scholarly journals Forward Link Optimization for the Design of VHTS Satellite Networks

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 473 ◽  
Author(s):  
Flor G. Ortiz-Gomez ◽  
Ramón Martínez ◽  
Miguel A. Salas-Natera ◽  
Andrés Cornejo ◽  
Salvador Landeros-Ayala
Keyword(s):  
Cost Model ◽  
High Capacity ◽  
Optimal Solution ◽  
Satellite Networks ◽  
Forward Link ◽  
User Terminal ◽  
Trade Offs ◽  
Service Areas ◽  
Optimum Solution ◽  
The Cost

The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse, in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity, and the figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link CINR (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs, including the constraints to achieve the optimum solution in terms of the user G/T, the number of beams, and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas.

scholarly journals Forward Link Optimization for the Design of VHTS Satellite Networks

2020 ◽  
Author(s):  
Flor G. Ortiz-Gomez ◽  
Ramón Martínez ◽  
Miguel A. Salas-Natera ◽  
Andrés Cornejo ◽  
Salvador Landeros-Ayala
Keyword(s):  
Cost Model ◽  
High Capacity ◽  
Optimal Solution ◽  
Satellite Networks ◽  
Forward Link ◽  
User Terminal ◽  
Trade Offs ◽  
Service Areas ◽  
Optimum Solution ◽  
The Cost

The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity and figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link CINR (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs including the constraints to achieve the optimum solution in terms of the user G/T, the number of beams and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas.

scholarly journals Greedy Algorithm for Minimizing the Cost of Routing Power on a Digital Microgrid

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3076 ◽  
Author(s):  
Zhengqi Jiang ◽  
Vinit Sahasrabudhe ◽  
Ahmed Mohamed ◽  
Haim Grebel ◽  
Roberto Rojas-Cessa
Keyword(s):  
Optimal Solution ◽  
High Complexity ◽  
Distributed Energy ◽  
Optimal Method ◽  
Cost Rate ◽  
Trade Offs ◽  
Lower Complexity ◽  
Cost Paths ◽  
The Cost ◽  
Selection Of

In this paper, we propose the greedy smallest-cost-rate path first (GRASP) algorithm to route power from sources to loads in a digital microgrid (DMG). Routing of power from distributed energy resources (DERs) to loads of a DMG comprises matching loads to DERs and the selection of the smallest-cost-rate path from a load to its supplying DERs. In such a microgrid, one DER may supply power to one or many loads, and one or many DERs may supply the power requested by a load. Because the optimal method is NP-hard, GRASP addresses this high complexity by using heuristics to match sources and loads and to select the smallest-cost-rate paths in the DMG. We compare the cost achieved by GRASP and an optimal method based on integer linear programming on different IEEE test feeders and other test networks. The comparison shows the trade-offs between lowering complexity and achieving optimal-cost paths. The results show that the cost incurred by GRASP approaches that of the optimal solution by small margins. In the adopted networks, GRASP trades its lower complexity for up to 18% higher costs than those achieved by the optimal solution.

OIL AND THE ENVIRONMENT - CALIFORNIA CASE STUDY

1976 ◽  
Vol 16 (1) ◽  
pp. 137
Author(s):  
D. W. Barnett
Keyword(s):  
Energy Production ◽  
Social Cost ◽  
Shadow Price ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Gulf Of Alaska ◽  
Outer Continental Shelf ◽  
Trade Offs ◽  
The Cost ◽  
Prudhoe Bay

USA environmentalists have tended to oppose all new energy developments. Their efforts may be counterproductive because opposition to, say, offshore oil directly leads to the continued use of other energy sources that may have a higher social cost. Rather than attempting to eliminate all pollution from energy production, which would be prohibitively expensive, one should minimize the social cost of energy production for the given demand.Linear programming is used to rank various oils (California State and Outer Continental Shelf (OCS), Gulf of Alaska, Prudhoe Bay, Athabasca tar sands, oil shale and certain foreign crudes) in terms of their social desirability. The objective is to minimize the cost of supplying the California market, subject to resource, sulphur and oil spill constraints.Social desirability is indicated by the inclusion of the oil in the optimal solution and the size of the associated shadow price. The larger the shadow price, the greater the benefits of increased production. The more negative, the greater the cost associated with forcing consumption of that fuel. The environmental shadow prices indicate the size of the trade-off between a particular environmental standard and minimum cost. The trade-offs can be surprisingly large. Any reasonable spill standard can be achieved by changing the development pattern. Generally, the further offshore, the smaller is the environmental degradation, but the more expensive is the oil. Foreign oils can be economically and environmentally inferior to domestic oils. Crude from the California OCS, San Joaquin Valley and Prudhoe Bay appears a valuable resource, while the Gulf of Alaska, synthetic and foreign crudes appear marginal to submarginal.The methodology could be readily adapted to the Australian scene.

