• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.191-191
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• 2017

Floods have become more widespread and frequent among natural disasters and consisted significant losses of lives and properties worldwide. Flood's impacts are threatening socio-economic and people's lives in the Mekong River Basin every year. The objective of this study is to identify the flood hazard areas and inundation depth in the Mekong River Basin. A rainfall-runoff and flood inundation model is necessary to enhance understanding of characteristic of flooding. Rainfall-Runoff-Inundation (RRI) model, a two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously, was applied in this study. HydoSHEDS Topographical data, APPRODITE precipitation, MODIS land use, and river cross section were used as input data for the simulation. The Shuffled Complex Evolution (SCE-UA) global optimization method was integrated with RRI model to calibrate the sensitive parameters. In the present study, we selected flood event in 2000 which was considered as 50-year return period flood in term of discharge volume of 500 km3. The simulated results were compared with observed discharge at the stations along the mainstream and inundation map produced by Dartmouth Flood Observatory and Landsat 7. The results indicated good agreement between observed and simulated discharge with NSE = 0.86 at Stung Treng Station. The model predicted inundation extent with success rate SR = 67.50% and modified success rate MSR = 74.53%. In conclusion, the RRI model was successfully used to simulate rainfall runoff and inundation processes in the large scale Mekong River Basin with a good performance. It is recommended to improve the quality of the input data in order to increase the accuracy of the simulation result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1765-1794
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• 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

• Convergence Security Journal
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• v.21 no.2
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• pp.67-79
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• 2021

Lessons Learned(LL) is a military term defined as all activities that promote future development by finding problems and need improvement in education and reality in the field of warfare development. In this paper, we focus on presenting actual examples and applying AI analysis inference techniques to solve revealed problems in promoting LL activities, such as long-term analysis, budget problems, and necessary expertise. AI legal advice services using cognitive computing-related technologies that have already been practical and in use, were judged to be the best examples to solve the problems of LL. This paper presents intelligent LL inference techniques, which utilize AI. To this end, we want to explore theoretical backgrounds such as LL analysis definitions and examples, evolution of AI into Machine Learning, cognitive computing, and apply it to new technologies in the defense sector using the newly proposed L²-OODA ensemble algorithm to contribute to implementing existing power improvement and optimization.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Journal of the Korean institute of surface engineering
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• v.55 no.3
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• pp.180-186
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• 2022

The production of hydrogen via water electrolysis (i.e., green hydrogen) using renewable energy is key to the development of a sustainable society. However, most current electrocatalysts are based on expensive precious metals and require the use of highly purified water in the electrolyte. We demonstrated the preparation of a non-precious metal catalyst based on CuCo₂O₄ (CCO) via simple electrodeposition. Further, an optimization process for electrodeposition potential, solution concentration and electrodeposition method was develop for a catalyst-substrate integrated electrode, which indicated the highly electrocatalytic performance of the material in electrochemical tests and when applied to an anion exchange membrane water electrolyzer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2945-2965
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• 2023

The laser inertial measurement unit is a precision device widely used in rocket navigation system and other equipment, and its quality is directly related to navigation accuracy. In the quality evaluation of laser inertial measurement unit, there is inevitably uncertainty in the index input information. First, the input numerical information is in interval form. Second, the index input grade and the quality evaluation result grade are given according to different national standards. So, it is a key step to transform the interval information input by the index into the data form consistent with the evaluation result grade. In the case of uncertain input, this paper puts forward a method based on probability distribution to solve the problem of asymmetry between the reference grade given by the index and the evaluation result grade when evaluating the quality of laser inertial measurement unit. By mapping the numerical relationship between the designated reference level and the evaluation reference level of the index information under different distributions, the index evidence symmetrical with the evaluation reference level is given. After the uncertain input information is transformed into evidence of interval degree distribution by this method, the information fusion of interval degree distribution evidence is carried out by interval evidential reasoning algorithm, and the evaluation result is obtained by projection covariance matrix adaptive evolution strategy optimization. Taking a five-meter redundant laser inertial measurement unit as an example, the applicability and effectiveness of this method are verified.

