The Experts below are selected from a list of 2877 Experts worldwide ranked by ideXlab platform
Oscar Castillo - One of the best experts on this subject based on the ideXlab platform.
-
modular neural networks architecture optimization with a new nature inspired method using a fuzzy combination of particle swarm optimization and genetic algorithms
Information Sciences, 2014Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. Also fuzzy logic is used to adjust parameters in the FPSO and FGA. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods for the optimization of modular neural networks. The new hybrid FPSO+FGA method is shown to be superior with respect to both the individual evolutionary methods.
-
an improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms
Applied Soft Computing, 2011Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using fuzzy logic to integrate the results of both methods and for parameters tuning. The new optimization method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid approach. Fuzzy logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The improved hybrid FPSO+FGA method is shown to outperform both individual optimization methods.
-
Evolutionary method combining Particle Swarm Optimisation and Genetic Algorithms using fuzzy logic for parameter adaptation and aggregation: the case neural network optimisation for face recognition
2010Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimisation combining Particle Swarm Optimisation (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic for parameter adaptation and to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO + FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. Also, fuzzy logic is used to adjust parameters in the FPSO and FGA. The new hybrid FPSO + FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of neural network architecture optimisation. The new hybrid FPSO + FGA method is shown to be superior with respect to the individual evolutionary methods. The tests were made with 2, 4, 8 and 16 variables.
-
fuzzy logic adaptation of a hybrid evolutionary method for pattern recognition
European Society for Fuzzy Logic and Technology Conference, 2009Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of modular neural network optimization. The new hybrid FPSO+FGA method is shown to be superior with respect to both the individual evolutionary methods.
En Sup Yoon - One of the best experts on this subject based on the ideXlab platform.
-
quantitative risk analysis of fire and explosion on the top side lng liquefaction process of lng FPSO
Process Safety and Environmental Protection, 2014Co-Authors: Dongil Shin, Seungkyu Dan, En Sup Yoon, Chang Jun Lee, Jeongpil ParkAbstract:Abstract Since the massive use and production of fuel oil and natural gas, the excavating locations of buried energy-carrying material are moving further away from onshore, eventually requiring floating production systems like floating production, storage and offloading (FPSO). Among those platforms, LNG-FPSO will play a leading role to satisfy the global demands for the natural gas in near future; the LNG-FPSO system is designed to deal with all the LNG processing activities, near the gas field. However, even a single disaster on an offshore plant would put the whole business into danger. In this research, the risk of fire and explosion in the LNG-FPSO is assessed by quantitative risk analysis, including frequency and consequence analyses, focusing on the LNG liquefaction process (DMR cycle). The consequence analysis is modeled by using a popular analysis tool PHAST. To assess the risk of this system, 5 release model scenarios are set for the LNG and refrigerant leakages from valves, selected as the most probable scenarios causing fire and explosion. From the results, it is found that the introduction of additional protection methods to reduce the effect of fire and explosion under ALARP criteria is not required, and two cases of the selection of independent protection layers are recommended to meet the SIL level of failure rate for safer design and operation in the offshore environment.
Jongsoo Seo - One of the best experts on this subject based on the ideXlab platform.
-
A study on the impact load acting on an FPSO bow by steep waves
Elsevier, 2017Co-Authors: Samkwon Hong, Jaemoon Lew, Dongwoo Jung, Heetaek Kim, Dongyeon Lee, Jongsoo SeoAbstract:Various offshore structures such as FPSO, FSO, Semi-submersible, TLP and Spar are operated to develop offshore oil and gas fields. Most of the offshore structures shall be operated over 20 years under the harsh environments at sites so that the offshore structures should be designed to endure the harsh environments. In this study, the effect of the impact load (so called slapping load) by the steep waves acting on the FPSO bow is investigated through the model test. For measurement of the impact pressures on the frontal area, a bow-shaped panel was fabricated, and installed the pressure sensors on the bow starboard side of the model FPSO. During the model test campaign, the impact load was investigated using the steep waves with Hs/λ greater than 1/16 of the representative wave condition. Consequently, it is confirmed through the model test that the impact loads acting on the FPSO bow are significantly increased with the steep waves (Hs/λ > 1/16) than the representative wave conditions of a maximum significant wave height and a pitch forcing period. Therefore, for safe design of North Sea FPSO, it is necessary to consider the steep waves in addition to the representative wave conditions and to be applied as proper structural load. Also, the effect of random seeds in irregular waves should be considered to build the safe FPSO
-
a study on the impact load acting on an FPSO bow by steep waves
ASME 2014 33rd International Conference on Ocean Offshore and Arctic Engineering, 2014Co-Authors: Samkwon Hong, Jaemoon Lew, Dongwoo Jung, Heetaek Kim, Dongyeon Lee, Jongsoo SeoAbstract:Among offshore floaters used to develop offshore resources, FPSO and FSO have a storage function whereas semi-submersible, Spar and TLP have only production function. The floaters with the storage function such as FPSO and FSO are designed as the typical ship type concept compared to the other floaters with small water plane area.In order to design the floaters for offshore resource development, it is needed to estimate the seakeeping performance under operating condition and survival conditions and then carry out the structural design based on seakeeping performance results. The environment conditions of 1yr, 10yrs, 100yrs or 1,000 yrs return periods are used based on the metocean data of the installation field to evaluate the seakeeping performance under operating and survival conditions. In general, the wave conditions with the maximum wave heights for each return