• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.755-760
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• 2016

The demand for independent generators using renewable energy has been increasing. Among those independent generators, small wind turbine generators have been actively developed. Permanent magnets are generally used for small wind turbine generators to realize a simple structure and small volume. On the other hand, cogging torque is included due to the structure of the permanent magnet synchronous machine, which can be the source of noise and vibration. The cogging torque can be varied by the shape of the permanent magnet and core, and it can be reduced using the appropriate design techniques. This paper proposes a design technique that can reduce the cogging torque by changing the shape of the permanent magnets for SPMSM (Surface Permanent Magnet Synchronous Motor), which is used widely for small wind turbine generators. Evolution Strategy, which is one of non-deterministic optimization techniques, was adopted to find the optimal shape of the permanent magnets that can reduce the cogging torque. The angle and outer diameter of permanent magnet were set as the design variable. A 300W class wind turbine generator, whose pole/slot combination was 8 poles/18 slots, was designed with the proposed design technique. The properties of the generator, including the cogging torque and output voltage, were calculated. The calculation results showed that the cogging torque of the optimized model was reduced compared to that of the initial model. The design technique proposed by this paper can be an effective measure to reduce the cogging torque.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.585-597
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• 2008

The aim of this study was to find an analytic solution to the problem of determining the optimal capacity (lot-size) of a batch-storage network to meet demand for a finished product in a system undergoing random failures of operating time and/or batch material. The superstructure of the plant considered here consists of a network of serially and/or parallel interlinked batch processes and storage units. The production processes transform a set of feedstock materials into another set of products with constant conversion factors. The final product demand flow is susceptible to short-term random variations in the cycle time and batch size as well as long-term variations in the average trend. Some of the production processes have random variations in product quantity. The spoiled materials are treated through regeneration or waste disposal processes. All other processes have random variations only in the cycle time. The objective function of the optimization is minimizing the total cost, which is composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis, the PSW (Periodic Square Wave) model, provides a judicious graphical method to find the upper and lower bounds of random flows. The advantage of this model is that it provides a set of simple analytic solutions while also maintaining a realistic description of the random material flows between processes and storage units; as a consequence of these analytic solutions, the computation burden is significantly reduced.

• Korean Chemical Engineering Research
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• v.43 no.5
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• pp.595-602
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• 2005

In order to purify diethylene glycol as main impurity included in p-dioxanone, SLE (solid-liquid equilibria) and mixture density on two components system of p-dioxanone and diethylene glycol were measured and a layered melt crystallization with seed has been applied. The SLE of p-dioxanone and diethylene glycol were a simple eutectic system and the temperature and PDX concentration at eutectic point were 0.08 and 246 K, respectively. Densities of their binary mixtures were well fitted by the best correlation equation, ${\rho}_l=0.405+1.361x+0.002T-0.004xT$. In the melt crystallization, the growth rate (G) was proportional to the 1.5th power of the subcooling degree. The effective distribution coefficient ($K_{eff}$) as the degree of impurity removal was observed to increase with increasing the growth rate and initial p-dioxanone concentration. And also, $K_{eff}$ was correlated with Z function using Wintermantel's model such as $K_{eef}=-0.0604+6.392{\times}Z$. Finally, PDX purity through the optimization of this process can be obtained over 99%.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.67-75
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• 2018

According to Symantec's Internet Security Threat Report(2018), Internet security threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing and diversifying. It means that detection of malwares requires not only the detection accuracy but also versatility. In the past, malware detection technology focused on qualitative performance due to the problems such as encryption and obfuscation. However, nowadays, considering the diversity of malware, versatility is required in detecting various malwares. Additionally the optimization is required in terms of computing power for detecting malware. In this paper, we present Stream Order(SO)-CNN and Incremental Coordinate(IC)-CNN, which are malware detection schemes using CNN(Convolutional Neural Network) that effectively detect intelligent and diversified malwares. The proposed methods visualize each malware binary file onto a fixed sized image. The visualized malware binaries are learned through GoogLeNet to form a deep learning model. Our model detects and classifies malwares. The proposed method reveals better performance than the conventional method.

• Applied Chemistry for Engineering
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• v.26 no.6
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• pp.730-736
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• 2015

Cyclotrimethylene trinitramine (RDX) is a high explosive commonly used for military applications. Submicronization of RDX particles has been a critical issue in order to alleviate the unintended and accidental stimuli toward safer and more powerful performances. The purpose of this study is to optimize experimental variables for drowning-out crystallization applied to produce submicron RDX particles. Effects of RDX concentration, anti-solvent temperature and anti-solvent mass were analyzed by the central composite rotatable design. The adjusted determination coefficient of regression model was calculated to be 0.9984 having the p-value less than 0.01. Response surface plots based on the central composite rotatable design determined the optimum conditions such as RDX concentration of 3 wt%, anti-solvent temperature of $0.2^{\circ}C$ and anti-solvent mass of 266 g. The optimum and experimental diameters of RDX particles were measured to be $0.53{\mu}m$ and $0.53{\mu}m$, respectively. The regression model satisfactorily predicts the average diameter of RDX particles prepared by drowning-out crystallization. Structure of RDX crystals was found to be ${\alpha}$-form by X-ray diffraction analysis and FT-IR spectroscopy.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.63-72
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• 2013

