scholarly journals Design of Air Cushion Vehicles Using Artificial Intelligence: Expert System and Genetic Algorithm

2021 ◽  
Author(s):  
Tae-Cheol Jung
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Expert System ◽  
Coast Guard ◽  
Ride Quality ◽  
Successful Implementation ◽  
Artificial Intelligence Techniques ◽  
Initial Design ◽  
Air Cushion ◽  
Thesis Work

In the thesis, initial design of an Air Cushion Vehicle (ACV)is performed with the expert system and its skirt system is further optimized with the genetic algorithm. Both the expert system and genetic algorithm are advanced computerized design techniques of artifical intelligence. Those techniques are specifically developed for the ACVs with programming codes in this thesis. Then the main objective is to show the successful implementation of those techniques in the design of ACVs. The thesis work is divided into two parts. In the first part, the general configuration of ACVs, including the overall dimensions, weight distribution, parametric properties, and several subsystems, is studied and designed by the expert system as an initial design phase. In the second part of the thesis, the skirt system of ACVs is further optimized. In particular, the properties of the bag and finger skirt are optimized for improved ride quality and stability by the genetic algorithm. For the validation of these two artificial intelligence techniques, the CCG (Canadian Coast Guard) 37 ton Waban-Aki and U.S. Navy's 150 ton LCAC (Landing Craft Air Cushion) are selected for the tests. The results of the tests proved that the expert system was successfully implemented and was a powerful tool for the initial design of ACVs. Furthermore, the genetic algorithm optimized the skirt system with significantly improved ride quality and stability. It was also shown that the skirt mass was an important design factor in the heave response of the bag and finger skirt. Hence, this thesis work opened the new possibility of designing ACVs with artificial intelligence techniques.

scholarly journals Design of Air Cushion Vehicles Using Artificial Intelligence: Expert System and Genetic Algorithm

2021 ◽  
Author(s):  
Tae-Cheol Jung
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Expert System ◽  
Coast Guard ◽  
Ride Quality ◽  
Successful Implementation ◽  
Artificial Intelligence Techniques ◽  
Initial Design ◽  
Air Cushion ◽  
Thesis Work

In the thesis, initial design of an Air Cushion Vehicle (ACV)is performed with the expert system and its skirt system is further optimized with the genetic algorithm. Both the expert system and genetic algorithm are advanced computerized design techniques of artifical intelligence. Those techniques are specifically developed for the ACVs with programming codes in this thesis. Then the main objective is to show the successful implementation of those techniques in the design of ACVs. The thesis work is divided into two parts. In the first part, the general configuration of ACVs, including the overall dimensions, weight distribution, parametric properties, and several subsystems, is studied and designed by the expert system as an initial design phase. In the second part of the thesis, the skirt system of ACVs is further optimized. In particular, the properties of the bag and finger skirt are optimized for improved ride quality and stability by the genetic algorithm. For the validation of these two artificial intelligence techniques, the CCG (Canadian Coast Guard) 37 ton Waban-Aki and U.S. Navy's 150 ton LCAC (Landing Craft Air Cushion) are selected for the tests. The results of the tests proved that the expert system was successfully implemented and was a powerful tool for the initial design of ACVs. Furthermore, the genetic algorithm optimized the skirt system with significantly improved ride quality and stability. It was also shown that the skirt mass was an important design factor in the heave response of the bag and finger skirt. Hence, this thesis work opened the new possibility of designing ACVs with artificial intelligence techniques.

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

Log analysis systems incorporating artificial intelligence techniques ? some examples

1989 ◽  
Vol 20 (2) ◽  
pp. 331 ◽  
Author(s):  
P.L. Baker
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Log Analysis ◽  
Rule Based ◽  
Artificial Intelligence Techniques ◽  
Lithology Identification ◽  
Well Correlation ◽  
Log Interpretation ◽  
Rule Based Approach ◽  
Mineral Identification

Artificial Intelligence (Al) systems have been used with some success in the areas of dipmeter interpretation, quantitative log interpretation and well-to-well correlation. A prototype expert system has been developed using a rule-based approach to lithology identification. Extensions of the system are now being considered to do mineral identification for the problem of mineral model construction for multi-mineral log interpretation algorithms.

scholarly journals Improved Forms for Controlling the Acrobot with Motors of Atypical Size Using Artificial Intelligence Techniques

2017 ◽  
Author(s):  
Gonzalo Mier ◽  
Javier de Lope
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Control Problem ◽  
Inverted Pendulum ◽  
Artificial Intelligence Techniques ◽  
Rest Position ◽  
Planar Robot ◽  
Trajectory Generator

An acrobot is a planar robot with a passive actuator in its first joint. The control problem of the acrobot tries to make it rise from the rest position to the inverted pendulum position. This control problem can be divided in the swing-up problem, when the robot has to rise itself through swinging up as a human acrobat does, and the balancing problem, when the robot has to maintain itself on the inverted pendulum position. We have developed three controllers for the swing-up problem applied to two types of motors: small and big. For small motors, we used the SARSA controller and the PD with a trajectory generator. For big motors, we propose a new controller to control the acrobot, a PWM controller. All controllers except SARSA are tuned using a Genetic Algorithm.

scholarly journals Survey on computational intelligence based image encryption techniques

2020 ◽  
Vol 19 (3) ◽  
pp. 1428
Author(s):  
Thirumalaimuthu Ramanathan ◽  
Md. Jakir Hossen ◽  
Md. Shohel Sayeed ◽  
Joseph Emerson Raja
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Image Encryption ◽  
Computational Intelligence ◽  
Visual Cryptography ◽  
Machine Learning Algorithms ◽  
Artificial Intelligence Techniques ◽  
Cryptographic Keys

Image encryption is an important area in visual cryptography that helps in protecting images when shared through internet. There is lot of cryptography algorithms applied for many years in encrypting images. In the recent years, artificial intelligence techniques are combined with cryptography algorithms to support image encryption. Some of the benefits that artificial intelligence techniques can provide are prediction of possible attacks on cryptosystem using machine learning algorithms, generation of cryptographic keys using optimization algorithms, etc. Computational intelligence algorithms are popular in enhancing security for image encryption. The main computational intelligence algorithms used in image encryption are neural network, fuzzy logic and genetic algorithm. In this paper, a review is done on computational intelligence-based image encryption methods that have been proposed in the recent years and the comparison is made on those methods based on their performance on image encryption.

scholarly journals A Feasibility Study of the Application of Artificial Intelligence Techniques for Turbomachinery Diagnostics

1985 ◽  
Author(s):  
Cyrus B. Meher-Homji
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Feasibility Study ◽  
Online Monitoring ◽  
System Development ◽  
Diagnostic Process ◽  
Computer Implementation ◽  
Diagnostic Systems ◽  
Artificial Intelligence Techniques

This paper presents a study into the application of Artificial Intelligence (AI) techniques (specifically Expert Systems) to the problem of turbomachine diagnostics. The diagnostic process is described and a model for computer implementation provided. Some diagnostic examples are provided to explain the methodology. Expert system development is underway at the author’s corporation for incorporation in online monitoring and diagnostic systems.

Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

2015 ◽  
pp. 446-477 ◽  
Author(s):  
Tianxing Cai
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Chemical Process ◽  
Chemical Engineering ◽  
Optimization Methods ◽  
Evolutionary Strategy ◽  
Artificial Intelligence Techniques ◽  
Bee Colony ◽  
Degree Of Satisfaction ◽  
Optimization And Control

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

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