periods are selected on each wave contour lines in the wave scatter diagram. Then the seakeeping performance is evaluated from the seakeeping model test.However, it was observed that the wave with the pitch forcing period, where the wave length is close to the ship length, is more important than the wave with the maximum wave height after several accidents caused by the green water in Northern North Sea and Norwegian Sea. Therefore, it became a common practice to include not only the wave conditions with maximum wave heights for each return period but also the wave conditions with the pitch forcing period to evaluate the seakeeping performance for offshore development floaters. Ship type floaters such as FPSO are more likely to experience higher impact force due to the large frontal area accompanied by large heave and pitch motions in head sea and bow quartering seas. Recently, it was reported that in an accident in North Sea of UK sector, the damage at the bow of the FPSO is caused due to the steep waves. Afterwards, studies on the steep waves have been made in several institutes such as UK HSE.In this study, the effect of the impact load (so called slapping load) by the steep waves acting on the FPSO bow is investigated throughout the model test. For measurement of the pressure and impact force on the frontal area, a bow-shaped panel was fabricated with the pressure and force sensors, and installed on the bow starboard side of the model FPSO. During the model test campaign, the impact load was investigated using the steep waves with Hw/λ greater than 1/16 in addition to the general wave conditions with maximum wave heights.Consequently, it is confirmed in the model test that the impact loads acting on the FPSO bow are significantly increased with the steep waves (Hw/λ > 1/16) compared to the general wave conditions. Therefore, it is necessary to consider whether the steep waves are additionally included in the wave conditions to estimate the seakeeping performance and how to apply the impact loads acting on the FPSO bow from the steep waves in structure design.© 2014 ASME
S. Mudi Mannan - One of the best experts on this subject based on the ideXlab platform.
-
fire and explosion assessment on oil and gas floating production storage offloading FPSO an effective screening and comparison tool
Process Safety and Environmental Protection, 2009Co-Authors: Jaffee A Suardin, Jeff A Mcphate, Anthony Willem Sipkema, Matt Childs, S. Mudi MannanAbstract:Abstract Fires and explosions have been identified as major potential hazards for Oil and Gas Floating Production Storage Offloading (FPSO) installations and pose risk to personnel, assets, and the environment. Current fire and explosion assessment (FEA) tools require physical effect modeling software and follows standards from API, ISO, and engineering practices. However, the tools are not specific to any particular system such as an FPSO, and do not provide comprehensive guidance for safety engineers to perform FEA. This paper discusses the development of a screening and comparison tool for FEA on FPSOs and the incorporation of an expert system into the tool. The results are computerized using MS Excel/VBA to provide a structured and comprehensive assessment on each equipment and module handling natural gas, crude oil, methanol and diesel on FPSO topsides. This tool features built-in calculations for jet and pool fire size estimation for gas/liquid releases, and the ability to perform Quantitative Risk Analysis (QRA) to specify the personnel and equipment risk for varying leak sizes and process conditions. Control and recovery measures are incorporated as an expert system based on report findings, engineering practices, and relevant standards. Bowtie analysis is applied in the tool to define detailed control and recovery measures for the FPSO based on the incident scenarios. An explosion assessment is performed by incorporating physical effect modeling software results. Unique features provided in the tool include fire and radiation contour mapping on an FPSO layout to help determine personnel and equipment risk more accurately and fire pump sizing that can be used to verify the amount of water deluge system required to mitigate fires and explosions. In addition, flexibility of data input (process data, failure rate data, etc.) and user interfaces assist safety engineers to screen and compare process alternatives, check design quality, and evaluate design options at any design stage.
Fevrier Valdez - One of the best experts on this subject based on the ideXlab platform.
-
modular neural networks architecture optimization with a new nature inspired method using a fuzzy combination of particle swarm optimization and genetic algorithms
Information Sciences, 2014Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. Also fuzzy logic is used to adjust parameters in the FPSO and FGA. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods for the optimization of modular neural networks. The new hybrid FPSO+FGA method is shown to be superior with respect to both the individual evolutionary methods.
-
an improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms
Applied Soft Computing, 2011Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using fuzzy logic to integrate the results of both methods and for parameters tuning. The new optimization method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid approach. Fuzzy logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The improved hybrid FPSO+FGA method is shown to outperform both individual optimization methods.
-
Evolutionary method combining Particle Swarm Optimisation and Genetic Algorithms using fuzzy logic for parameter adaptation and aggregation: the case neural network optimisation for face recognition
2010Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimisation combining Particle Swarm Optimisation (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic for parameter adaptation and to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO + FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. Also, fuzzy logic is used to adjust parameters in the FPSO and FGA. The new hybrid FPSO + FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of neural network architecture optimisation. The new hybrid FPSO + FGA method is shown to be superior with respect to the individual evolutionary methods. The tests were made with 2, 4, 8 and 16 variables.
-
fuzzy logic adaptation of a hybrid evolutionary method for pattern recognition
European Society for Fuzzy Logic and Technology Conference, 2009Co-Authors: Fevrier Valdez, Patricia Melin, Oscar CastilloAbstract:We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of modular neural network optimization. The new hybrid FPSO+FGA method is shown to be superior with respect to both the individual evolutionary methods.
