• Proceedings of the KSME Conference
• /
• 2001.06c
• /
• pp.369-375
• /
• 2001

For Optimization technology Was Developed in 1960, the Optimization Technology have grown into a full-featured, robust, highly rated and highly used. And Optimization techniques, having reached a degree of maturity over the past several years, are being used in a wide spectrum of industries, including aerospace, automotive, chemical, electrical, and manufacturing industries. With rapidly advancing computer technology, computers are becoming more powerful, and correspondingly, the size and the complexity of the problems being solved using Optimization techniques are also increasing. But Optimization techniques with analysis solver have many problems. For instance, the difficulties that a particular interface must be coded for each design problem and that the designer should be familiar with the optimization program as well as the analysis program. The purpose of this paper is Optimal Design Framework for Mechanical systems design. This Design Framework has two Optimizers, ADS (local optimizer) and RSM(Response Surface Method), and graphic user interfaces for formulation and optimum design problem and controlling the design process. Current Design Framework tested by two analysis solver, ADAMS and ANSYS. First this paper focused on the core Framework and their conception. In the second of the paper, I cover subjects such as Design Framework Operation. Next, The validity and effectiveness of Design Framework are shown by applying it to many practical design problems and obtaining satisfactory results. Finally, if you are an advanced Operator, you might want to use Response Surface Method, so that cover the result applied by RSM. here.

• Journal of Korean Society of Steel Construction
• /
• v.17 no.6 s.79
• /
• pp.661-672
• /
• 2005

For the efficient stochastic optimization of steel structures for which a large number of analyses is required, artificial neural networks,which have emerged as a powerful tool that could have been used to replace time-consuming procedures in many scientific or engineering applications, are applied. They are utilized for the solution of the equilibrium equations resulting from the application of the finite element method in connection with the reanalysis type of problem, for which a large number of finite element analyses are required in this study. As such, the use of artificial neural networks to predict finite element analysis outputs simplifies and facilitates the performance of the stochastic optimal design of structural systems where a trained neural network is used to replace the structural reanalysis phase. Moreover, to improve efficiency of used artificial neural networks, genetic algorithm is utilized. The stochastic optimizer used in this study is an algorithm based on the evolution theory. The efficiency of the proposed procedure is examined in problems with both volume (weight) functions and real-world cost functions

• KIISE Transactions on Computing Practices
• /
• v.23 no.7
• /
• pp.440-445
• /
• 2017

Due to the advance of e-commerce systems, the number of people using online shopping and products has significantly increased. Therefore, the need for an accurate recommendation system is becoming increasingly more important. Recurrent neural network is a deep-learning algorithm that utilizes sequential information in training. In this paper, an evaluation is performed on the application of recurrent neural networks to recommendation systems. We evaluated three recurrent algorithms (RNN, LSTM and GRU) and three optimal algorithms(Adagrad, RMSProp and Adam) which are commonly used. In the experiments, we used the TensorFlow open source library produced by Google and e-commerce session data from RecSys Challenge 2015. The results using the optimal hyperparameters found in this study are compared with those of RecSys Challenge 2015 participants.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.36C no.10
• /
• pp.8-16
• /
• 1999

This paper presents a new simulation-based analog cell synthesis approach with improved simulation efficiency For the hierarchical synthesis of analog cells we developed the sub-circuit optimizers such as current mirror and differential input stage. Each sub-circuit optimizer can be used for synthesis of analog cells such as OTA(operational transconductance amplifier), 2-stage OP-AMP and comparator. To reduce the time spending of the simulation-based synthesis we propose 2-stage searching scheme and simulation data reusing scheme. With those schemes the synthesis time spending of OTA was reduced from 301.05sec to 56.52sec by 81.12%. Since our synthesis system doesn't need other additional physical parameters except SPICE parameters, and is independent of the process and its model level, the time spending to port to other process is minimized. We synthesized OTA and 2-stage OP-AMP respectively with our approach to show its usefulness.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.6
• /
• pp.108-114
• /
• 1989

Introducing and optimizing bit-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a $6{}8$ node system. Unlike many other neural network models, this model has stronger error correction capability for correlated images such as "6","8","3", and "9". The bit significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with Widrow-Hoff neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.16 no.4
• /
• pp.630-648
• /
• 1992

