scholarly journals Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

2020 ◽  
Vol 12 (18) ◽  
pp. 3096
Author(s):  
Gideon Okpoti Tetteh ◽  
Alexander Gocht ◽  
Marcel Schwieder ◽  
Stefan Erasmi ◽  
Christopher Conrad
Keyword(s):  
Evaluation System ◽  
Reference Data ◽  
Cost Effective ◽  
Agricultural Landscapes ◽  
Bayesian Optimization ◽  
Absolute Difference ◽  
Optimization Approach ◽  
Lower Saxony ◽  
Optimal Segmentation ◽  
Unsupervised Methods

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.

Bioavailability of phosphorus as a tool for efficient P reduction schemes

1998 ◽  
Vol 37 (3) ◽  
pp. 241-247 ◽  
Author(s):  
Peter Gerdes ◽  
Sabine Kunst
Keyword(s):  
Sewage Treatment ◽  
Cost Effective ◽  
Effective Management ◽  
Lower Saxony ◽  
Effective Strategies ◽  
P Balance ◽  
Different Sources ◽  
Total P ◽  
P Sources ◽  
P Bioavailability

The bioavailability of phosphorus from different sources has been evaluated in the catchment area of the River Ilmenau (Lower-Saxony, Germany) by using algal assays. The P bioavailability describes the different potential of P from various sources of supporting eutrophication. Effluents from sewage treatment plants were highly bioavailable (72% of TP) whereas rainwater (26%) and erosion effluents (30%) showed a low bioavailability. In order to develop effective strategies to minimize P inputs into the river, source specific P bioavailability indices were determined and combined with a P balance to calculate inputs of vioavailable P (BAP) instead of total P (TP). It could be shown that the relative importance of the different P sources changes when applying BAP. Measures to reduce P inputs into the River Ilmenau will take P bioavailability into consideration and therefore lead to a more cost-effective management.

scholarly journals Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 934
Author(s):  
Mariacrocetta Sambito ◽  
Gabriele Freni
Keyword(s):  
Treatment Plant ◽  
Optimal Solution ◽  
Monitoring Network ◽  
Computational Effort ◽  
Bayesian Optimization ◽  
Urban Drainage ◽  
Optimization Approach ◽  
Sensor Location ◽  
Contamination Source ◽  
Monitoring Point

In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal 'grey' information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.

scholarly journals Toward Decentralised Sanitary Sewage Collection Systems: A Multiobjective Approach for Cost-Effective and Resilient Designs

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1886
Author(s):  
Arezoo Zahediasl ◽  
Amin E. Bakhshipour ◽  
Ulrich Dittmer ◽  
Ali Haghighi
Keyword(s):  
Treatment Plant ◽  
Drainage System ◽  
Cost Effective ◽  
Optimization Approach ◽  
Hydraulic Design ◽  
Multiobjective Approach ◽  
Layout Generation ◽  
Plant Allocation ◽  
Problem Solving Process

In recent years, the concept of a centralized drainage system that connect an entire city to one single treatment plant is increasingly being questioned in terms of the costs, reliability, and environmental impacts. This study introduces an optimization approach based on decentralization in order to develop a cost-effective and sustainable sewage collection system. For this purpose, a new algorithm based on the growing spanning tree algorithm is developed for decentralized layout generation and treatment plant allocation. The trade-off between construction and operation costs, resilience, and the degree of centralization is a multiobjective problem that consists of two subproblems: the layout of the networks and the hydraulic design. The innovative characteristics of the proposed framework are that layout and hydraulic designs are solved simultaneously, three objectives are optimized together, and the entire problem solving process is self-adaptive. The model is then applied to a real case study. The results show that finding an optimum degree of centralization could reduce not only the network’s costs by 17.3%, but could also increase its structural resilience significantly compared to fully centralized networks.

scholarly journals A delayed differentiation multiproduct model with the outsourcing of common parts, overtime strategy for end products, and quality reassurance