Business Process Intelligence (BPI) is one of the emerging technologies in the knowledge discovery and analysis area. BPI deals with a series of techniques from discovering knowledge to analyzing the discovered knowledge in BPM-supported organizations. By means of the BPI technology, we are able to provide the full functionality of control, monitoring, prediction, and optimization of process-supported organizational knowledge. Particularly, we focus on the focal organizational knowledge, which is so-called the BPM activity-performer affiliation networking knowledge that represents the affiliated relationships between performers and activities in enacting a specific business process model. That is, in this paper we devise a statistical analysis method to be applied to the BPM activity-performer affiliation networking knowledge, and dubbed it the activity-performer correspondence analysis method. The devised method consists of a series of pipelined phases from the generation of a bipartite matrix to the visualization of the analysis result, and through the method we are eventually able to analyze the degree of correspondences between a group of performers and a group of activities involved in a business process model or a package of business process models. Conclusively, we strongly expect the effectiveness and efficiency of the human resources allotments, and the improvement of the correlational degree between business activities and performers, in planning and designing business process models and packages for the BPM-supported organization, through the activity-performer correspondence analysis method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.47-47
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• 2010

Over the past 20 years, constant progress in noise and vibration (NVH) engineering has enabled to constantly advance quality and comfort of operation and use of really any products - from automobiles to aircraft, to all kinds of industrial vehicles and machines - to the extend that for many products, supreme NVH performance has becomes part of its brand image in the market. At the same time, the product innovation agenda in the automotive, aircraft and really many other industries, has been extended very much in recent years by meeting ever more strict environmental regulations. Like in the automotive industry, the drive towards meeting emission and CO2 targets leads to very much accelerated adoption of new powertrain concepts (downsizing of ICE, hybrid-electrical...), and to new vehicle architectures and the application of new materials to reduce weight, which bring new challenges for not only maintaining but further improving NVH performance. This drives for innovation in NVH engineering, so as to succeed in meeting a product brand performance for NVH, while as the same time satisfying eco-constraints. Product innovation has also become increasingly dependent on the adoption of electronics and software, which drives for new solutions for NVH engineering that can be applied for NVH performance optimization of mechatronic products. Finally, relentless pressure to shorten time to market while maintaining overall product quality and reliability, mandates that the practice and solutions for NVH engineering can be optimally applied in all phases of product development. The presentation will first review the afore trends for product and process innovation, and discuss the challenges they represent for NVH engineering. Next, the presentation discusses new solutions for NVH engineering of products, so as to meet target brand values, while at the same time meeting ever more strict eco constraints, and this within a context of increasing adoption of electronics and controls to drive product innovation. NVH being very much defined by system level performance, these solutions implement the approach of "Model Based System Engineering" to increase the impact of system level analysis for NVH in all phases of product development: - At the Concept Phase, to be able to do business case analysis of new product concepts; to arrive at an optimized and robust product architecture (e.g. to hybrid powertrain lay-out, to optimize fuel economy); to enable target cascading, to subsystem and component level. - In Development Phase, to increase realism and productivity of simulation, so as to frontload virtual validation of components and subsystems and to further reduce reliance on physical testing. - During the final System Testing Phase, to enable subsystem testing by a combination of physical testing and simulation: using simulation models to simulate the final integration context when testing a subsystem, enabling to frontload subsystem testing before final system integration is possible. - To interconnect Mechanical, Electronical and Controls engineering, in all phases of development, by supporting model driven controls engineering (MIL, SIL, HIL). Finally, the presentation reviews examples of how LMS is implementing such new applications for NVH engineering with lead customers in Europe, Asia and US, with demonstrated benefits both in terms of shortening development cycles, and/or enabling a simulation based approach to reduce reliance on physical testing.

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

• Journal of the Korean Wood Science and Technology
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• v.38 no.3
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• pp.191-198
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• 2010

This research was performed to investigate the effect of hydrogen peroxide on the stain removal in japanese hackberry. Response surface method (RSM) was used to optimize the bleaching conditions such as reaction temperature, reaction time and the concentration of hydrogen peroxide. Fifteen different bleaching conditions were selected according to $2^3$ factorial central composite design (CCD). The bleaching effect were evaluated by lightness differences of wood surface before and after the bleaching. The RSM model was determined and its $R^2$ values were 0.93, showing it well represented the bleaching effect. The most affecting factor on the stain removal was the concentration of hydrogen peroxide, followed by reaction time and reaction temperature. Second degree of concentration was proved to have an effect on the bleaching. Bleaching rates above 3% concentrations of hydrogen peroxide were tended to be slightly decreased, and low bleaching effect was found at $20^{\circ}C$. The determined RSM model may offer very practical ways to obtain the desired levels of bleaching because it offers multiple solutions.

• Korean journal of food and cookery science
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• v.31 no.2
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• pp.175-184
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• 2015

The purpose of this study was to determine the optimal mixing ratio of Agaricus blazei Murill powder and butter in the preparation of cookies. The experimental design utilized herein was based on central composite design for response surface methodology, which included 10 experimental points, including 2 replicates for Agaricus blazei Murill and butter. The physical, mechanical, and sensory properties of the test were measured, and these values were applied to the mathematical models. A canonical form and perturbation plot showed the influence of each ingredient on the final mixed product. The spread ratio increased significantly with an increase in Agaricus blazei Murill powder and butter (p<0.05). The response surface methodology was applied to evaluate the effect of Agaricus blazei Murill powder and butter on cookie moisture and color (L, a) (p<0.001). Sensory evaluation showed significant values for color (p<0.05), flavor (p<0.05), texture (p<0.05) and overall quality (p<0.01) in the predicted model. The optimum formulation by numerical and graphical methods was calculated as follows: Agaricus blazei Murill powder 3.63 g, butter 55.37 g.