• Journal of KIISE:Software and Applications
• /
• v.30 no.3_4
• /
• pp.326-338
• /
• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.10
• /
• pp.2627-2640
• /
• 1998

In this paper, we analyze the performance of the virtual cell system[1] for the transmission of IP datagrams in mobile computer communications. A virtual cell consistsof a group of physical cells shose base stationsl are implemented b recote bridges and interconnected via high speed datagram packet switched networks. Host mobility is supported at the data link layer using the distributed hierachical location information of mobile hosts. Given mobility and communication ptems among physical cells, the problem of deploying virtual cells is equivalent to the optimization cost for the entire system where interclster communication is more expesive than intracluster communication[2]. Once an iptimal partitionof disjoint clusters is obtained, we deploy the virtual cell system according to the topology of the optimal partition such that each virtual cell correspods to a cluser. To analyze the performance of the virtual cell system, we adopt a BCMP open multipel class queueing network model. In addition to mobility and communication patterns, among physical cells, the topology of the virtual cell system is used to determine service transition probabilities of the queueing network model. With various system parameters, we conduct interesting sensitivity analyses to determine network design tradeoffs. The first application of the proposed model is to determine an adequate network bandwidth for base station networking such that the networks would not become an bottleneck. We also evaluate the network vlilization and system response time due to various types of messages. For instance, when the mobile hosts begin moving fast, the migration rate will be increased. This results of the performance analysis provide a good evidence in demonsratc the sysem effciency under different assumptions of mobility and communication patterns.

• Journal of Technology Innovation
• /
• v.18 no.2
• /
• pp.221-252
• /
• 2010

This study presents a concept of technology trade network and management, and proposes a procedural method for optimally selecting the technology transferor when a technology transferee needs to buy a specific technology. We develop a technology trade network where technology supplier, technology marketer, and technology transferee are informatively linked. And a technology trade management consists of three step of estimating technology, trading technology, and commercialization technology. Technology transferees could import the best appropriate technology which they want through these technology network method and cost optimization method. And we hope that these methodologies can be used in selecting new technology. A methodology can be classified into an estimating technology process and a choice of technology supplier process. In an estimating technology process, we calculate the technology similarity quantitatively through developing method of estimating technology which is focused on its technological characteristics. After defining the related cost of technology introduction, we suggest goal programming model to find a solution which can be acceptable both maximizing the technology similarity and minimizing the cost of technology. And suggested model is verified with a supplier selection problem of next generation tanks.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1440-1447
• /
• 2019

Currently, the prevalence of autism spectrum disorders in children is reported to be higher and shows various types of disorders. In particular, they are having difficulty in communication due to communication impairment in the area of social communication and need to be improved through training. Thus, this study proposes a method of acquiring voice information through a microphone mounted on a robot designed through preliminary research and using this information to make intelligent motions. An ANN(Artificial Neural Network) was used to classify the speech data into robot motions, and we tried to improve the accuracy by combining the Recurrent Neural Network based on Convolutional Neural Network. The preprocessing of input speech data was analyzed using MFCC(Mel-Frequency Cepstral Coefficient), and the motion of the robot was estimated using various data normalization and neural network optimization techniques. In addition, the designed ANN showed a high accuracy by conducting an experiment comparing the accuracy with the existing architecture and the method of human intervention. In order to design robot motions with higher accuracy in the future and to apply them in the treatment and education environment of children with autism.

