• Korean Journal of Environmental Biology
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• v.36 no.2
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• pp.190-198
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• 2018

The petroleum industry is an important part of the world economy. However, the massive exposure of petroleum in nature is a major cause of environmental pollution. Therefore, the microbial mediated biodegradation of petroleum residues is an emerging scientific approach used to resolve these problem. Through the screening of diesel contaminated soil we isolated a rapid phenanthrene and a diesel degrading bacterium identified as Enterobacter cancerogenus DA1 strain through 16S rRNA gene sequence analysis. The strain was registered in NCBI with an accession number MG270576. The optimal growth condition of the DA1 strain was determined at pH 8 and $35^{\circ}C$, and the highest degradation rate of the diesel was achieved at this condition. At the optimal condition, growth of the strain on the medium containing 0.05% phenanthrene and 0.1% of diesel-fuel was highest at 45 h and 60 h respectively after the incubation period. Biofilm formation was found significantly higher at $35^{\circ}C$ as compared to $30^{\circ}C$ and $40^{\circ}C$. Likewise, the lipase activity was found significantly higher at 48 h after the incubation compared to 24 h and 72 h. These results suggest that the Enterobacter cancerogenus DA1 could be an efficient candidate, for application through ecofriendly scientific approach, for the biodegradation of petroleum products like diesel.

• International Journal of Railway
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• v.4 no.4
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• pp.109-115
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• 2011

An analysis and design method for reducing aerodynamic noise in high-speed trains based on biomimetics of noiseless flight of owl is proposed. Five factors related to the morphology of the flight feather have been selected, and the candidate optimal shape of the flight feather is determined. The turbulent flow field analysis demonstrates that the optimal shape leads to diminished vortex formation by causing separation of the flow as well as allowing the fluid to climb up along the surface of the flight feather. To determine the effect of scaling of the owl's flight feather on the noise reduction, a two-fold and a four-fold scaled up model of the feather are constructed, and the numerical simulations are carried out to obtain the aerodynamic noise levels for each scale. Original model is found to reduce the noise level by 10 dBA, while two-fold increase in length dimensions reduces the noise by 12 dBA. Validation of numerical solution using wind tunnel experimental measurements is presented as well.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.57-63
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• 2018

The operating system for an isolated microgrid, which is completely disconnected from the central power system, aims at preventing blackouts and minimizing power generation costs of diesel generators through efficient operation of the energy storage system (ESS) that stores energy produced by renewable energy generators and diesel generators. In this paper, we predict the amount of renewable energy generation using the weather forecast and build an optimal diesel power generation plan using a genetic algorithm. In order to avoid inefficiency due to inaccurate prediction of renewable energy generation, our search algorithm imposes penalty on candidate diesel power generation plans that fail to maintain the SOC (state of charge) of ESS at an appropriate level. Simulation experiments show that our optimization method for maintaining an appropriate SOC balance can prevent the blackout better when compared with the previous method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.388-394
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• 1999

This paper is concerned with an application of Parallel Genetic Algorithms(PGA) to optimal econmic load dispatch(ELD) in power systems. The ELD problem is to minimize the total generation fuel cost of power outputs for all generating units while satisfying load balancing constraints. Genetic Algorithms(GA) is a good candidate for effective parallelization because of their inherent principle of evolving in parallel a population of individuals. Each individual of a population evaluates the fitness function without data exchanges between individuals. In application of the parallel processing to GA, it is possible to use Single Instruction stream, Multiple Data stream(SIMD), a kind of parallel system. The architecture of SIMD system need not data communications between processors assigned. The proposed ELD problem with C code is implemented by SIMSCRIPT language for parallel processing which is a powerfrul, free-from and versatile computer simulation programming language. The proposed algorithms has been tested for 38 units system and has been compared with Sequential Quadratic programming(SQP).

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.2
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• pp.236-247
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• 1992

Optimization has been developed to minimize the cost function while satisfying constraints. Nonlinear Programming method is used as a tool for the optimization. Usually, cost and constraint function calculations are required in the engineering applications, but those calculations are extremely expensive. Especially, the function and sensitivity analyses cause a bottleneck in structural optimization which utilizes the Finite Element Method. Also, when the functions are quite noisy, the informations do not carry out proper role in the optimization process. An algorithm called "Second-order Approximation Method" has been proposed to overcome the difficulties recently. The cost and constraint functions are approximated by the second-order Taylor series expansion on a nominal points in the algorithm. An optimal design problem is defined with the approximated functions and the approximated problem is solved by a nonlinear programming numerical algorithm. The solution is included in a candidate point set which is evaluated for a new nominal point. Since the functions are approximated only by the function values, sensitivity informations are not needed. One-dimensional line search is unnecessary due to the fact that the nonlinear algorithm handles the approximated functions. In this research, the method is analyzed and the performance is evaluated. Several mathematical problems are created and some standard engineering problems are selected for the evaluation. Through numerical results, applicabilities of the algorithm to large scale and complex problems are presented.presented.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.149-163
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• 2023