New Approach in Cost Structuring of High Speed Hard Turning

2011 ◽  
Vol 264-265 ◽  
pp. 1003-1008 ◽  
Author(s):  
Muataz H.F. Al Hazza ◽  
Erry Yulian Triblas Adesta
Keyword(s):  
Cost Estimation ◽  
High Speed ◽  
Hard Turning ◽  
Cost Model ◽  
New Technology ◽  
Cost Estimating ◽  
New Approach ◽  
Cost Drivers ◽  
Optimum Solution ◽  
The Cost

Cost structuring of new technology is a critical mission which needs to be developed systematically to get accurate cost estimation. In this research a new approach was proposed and developed for cost structuring a new process. Cost modeling roadmap was proposed to guide the development of genetic cost model by integrating different cost estimating methods and supporting the optimum solution by using statistical techniques in modeling the cost in high speed hard turning, then by building logical relationships between the different effective variables through three levels of cost drivers; main drivers, process and technical drivers and final drivers. Finally a matlab model was developed for simulating the final cost drivers to study the effect of different parameters on the cost drivers.

Application of a New Nonlinear Programming Code with Decomposition to the Regional Wastewater-Collection and Treatment-Location Problem

1976 ◽  
Vol 8 (5) ◽  
pp. 563-571
Author(s):  
L R Padgett ◽  
A H Montgomery ◽  
L Romino
Keyword(s):  
Nonlinear Programming ◽  
Economies Of Scale ◽  
Location Problem ◽  
Sewage Treatment ◽  
Optimal Solution ◽  
Cost Savings ◽  
Regional Model ◽  
Programming Algorithm ◽  
Trade Offs ◽  
The Cost

A new, highly reliable nonlinear programming algorithm is coupled with decomposition to find the optimal solution to a regional sewage-treatment system for an area in Monongalia County surrounding Morgantown, West Virginia. A regional model with trade-offs between the cost of transporting wastewater to centrally located plants for treatment and the economies of scale accruing to large centralized treatment plants is developed. Possible cost savings of an optimal system are demonstrated.

scholarly journals Evolutionary Algorithm Technique to Optimize Configurational Cost in Reconfigurable Manufacturing System

2020 ◽  
Vol 8 (6) ◽  
pp. 1283-1290
Keyword(s):  
Evolutionary Algorithm ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Optimal Solution ◽  
Reconfigurable Manufacturing ◽  
Hardware Configuration ◽  
Software And Hardware ◽  
Optimum Solution ◽  
The Cost

In past few decades, manufacturing industry has been trying to achieve the high quality customized products in the flexible batches. Moreover due to the dynamic demand of market, the product lifecycle and flexibility is one of the major concern and Reconfiguration Manufacturing System is one of the discovery that matches these criteria such as flexibility, reliability and others, the main advantage of RMS is its adoptability to cope up with change in software and hardware configuration for adjusting the promptly. Hence in this paper we have proposed a optimized RMS , the main objective is to reduce the cost of the configuration through task scheduling, we propose Evolutionary algorithm based approach for configuration, moreover here we find the optimal solution through utilizing the machine over the operation set for each machine part, later we find the optimal from the probable assignment. In order to evaluate the algorithm we have performed the case study, a case study shows that Evolutionary algorithm based approach achieves the optimum solution when compared to the existing.

Large-scale cost function learning for path planning using deep inverse reinforcement learning

2017 ◽  
Vol 36 (10) ◽  
pp. 1073-1087 ◽  
Author(s):  
Markus Wulfmeier ◽  
Dushyant Rao ◽  
Dominic Zeng Wang ◽  
Peter Ondruska ◽  
Ingmar Posner
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Cost Model ◽  
High Capacity ◽  
Urban Environments ◽  
Training Dataset ◽  
Inverse Reinforcement Learning ◽  
Dataset Size ◽  
And Performance ◽  
The Cost

We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of manually designing parts of the pipeline, exploits a large number of data samples, and can be framed additionally to refine handcrafted cost maps produced based on manual hand-engineered features. To achieve this, we introduce a maximum-entropy-based, non-linear inverse reinforcement learning (IRL) framework which exploits the capacity of fully convolutional neural networks (FCNs) to represent the cost model underlying driving behaviours. The application of a high-capacity, deep, parametric approach successfully scales to more complex environments and driving behaviours, while at deployment being run-time independent of training dataset size. After benchmarking against state-of-the-art IRL approaches, we focus on demonstrating scalability and performance on an ambitious dataset collected over the course of 1 year including more than 25,000 demonstration trajectories extracted from over 120 km of urban driving. We evaluate the resulting cost representations by showing the advantages over a carefully, manually designed cost map and furthermore demonstrate its robustness towards systematic errors by learning accurate representations even in the presence of calibration perturbations. Importantly, we demonstrate that a manually designed cost map can be refined to more accurately handle corner cases that are scarcely seen in the environment, such as stairs, slopes and underpasses, by further incorporating human priors into the training framework.