• Journal of Korean Society of Transportation
• /
• v.30 no.5
• /
• pp.71-82
• /
• 2012

Some previous studies adopted a method statistically based on the observed traffic volumes and travel times to estimate the parameters. Others tried to find an optimal set of parameters to minimize the gap between the observed and estimated traffic volumes using, for instance, a combined optimization model with a traffic assignment model. The latter is frequently used in a large-scale network that has a capability to find a set of optimal parameter values, but its appropriateness has never been demonstrated. Thus, we developed a methodology to estimate a set of parameter values of BPR(Bureau of Public Road) function using Harmony Search (HS) method. HS was developed in early 2000, and is a global search method proven to be superior to other global search methods (e.g. Genetic Algorithm or Tabu search). However, it has rarely been adopted in transportation research arena yet. The HS based transportation network calibration algorithm developed in this study is tested using a grid network, and its outcomes are compared to those from incremental method (Incre) and Golden Section (GS) method. It is found that the HS algorithm outperforms Incre and GS for copying the given observed link traffic counts, and it is also pointed out that the popular optimal network calibration techniques based on an objective function of traffic volume replication are lacking the capability to find appropriate free flow travel speed and ${\alpha}$ value.

• International Journal of Automotive Technology
• /
• v.6 no.1
• /
• pp.71-77
• /
• 2005

This paper presents a systematic approach which determines the optimal period to minimize performance measure subject to the schedulability constraints of a real-time control system by formulating the scheduling problem as an optimal problem. The performance measure is derived from the summation of end-to-end response times of processed I/Os scheduled by the static cyclic method. The schedulability constraint is specified in terms of allowable resource utilization. At first, a uniprocessor case is considered and then it is extended to a distributed system connected through a communication link, local-inter network, UN. This approach is applied to the design of an automotive body control system in order to validate the feasibility through a real example. By using the approach, a set of optimal periods can easily be obtained without complex and advanced methods such as branch and bound (B&B) or simulated annealing.

• Journal of Korea Multimedia Society
• /
• v.15 no.12
• /
• pp.1499-1505
• /
• 2012

Recently, the distributed processing system for big data has been actively investigated owing to the development of high speed network and storage technologies. In addition, virtual system that can provide efficient use of system resources through the consolidation of servers has been increasingly recognized. But, when we configure distributed processing system for big data in virtual machine environments, many problems occur. In this paper, we did an experiment on the optimization of I/O bandwidth according to the creation and placement of VMs and tasks with composing Hadoop cluster in virtual environments and evaluated the results of an experiment. These results conducted by this paper will be used in the study on the development of Hadoop Scheduler supporting I/O bandwidth balancing in virtual environments.

• Industrial Engineering and Management Systems
• /
• v.13 no.3
• /
• pp.258-266
• /
• 2014

This paper proposes a management system for the educational program of production managers on the basis of value co-creation by the learner and the instructor. The program combines an intelligent knowledge-based approach with the kaizen activity program. The program helps individuals acquire knowledge and skills to ensure the total rather than the partial optimization of processes and operations facilitating continuous improvement in the workplace. To achieve these goals, the program uses models of a learning process and a swing of enlightenment. In addition, the program is supported by a framework of academic, business people, consultants, and government officers. The program was developed using an instructional design approach. This paper discusses the process of developing and managing the educational program between 2006 and 2012 as well as the results obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.3
• /
• pp.735-748
• /
• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• Journal of Korean Society of Water and Wastewater
• /
• v.12 no.1
• /
• pp.70-80
• /
• 1998

Many algorithms to find a minimum cost design of water distribution network (WDN) have been developed during the last decades. Most of them have tried to optimize cost only while satisfying other constraining conditions. For this, a certain degree of simplification is required in their calculation process which inevitably limits the real application of the algorithms, especially, to large networks. In this paper, an optimum design method using the Genetic Algorithms (GA) is developed which is designed to increase the applicability, especially for the real world large WDN. The increased to applicability is due to the inherent characteristics of GA consisting of selection, reproduction, crossover and mutation. Just for illustration, the GA method is applied to find an optimal solution of the New York City water supply tunnel. For the calculation, the parameter of population size and generation number is fixed to 100 and the probability of crossover is 0.7, the probability of mutation is 0.01. The yielded optimal design is found to be superior to the least cost design obtained from the Linear Program method by $4.276 million.