scholarly journals Ground station scheduling optimization for a model of a real-world problem instance

2021 ◽  
Author(s):  
Bryce Wildish
Keyword(s):  
Real World ◽  
Heuristic Algorithms ◽  
Complex Problem ◽  
Initial Guess ◽  
Fine Tuning ◽  
Problem Instance ◽  
Ground Station ◽  
Real World Problem ◽  
Crucial Component ◽  
Orbiting Spacecraft

Effective scheduling of communication windows between orbiting spacecraft and ground stations is a crucial component of efficiently using spacecraft resources. In all but the most trivial cases, this forces the operator to choose a subset of the potentially available access windows such that they can achieve the best possible usage of their hardware and other resources. This is a complex problem not normally solvable analytically, and as a result the standard approach is to apply heuristic algorithms which take an initial guess at a solution and improve upon it in order to increase its quality. Various such algorithms exist, with some being in common practice for this particular problem. This thesis covers the application of several of the most commonly-used algorithms on a problem instance. Additionally, a real-world problem instance is used, and the resultant practical constraints are addressed when applying the heuristics and fine-tuning them for this application.

scholarly journals Ground station scheduling optimization for a model of a real-world problem instance

2021 ◽  
Author(s):  
Bryce Wildish
Keyword(s):  
Real World ◽  
Heuristic Algorithms ◽  
Complex Problem ◽  
Initial Guess ◽  
Fine Tuning ◽  
Problem Instance ◽  
Ground Station ◽  
Real World Problem ◽  
Crucial Component ◽  
Orbiting Spacecraft

Effective scheduling of communication windows between orbiting spacecraft and ground stations is a crucial component of efficiently using spacecraft resources. In all but the most trivial cases, this forces the operator to choose a subset of the potentially available access windows such that they can achieve the best possible usage of their hardware and other resources. This is a complex problem not normally solvable analytically, and as a result the standard approach is to apply heuristic algorithms which take an initial guess at a solution and improve upon it in order to increase its quality. Various such algorithms exist, with some being in common practice for this particular problem. This thesis covers the application of several of the most commonly-used algorithms on a problem instance. Additionally, a real-world problem instance is used, and the resultant practical constraints are addressed when applying the heuristics and fine-tuning them for this application.

scholarly journals Improving Local Search for Minimum Weight Vertex Cover by Dynamic Strategies

2018 ◽  
Author(s):  
Shaowei Cai ◽  
Wenying Hou ◽  
Jinkun Lin ◽  
Yuanjie Li
Keyword(s):  
Local Search ◽  
Real World ◽  
Heuristic Algorithms ◽  
Minimum Weight ◽  
Vertex Cover ◽  
Independent Set ◽  
Map Labeling ◽  
Massive Graphs ◽  
Real World Problem ◽  
Np Hardness

The minimum weight vertex cover (MWVC) problem is an important combinatorial optimization problem with various real-world applications. Due to its NP hardness, most works on solving MWVC focus on heuristic algorithms that can return a good quality solution in reasonable time. In this work, we propose two dynamic strategies that adjust the behavior of the algorithm during search, which are used to improve a state of the art local search for MWVC named FastWVC, resulting in two local search algorithms called DynWVC1 and DynWVC2. Previous MWVC algorithms are evaluated on graphs with random or hand crafted weights. In this work, we evaluate the algorithms on the vertex weighted graphs that obtained from an important real world problem, the map labeling problem. Experiments show that our algorithm obtains better results than previous algorithms for MWVC and maximum weight independent set (MWIS) on these real world instances. We also test our algorithms on massive graphs studied in previous works, and show significant improvements there.

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

scholarly journals A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 443-449
Author(s):  
P. Zhong ◽  
Z. Q. Gong ◽  
C. Schönlieb
Keyword(s):  
Image Classification ◽  
Real World ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Fine Tuning ◽  
Hyperspectral Image Classification ◽  
Latent Factors ◽  
Deep Model ◽  
Training Samples ◽  
Limited Training Samples

In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work turns to investigate the deep belief networks (DBNs), which allow unsupervised training. The DBN trained over limited training samples usually has many “dead” (never responding) or “potential over-tolerant” (always responding) latent factors (neurons), which decrease the DBN’s description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.

scholarly journals Finding A Small Vertex Cover in Massive Sparse Graphs: Construct, Local Search, and Preprocess

2017 ◽  
Vol 59 ◽  
pp. 463-494 ◽  
Author(s):  
Shaowei Cai ◽  
Jinkun Lin ◽  
Chuan Luo
Keyword(s):  
Local Search ◽  
Real World ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Vertex Cover ◽  
Search Algorithms ◽  
Theory And Practice ◽  
Sparse Graphs ◽  
Massive Graphs