Affected by external factors, errors in time series data collected by sensors are common. Using the traditional method of constraining the speed change rate to clean the errors can get good performance. However, they are only limited to the data of stable changing speed because of fixed constraint rules. Actually, data with uneven changing speed is common in practice. To solve this problem, an online cleaning algorithm for time series data based on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changes periodically, we use the extreme learning machine to learn the law of speed changes from past data and predict the speed ranges that change over time to detect the data. In order to realize online data repair, a dual-window mechanism is proposed to transform the global optimal into the local optimal, and the traditional minimum change principle and median theorem are applied in the selection of the repair strategy. Aiming at the problem that the repair method based on the minimum change principle cannot correct consecutive abnormal points, through quantitative analysis, it is believed that the repair strategy should be the boundary of the repair candidate set. The experimental results obtained on the dataset show that the method proposed in this paper can get a better repair effect.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.117-129
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• 2023

High-utility itemset mining (HIUM) has emerged as a key data-mining paradigm for object-of-interest identification and recommendation systems that serve as frequent itemset identification tools, product or service recommendation systems, etc. Recently, it has gained widespread attention owing to its increasing role in business intelligence, top-N recommendation, and other enterprise solutions. Despite the increasing significance and the inability to provide swift and more accurate predictions, most at-hand solutions, including frequent itemset mining, HUIM, and high average- and fast high-utility itemset mining, are limited to coping with real-time enterprise demands. Moreover, complex computations and high memory exhaustion limit their scalability as enterprise solutions. To address these limitations, this study proposes a model to extract high-utility frequent closed itemsets based on an improved cumulative summary list structure (CSLFC-HUIM) to reduce an optimal set of candidate items in the search space. Moreover, it employs the lift score as the minimum threshold, called the cumulative utility threshold, to prune the search space optimal set of itemsets in a nested-list structure that improves computational time, costs, and memory exhaustion. Simulations over different datasets revealed that the proposed CSLFC-HUIM model outperforms other existing methods, such as closed- and frequent closed-HUIM variants, in terms of execution time and memory consumption, making it suitable for different mined items and allied intelligence of business goals.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.115-120
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• 2019

In this paper, we present the results of the performance statistical analysis of the multi-robot formation control based on receding horizon particle swarm optimization (RHPSO). The formation control problem of multi-robot system can be defined as a constrained nonlinear optimization problem when considering collision avoidance between robots. In general, the constrained nonlinear optimization problem has a problem that it takes a long time to find the optimal solution. The RHPSO algorithm was proposed to quickly find a suboptimal solution to the optimization problem of multi-robot formation control. The computational complexity of the RHPSO increases as the number of candidate solutions and generations increases. Therefore, it is important to find a suboptimal solution that can be used for real-time control with minimal candidate solutions and generations. In this paper, we compared the formation error according to the number of candidate solutions and the number of generations. Through numerical simulations under various conditions, the results are analyzed statistically and the minimum number of candidate solutions and the minimum number of generations of the RHPSO algorithm are derived within the allowable control error.

• Journal of Environmental Science International
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• v.22 no.11
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• pp.1457-1464
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• 2013

We investigated the optimal conditions for keratinase production by feather-degrading Pseudomonas geniculata H10 using one variable at a time (OVT) method. The optimal medium composition and cultural condition for keratinase production were determined to be glucose 0.15% (w/v), beef extract 0.08% (w/v), $KH_2PO_4$ 0.12% (w/v), $K_2HPO_4$ 0.02% (w/v), NaCl 0.07% (w/v), $MgSO_4{\cdot}7H_2O$ 0.03%, $MgCl_2{\cdot}6H_2O$ 0.04% along with initial pH 10 at 200 rpm and $25^{\circ}C$, respectively. The production yield of keratinase was 31.6 U/ml in an optimal condition, showing 4.6-fold higher than that in basal medium. The strain H10 also showed plant growth promoting activities. This strain had ammonification activity and produced indoleacetic acid (IAA), siderophore and a variety of hydrolytic enzymes such as protease, lipase and chitinase. Therefore, this study showed that P. geniculata H10 could be not only used to upgrade the nutritional value of feather wastes but also useful in situ biodegradation of feather wastes. Moreover, it is also a potential candidate for the development of biofertilizing agent applicable to crop plant soil.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.249-257
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• 2015

This paper introduces a heuristic algorithm to NP-hard travelling salesman problem. The proposed algorithm, in its bid to determine initial path, applies SW-DNN, DW-DNN, and DC-DNN, which are modified forms of the prevalent Double-sided Nearest Neighbor Search and searches the minimum value. As a part of its optimization process on the initial solution, it employs 2, 2.5, 3-opt of a local search k-opt on candidate delete edges and 4-opt on undeleted ones among them. When tested on TSP-1 of 26 European cities and TSP-2 of 49 U.S. cities, the proposed algorithm has successfully obtained optimal results in both, disproving the prevalent disbelief in the attainability of the optimal solution and making itself available as a general algorithm for the travelling salesman problem.