A state-based and optimal path dependant short-term flood planning 

2021 ◽  
Author(s):  
Mengke Ni ◽  
Tohid Erfani
Keyword(s):  
Large Scale ◽  
Optimal Path ◽  
Weather Condition ◽  
Optimal Solution ◽  
Scenario Tree ◽  
Short Term ◽  
Planning Decisions ◽  
Proposed Model ◽  
Flood Planning ◽  
Deployment Planning

<p>Temporary flood protective defences (TFPD) are supplementary to permanent engineering solutions. In a flood event, asset managers are faced with a challenging task of deploying large-scale temporary defences at multiple locations. As the performance of temporary defences is sensitive to various uncertain weather condition factors, it is difficult to fix a single specific deployment plan as the optimal solution. This, moreover, leads to insufficient and/or underused defences on flood-affected locations. This paper describes a state-based (SB) mathematical modelling approach to deal with above challenge by adapting TFPD strategies consistently to short-term future as they unfold. We employ multistage stochastic and scenario tree to identify a set of alternative SB optimal paths for deployment planning. The proposed model is applied to nine flood-affected locations in Carlisle, northwest England. The results indicate that the inclusion of SB path-dependant solution strategy are beneficial for the flood asset manager faced with making short-term deployment planning decisions.</p>

Robot perceptual adaptation to environment changes for long-term human teammate following

2020 ◽  
pp. 027836491989662
Author(s):  
Sriram Siva ◽  
Hao Zhang
Keyword(s):  
Large Scale ◽  
Feature Learning ◽  
Optimal Solution ◽  
Human Detection ◽  
Perceptual Adaptation ◽  
Short Term ◽  
Environment Changes ◽  
Depth Sensors ◽  
Robot Perception

Perception is one of the several fundamental abilities required by robots, and it also poses significant challenges, especially in real-world field applications. Long-term autonomy introduces additional difficulties to robot perception, including short- and long-term changes of the robot operation environment (e.g., lighting changes). In this article, we propose an innovative human-inspired approach named robot perceptual adaptation (ROPA) that is able to calibrate perception according to the environment context, which enables perceptual adaptation in response to environmental variations. ROPA jointly performs feature learning, sensor fusion, and perception calibration under a unified regularized optimization framework. We also implement a new algorithm to solve the formulated optimization problem, which has a theoretical guarantee to converge to the optimal solution. In addition, we collect a large-scale dataset from physical robots in the field, called perceptual adaptation to environment changes (PEAC), with the aim to benchmark methods for robot adaptation to short-term and long-term, and fast and gradual lighting changes for human detection based upon different feature modalities extracted from color and depth sensors. Utilizing the PEAC dataset, we conduct extensive experiments in the application of human recognition and following in various scenarios to evaluate ROPA. Experimental results have validated that the ROPA approach obtains promising performance in terms of accuracy and efficiency, and effectively adapts robot perception to address short-term and long-term lighting changes in human detection and following applications.

scholarly journals An Integrated Multi-Echelon Supply Chain Network Design Considering Stochastic Demand: A Genetic Algorithm Based Solution

2017 ◽  
Vol 29 (4) ◽  
pp. 391-400 ◽  
Author(s):  
Sara Nakhjirkan ◽  
Farimah Mokhatab Rafiei
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Large Scale ◽  
Human Life ◽  
Optimal Solution ◽  
Green Supply Chain ◽  
Inventory Routing ◽  
Model Object ◽  
Proposed Model ◽  
Practical Tool

The growing trend of natural resources consumption has caused irreparable losses to the environment. The scientists believe that if environmental degradation continues at its current pace, the prospect of human life will be shrouded in mystery. One of the most effective ways to deal with the environmental adverse effects is by implementing green supply chains. In this study a multilevel mathematical model including supply, production, distribution and customer levels has been presented for routing–location–inventoryin green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q) to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.

