scholarly journals Optimised ExpTime Tableaux for𝒮ℋℐ𝒩over Finite Residuated Lattices

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Jian Huang ◽  
Xinye Zhao ◽  
Jianxing Gong
Keyword(s):  
Experimental Evaluation ◽  
Decision Procedure ◽  
Description Logic ◽  
Optimization Technique ◽  
Residuated Lattices ◽  
Optimization Techniques ◽  
Satisfiability Problem ◽  
Reasoning Algorithm ◽  
De Morgan Lattice

This study proposes to adopt a novel tableau reasoning algorithm for the description logic𝒮ℋℐ𝒩with semantics based on a finite residuated De Morgan lattice. The syntax, semantics, and logical properties of this logic are given, and a sound, complete, and terminating tableaux algorithm for deciding fuzzy ABox consistency and concept satisfiability problem with respect to TBox is presented. Moreover, based on extended and/or completion-forest with a series of sound optimization technique for checking satisfiability with respect to a TBox in the logic, a new optimized ExpTime (complexity-optimal) tableau decision procedure is presented here. The experimental evaluation indicates that the optimization techniques we considered result in improved efficiency significantly.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

scholarly journals Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

2022 ◽  
Vol 2022 ◽  
pp. 1-18
Author(s):  
Dereje Tekilu Aseffa ◽  
Harish Kalla ◽  
Satyasis Mishra
Keyword(s):  
Optimization Technique ◽  
Research Work ◽  
Optimization Techniques ◽  
Magnetic Sensors ◽  
Batch Size ◽  
Raspberry Pi ◽  
Prototype Development ◽  
Embedded Platform ◽  
Artificial Intelligence Technology ◽  
Banknote Recognition

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.

scholarly journals A Roadmap to Gradually Compare and Benchmark Description Logic Calculi

10.29007/tgc4 ◽  
2019 ◽  
Author(s):  
Fred Freitas
Keyword(s):  
Asymptotic Analysis ◽  
Description Logic ◽  
Basic Research ◽  
Detailed Comparison ◽  
Optimization Techniques ◽  
Cutting Edge ◽  
Memory Usage ◽  
Inference Methods

DL reasoners were developed with cutting-edge performance, implementing plenty of specific optimization techniques over tableaux-based methods, which took over the field. However, promising methods may have been neglected in such a scenario, in which the tough competition is often focused on gains through optimizations. Therefore, perhaps there is still room available for “basic research” on DL reasoning. The purpose of this work is to stimulate research on trying out DL calculi other than tableaux. Such endeavors should be carried out by making a careful, detailed comparison between tableaux and other inference methods in a systematic way: first starting with simpler languages (like ALC) without any optimizations. Then gradually including optimizations and comparing them; and continuing these interactive steps: enhancing language expressivity, including optimizations, and testing until reaching the most expressive DL fragments such as SROIQ. The comparison can also be done by in terms memory usage and algorithm asymptotic analysis, with worst and average cases, etc. The rationale is identifying whether there are fragments which are more suitable to certain inference methods, as well as which aspects or constructs (e.g., the costliest combinations, which usually involve inverses, nominals, equalities, etc) are sensitive to which calculus.

scholarly journals Adaptive Day-Ahead Prediction of Resilient Power Distribution Network Partitions

2021 ◽  
Author(s):  
Chinmay Shah ◽  
Richard Wies
Keyword(s):  
Power Distribution ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Distribution Network ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Power Distribution Networks ◽  
Power Distribution Network ◽  
High Penetration ◽  
Islanded Mode

The conventional power distribution network is being transformed drastically due to high penetration of renewable energy sources (RES) and energy storage. The optimal scheduling and dispatch is important to better harness the energy from intermittent RES. Traditional centralized optimization techniques limit the size of the problem and hence distributed techniques are adopted. The distributed optimization technique partitions the power distribution network into sub-networks which solves the local sub problem and exchanges information with the neighboring sub-networks for the global update. This paper presents an adaptive spectral graph partitioning algorithm based on vertex migration while maintaining computational load balanced for synchronization, active power balance and sub-network resiliency. The parameters that define the resiliency metrics of power distribution networks are discussed and leveraged for better operation of sub-networks in grid connected mode as well as islanded mode. The adaptive partition of the IEEE 123-bus network into resilient sub-networks is demonstrated in this paper.

