scholarly journals Automation of the one-storey underground parking space organization

2021 ◽  
Vol 284 ◽  
pp. 06012
Author(s):  
Natalia Knyazeva ◽  
Anastasia Kolosova
Keyword(s):  
Evolutionary Algorithms ◽  
Urban Area ◽  
Optimal Solution ◽  
Parking Space ◽  
Space Planning ◽  
Parking Lots ◽  
Legal Documents ◽  
Space Organization ◽  
Big Cities ◽  
The One

With the growing car population in big cities, the problem of its keeping in conditions of a compact urban area has happened. The organisation of parking space in a different way has resolved this issue. Underground parking was in demand in many countries even in the XX century. By the way, they are becoming more and more popular now. The design of car parking is based on legal documents, which regulate the size of car parking seats and the width of the passage inside the garage. It is expedient to use evolutionary algorithms as one of the tools of algorithmic modelling for automation of design the car parking lots and for identifying the most effective and profitable way of the car parking space planning. So, the process of looking for the most optimal solution in underground car parking designing.

scholarly journals Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zhendong Liu ◽  
Dongyan Li ◽  
Yurong Yang ◽  
Xi Chen ◽  
Xinrong Lv ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Guidance System ◽  
Parking Space ◽  
Dijkstra Algorithm ◽  
Parking Lots ◽  
Design And Implementation ◽  
Parking Lot ◽  
Big Cities

The information guidance system for parking spaces in large- and medium-sized parking lots is not efficient at present. It tends to be difficult to find an empty parking space in parking lots in big cities. One of the problems is the large amount of calculation in the traditional Dijkstra algorithm. In this paper, an improved Dijkstra algorithm is presented and optimized to find the best parking path with the purpose of looking for the nearest free parking space based on the layout model in parking lot parking guidance. The experiments show that the improved Dijkstra algorithm can find the optimal parking space and the optimal parking path and improve the parking efficiency.

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

2019 ◽  
Author(s):  
Anders Andreasen
Keyword(s):  
Evolutionary Algorithms ◽  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

scholarly journals Occurrence and Activity of Roe Deer in Urban Forests of Warsaw

2020 ◽  
Vol 3 (1) ◽  
pp. 35
Author(s):  
Karolina D. Jasińska ◽  
Mateusz Jackowiak ◽  
Jakub Gryz ◽  
Szymon Bijak ◽  
Katarzyna Szyc ◽  
...  
Keyword(s):  
Predation Risk ◽  
Urban Area ◽  
Roe Deer ◽  
Urban Forests ◽  
Anthropogenic Disturbances ◽  
Camera Traps ◽  
Human Disturbances ◽  
Human Presence ◽  
Big Cities ◽  
High Level

Human presence or activities are perceived by animals as those associated with predation risk so activity and exploration patterns of animals should be shaped by indices of anthropogenic disturbances. The high level of human disturbances is noticed in big cities. Therefore, the aim of the study was to determine the occurrence of roe deer in Warsaw and its activity in the Warsaw urban forests. We used snow tracking on transect routes (winter seasons 2016, 2017, 2018; 115.1 km in total) to determine roe deer occurrence in four habitats: forests, open areas, parks, and built-up areas. The number of tracks was highest in forests (4.6 tracks/1 km/24 h), followed by open areas, built-up areas, and parks. We used camera traps to determine the activity of roe deer in selected urban forests. We collected 697 observations of roe deer in Warsaw forests in the years 2016–2019 (per 4826 trap-days in total). The peak of roe deer activity was noticed between 4:00 and 5:00 a.m. Animals were least active at 1:00–2:00 p.m. and between 11:00 p.m.–01:00 a.m. Our research showed that roe deer inhabiting the urban area avoided human presence by using well-covered habitats and being active in periods when humans’ disturbances’ level is lower.

scholarly journals Differentiation of the concept of legal aid

1970 ◽  
pp. 98-102
Author(s):  
Oksana Krushnitska
Keyword(s):  
Legal Assistance ◽  
Legal Aid ◽  
Legal Decisions ◽  
Legal Advice ◽  
Legal Documents ◽  
Basic Law ◽  
Evidence Analysis ◽  
The Subject ◽  
The One ◽  
The Relationship

This article discusses the relationship between legal, legal aid and legal assistance. The lack of a clear distinction between the term "legal assistence" and the terms "legal aid" and "legal" has led, in our observations, to the conclusions of individual authors and entire institutions that Ukraine's law enshrines in fact a triple system legal aid. Studies have shown that the legislator distinguishes between "legal aid" and "legal" (or legal) assistance, depending on the subject of assistance. Positive trends in the replacement of legal aid terminology with professional legal aid have been identified and shown. At he article notes that the development and establishment of independent professional legal assistance continues in the future. A large number of reforms and changes, especially at the constitutional level, on the one hand, contribute to improving and improving the development of the institution of professional legal assistance, and on the other hand, there are many contradictions and inconsistencies in this regard, because the introduction of new terms is always a supporter for its introduction and against it. Legal aid is the most successful term and should be interpreted as a multidimensional legal practice aimed at ensuring the rule of law and the realization of the rights of each person who enters into a specific legal relationship, the content of which is the implementation of legally defined means, including legal advice and clarification of the rights and procedures for their implementation, assistance in the preparation and filing of applications, petitions, complaints and other legal documents, initiation and participation in procedural actions and proper recording of their course and results, assessment of the adherence, validity and admissibility of evidence, analysis of the legality of legal decisions, taking measures to remedy infringed cases. to, damages caused offense. It also includes some of the problems that need to be addressed by further consolidating professional legal assistance in other regulations to ensure their compliance with the Basic Law of Ukraine.

