• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.85-98
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• 1998

This paper proposes a new design method of neuro-FLC by the Lamarckian co-adaptation scheme that incorporates the backpropagation learning into the GA evolution in an attempt to find optimal design parameters (fuzzy rule base and membership functions) of application-specific FLC. The design parameters are determined by evolution and learning in a way that the evolution performs the global search and makes inter-FLC parameter adjustments in order to obtain both the optimal rule base having high covering value and small number of useful fuzzy rules and the optimal membership functions having small approximation error and good control performance while the learning performs the local search and makes intra-FLC parameter adjustments by interacting each FLC with its environment. The proposed co-adaptive design method produces better approximation ability because it includes the backpropagation learning in every generation of GA evolution, shows better control performance because the used COG defuzzifier computes the crisp value accurately, and requires small workspace because the optimization procedure of fuzzy rule base and membership functions is performed concurrently by an integrated fitness function on the same fuzzy partition. Simulation results show that the Lamarckian co-adapted FLC produces the most superior one among the differently generated FLCs in all aspects such as the number of fuzzy rules, the approximation ability, and the control performance.

• Journal of Life Science
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• v.30 no.10
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• pp.919-929
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• 2020

Aromatic compounds are widely used in the chemical, food, polymer, cosmetic, and pharmaceutical industries and are produced by mainly chemical synthesis using benzene, toluene, and xylene or by plant extraction methods. Due to many rising threats, including the depletion of fossil fuels, global warming, the strengthening of international environmental regulations, and the excessive harvesting of plant resources, the microbial production of aromatic compounds using renewable biomass is regarded as a promising alternative. By integrating metabolic engineering with synthetic and systems biology, artificial biosynthetic pathways have been reconstituted from L-tryptophan biosynthetic pathway in relevant microorganisms, such as Escherichia coli and Corynebacterium glutamicum, enabling the production of a variety of value-added aromatic compounds, such as 5-hydroxytryptophan, serotonin, melatonin, 7-chloro-L-tryptophan, 7-bromo-L-tryptophan, indigo, indirubin, indole-3-acetic acid, violacein, and dexoyviolacein. In this review, we summarize the characteristics, usage, and biosynthetic pathways of these aromatic compounds and highlight the latest metabolic engineering strategies for the microbial production of aromatic compounds and suitable solution strategies to overcome problems in increasing production titers. It is expected that strain development based on systems metabolic engineering and the optimization of media and bioprocesses using renewable biomass will enable the development of commercially viable technologies for the microbial production of many aromatic compounds.

• Journal of Korea Water Resources Association
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• v.41 no.8
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• pp.807-823
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• 2008

This paper presents a technique for estimating discharge rating curve parameters. In typical practical applications, the original non-linear rating curve is transformed into a simple linear regression model by log-transforming the measurement without examining the effect of log transformation. The model of pseudo-likelihood estimation is developed in this study to deal with heteroscedasticity of residuals in the original non-linear model. The parameters of rating curves and variance functions of errors are simultaneously estimated by the pseudo-likelihood estimation(P-LE) method. Simulated annealing, a global optimization technique, is adapted to minimize the log likelihood of the weighted residuals. The P-LE model was then applied to a hypothetical site where stage-discharge data were generated by incorporating various errors. Results of the P-LE model show reduced error values and narrower confidence intervals than those of the common log-transform linear least squares(LT-LR) model. Also, the limit of water levels for segmentation of discharge rating curve is estimated in the process of P-LE using the Heaviside function. Finally, model performance of the conventional log-transformed linear regression and the developed model, P-LE are computed and compared. After statistical simulation, the developed method is then applied to the real data sets from 5 gauge stations in the Geum River basin. It can be suggested that this developed strategy is applied to real sites to successfully determine weights taking into account error distributions from the observed discharge data.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.96-110
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• 2020

Recently, due to global warming, it is easily exposed to various disasters such as fire, flood, and earthquake. In particular, large-scale disasters have continuously been occurring in crowded areas such as traditional markets, facilities for the elderly and children, and public facilities where various people stay. Purpose: This study aims to detect a fire occurred in crowded facilities early in the event to analyze and provide an optimal evacuation route using big data and advanced technology. Method: The researchers propose a new algorithm through context-aware 3D object model technology and A* algorithm optimization and propose a scenario-based optimal evacuation route selection technique. Result: Using the HPA* E algorithm, the evacuation simulation in the event of a fire was reproduced as a 3D model and the optimal evacuation route and evacuation time were calculated for each scenario. Conclusion: It is expected to reduce fatalities and injuries through the evacuation induction technique that enables evacuation of the building in the shortest path by analyzing in real-time via fire detection sensors that detects the temperature, flame, and smoke.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.131-138
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• 2010