A Web-Based Process for Creating Parametric Cost Models for Product Design Trade Studies

2004 ◽  
Author(s):  
Alexandre E. Gue´rinot ◽  
Gregory M. Roach ◽  
Jordan J. Cox
Keyword(s):  
Product Design ◽  
Cost Estimation ◽  
Cost Model ◽  
Production Costs ◽  
Preliminary Design ◽  
Cost Models ◽  
Design Decisions ◽  
Web Based ◽  
Trade Offs ◽  
The Cost

This paper proposes a method for creating a parametric cost model established on the foundation of the product design generator methodology to provide early estimates of production cost and manufacturing cycle-time during preliminary design. This is accomplished by capturing the manufacturing process and knowledge associated with the product and its production. The relationships between design decisions and manufacturing costs are explicitly exposed making the cost estimation process reusable and repeatable. Designers can now clearly assess the profitability of their design, identify appropriate trade-offs between engineering requirements and production costs, and alter the design accordingly.

scholarly journals Economic evaluation of alternative testing regimes and settings to detect undiagnosed HIV in Australia

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Owain D. Williams ◽  
Judith A. Dean ◽  
Anna Crothers ◽  
Charles F. Gilks ◽  
Jeff Gow
Keyword(s):  
Point Of Care ◽  
Cost Model ◽  
Cost Effective ◽  
Positive Diagnosis ◽  
Trade Offs ◽  
Self Testing ◽  
Undiagnosed Hiv ◽  
Sex With Men ◽  
The Individual ◽  
The Cost

Abstract Background The study aimed to estimate the comparative costs per positive diagnosis of previously undetected HIV in three testing regimes: conventional; parallel and point of care (POC) testing. The regimes are analysed in six testing settings in Australia where infection is concentrated but with low prevalence. Methods A cost model was developed to highlight the trade-offs between test and economic efficiency from a provider perspective. First, an estimate of the number of tests needed to find a true (previously undiagnosed) positive diagnosis was made. Second, estimates of the average cost per positive diagnosis in whole of population (WoP) and men who have sex with men (MSM) was made, then third, aggregated to the total cost for diagnosis of all undetected infections. Results Parallel testing is as effective as conventional testing, but more economically efficient. POC testing provide two significant advantages over conventional testing: they screen out negatives effectively at comparatively lower cost and, with confirmatory testing of reactive results, there is no loss in efficiency. The average and total costs per detection in WoP are prohibitive, except for Home Self Testing. The diagnosis in MSM is cost effective in all settings, but especially using Home Self Testing when the individual assumes the cost of testing. Conclusions This study illustrates the trade-offs between economic and test efficiency and their interactions with population(s) prevalence. The efficient testing regimes and settings are presently under or not funded in Australia. Home Self Testing has the potential to dramatically increase testing rates at very little cost.

OPTIMAL FEASIBLE SOLUTIONS TO A ROAD FREIGHT TRANSPORTATION PROBLEM

2020 ◽  
Vol 5 (1) ◽  
pp. 456
Author(s):  
Tolulope Latunde ◽  
Joseph Oluwaseun Richard ◽  
Opeyemi Odunayo Esan ◽  
Damilola Deborah Dare
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Transportation Cost ◽  
North West ◽  
Life Problems ◽  
Basic Feasible Solutions ◽  
Feasible Solutions ◽  
Road Freight Transportation ◽  
The Cost ◽  
Cost Of Transportation

For twenty decades, there is a visible ever forward advancement in the technology of mobility, vehicles and transportation system in general. However, there is no "cure-all" remedy ideal enough to solve all life problems but mathematics has proven that if the problem can be determined, it is most likely solvable. New methods and applications will keep coming to making sure that life problems will be solved faster and easier. This study is to adopt a mathematical transportation problem in the Coca-Cola company aiming to help the logistics department manager of the Asejire and Ikeja plant to decide on how to distribute demand by the customers and at the same time, minimize the cost of transportation. Here, different algorithms are used and compared to generate an optimal solution, namely; North West Corner Method (NWC), Least Cost Method (LCM) and Vogel’s Approximation Method (VAM). The transportation model type in this work is the Linear Programming as the problems are represented in tables and results are compared with the result obtained on Maple 18 software. The study shows various ways in which the initial basic feasible solutions to the problem can be obtained where the best method that saves the highest percentage of transportation cost with for this problem is the NWC. The NWC produces the optimal transportation cost which is 517,040 units.

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