• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.125-128
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent recycled to the pigsty. This system significantly removes offensive smells (at both pigsty and treatment plant), BOD and other loads, and appears to be costeffective for the small-scale farms. The most dominant heterotrophs were Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp. in order while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment such as population densities of heterotrophic and lactic acid bacteria, suspended solids (SS), COD, $NH_3-N$, ortho-P, and total-P) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• Journal of Sensor Science and Technology
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• v.9 no.2
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• pp.127-133
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• 2000

We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.751-756
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• 2003

Ant Colony Optimization (ACO) is new meta heuristics method to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was firstly proposed for tackling the well known Traveling Salesman Problem (TSP) . In this paper, we introduce Multi Colony Ant Model that achieve positive interaction and negative interaction through Intensification and Diversification to improve original ACS performance. This algorithm is a method to solve problem through interaction between ACS groups that consist of some agent colonies to solve TSP problem. In this paper, we apply this proposed method to TSP problem and evaluates previous method and comparison for the performance and we wish to certify that qualitative level of problem solution is excellent.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.67-75
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• 2023

This paper deals with the operation of air taxis, which is one of the latest innovative technologies aimed at solving the issue of traffic congestion in cities. A key challenge for the successful introduction of the technology and efficient operation is a vertiport location problem. This paper employs a discrete choice model to calculate choice probabilities of transportation modes for each route, taking into account factors such as cost and travel time associated with different modes. Based on this probability, a mathematical formulation to maximize the utilization rate for air taxi is proposed. However, the proposed model is NP-hard, effective and efficient solution methodology is required. Compared to previous studies that simply proposed the optimization models, this study presents a solution methodology using the cross-entropy algorithm and confirms the effectiveness and efficiency of the algorith through numerical experiments. In addition to the academic excellence of the algorithm, it suggests that decision-making that considers actual data and air taxi utilization plans can increase the practial usability.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.35-47
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• 2005

This paper is to review a literature concerning Bus Transit Route Network Design(BTRNDP), to describe a future study direction for a systematic application for the BTRNDP. Since a bus transit uses a fixed route, schedule, stop, therefore an approach methodology is different from that of auto network design problem. An approach methodology for BTRNDP is classified by 8 categories: manual & guideline, market analysis, system analytic model. heuristic model. hybrid model. experienced-based model. simulation-based model. mathematical optimization model. In most previous BTRNDP, objective function is to minimize user and operator costs, and constraints on the total operator cost, fleet size and service frequency are common to several previous approach. Transit trip assignment mostly use multi-path trip assignment. Since the search for optimal solution from a large search space of BTRNDP made up by all possible solutions, the mixed combinatorial problem are usually NP-hard. Therefore, previous researches for the BTRNDP use a sequential design process, which is composed of several design steps as follows: the generation of a candidate route set, the route analysis and evaluation process, the selection process of a optimal route set Future study will focus on a development of detailed OD trip table based on bus stop, systematic transit route network evaluation model. updated transit trip assignment technique and advanced solution search algorithm for BTRNDP.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.57-78
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• 2005

The vehicle routing problem (VRP) is to determine a set of feasible vehicle routes, one for each vehicle, such that each customer is visited exactly once and the total distance travelled by the vehicles is minimized. A feasible route is defined as a simple circuit including the depot such that the total demand of the customers in the route does not exceed the vehicle capacity. While there have been significant advances recently in exact solution methodology, the VRP is not a well solved problem. We find most approaches still relying on the branch and bound method. These approaches employ various methodologies to compute a lower bound on the optimal value. We introduce a new modelling approach, termed route-splitting, for the VRP that allows us to address problems whose size is beyond the current computational range of set-partitioning models. The route-splitting model splits each vehicle route into segments, and results in more tractable subproblems. Lifting much of the burden of solving combinatorially hard subproblems, the route-splitting approach puts more weight on the LP master problem, Recent breakthroughs in solving LP problems (Nemhauser, 1994) bode well for our approach. Lower bounds are computed on five symmetric VRPs with up to 199 customers, and eight asymmetric VRPs with up to 70 customers. while it is said that the exact methods developed for asymmetric instances have in general a poor performance when applied to symmetric ones (Toth and Vigo, 2002), the route splitting approach shows a competent performance of 93.5% on average in the symmetric VRPs. For the asymmetric ones, the approach comes up with lower bounds of 97.6% on average. The route-splitting model can deal with asymmetric cost matrices and non-identical vehicles. Given the ability of the route-splitting model to address a wider range of applications and its good performance on asymmetric instances, we find the model promising and valuable for further research.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.105-117
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• 2010

In recent years, dynamic collaboration (DC) among cloud providers (CPs) is becoming an inevitable approach for the widely use of cloud computing and to realize the greatest value of it. In our previous paper, we proposed a combinatorial auction (CA) based cloud market model called CACM that enables a DC platform among different CPs. The CACM model allows any CP to dynamically collaborate with suitable partner CPs to form a group before joining an auction and thus addresses the issue of conflicts minimization that may occur when negotiating among providers. But how to determine optimal group bidding prices, how to obtain the stability condition of the group and how to distribute the winning prices/profits among the group members in the CACM model have not been studied thoroughly. In this paper, we propose to formulate the above problems of cooperative negotiation in the CACM model as a bankruptcy game which is a special type of N-person cooperative game. The stability of the group is analyzed by using the concept of the core and the amount of allocationsto each member of the group is obtained by using Shapley value. Numerical results are presented to demonstrate the behaviors of the proposed approaches.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.2
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• pp.21-30
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• 2000

If there exists a model that calculates the proper candidate position whenever the game of Go is in progress, it can be used for setting up the prototype of the candidate generation algorithm without using case-based reasoning. In this paper, we analyze Go through combinatorial game theory and on the basis of probability matrix (PM) showing the difference of the territory of the black and the white. We design and implement a candidate generation system(CGS) to find the candidates at a situation in Go. CGS designed in this paper can compute Influence power, safety, probability value(PV), and PM and then generate candidate positions for a present scene, once a stone is played at a scene. The basic strategy generates five candidates for the Present scene, and then chooses one with the highest PV. CGS generates the candidate which emphasizes more defence tactics than attack ones. In the opening game of computer Go, we can know that CGS which has no pattern is somewhat superior to NEMESIS which has the Joseki pattern.