• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.232-235
• /
• 2000

In this paper, we introduce a new genetic reordering operator based on the concept of schema to solve the Traveling Salesman Problem(TSP). Because TSP is a well-known combinatorial optimization problem and belongs to a NP-complete problem, there is a huge solution space to be searched. For robustness to local minima, the operator separates selected strings into two parts to reduce the destructive probability of good building blocks. And it applies inversion to the schema part to prevent the premature convergence. At the same time, it searches new spaces of solutions. In addition, we have the non-schema part to be applied to inversion as well as for robustness to local minima. By doing so, we can preserve diversity of the distributions in population and make GA be adaptive to the dynamic environment.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.05a
• /
• pp.141-146
• /
• 1999

A new method, so called MMA(Method of Moving Asymptotes) was applied to the optimization problems of non-linear functions and non-linear structures. In each step of the iterative process, tile MMA generates a strictly convex approximation subproblems and solves them by using the dual problems. The generation of these subproblems is controlled by so called 'moving asymptotes', which may both make no oscillation and speed up tile convergence rate of optimization process. By contrast in generalized dual function, the generated function by MMA is always explicit type. Both the objective and behaviour constraints which were approximated are optimized by dual function. As the results of some examples, it was found that this method is very effective to obtain the global solution for problems with many local solutions. Also it was found that MMA is a very effective approximate method using the original function and its 1st derivatives.

• Bulletin of the Society of Naval Architects of Korea
• /
• v.23 no.3
• /
• pp.10-16
• /
• 1986

This paper deals with a simple, approximate formula to predict the natural frequencies of the sagged cable structures. Assuming that the propagation velocity of the lateral wave is dependent only on the local mass per unit length and local tension, the explicit simple formula to predict the fundamental period is newly derived. The modified form of these formula is also presented for the prediction of the fundamental period of general shaped cable structures. The results of comparison shows fairly good agreements with experimental results and with theoretical ones. This formula is also used to predict the natural frequencies of a long vertical cable and the derived approximate formula in that case, becomes identical to the exact solution.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.5
• /
• pp.1303-1311
• /
• 1999

In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.7
• /
• pp.167-174
• /
• 1998

This paper presents an unoccluded cylindrical object pose measurement using a slit beam laser in which a robot recognizes all of the unoccluded objects from the top of jumbled objects, and picks them up one by one. The elliptical equation parameters of a projected curve edge on a slice are calculated using LSM. The coefficients of standard elliptical equation are compared with these parameters to estimate the object pose. The hamming distances between the estimated coordinates and the calculated ones are extracted as measures to evaluate a local constraint and a smoothing surface curvature. The edges between slices are linked using error function based on the edge types and the hamming distances. The linked edges on slices are compared with the model object's length to recognize the unoccluded object. This proposed method may provide a solution to the automation of part handling in manufacturing environments such as punch press operation or part assembly.

• Bulletin of the Korean Chemical Society
• /
• v.16 no.12
• /
• pp.1153-1162
• /
• 1995

The conformational details of β-lactose are investigated through molecular dynamics simulations in conjunction with the adiabatic potential energy map. The adiabatic energy map generated in vacuo contains five local minima. The lowest energy structure on the map does not correspond to the structure determined experimentally by NMR and the X-ray crystallography. When aqueous solvent effect is incorporated into the energy map calculation by increasing the dielectric constant, one of the local minima in the vacuum energy map becomes the global minimum in the resultant energy map. The lowest energy structure of the energy map generated in aquo is consistent with the one experimentally determined. Molecular dynamics simulations starting from those fivelocal minima on the vacuum energy map reveal that conformational transitions can take place among various conformations. Molecular dynamics simulations of the lactose and ricin B chain complex system in a stochastic boundary indicate that the most stable conformation in solution phase is bound to the binding site and that there are conformational changes in the exocyclic region of the lactose molecule upon binding.

• Advances in aircraft and spacecraft science
• /
• v.6 no.4
• /
• pp.273-282
• /
• 2019

This article is concerned with the investigation of geometrically non-linear vibration response of refined thick porous nanobeams. To this end, non-local theory of elasticity has been adopted to provide the nanobeam formulation. Voids or pores can affect the material characteristics of the nanobeam. So, their effects have been considered in this research and also there are various void distributions. The closed form solution of the non-linear problem has been used that is adopted from previous articles. Then, it is focused on the impacts of non-local field, void distribution, void amount and geometrical properties on non-linear vibrational characteristic of a nano-size beam.

• Proceedings of the PSK Conference
• /
• 2002.10a
• /
• pp.410.1-410.1
• /
• 2002

Local anesthetics are used to reduce pain. but they are so frequently injected to patients. So we prepared lidocaine solid lipid nanopaticles for long acting subcutaneous injection to decrease the number of times of injection. Solid lipid nanoparticles were prepared by spray drying method. First. drug. lipid. plasticizer and surfactant were dissolved in methylene chloride. and we operated spray dryer using this solution at setting value. (omitted)

• Journal of Information Processing Systems
• /
• v.17 no.3
• /
• pp.441-452
• /
• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.313-326
• /
• 2022

This study presents a noble scheme for distributed and parallel simulations with optimized agent placement for simulation instances. The traditional parallel simulation has some limitations in that it does not provide sufficient performance even though using multiple resources. The main reason for this discrepancy is that supporting parallelism inevitably requires additional costs in addition to the base simulation cost. We present a comprehensive study of parallel simulation architectures, execution flows, and characteristics. Then, we identify critical challenges for optimizing large simulations for parallel instances. Based on our cost-benefit analysis, we propose a novel approach to overcome the performance constraints of agent-based parallel simulations. We also propose a solution for eliminating the synchronizing cost among local instances. Our method ensures balanced performance through optimal deployment of agents to local instances and an adaptive agent placement scheme according to the simulation load. Additionally, our empirical evaluation reveals that the proposed model achieves better performance than conventional methods under several conditions.