112 research outputs found

    A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

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    The purpose of this article is to present a novel strategy for the coordinated design of the Thyristor Controlled Series Compensator (TCSC) controller and the Power System Stabilizer (PSS). A time domain objective function that is based on an optimization problem has been defined. This objective function takes into account not only the influence that disturbances have on the mechanical power, but also, and this is more accurately the case, the impact that disturbances have on the reference voltage. When the objective function is minimized, potential disturbances are quickly mitigated, and the deviation of the speed of the generator's rotor is limited; as a result, the system's stability is ultimately improved. Particle Swarm Optimization (PSO) and the Shuffled Frog Leaping Algorithm are both components of a composite strategy that is utilized in the process of determining the optimal controller parameters. (SFLA). An independent controller design as well as a collaborative controller design utilizing PSS and TCSC are developed, which enables a direct evaluation of the functions performed by each. The presentation of the eigenvalue analysis and the findings of the nonlinear simulation can help to provide a better understanding of the efficacy of the outcomes. The findings indicate that the coordinated design is able to successfully damp low-frequency oscillations that are caused by a variety of disturbances, such as changes in the mechanical power input and the setting of the reference voltage, and significantly enhance system stability in power systems that are connected weekly

    Time-Cost-Quality Trade-off Model for Optimal Pile Type Selection Using Discrete Particle Swarm Optimization Algorithm

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    The cost of pile foundations is part of the super structure cost, and it became necessary to reduce this cost by studying the pile types then decision-making in the selection of the optimal pile type in terms of cost and time of production and quality .So The main objective of this study is to solve the timeโ€“costโ€“quality trade-off (TCQT) problem by finding an optimal pile type with the target of "minimizing" cost and time while "maximizing" quality. There are many types In the world of piles butย  in this paper, the researcher proposed five pile types, one of them is not a traditional, andย ย  developed a model for the problem and then employed particle swarm optimization (PSO) algorithm, as one of evolutionary algorithms with the help of (Mat lab software), as a tool for decision making problem about choosing the best alternative of the traded piles, and proposes a multi objective optimization model, which aims to optimize the time, cost and quality of the pile types, and assist in selecting the most appropriate pile types. The researcher selected 10 of senior engineers to conduct interviews with them.ย  And prepared some questions for interviews and open questionnaire. The individuals are selected from private and state sectors each one have 10 years or more experience in pile foundations work. From personal interviews and field survey the research has shown that most of the experts, engineers are not fully aware of new soft wear techniques to helps them in choosing alternatives, despite their belief in the usefulness of using modern technology and software. The Problem is multi objective optimization problem, so after running the PSO algorithm it is usual to have more than one optimal solution, for five proposed pile types, finally the researcherย  evaluated andย  discussed the output results andย  found out that pre-high tension spun (PHC)pile type was the optimal pile type

    An Energy Efficient Service Composition Mechanism Using a Hybrid Meta-heuristic Algorithm in a Mobile Cloud Environment

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    By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms

    Navigational Strategies for Control of Underwater Robot using AI based Algorithms

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    Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robotโ€™s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

