• Journal of Information Processing Systems
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• v.18 no.6
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• pp.763-769
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• 2022

Transportation allocation requires information such as storage location and order information. In order to guarantee the safe transmission and real-time sharing of information in all links, an intelligent transportation control system based on blockchain technology is designed. Firstly, the technical architecture of intelligent transportation information traceability blockchain and the overall architecture of intelligent transportation control system were designed. Secondly, the transportation management demand module and storage demand management module were designed, and the control process of each module was given. Then, the type of intelligent transportation vehicle was defined, the objective function of intelligent transportation control was designed, and the objective function of intelligent transportation control was constructed. Finally, the intelligent transportation control was realized by genetic algorithm. It was found that when the transportation order volume was 50×103, and the CPU occupancy of the designed system was only 11.8%. The reliability attenuation of the code deletion scheme was lower, indicating better performance of the designed system.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.158-160
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• 2015

Efficient scheduling and resource management are the key factor to reduce construction project budget (e.g., labor cost, equipment cost, material cost, etc.). Resource-based line of balance (LOB) technique has been used to complement the limitations of existing time-driven scheduling techniques (e.g., critical-path method). Optimizing construction alternatives contributes to cost savings while honoring the project deadline. However, existing LOB scheduling is lack of identifying optimal resource combination. This study presents a method which identifies the optimal construction alternatives, hence achieving resource minimization in a repetitive construction by using genetic algorithm (GA). The method provides efficient planning tool that enhances the usability of the system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.252-256
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• 1997

The success of a fuzzy neural network(FNN) control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. A messy genetic algorithm is used to obtain structurally optimized FNN models. Structural optimization is regarded important before neural networks based learning is switched into. We have applied the method to the problem of a numerical approximation

• Computers and Concrete
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• v.5 no.5
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• pp.475-490
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• 2008

The paper explores the potential of applicability of Genetic programming approach (GP), adopted in this investigation, to model the combined effects of five independent variables to predict the mini-slump, the plate cohesion meter, the induced bleeding test, the J-fiber penetration value, and the compressive strength at 7 and 28 days of self-compacting slurry infiltrated fiber concrete (SIFCON). The variables investigated were the proportions of limestone powder (LSP) and sand, the dosage rates of superplasticiser (SP) and viscosity modifying agent (VMA), and water-to-binder ratio (W/B). Twenty eight mixtures were made with 10-50% LSP as replacement of cement, 0.02-0.06% VMA by mass of cement, 0.6-1.2% SP and 50-150% sand (% mass of binder) and 0.42-0.48 W/B. The proposed genetic models of the self-compacting SIFCON offer useful modelling approach regarding the mix optimisation in predicting the fluidity, the cohesion, the bleeding, the penetration, and the compressive strength.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.5
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• pp.378-386
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• 2017

In this paper, a method is presented for the optimal design of submarine pressure hull structures by taking advantage of genetic algorithm techniques. The objective functions and design constraints in the process of structural optimization are based on the ultimate limit states of hull structures. One of the benefits associated with the utilization of genetic algorithm is that the optimization process can be completed within short generations of design variables for the pressure hull structure model. Applied examples confirm that the proposed method is useful for the optimal design of submarine pressure hull structures. Details of the design procedure with applied examples are documented. The conclusions and insights obtained from the study are summarized.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1355-1360
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• 2005

In this paper, we approach the problem of image filter design automation using a kind of intrinsic evolvable hardware architecture. For the purpose of implementing the intrinsic evolution process in a common FPGA chip and evolving a complicated digital circuit system-image filter, the design automation system employs the reconfigurable circuit architecture as the reconfigurable component of the EHW. The reconfigurable circuit architecture is inspired by the Cartesian Genetic Programming and the functional level evolution. To increase the speed of the hardware evolution, the whole evolvable hardware system which consists of evolution algorithm unit, fitness value calculation unit and reconfigurable unit are implemented by a commercial FPGA chip. The Celoxica RC1000 card which is fitted with a Xilinx Virtex xcv2000E FPGA chip is employed as the experiment platform. As the result, we conclude the terms of the synthesis report of the image filter design automation system and hardware evolution speed in the Celoxica RC1000 card. The evolved image filter is also compared with the conventional image filter form the point of filtered image quality.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.1
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• pp.77-87
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• 2015

For a floating offshore plant such as FPSO(Floating, Production, Storage, and Off-loading unit), various equipment should be installed in the restricted space, as compared with an onshore plant. The requirement for an optimal layout method of the plant has been increased in these days. Thus, a layout method of the floating offshore plant was proposed in this study. For this, an optimization problem for layout design was mathematically formulated, and then an optimization algorithm based on the genetic algorithm was implemented with C++ language in order to solve it. Finally, the proposed method was applied to an example of FPSO topsides. As a result, it was shown that the proposed method can be applied to layout design of the floating offshore plant such as FPSO.

• Korean Journal of Computational Design and Engineering
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• v.18 no.6
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• pp.439-450
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• 2013

In the case of a floating offshore structure such as FPSO(Floating, Production, Storage, and Offloading unit), many equipment should be installed in the limited space, as compared with an onshore structure. Recently, the requirement for an optimal layout method of the structure has been raised. Thus, a layout method of the floating offshore structure was proposed in this study. First, an optimization problem for layout design was mathematically formulated, and then an optimization algorithm was implemented based on the genetic algorithm in order to solve it. To evaluate the applicability of the proposed method, it was applied to examples ofFPSO topsides and an offshore wind turbine. As a result, it was shown that the proposed method can be applied to layout design of the floating offshore structure.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.443-445
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• 2009

Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.463-464
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• 2007

This paper presents the hardware-based genetic algorithm, written in VHDL. Due to parallel computation and no function call overhead, a hardware-based GA advantage a speedup over a software-based GA. The proposed architecture is constructed on a field-programmable gate arrays, which are easily reconfigured. Since a general-purpose GA requires that the fitness function be easily changed, the hardware implementation must exploit the reprogrammability.