• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.231-236
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• 2001

The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.5
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• pp.538-544
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• 2012

In this paper, design optimization of C-frame of a precision drilling and autorivet machine has been performed. The machine, Autoriveter has been developed by Korea Aerospace Industry (KAI), For current autoriveter, it is hard to achieve high efficiency because of heavy weight of the machine. In this paper, we suggest new structure of the current C-frame, a part of autoriveter, by optimization. The result of the study can give much profit for mass-production of the machine.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1672-1678
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• 2024

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.63-66
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• 2001

Recently, as the demand for high efficiency, multi function machine tools is increasing, domestic machine tool industries are investing in research and development for precision machine tools with high speed. This trend is closely correlated with the design technique which is necessary to make new type machine tool compatible with new production system. To achieve high precision, high speed machine tools with reduced chatter, it is needed to develop dynamically rigid structure. In this paper, dynamic optimization of machine structure is presented. At this procedure of dynamic design, dynamic compliance is minimized using Simple Genetic Algorithm(SGA)

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.5
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• pp.313-320
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• 2016

The need for high-strength, multi-axis, and multi-functional machine tools has recently increased because of part complexity and workpiece strength. However, most of the machine tool manufacturers rely on experience for a detailed design because of the shortcomings in the existing design technology. This study uses a topology optimization method to more effectively design a large multi-functional machine tool considering static stiffness. The ram, saddle, and column parts are important structures in a machine tool. Hence, they are selected for the finite element method analysis. Based on this analysis, the optimized internal rib structure for those parts is designed for desirable rigidity and weight. This structure could possibly provide the required design technology for machine tool manufacturers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• The KIPS Transactions:PartA
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• v.14A no.3 s.107
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• pp.167-172
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• 2007

VM(Virtual Machine) can be considered as a software processor which interprets the machine code. Also, it is considered as a conceptional computer that consists of logical system configuration. But, the execution speed of VM system is much slower than that of a real processor system. So, it is very important to optimize the code for virtual machine to enhance the execution time. Especially the optimizer for a virtual machine code on embedded devices requires the highly efficient performance to the ordinary optimizer in the respect to the optimized ratio about cost. Fundamentally, functions and basic blocks which influence the execution time of virtual machine is found, and then an optimization for them nay get the high efficiency. In this paper, we designed and implemented the optimizer for the virtual(or abstract) machine code(VMC) using profiling. Firstly, we defined the profiling information which is necessary to the optimization of VMC. The information can be obtained from dynamically executing the machine code. And we implemented VMC optimizer using the profiling information. In our implementation, the VMC is SIL(Standard Intermediate Language) that is an intermediate code of EVM(Embedded Virtual Machine). Also, we tried a benchmark test for the VMC optimizer and obtained reasonable results.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.7
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• pp.1025-1032
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• 1990

We proposed a neural network energy function for the optimal graph partitioning and its optimization method using Boltzmann Machine. We composed a Boltzmann Machine with the proposed neural network energy function, and the simulation results show that we can obtain an optimal solution with the energy function parameters of A=50, B=5, c=14 and D=10, at the Boltzmann Machine parameters of To=80 and \ulcorner0.07 for a 6-node 3-partition problem. As a result, the proposed energy function and optimization parameters are proved to be feasible for the optimal graph partitioning.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.81-85
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• 2005

In this paper, the structural design optimization of a wafer grinding machine using a multi-step optimization with genetic algorithm is presented. The design problem, in this study, is to find out the optimum configuration and dimensions of structural members which minimize the static compliance, the dynamic compliance, and the weight of the machine structure simultaneously under several design constraints. The first design step is shape optimization, in which the best structural configuration is found by getting rid of structural members that have no contributions to the design objectives from the given initial design configuration. The second and third steps are sizing optimization. The second design step gives a set of good design solutions having higher fitness for lightweight and minimum static compliance. Finally the best solution, which has minimum dynamic compliance and weight, is extracted among those good solution set. The proposed design optimization method was successfully applied to the structural design optimization of a high precision wafer grinding machine. After optimization, both static and dynamic compliances are reduced more than $92\%\;and\;93\%$ compared with the initial design, which was designed empirically by experienced engineers. Moreover the weight of the optimized structure are also slightly reduced than before.