    ํ•˜์ฒœ ์˜ค์—ผ๋ฌผ์งˆ ํ˜ผํ•ฉ ํ•ด์„์„ ์œ„ํ•œ ์ €์žฅ๋Œ€ ๋ชจํ˜•์˜ ๋งค๊ฐœ๋ณ€์ˆ˜ ์‚ฐ์ •๋ฒ• ๋ฐ ๊ฒฝํ—˜์‹ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€,2019. 8. ์„œ์ผ์›.Analyses of solute transport and retention mechanism are essential to manage water quality and river ecosystem. As reported by tracer injection studies that have been conducted to identify solute transport mechanism, concentration curves measured in natural stream have steep rising and long tail parts. This phenomenon is due to solute exchange process between transient storage zones and the main river stream. The transient storage model (TSM) is one of the most widely used models for describing solute transport in natural stream, taking transient storage exchange process into consideration. In order to use this model, calibration of four TSM parameters is necessary. Inverse modelling using measured breakthrough curves (BTCs) from tracer injection test is general method for TSM parameter calibration. However, it is not feasible to carry out performing tracer injection tests, for every parameter calibration. For that reasons, empirical formulae with hydraulic data, which is comparatively easier to obtain, have been proposed for the purpose of parameter estimation. This study presents two methods for TSM parameter estimation. At first, inverse modelling method employing global optimization framework Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), that incorporating famous evolutionary algorithms in water resource management field, was suggested. Second, TSM parameter empirical equations were derived adopting Multigene Genetic Programming (MGGP) based symbolic regression library GPTIPS and using Principal Components Regression (PCR). In terms of general performance, equations of this study were superior to published empirical equations.ํ•˜์ฒœ์˜ ์ˆ˜์งˆ์„ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ž์—ฐํ•˜์ฒœ์—์„œ ์œ ์ž…๋œ ๋ฌผ์งˆ์ด ์ด์†ก๋˜๊ณ  ์ง€์ฒด๋˜๋Š” ๋ฉ”์นด๋‹ˆ์ฆ˜์„ ๊ทœ๋ช…ํ•˜๊ณ  ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ํ•˜์ฒœ์—์„œ์˜ ๋ฌผ์งˆ ํ˜ผํ•ฉ์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ–‰๋œ ์ถ”์ ์ž ์‹คํ—˜ ์—ฐ๊ตฌ๋“ค์— ๋”ฐ๋ฅด๋ฉด ์ž์—ฐํ•˜์ฒœ์—์„œ ๊ณ„์ธก๋˜๋Š” ๋†๋„๊ณก์„ ์—์„œ๋Š” ๊ฐ€ํŒŒ๋ฅธ ์ƒ์Šน๋ถ€์™€ ๊ธด ๊ผฌ๋ฆฌ๊ธฐ ๊ด€์ธก๋˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์กŒ๋‹ค. ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ์ฃผ๋กœ ๋ฌผ์งˆ์ด ํ๋ฅด๋Š” ๋ณธ๋ฅ˜๋Œ€์™€ ์ž ์‹œ ๋ฌผ์งˆ์ด ํฌํš๋˜์—ˆ๋‹ค๊ฐ€ ์žฌ๋ฐฉ์ถœ๋˜๋Š” ๋ณธ๋ฅ˜๋Œ€์™€ ์ €์žฅ๋Œ€ ๊ฐ„์˜ ๋ฌผ์งˆ๊ตํ™˜ ํšจ๊ณผ ๋•Œ๋ฌธ์— ์ผ์–ด๋‚œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ €์žฅ๋Œ€ ๋ฌผ์งˆ๊ตํ™˜ ํšจ๊ณผ๋ฅผ ๋ชจ์‚ฌํ•˜๋Š” ์ €์žฅ๋Œ€๋ชจํ˜• ์ค‘ Transient Storage zone Model (TSM)์€ ๊ฐ€์žฅ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์ด์šฉ๋˜๋Š” ๋ชจํ˜•์œผ๋กœ, ์ด๋ฅผ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„  ๋„ค ๊ฐ€์ง€์˜ ์ €์žฅ๋Œ€ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ๋ณด์ •ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋„ค ๊ฐ€์ง€ ์ €์žฅ๋Œ€ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ํ˜„์žฅ์‹คํ—˜์—์„œ ์ธก์ •๋œ ๋†๋„๊ณก์„ ์„ ์ด์šฉํ•œ ์—ญ์‚ฐ๋ชจํ˜•์ด ์ด์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งค๊ฐœ๋ณ€์ˆ˜๊ฐ€ ํ•„์š”ํ•  ๋•Œ๋งˆ๋‹ค ์ถ”์ ์ž์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์—ญ์‚ฐ๋ชจํ˜•์„ ์ด์šฉํ•˜๋Š” ๊ฒƒ์€ ํ˜„์‹ค์ ์œผ๋กœ ๋ถˆ๊ฐ€๋Šฅํ•œ ๊ฒฝ์šฐ๊ฐ€ ์žˆ์–ด ์ด๋Ÿฌํ•œ ๊ฒฝ์šฐ์—๋Š” ๋น„๊ต์  ์ทจ๋“ํ•˜๊ธฐ ์‰ฌ์šด ์ˆ˜๋ฆฌ์ง€ํ˜•ํ•™์  ์ธ์ž๋“ค์„ ์ด์šฉํ•ด ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ์‚ฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์ด์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” TSM ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ๊ฒฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, ์ „์—ญ ์ตœ์ ํ™” ํ”„๋ ˆ์ž„์›Œํฌ์ธ Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL)์„ ์ด์šฉํ•œ ์—ญ์‚ฐ๋ชจํ˜• ๊ธฐ๋ฐ˜ TSM ๋งค๊ฐœ๋ณ€์ˆ˜ ์‚ฐ์ • ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ๋‘˜์งธ๋กœ๋Š” ๊ธฐํ˜ธํšŒ๊ท€๋ฒ• ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์ธ GPTIPS๋ฅผ ์ด์šฉํ•œ ๋‹ค์ค‘์œ ์ „์ž ์œ ์ „ ํ”„๋กœ๊ทธ๋ž˜๋ฐ(Multigene Genetic Programming, MGGP) ๊ณผ ์ฃผ์„ฑ๋ถ„ํšŒ๊ท€๋ฒ•(Principal Components Regression, PCR)์„ ํ†ตํ•ด ๋„ค ๊ฐ€์ง€ ๋งค๊ฐœ๋ณ€์ˆ˜ ๋ณ„๋กœ ๊ฐ ๋‘ ๊ฐœ์”ฉ์˜ ๊ฒฝํ—˜์‹์ด ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ๊ฐœ๋ฐœ๋œ ๊ฒฝํ—˜์‹๋“ค์˜ ์„ฑ๋Šฅํ‰๊ฐ€ ๊ฒฐ๊ณผ, ์„ ํ–‰ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œ๋œ ์ €์žฅ๋Œ€ ๋งค๊ฐœ๋ณ€์ˆ˜ ์‹์— ๋น„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œ๋œ ๋ฐฉ๋ฒ•์ด ๋Œ€์ฒด์ ์œผ๋กœ ์šฐ์ˆ˜ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ถ„์„์„ ํ†ตํ•ด ์‹ค๋ฌด์ ์œผ๋กœ ํ™œ์šฉ ๊ฐ€๋Šฅํ•œ TSM ๋งค๊ฐœ๋ณ€์ˆ˜ ์‚ฐ์ • ํ”„๋ ˆ์ž„์›Œํฌ์™€ ๊ฒฝํ—˜์‹๋“ค์ด ์ œ์‹œ๋˜์—ˆ์œผ๋ฉฐ, ์ด ๋ฐฉ๋ฒ•๋“ค์€ ์ถ”์ ์ž ์‹คํ—˜ ์ž๋ฃŒ์˜ ์œ ๋ฌด์— ๋”ฐ๋ผ TSM์˜ ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ฒฐ์ •์— ์œ ์šฉํ•˜๊ฒŒ ์‚ฌ์šฉ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Chapter 1. Introduction 1 1.1 Necessity and Background of Research 1 1.2 Objectives 12 Chapter 2. Theoretical Background 15 2.1 Transient Storage Model 15 2.1.1. Mechanisms of Transient Storage 15 2.1.2. Models Accounting for Transient Storage 21 2.1.2.1 The one Zone Transient Storage Model (1Z-TSM) 24 2.1.2.2 The two Zone Transient Storage Model (2Z-TSM) 25 2.1.2.3 The Continuous Time Random Walk Approach (CTRW) 26 2.1.2.4 The Modified Advection Dispersion Model (MADE) 27 2.1.2.5 The Fractional Advection Dispersion Equation Model (FADE) 28 2.1.2.6 The Multirate Mass Transfer Model (MRMT) 29 2.1.2.7 The Advective Storage Path Model (ASP) 30 2.1.2.8 The Solute Transport in Rivers Model (STIR) 31 2.1.2.9 The Aggregate Dead Zone Model (ADZ) 34 2.2 Empirical Equations for Predicting Transient Storage Model Parameters 39 2.3 Parameter Estimation 47 2.3.1. The SC-SAHEL Framework 50 2.3.1.1 Modified Competitive Complex Evolution (MCCE) 52 2.3.1.2 Modified Frog Leaping (MFL) 52 2.3.1.3 Modified Grey Wolf Optimizer (GWO) 53 2.3.1.4 Modified Differential Evolution (DE) 53 2.4 Regression Method 54 2.4.1. The Multi-Gene Genetic Programming (MGGP) 56 2.4.1.1 The Simple Genetic Programming 56 2.4.1.2 Scaled Symbolic Regression via Multi-Gene Genetic Programming 57 2.4.2. Evolutionary Polynomial Regression (EPR) 61 2.4.2.1 Main Flow of EPR Procedure 62 Chapter 3. Model Development 66 3.1 Numerical Model 66 3.1.1. Model Validation 69 3.2 Merger of TSM-SC-SAHEL 73 3.3 Further assessments for the parameter estimation framework 76 3.3.1. Tracer Test Description 76 3.3.2. Grid Independency of Estimation 81 3.3.3. Choice of Optimization Setting 85 Chapter 4. Development of Formulae for Predicting TSM Parameter 91 4.1 Dimensional Analysis 91 4.2 Data Collection via Meta Analysis 95 4.3 Formulae Development 106 Chapter 5. Result and Discussion 110 5.1 Model Performances 110 5.2 Sensitivity Analysis 118 5.3 In-stream Application of Empirical Equations 130 Chapter 6. Conclusion 140 References 144 Appendix. I. The mean, minimum, and maximum values of the model fitness value and number of evolution using the SC-SAHEL with single-EA and multi-EA 159 Appendix. II. Used dimensionless datasets for development of empirical equations 161 ๊ตญ๋ฌธ์ดˆ๋ก 165Maste