Due to the increased water demand and severe drought as an effect of the global warming, the effluent from wastewater treatment plants becomes considered as an alternative water source to supply agricultural, industrial, and public (gardening) water demand. The effluent from the wastewater treatment plant is a sustainable water source because of its good quality and stable amount of water discharge. In this study, the water reuse system was developed to minimize total construction cost to cope with the uncertain water demand in future using two-stage stochastic linear programming with binary variables. The pipes in the water reuse network were constructed in two stages of which in the first stage, the water demands of users are assumed to be known, while the water demands in the second stage have uncertainty in the predicted value. However, the water reuse system has to be designed now when the future water demands are not known precisely. Therefore, the construction of a pipe parallel with the existing one was allowed to meet the increased water demands in the second stage. As a result, the trade-off of construction costs between a pipe with large diameter and two pipes having small diameters was evaluated and the optimal solution was found. Three scenarios for the future water demand were selected and a hypothetical water reuse network considering the uncertainties was optimized. The results provide the information about the economies of scale in the water reuse network and the long range water supply plan.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.115-121
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• 2015

In this paper, we proposed an iterative data-flow optimal scheduling algorithm based on genetic algorithm for high-performance multiprocessor. The basic hardware model can be extended to include detailed features of the multiprocessor architecture. This is illustrated by implementing a hardware model that requires routing the data transfers over a communication network with a limited capacity. The scheduling method consists of three layers. In the top layer a genetic algorithm takes care of the optimization. It generates different permutations of operations, that are passed on to the middle layer. The global scheduling makes the main scheduling decisions based on a permutation of operations. Details of the hardware model are not considered in this layer. This is done in the bottom layer by the black-box scheduling. It completes the scheduling of an operation and ensures that the detailed hardware model is obeyed. Both scheduling method can insert cycles in the schedule to ensure that a valid schedule is always found quickly. In order to test the performance of the scheduling method, the results of benchmark of the five filters show that the scheduling method is able to find good quality schedules in reasonable time.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.29-37
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• 1999

In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.540-548
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• 2012

The Weapon Target Allocation(WTA) problem is the NP-Complete problem. The WTA problem is that the threatful air targets are assigned by weapon of allies for killing the targets. A good solution of NP-complete problem is heuristic algorithms. Genetic algorithms are commonly used heuristic for global optimization, and it is good solution on the diverse problem domain. But there has been very little research done on the generation of their initial population. The initialization of population is one of the GA step, and it decide to initial value of individuals. In this paper, we propose to the population initialization method to improve a Genetic Algorithm. When it initializes population, the proposed algorithm reflects the characteristics of the WTA problem domain, and inherits the dominant gene. In addition, the search space widely spread in the problem space to find efficiently the good quality solution. In this paper, the proposed algorithm to verify performance examine that an analysis of various properties and the experimental results by analyzing the performance compare to other algorithms. The proposed algorithm compared to the other initialization methods and a general genetic algorithm. As a result, the proposed algorithm showed better performance in WTA problem than the other algorithms. In particular, the proposed algorithm is a good way to apply to the variety of situation WTA problem domain, because the proposed algorithm can be applied flexibly to WTA problem by the adjustment of RMI.

• Korean Chemical Engineering Research
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• v.55 no.2
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• pp.141-155
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• 2017

In the present paper, we investigated the development status of precipitated calcium carbonate (PCC) production using steel slag, which is one of mineral carbonation (MC) technologies, from the standpoint of $CO_2$ utilization. Principle, feature, and global and domestic development status of the mineral carbonation technology were discussed together with the overview of the production method and market of PCC. Mineral carbonation is known as stable and environmentally-friendly technology enabling economical treatment of industrials wastes. Typically, PCC is produced by the reaction of $CO_2$ with supernatant solution after Ca extraction from steel slag followed by the separation of solid and liquid. The development status of MC using steel slag is at the pilot stage (Slag2PCC at Aalto University), and there remains the process economics improvement for commercialization. Key technologies for the further development are efficient extraction of Ca ions from steel slag including impurities removal, valorization of PCC via shape and size control, usage development and value-addition of residual slag, and optimization of reaction conditions for continuous process setup, etc.

• Korean Chemical Engineering Research
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• v.56 no.5
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• pp.647-654
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• 2018

In order to solve the global warming and reduce greenhouse gas emissions, it has been developed the $CO_2$ capture technology by oxy-fuel combustion. But there is a problem that the economic efficiency is low because the oxygen production cost is high. ASU (Air Separation Unit) is known to be most suitable method for producing large capacity of oxygen (>2,000 tpd). But most of them are optimized for high purity (>99.5%) oxygen production. If the ASU process is optimized for low purity(90~97%) oxygen producing, it is possible to reduce the production cost of oxygen by improving the process efficiency. In this study, the process analysis and comparative evaluation was conducted for developing large capacity ASU for oxy-fuel combustion. The process efficiency was evaluated by calculating the recovery rate and power consumption according to the oxygen purity using the AspenHysys. As a result, it confirmed that the optimal purity of oxygen for oxyfuel combustion is 95%, and the power consumption can be reduced by process optimization to 12~18%.