Adapting Ant Colony for Topology Optimization of Compliant Mechanisms With Variable Material Density

2017 ◽  
Author(s):  
Nadim Diab
Keyword(s):  
Topology Optimization ◽  
Ant Colony Optimization ◽  
High Performance ◽  
Compliant Mechanisms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Intensity Level ◽  
Material Density ◽  
Swarm Intelligence Optimization

Swarm intelligence optimization techniques are widely used in topology optimization of compliant mechanisms. The Ant Colony Optimization has been implemented in various forms to account for material density distribution inside a design domain. In this paper, the Ant Colony Optimization technique is applied in a unique manner to make it feasible to optimize for the beam elements’ cross-section and material density simultaneously. The optimum material distribution algorithm is governed by two various techniques. The first technique treats the material density as an independent design variable while the second technique correlates the material density with the pheromone intensity level. Both algorithms are tested for a micro displacement amplifier and the resulting optimized topologies are benchmarked against reported literature. The proposed techniques culminated in high performance and effective designs that surpass those presented in previous work.

Comparison of Two Multiobjective Optimization Techniques With and Within Genetic Algorithms

1999 ◽  
Author(s):  
Shapour Azar ◽  
Brian J. Reynolds ◽  
Sanjay Narayanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Pareto Set ◽  
Design Solution ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Continuous Variables ◽  
Optimal Set

Abstract Engineering decision making involving multiple competing objectives relies on choosing a design solution from an optimal set of solutions. This optimal set of solutions, referred to as the Pareto set, represents the tradeoffs that exist between the competing objectives for different design solutions. Generation of this Pareto set is the main focus of multiple objective optimization. There are many methods to solve this type of problem. Some of these methods generate solutions that cannot be applied to problems with a combination of discrete and continuous variables. Often such solutions are obtained by an optimization technique that can only guarantee local Pareto solutions or is applied to convex problems. The main focus of this paper is to demonstrate two methods of using genetic algorithms to overcome these problems. The first method uses a genetic algorithm with some external modifications to handle multiple objective optimization, while the second method operates within the genetic algorithm with some significant internal modifications. The fact that the first method operates with the genetic algorithm and the second method within the genetic algorithm is the main difference between these two techniques. Each method has its strengths and weaknesses, and it is the objective of this paper to compare and contrast the two methods quantitatively as well as qualitatively. Two multiobjective design optimization examples are used for the purpose of this comparison.

Efficient Structural Design Using a Numerical Optimization Technique

2019 ◽  
Author(s):  
Qian Wang ◽  
Lucas Schmotzer ◽  
Yongwook Kim
Keyword(s):  
Structural Design ◽  
Numerical Optimization ◽  
Optimization Technique ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Convergence Criteria ◽  
Efficient Design ◽  
Concrete Building ◽  
Gradient Based ◽  
Structural Responses

<p>Structural designs of complex buildings and infrastructures have long been based on engineering experience and a trial-and-error approach. The structural performance is checked each time when a design is determined. An alternative strategy based on numerical optimization techniques can provide engineers an effective and efficient design approach. To achieve an optimal design, a finite element (FE) program is employed to calculate structural responses including forces and deformations. A gradient-based or gradient-free optimization method can be integrated with the FE program to guide the design iterations, until certain convergence criteria are met. Due to the iterative nature of the numerical optimization, a user programming is required to repeatedly access and modify input data and to collect output data of the FE program. In this study, an approximation method was developed so that the structural responses could be expressed as approximate functions, and that the accuracy of the functions could be adaptively improved. In the method, the FE program was not required to be directly looped in the optimization iterations. As a practical illustrative example, a 3D reinforced concrete building structure was optimized. The proposed method worked very well and optimal designs were found to reduce the torsional responses of the building.</p>

Evolutionary Topology Optimization of Spatial Steel-Concrete Structures

2021 ◽  
Vol 62 (2) ◽  
pp. 102-112
Author(s):  
Yu Li ◽  
Yi Min Xie
Keyword(s):  
Topology Optimization ◽  
Composite Structures ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Principal Stresses ◽  
Element Analysis ◽  
Floor Systems ◽  
Beso Method ◽  
Multiple Materials ◽  
Single Material

Topology optimization techniques based on finite element analysis have been widely used in many fields, but most of the research and applications are based on single-material structures. Extended from the bi-directional evolutionary structural optimization (BESO) method, a new topology optimization technique for 3D structures made of multiple materials is presented in this paper. According to the sum of each element's principal stresses in the design domain, a material more suitable for this element would be assigned. Numerical examples of a steel- concrete cantilever, two different bridges and four floor systems are provided to demonstrate the effectiveness and practical value of the proposed method for the conceptual design of composite structures made of steel and concrete.

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