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard combinatorial optimization problem of great importance in theory and practice. Due to its NP-hardness, there has been much interest in developing heuristic algorithms for finding a small vertex cover in reasonable time. Previously, heuristic algorithms for MinVC have focused on solving graphs of relatively small size, and they are not suitable for solving massive graphs as they usually have high-complexity heuristics. This paper explores techniques for solving MinVC in very large scale real-world graphs, including a construction algorithm, a local search algorithm and a preprocessing algorithm. Both the construction and search algorithms are based on low-complexity heuristics, and we combine them to develop a heuristic algorithm for MinVC called FastVC. Experimental results on a broad range of real-world massive graphs show that, our algorithms are very fast and have better performance than previous heuristic algorithms for MinVC. We also develop a preprocessing algorithm to simplify graphs for MinVC algorithms. By applying the preprocessing algorithm to local search algorithms, we obtain two efficient MinVC solvers called NuMVC2+p and FastVC2+p, which show further improvement on the massive graphs.

scholarly journals TSAL: Two steps Adversarial learning based domain adaptation

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Haidi Hasan Badr ◽  
Nayer Mahmoud Wanas ◽  
Magda Fayek
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Data Availability ◽  
Fine Tuning ◽  
Target Domain ◽  
Adversarial Learning ◽  
Prior Art ◽  
Real World Problem ◽  
Wide Range ◽  
Benchmark Datasets

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasize the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions.

scholarly journals Preparing Biomedical Engineers For Real World Problem Solving

2020 ◽  
Author(s):  
David Kelso ◽  
John D. Enderle ◽  
Kristina Ropella
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Real World Problem

scholarly journals Virtual Physics Laboratory with Real World Problem Based on Ngada Local Wisdom in Basic Physics Practicum

2021 ◽  
Vol 5 (4) ◽  
pp. 520
Author(s):  
Maria Yuliana Kua ◽  
Ni Wayan Suparmi ◽  
Dek Ngurah Laba Laksana
Keyword(s):  
Data Collection ◽  
Real World ◽  
Average Score ◽  
Study Program ◽  
Face To Face ◽  
Physics Laboratory ◽  
Real World Problem ◽  
Validation Score ◽  
Basic Physics ◽  
The Media

This research is based on the problem where practical activities in the Basic Physics Practicum course can no longer be carried out optimally due to changes in the learning model from face-to-face (offline) to online (online) during the COVID-19 pandemic. The purpose of this study was to develop a virtual physics laboratory as a medium in carrying out practical activities and to analyze the feasibility of the product through the validation results of experts and the results of product trials on prospective users. This type of research is Research & Development with ADDIE development model. The subjects of this study were 12 lecturers and 47 students of the STKIP Citra Bakti science education study program. Data collection techniques using validation sheets and questionnaires. The data collection instruments are in the form of validation assessment sheets and response questionnaires of prospective users. The data from this study were analyzed qualitatively descriptive to decide the feasibility of the product being developed. The results of the research showed that the average validation score of the material expert was 4.63, the media expert was 4.41, the learning design expert was 4.30, and the linguist was 4.51. The validation results of the four validators are in the very good category. Meanwhile, the results of product trials to lecturers and students as potential users are in the very good category with an average score of 4.53 and 4.57, respectively. Based on these data, this virtual physics laboratory product with real world problems based on Ngada local wisdom is recommended to be applied to the Basic Physics Practicum course and to help students in their independent practicum activities.

scholarly journals Pesantren Dan Pendidikan Politik Di Indonesia: Sebuah Reformulasi Kepemimpinan Islam Futuristik

2016 ◽  
Vol 16 (2) ◽  
pp. 461
Author(s):  
Mukodi Mukodi
Keyword(s):  
Real World ◽  
Life Skills ◽  
Boarding School ◽  
Political Awareness ◽  
The Real ◽  
Real World Problem ◽  
Agent Of Change ◽  
As If

Abstract: There is an increasing concern as if discussing politics in pesantren (Islamic Boarding School) was uncommon. This oddity is due to the conception of a person who puts pesantren merely a decontextualised scholarly reproduction of an-sich (from the real world problem or real politics) and not as an agent of change. In fact, pesantren is a replica of life integrating various life skills, including politics. The most interesting finding was that the diverse activities of life in the boarding school had raised the seedling of students’ political sense. This article also recommends the presence of political boarding school establishment, as a political incubator for Islamic activists as the continuity of conditioning political awareness in pesantren. Its realization is believed to be able to trigger the acceleration of the Islamic ideal leader candidate in Indonesia.

scholarly journals Thematic analysis of students’ talk while solving a real-world problem in geometry

2016 ◽  
Vol 35 ◽  
pp. 37-49 ◽  
Author(s):  
Anna F. DeJarnette ◽  
Gloriana González
Keyword(s):  
Thematic Analysis ◽  
Real World ◽  
Real World Problem
Sign in / Sign up

Export Citation Format

Share Document