2021 ◽  
pp. 143-158 ◽  
Author(s):  
Singa Wang Chiu ◽  
Victoria Chiu ◽  
Ming-Hon Hwang ◽  
Yuan-Shyi Peter Chiu
Keyword(s):  
Cost Effective ◽  
Sensitivity Analyses ◽  
Optimization Approach ◽  
Limited Capacity ◽  
Numerical Illustration ◽  
Delayed Differentiation ◽  
Modeling Analysis ◽  
End Products ◽  
Effective Decision ◽  
Effective Decision Making

Production planners today must simultaneously face with the time and quality demands of various goods externally and meet limited capacity internally. This study presents a two-stage delayed- differentiation multiproduct model that considers the outsourcing options for common parts, overtime strategy for end products, and quality reassurance to assist in making fabrication runtime decisions that are cost-effective. Stage one produces all necessary common intermediate components for end products. To reduce stage one’s utilization/uptime, this study adopts a partial outsourcing option. Stage two uses an overtime strategy to fabricate end products that further shorten the uptime. The production processes in both phases are assumed to be imperfect. This study employs the reworking/scrapping of random faulty items to reassure product quality. The researchers build a model to depict the proposed problem’s characteristics and used the mathematical modeling, analysis, and optimization approach to determine the best rotation cycle length that minimizes the system’s expenses. Further, in this study, the researchers provide sensitivity analyses and a numerical illustration, which validate the result’s applicability and exhibit its capability. This result contributes to practical multiproduct-fabrication by (1) deriving the optimal manufacturing policy for a delayed-differentiation multiproduct system with dual uptime reduction policies and quality reassurance; and (2) offering a decisional model that allows production planners to explore the collective/separate effect of a quality-ensured and dual uptime reduction strategy on a problem’s operating policy and crucial system performance indicators, which assists in cost-effective decision-making.

Design and Analysis of Automotive Bumper Covers in Transient Loading Conditions

2016 ◽  
Vol 715 ◽  
pp. 174-179 ◽  
Author(s):  
Chih Hsing Liu ◽  
Ying Chia Huang ◽  
Chen Hua Chiu ◽  
Yu Cheng Lai ◽  
Tzu Yang Pai
Keyword(s):  
Optimal Design ◽  
Numerical Model ◽  
Design Method ◽  
Experimental Tests ◽  
Cost Effective ◽  
Limited Range ◽  
Optimization Approach ◽  
Loading Conditions ◽  
Element Analysis ◽  
Design Guideline

This paper presents the analysis methods for design of automotive bumper covers. The bumper covers are plastic structures attached to the front and rear ends of an automobile and are expected to absorb energy in a minor collision. One requirement in design of the bumper covers is to minimize the bumper deflection within a limited range under specific loadings at specific locations based on the design guideline. To investigate the stiffness performance under various loading conditions, a numerical model based on the explicit dynamic finite element analysis (FEA) using the commercial FEA solver, LS-DYNA, is developed to analyze the design. The experimental tests are also carried out to verify the numerical model. The thickness of the bumper cover is a design variable which usually varies from 3 to 4 mm depending on locations. To improve the stiffness of the bumper, an optimal design for the bumper under a pre-defined loading condition is identified by using the topology optimization approach, which is an optimal design method to obtain the optimal layout of an initial design domain under specific boundary conditions. The outcome of this study provides an efficient and cost-effective method to predict and improve the design of automotive bumper covers.

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

2021 ◽  
Author(s):  
Federico Peralta Samaniego ◽  
Sergio Toral Marín ◽  
Daniel Gutierrez Reina
Keyword(s):  
Water Quality ◽  
Mean Squared Error ◽  
Bayesian Optimization ◽  
Water Quality Parameter ◽  
Optimization Approach ◽  
Decision Strategy ◽  
Sequential Decision ◽  
Partition System ◽  
Autonomous Surface Vehicle ◽  
Voronoi Partition

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km<sup>2</sup>) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

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