocation maps (RIAMs) distribution functions that abstract the event that we want to detect/monitor in a given area. The agent's location in the successive stage is determined by formulating it as an optimization problem. Three different objective functions have been developed and proposed in this paper: (1) L2 difference, (2) sequential minimum energy design (SMED), and (3) the weighted L2 and SMED. Pattern search (PS), an efficient heuristic algorithm has been used as optimization algorithm to arrive at the solutions for the formulated optimization problems. The developed approach has been tested on two- and higher dimensional functions. The results analyzing real-life applications of windmill placement inside a wind farm in multiple stages are also presented.

Sign in / Sign up

Export Citation Format

Share Document