scholarly journals Study on Profitable Shared Parking Management Based on Day-to-Day Evolution Model

2021 ◽  
Vol 33 (1) ◽  
pp. 17-33
Author(s):  
Duo Xu ◽  
Huijun Sun
Keyword(s):  
Urban Area ◽  
Optimal Allocation ◽  
Cost Minimization ◽  
Profit Maximization ◽  
Evolution Model ◽  
Residential Areas ◽  
Short Supply ◽  
Parking Lots ◽  
Morning Commute ◽  
Quantity Control

Parking problems are getting increasingly serious in the urban area. However, the parking spots in the urban area are underutilized rather than really scarce. There is a large number of private spots in the residential areas that have the potential of being shared. Due to its private nature, shared parking is usually operated by a profitable mode. To study the utilization of shared parking and its impact on the morning commute, this paper proposes an evolution model. The supply side is a profit-chasing manager who decides on the selling prices and the business scale, while the demand side refers to travellers who respond to costs and choose the trip mode. By analysing the behaviour (strategy) of both sides, the study covers: 1 - the attraction and competition between parking lots and trip modes, 2 - the utilization and user composition of the parking lots. By inducing two numerical examples, the conclusions are that 1 - managers can achieve maximum profit and optimal allocation through price adjustment and quantity control; 2 - publicity (system cost minimization) and profitability (profit maximization) are consistent under certain threshold conditions; 3 - competition exists between parking lots as well as trip modes; some parking lots are even in short supply; profitable management does not create a market monopoly.

scholarly journals Multiple infill criterion-assisted hybrid evolutionary optimization for medium-dimensional computationally expensive problems

2021 ◽  
Author(s):  
Shufen Qin ◽  
Chan Li ◽  
Chaoli Sun ◽  
Guochen Zhang ◽  
Xiaobo Li
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Global Search ◽  
Model Management ◽  
The Other ◽  
Local Searches ◽  
Fitness Value ◽  
Expensive Problems ◽  
Computationally Expensive

AbstractSurrogate-assisted evolutionary algorithms have been paid more and more attention to solve computationally expensive problems. However, model management still plays a significant importance in searching for the optimal solution. In this paper, a new method is proposed to measure the approximation uncertainty, in which the differences between the solution and its neighbour samples in the decision space, and the ruggedness of the objective space in its neighborhood are both considered. The proposed approximation uncertainty will be utilized in the surrogate-assisted global search to find a solution for exact objective evaluation to improve the exploration capability of the global search. On the other hand, the approximated fitness value is adopted as the infill criterion for the surrogate-assisted local search, which is utilized to improve the exploitation capability to find a solution close to the real optimal solution as much as possible. The surrogate-assisted global and local searches are conducted in sequence at each generation to balance the exploration and exploitation capabilities of the method. The performance of the proposed method is evaluated on seven benchmark problems with 10, 20, 30 and 50 dimensions, and one real-world application with 30 and 50 dimensions. The experimental results show that the proposed method is efficient for solving the low- and medium-dimensional expensive optimization problems by compared to the other six state-of-the-art surrogate-assisted evolutionary algorithms.

scholarly journals Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms

2018 ◽  
Author(s):  
Hsu-Tan Tan ◽  
Bor-An Chen ◽  
Yung-Fa Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Communication Systems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
System Capacity ◽  
Swarm Optimization ◽  
Device To Device ◽  
Simulation Results ◽  
Capacity Performance

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

scholarly journals How Do We See The Non Luminous Objects? 12-13 Years Old Students’ Mental Representation of Vision

2018 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
Konstantinos Ravanis
Keyword(s):  
Data Analysis ◽  
Urban Area ◽  
Mental Representation ◽  
The Other ◽  
Artificial Light ◽  
Scientific Thinking ◽  
Teaching Intervention ◽  
Reflection Of Light ◽  
Luminous Objects ◽  
The One

In this paper, we present the findings of a research which has two objectives: firstly, it recorded 12-13 years old (7th grade) students’ mental representation regarding the vision of non-luminous objects, and, secondly, it emphasized on the relative cognitive fields. The research was done through interviews of 107 urban area students in Greece. The students were asked to explain how objects become visible, stressing the following themes: The manner in which our eyes help us see the objects, whether natural or artificial light helps us see the objects and in what way, and if the objects emit light. The data analysis led to the recording of the students' basic mental representation on the one hand, while on the other hand emphasized the reemission or reflection of light by the luminous objects as a basic mental representation.From the research results, it can be concluded that through a teaching intervention based on mental representation we can foster and enhance scientific thinking and learning about light and vision. 

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Evolutionary Algorithms for Global Decision Tree Induction

2018 ◽  
pp. 2132-2141 ◽  
Author(s):  
Marek Kretowski ◽  
Marcin Czajkowski
Keyword(s):  
Evolutionary Algorithms ◽  
Decision Tree ◽  
Knowledge Discovery ◽  
Decision Trees ◽  
Optimal Solution ◽  
Top Down ◽  
Local Optima ◽  
Optimal Decisions ◽  
Decision Tree Induction ◽  
Regression Functions

Decision trees represent one of the main predictive techniques in knowledge discovery. This chapter describes evolutionary induced trees, which are emerging alternatives to the greedy top-down solutions. Most typical tree-based system searches only for locally optimal decisions at each node and do not guarantee the optimal solution. Application of evolutionary algorithms to the problem of decision tree induction allows searching for the structure of the tree, tests in internal nodes and regression functions in the leaves (for model trees) at the same time. As a result, such globally induced decision tree is able to avoid local optima and usually leads to better prediction than the greedy counterparts.

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