    Artificial Intelligence and Its Applications

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    A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

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    Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

    An Integrated Method for Optimizing Bridge Maintenance Plans

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    Bridges are one of the vital civil infrastructure assets, essential for economic developments and public welfare. Their large numbers, deteriorating condition, public demands for safe and efficient transportation networks and limited maintenance and intervention budgets pose a challenge, particularly when coupled with the need to respect environmental constraints. This state of affairs creates a wide gap between critical needs for intervention actions, and tight maintenance and rehabilitation funds. In an effort to meet this challenge, a newly developed integrated method for optimized maintenance and intervention plans for reinforced concrete bridge decks is introduced. The method encompasses development of five models: surface defects evaluation, corrosion severities evaluation, deterioration modeling, integrated condition assessment, and optimized maintenance plans. These models were automated in a set of standalone computer applications, coded using C#.net in Matlab environment. These computer applications were subsequently combined to form an integrated method for optimized maintenance and intervention plans. Four bridges and a dataset of bridge images were used in testing and validating the developed optimization method and its five models. The developed models have unique features and demonstrated noticeable performance and accuracy over methods used in practice and those reported in the literature. For example, the accuracy of the surface defects detection and evaluation model outperforms those of widely-recognized machine leaning and deep learning models; reducing detection, recognition and evaluation of surface defects error by 56.08%, 20.2% and 64.23%, respectively. The corrosion evaluation model comprises design of a standardized amplitude rating system that circumvents limitations of numerical amplitude-based corrosion maps. In the integrated condition, it was inferred that the developed model accomplished consistent improvement over the visual inspection procedures in-use by the Ministry of Transportation in Quebec. Similarly, the deterioration model displayed average enhancement in the prediction accuracies by 60% when compared against the most commonly-utilized weibull distribution. The performance of the developed multi-objective optimization model yielded 49% and 25% improvement over that of genetic algorithm in a five-year study period and a twenty five-year study period, respectively. At the level of thirty five-year study period, unlike the developed model, classical meta-heuristics failed to find feasible solutions within the assigned constraints. The developed integrated platform is expected to provide an efficient tool that enables decision makers to formulate sustainable maintenance plans that optimize budget allocations and ensure efficient utilization of resources
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