How an adaptive and flexible short-term flood planning can be beneficial - a UK case study application

2020 ◽  
Author(s):  
Mengke Ni ◽  
Tohid Erfani
Keyword(s):  
Flood Protection ◽  
Scenario Tree ◽  
Mixed Integer ◽  
Model Parameters ◽  
Integer Optimization ◽  
Short Term ◽  
Location Selection ◽  
Multi Stage ◽  
Flood Planning

<p>Short term flood intervention planning includes identifying how the limited resources should be allocated to the most appropriate affected locations. The water level is an important factor for temporary flood protection planning for which adaptability of the plan to its changing future condition is regarded valuable. Moreover, flexibility in activation, delaying and replacement of the existing plans should be considered to mitigate the damages caused by future unknown condition. This research applies real options analysis which incorporates adaptability and flexibility in addressing “least-cost alternative”  location selection via multi-stage stochastic programming. We apply the proposed model to a case study in Eden catchment with nine different flood-affected cities with different degrees of uncertainty along Eden River in England. A multi-objective and mixed integer optimization model was formulated to solve on a scenario tree capable to choose most appropriate locations for deploying intervention measures of temporary flood protection. We examine the solution under various model parameters uncertainty and compare the results with the business as usual case presenting the benefits of proposed formulation in terms of expected damage and cost.</p>

High-Dimensional Least-Squares with Perfect Positive Correlation

2019 ◽  
Vol 36 (04) ◽  
pp. 1950016
Author(s):  
Zhiyong Huang ◽  
Ziyan Luo ◽  
Naihua Xiu
Keyword(s):  
Least Squares ◽  
Correlation Coefficient ◽  
Large Scale ◽  
Rank Correlation ◽  
Optimal Solution ◽  
Ordinary Least Squares ◽  
High Dimensional ◽  
Dual Algorithm ◽  
Positive Correlation ◽  
Proposed Model

The least-squares is a common and important method in linear regression. However, it often leads to overfitting phenomenon as dealing with high-dimensional problems, and various regularization schemes regarding prior information for specific problems are studied to make up such a deficiency. In the sense of Kendall’s [Formula: see text] from the community of nonparametric analysis, we establish a new model wherein the ordinary least-squares is equipped with perfect positive correlation constraint, sought to maintain the concordance of the rankings of the observations and the systematic components. By sorting the observations into an ascending order, we reduce the perfect positive correlation constraint into a linear inequality system. The resulting linearly constrained least-squares problem together with its dual problem is shown to be solvable. In particular, we introduce a mild assumption on the observations and the measurement matrix which rules out the zero vector from the optimal solution set. This indicates that our proposed model is statistically meaningful. To handle large-scale instances, we propose an efficient alternating direction method of multipliers (ADMM) to solve the proposed model from the dual perspective. The effectiveness of our model compared to ordinary least-squares is evaluated in terms of rank correlation coefficient between outputs and the systematic components, and the efficiency of our dual algorithm is demonstrated with the comparison to three efficient solvers via CVX in terms of computation time, solution accuracy and rank correlation coefficient.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Wind Farm Cable Connection Layout Optimization with Several Substations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3615
Author(s):  
Adelaide Cerveira ◽  
Eduardo J. Solteiro Pires ◽  
José Baptista
Keyword(s):  
Energy Production ◽  
Wind Farm ◽  
Programming Model ◽  
Optimal Solution ◽  
Wind Farms ◽  
Green Energy ◽  
Fossil Energy ◽  
Proposed Model ◽  
Short Time ◽  
Cable Connection

Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms’ cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

scholarly journals Blastocyst formation, embryo transfer and breed comparison in the first reported large scale cloning of camels

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
P. O. Olsson ◽  
A. H. Tinson ◽  
N. Al Shamsi ◽  
K. S. Kuhad ◽  
R. Singh ◽  
...  
Keyword(s):  
Embryo Transfer ◽  
Nuclear Transfer ◽  
Large Scale ◽  
Donor Cell ◽  
Blastocyst Formation ◽  
Short Term ◽  
Embryo Formation ◽  
Large Expansion ◽  
Breed Differences ◽  
Breed Comparison

AbstractCloning, through somatic cell nuclear transfer (SCNT), has the potential for a large expansion of genetically favorable traits in a population in a relatively short term. In the present study we aimed to produce multiple cloned camels from racing, show and dairy exemplars. We compared several parameters including oocyte source, donor cell and breed differences, transfer methods, embryo formation and pregnancy rates and maintenance following SCNT. We successfully achieved 47 pregnancies, 28 births and 19 cloned offspring who are at present healthy and have developed normally. Here we report cloned camels from surgical embryo transfer and correlate blastocyst formation rates with the ability to achieve pregnancies. We found no difference in the parameters affecting production of clones by camel breed, and show clear differences on oocyte source in cloning outcomes. Taken together we demonstrate that large scale cloning of camels is possible and that further improvements can be achieved.

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