• Journal of the Korean Geosynthetics Society
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• v.6 no.1
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• pp.39-43
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• 2007

Geocomposites of needle punched and spunbonded nonwovens having the reinforcement and drainage functions were manufactured by use of thermal bonding method. The physical properties (e.g. tensile, tear and bursting strength, permittivity) of these multi-layered nonwovens were dependent on the processing parameters of temperatures, pressures, bonding periods etc. - in manufacturing by use of thermal bonding method. Therefore, it is very meaningful to optimize the processing parameters and physical properties of the geocomposites by thermal bonding method. In this study, an algorithm has been developed to optimize the process of the geocomposites using an artificial neural network (ANN). Geocomposites were employed to examine the effects of manufacturing methods on the analysis results and the neural network simulations have been applied to predict the changes of the nonwovens performances by varying the processing parameters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of Mechanical Science and Technology
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• v.20 no.6
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• pp.864-873
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• 2006

As fuel cells approach commercialization, hydrogen production becomes a critical step in the overall energy conversion pathway. Reforming is a process that produces a hydrogen-rich gas from hydrocarbon fuels. Hydrogen production via autothermal reforming (ATR) is particularly attractive for applications that demand a quick start-up and response time in a compact size. However, further research is required to optimize the performance of autothermal reformers and accurate models of reactor performance must be developed and validated. The design includes the requirement of accommodating a wide range of experimental set ups. Factors considered in the design of the reformer are capability to use multiple fuels, ability to vary stoichiometry, precise temperature and pressure control, implementation of enhancement methods, capability to implement variable catalyst positions and catalyst arrangement, ability to monitor and change reactant mixing, and proper implementation of data acquisition. A model of the system was first developed in order to calculate flowrates, heating, space velocity, and other important parameters needed to select the hardware that comprises the reformer. Predicted performance will be compared to actual data once the reformer construction is completed. This comparison will quantify the accuracy of the model and should point to areas where further model development is required. The end result will be a research tool that allows engineers to optimize hydrogen production via autothermal reformation.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.12
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• pp.116-123
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• 2001

An automated injection molding design methodology has been developed to optimize multiple quality attributes, which are usually in conflict with each other, in injection molded parts. For the optimization, commercial CAE simulation tools and optimization techniques are integrated into the methodology. To decal with the multiple objective problem the relative closeness computed in TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) is used as a performance measurement index for optimization multiple part defects. To attain robustness against process variation, Taguchi's quadratic loss function is introduced in the TOPSIS. Also, the modified complex method is used as an optimization tool to optimize objective function. The verification of the developed design methodology was carried out on simulation software with an actual model. Applied to production this methodology will be useful to companies in reducing their product development time and enhancing their product quality.

• Journal of Astronomy and Space Sciences
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• v.38 no.1
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• pp.55-63
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• 2021

The universe is well known to be consists of dark energy, dark matter and the standard model (SM) particles. The dark matter dominates the density of matter in the universe. The dark matter is thought to be linked with dark photon which are hypothetical hidden sector particles similar to photons in electromagnetism but potentially proposed as force carriers. Due to the extremely small cross-section of dark matter, a large amount of data is needed to be processed. Therefore, we need to optimize the central processing unit (CPU) time. In this work, using MadGraph5 as a simulation tool kit, we examined the CPU time, and cross-section of dark matter at the electron-positron collider considering three parameters including the center of mass energy, dark photon mass, and coupling constant. The signal process pertained to a dark photon, which couples only to heavy leptons. We only dealt with the case of dark photon decaying into two muons. We used the simplified model which covers dark matter particles and dark photon particles as well as the SM particles. To compare the CPU time of simulation, one or more cores of the KISTI-5 supercomputer of Nurion Knights Landing and Skylake and a local Linux machine were used. Our results can help optimize high-energy physics software through high-performance computing and enable the users to incorporate parallel processing.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.273-276
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• 2023

This paper presents the integration of Digital twin and Blockchain-based Supply Chain Management (DB-SCM) in a smart warehouse to create a more efficient, secure, and transparent facility. The process involves creating a digital twin of the warehouse using sensors and IoT devices and then integrating it with a blockchain-based supply chain management system to connect all stakeholders. All data are collected and tracked in real-time as goods move through the warehouse, and smart contracts are automatically executed to ensure accountability for all parties involved. The study also highlights the critical role of effective supply chain management in modern business operations and the significance of smart warehouses, which leverage advanced technologies such as robotics, AI, and data analytics to optimize warehouse operations. Later, we discuss the importance of digital twins, which allow for creating a virtual representation of a physical object or system, and their potential to revolutionize a wide range of industries. Therefore, DB-SCM offers numerous benefits, including enhanced efficiency, improved customer satisfaction, and increased sustainability, and provides a valuable case study for organizations seeking to optimize their supply chain operations.

• Journal of Hydrogen and New Energy
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• v.34 no.6
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• pp.734-740
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• 2023

Hydrogen emphasis on safety management due to its high potential for accidents from wide explosive limits and low ignition energy. To prevent accidents, appropriate explosion-proof electrical equipment with installed to safe management of ignition sources. However, designing all facilities with explosion-proof structures can significantly increase costs and impose limitations. In this study, we optimize the barrier to effectively control the initial momentum in case of hydrogen release and form the control room as a non-hazardous area. We employed response surface method (RSM), the barrier distance, width and height of the barrier were set as variables. The Box-Behnken design method the selection of 15 cases, and FLACS assessed the presence of hazardous area. Analysis of variance (ANOVA) analysis resulting in an optimized barrier area. Through this methodology, the workplace can optimize the barrier according to the actual workplace conditions and classify reasonable hazardous area, which is believed to secure safety in hydrogen facilities and minimize economic burden.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1132-1138
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• 2009

The spring constants of primary suspensions for a railway vehicle are optimized by a robust design process, in which the response surface models(RSMs) of their dynamic responses are constructed via the design of experiment(DOE). The robust design process requires an intensive computation to evaluate exactly a probabilistic feasibility for the robustness of dynamic responses with their probabilistic variances for the railway vehicle. In order to overcome the computational process, the process capability index $C_{pk}$ is introduced which enables not only to show the mean value and the scattering of the product quality to a certain extent, but also to normalize the objective functions irrespective of various different dimensions. This robust design, consequently, becomes to optimize the $C_{pk}$ subjected to constraints, i.e. 2, satisfying six sigma. The proposed method shows not only an improvement of some $C_{pk}$ violating the constraints obtained by the conventional optimization, but also a significant decrease of the variance of the $C_{pk}$.

• Current Photovoltaic Research
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• v.8 no.1
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• pp.17-26
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• 2020

It is clear that monocrystalline Silicon (Si) ingots are the key raw material for semiconductors devices. In the present industries markets, most of monocrystalline Silicon (Si) ingots are made by Czochralski Process due to their advantages with low production cost and the big crystal diameters in comparison with other manufacturing process such as Float-Zone technique. However, the disadvantage of Czochralski Process is the presence of impurities such as oxygen or carbon from the quartz and graphite crucible which later will resulted in defects and then lowering the efficiency of Si wafer. The heat transfer plays an important role in the formation of Si ingots. However, the heat transfer generates convection in Si molten state which induces the defects in Si crystal. In this study, a crystal growth simulation software was used to optimize the Si crystal growth process. The furnace and system design were modified. The results showed the melt-crystal interface shape can affect the Si crystal growth rate and defect points. In this study, the defect points and desired interface shape were controlled by specific crystal growth rate condition.