• The Journal of Bigdata
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• v.9 no.1
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• pp.141-152
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• 2024

In the biopharmaceutical contract manufacturing organization (CMO) business, establishing a production schedule that satisfies the due date for various customer orders is crucial for competitiveness. In a CMO process, each order consists of multiple batches that can be allocated to multiple production lines in small batch units for parallel production. This study proposes a meta-heuristic algorithm to establish a scheduling plan that minimizes the total delivery delay of orders in a CMO process with identical parallel machine. Inspired by biological evolution, the proposed algorithm generates random data structures similar to chromosomes to solve specific problems and effectively explores various solutions through operations such as crossover and mutation. Based on real-world data provided by a domestic CMO company, computer experiments were conducted to verify that the proposed algorithm produces superior scheduling plans compared to expert algorithms used by the company and commercial optimization packages, within a reasonable computation time.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.343-356
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• 2014

The SWMM (storm water management model) has been widely used in the world and is a watershed runoff simulation model used for a single event or a continuous simulation of runoff quantity and quality. However, there are many uncertain parameters in the watershed runoff continuous simulation module and the water quality module, which make it difficult to use the SWMM. The purpose of the study is to develop an automatic calibration module of the SWMM not only for watershed runoff continuous simulation, but also water quality simulation. The automatic calibration module was developed by linking the SWMM with the SCE-UA (shuffled complex evolution-University of Arizona) that is a global optimization algorithm. Estimation parameters of the SWMM were selected and search ranges of them were reasonably configured. The module was validated by calibration and verification of the watershed runoff continuous simulation model and the water quality model for the Donghyang Stage Station Basin. The calibration results for watershed runoff continuous simulation model were excellent and those for water quality simulation model were generally satisfactory. The module could be used in various studies and designs for watershed runoff and water quality analyses.

• Korean journal of applied entomology
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• v.42 no.4
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• pp.367-381
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• 2003

Dispersal polymorphism in insects Is a kind of adaptive strategy of the life history together with the diapause, consisting of the “long-winged or alate forms” of migratory phase and the “short-winged or apterous forms” of stationary phase. Dispersal polymorphism is a polymorphism related with the flight capability, and has three categories ; the wing polymorphisms, flight muscle polymorphisms, and flight behavior variations. Phase variation is another type of dispersal polymorphism varying in morphology, physiology and wing forms in response to the density of the population. The dispersal migration is a very adaptive trait that enables a species to keep pace with the changing mosaic of its habitat, but requires some costs. In general, wing reduction has a positive effect on the reproductive potential such as earlier reproduction and larger fecundity The dispersal polymorphism is a kind of optimization in the evolutionary strategies of the life history in insects; a trade-off between the advantages and disadvantages of migration. Wing polymorphism is a phenotypically plastic trait. Wing form changes with the environmental conditions even though the species is the same. Various environmental factors have an effect on the dispersal polymorphisms. Density dependent dispersal polymorphism plays an important role In population dynamics, but it is not a simple function of the density; the individuals of a population may be different in response to the density resulting different outcomes in the population biology, and the detailed information on the genotypic variation of the individuals in the population is the fundamental importance in the prediction of the population performances in a given environment. In conclusion, the studies on the dispersal polymorphisms are a complicated field in relation with both physiology and ecology, and studies on the ecological and quantitative genetics have indeed contributed to understanding of its important nature. But the final factors of evolution; the mechanisms of natural selections, might be revealed through the studies on the population biology.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.417-423
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• 2007

Oxy-gasification or oxygen-blown gasification, enables a clean and efficient use of coal and opens a promising way to CO2 capture. The coal gasification process of a slurry feed type, entrained-flow coal gasifier was numerically predicted in this paper. The purposes of this study are to develop an evaluation technique for design and performance optimization of coal gasifiers using a numerical simulation technique, and to confirm the validity of the model. By dividing the complicated coal gasification process into several simplified stages such as slurry evaporation, coal devolatilization, mixture fraction model and two-phase reactions coupled with turbulent flow and two-phase heat transfer, a comprehensive numerical model was constructed to simulate the coal gasification process. The influence of turbulence on the gas properties was taken into account by the PDF (Probability Density Function) model. A numerical simulation with the coal gasification model is performed on the Conoco-Philips type gasifier for IGCC plant. Gas temperature distribution and product gas composition are also presented. Numerical computations were performed to assess the effect of variation in oxygen to coal ratio and steam to coal ratio on reactive flow field. The concentration of major products, CO and H2 were calculated with varying oxygen to coal ratio (0.2-1.5) and steam to coal ratio(0.3-0.7). To verify the validity of predictions, predicted values of CO and H2 concentrations at the exit of the gasifier were compared with previous work of the same geometry and operating points. Predictions showed that the CO and H2 concentration increased gradually to its maximum value with increasing oxygen-coal and hydrogen-coal ratio and decreased. When the oxygen-coal ratio was between 0.8 and 1.2, and the steam-coal ratio was between 0.4 and 0.5, high values of CO and H2 were obtained. This study also deals with the comparison of CFD (Computational Flow Dynamics) and STATNJAN results which consider the objective gasifier as chemical equilibrium to know the effect of flow on objective gasifier compared to equilibrium. This study makes objective gasifier divided into a few ranges to study the evolution of the gasification locally. By this method, we can find that there are characteristics in the each scope divided.