The minimum weight design for simply-supported isotropic or symmetrically laminated stiffened cylindrical shells subjected to various loads (axial compression or combined loads) is studied by a nonlinear mathematical search algorithm. The minimum weight design in accomplished with the CONMIN optimizer by Vanderplaats. Several types of buckling modes with maximum allowable stresses and strains are included as constraints in the minimum weight design process, such as general buckling, panel buckling with either stingers or rings smeared out, local skin buckling, local crippling of stiffener segments, and general, panel and local skin buckling including stiffener rolling. The approach allows the consideration of various shapes of stiffening members. Rectangular, I, or T type stringers and rectangular rings are used for stiffened cylindrical shells. Several design examples are analyzed and compared with those in the previous literatures. The unstiffened glass/epoxy, graphite/epoxy(T300/5208), and graphite/epoxy aluminum honeycomb cylindrical shells and stiffened graphite/epoxy cyindrical shells under axial compression are analyzed through the present approach.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.2
• /
• pp.28-36
• /
• 2016

Recently, owing to the development of ICT industry and wide spread of smart phone, the number of people who use car sharing service are increased rapidly. Currently two-way car sharing system with same rental and return locations are mainly operated since this system can be easily implemented and maintained. Currently the demand of one-way car sharing service has increase explosively. But this system have several obstacle in operation, especially, vehicle stock imbalance issues which invoke vehicle relocation. Hence in this study, we present an optimization approach to depot location and relocation policy in one-way car sharing systems. At first, we modelled as mixed-integer programming models whose objective is to maximize the profits of a car sharing organization considering all the revenues and costs involved and several constraints of relocation policy. And to solve this problem efficiently, we proposed a new method based on particle swarm optimization, which is one of powerful meta-heuristic method. The practical usefulness of the approach is illustrated with a case study involving satellite cities in Seoul Metrolitan Area including several candidate area where this kind systems have not been installed yet and already operating area. Our proposed approach produced plausible solutions with rapid computational time and a little deviation from optimal solution obtained by CPLEX Optimizer. Also we can find that particle swarm optimization method can be used as efficient method with various constraints. Hence based on this results, we can grasp a clear insight into the impact of depot location and relocation policy schemes on the profitability of such systems.

• Journal of Ocean Engineering and Technology
• /
• v.29 no.5
• /
• pp.366-372
• /
• 2015

Ocean energy is one of the most promising renewable energy resources. In particular, South Korea is one of the countries where it is economically and technically feasible to develop tidal current power plants to use tidal current energy. In this study, based on the design code for HARP_Opt (Horizontal axis rotor performance optimizer) developed by NREL (National Renewable Energy Laboratory) in the United States, and applying the BEMT (Blade element momentum theory) and GA (Genetic algorithm), the optimal shape design and performance evaluation of the horizontal axis rotor for a 200-kW-class tidal current turbine were performed using different numbers of blades (two or three) and a pitch control method (variable pitch or fixed pitch). As a result, the VSFP (Variable Speed Fixed Pitch) turbine with three blades showed the best performance. However, the performances of four different cases did not show significant differences. Hence, it is necessary when selecting the final design to consider the structural integrity related to the fatigue, along with the economic feasibility of manufacturing the blades.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.10 no.3
• /
• pp.7-17
• /
• 1990

The objective of this paper is to provide a method of optimizing are as of the members as well as shape of both truss and arch structures. The design process includes satisfaction of stress and Euler buckling stress constraints for truss and combined stress constraints for arch structures. In order to reduce the number of detailed finite element analysis, the Force Approximation Method is used. A finite element analysis of the initial structure is performed and the gradients of the member end forces are calculated with respect to the areas and nodal coordinates. The gradients are used to form an approximate structural analysis based on first order Taylor series expansions of the member end forces. Using move limits, a numerical optimizer minimizes the volume of the structure with information from the approximate structural analysis. Numerical examples are performed and compared with other methods to demonstrate the efficiency and reliability of the Force Approximation Method for shape optimization. It is shown that the number of finite element analysis is greatly reduced and that it leads to a highly efficient method of shape optimization of structures.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.19 no.7
• /
• pp.7-15
• /
• 2020

Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.