• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.854-861
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• 2005

The functional behavior of a portable consumer electronic (PCE) product is nearly all expressed with human-machine interaction (HMI) tasks. Although physical prototyping and computer aided design (CAD) software can show the appearance of the product, they cannot properly reflect its functional behavior. In this paper, we propose a virtual prototyping (VP) system that incorporates virtual reality and HMI functional simulation in order to enables users to capture not only the realistic look of a PCE product but also its functional behavior. We obtain geometric part models of the product and their assembly and kinematics information with the help of CAD and reverse engineering tools, and visualize them with various display tools. We adopt state transition methodology to capture the HMI functional behavior of the product into a state transition chart, which is later used to construct a finite state machine (FSM) for the functional simulation of the product. The FSM plays an important role to control the transition between states of the product. The proposed VP system receives input events such as mouse clicks on buttons and switches of the virtual prototype model, and it reacts to the events based on the FSM by activating associated activities. The VP system provides the realistic visualization of the product and the vivid simulation of its functional behavior. It can easily allow users to perform functional evaluation and usability testing. Moreover, it can greatly reduce communication errors occurring in a typical product development process. A case study about VP of an MP3 player is given to show the usefulness of the proposed VP system.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.1-15
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• 2001

Recently, Internet based Electronic Commerce is recognized as one of the alternatives for strengthening sales power of small and medium companies. However, small and medium manufacturers can't adjust properly to the new environment because they are in short of money, personnel, and technology. To cope with this problem, this paper deals with the development of virtual manufacturing agent to support sales agent. The sales activity of most of parts manufacturing companies is based on orders of buyers. The process of promotion, receipt and selection of orders of the parts manufacturing is closely coupled with the load status of the production lines. On deciding whether to accept an order or not, as well as negotiating with buyers, sales person needs information such as load and schedule of production lines, manufacturability of the order. Therefore, the functions of virtual manufacturing agents manufacturability analysis, process planning, and scheduling are key features in developing an agent of sales activity for the parts manufacturing business. While most of research on virtual manufacturing system so far is focused on the simulation of each product, this paper deals with the development of agent assisting internet-based product sales by supporting production information promptly. The pilot system of virtual manufacturing agent is implemented using KQML-based agent template and Java-based expert system shell for a small molding company.

• Design & Manufacturing
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• v.7 no.1
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• pp.45-49
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• 2013

Since the shell-molds are used to make casting the metal parts for the automobile industry, the quality may well be inconsistent with the lower productivity, increasing the cost of the end products. The primary elbow design shell molded steel castings being produced through extrusion process has $180^{\varnothing}$ O.D., $150^{\varnothing}$ I.D., 14mm thickness and 400mm length, while being processed onto the left side of the tubing. The primary cause for the poor processing is the uneven manual shell molding. If the manual shell molds should be produced to have even quality, they would not be processed for tube linking. The purpose of this study was to develop the flask-molds for manufacturing of the shell molds to ensure mass-production, consistent quality, ommission of processing and comfortable working environment. For this purpose, four flask-molds were produced and thereby, four shell molds were assembled. In particular, the shell molds for processing were formed of the fine coated sand to be blown. As a result, productivity increased about three times, while a consistent quality was ensured. Furthermore, the tubes could be linked with each other without being processed, while pallets could be stacked, stored, transported and managed more easily. In a nut-shell, the molding theory could be applied more effectively. However, it is conceived that this study should be followed up by future studies which will research into reliability and endurability of the end products.

• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.168-177
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• 2016

In this paper we present an implantable pressure sensor to measure real-time blood pressure by monitoring mechanical movement of artery. Sensor is composed of inductors (L) and capacitors (C) which are formed by microfabrication and direct bonding on two biocompatible substrates (quartz). When electrical potential is applied to the sensor, the inductors and capacitors generates a LC resonance circuit and produce characteristic resonant frequencies. Real-time variation of the resonant frequency is monitored by an external measurement system using inductive coupling. Structural and electrical simulation was performed by Computer Aided Engineering (CAE) programs, ANSYS and HFSS, to optimize geometry of sensor. Ultrafast laser (femto-second) cutting and MEMS process were executed as sensor fabrication methods with consideration of brittleness of the substrate and small radial artery size. After whole fabrication processes, we got sensors of $3mm{\times}15mm{\times}0.5mm$. Resonant frequency of the sensor was around 90 MHz at atmosphere (760 mmHg), and the sensor has good linearity without any hysteresis. Longterm (5 years) stability of the sensor was verified by thermal acceleration testing with Arrhenius model. Moreover, in-vitro cytotoxicity test was done to show biocompatiblity of the sensor and validation of real-time blood pressure measurement was verified with animal test by implant of the sensor. By integration with development of external interrogation system, the proposed sensor system will be a promising method to measure real-time blood pressure.

• Journal of Biomedical Engineering Research
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• v.38 no.1
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• pp.25-31
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• 2017

Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.

• Advances in Computational Design
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• v.3 no.4
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• pp.339-366
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• 2018

Parametric design systems serve as powerful assistive tools in the design process by providing a flexible approach for the generation of a vast number of design alternatives. However, contemporary parametric design systems focus primarily on low-level engineering and structural forms, without an explicit means to also take into account high-level, cognitively motivated people-centred design goals. We present a precedent-based parametric design method that integrates people-centred design "precedents" rooted in empirical evidence directly within state of the art parametric design systems. As a use-case, we illustrate the general method in the context of an empirical study focusing on the multi-modal analysis of wayfinding behaviour in two large-scale healthcare environments. With this use-case, we demonstrate the manner in which: (1). a range of empirically established design precedents -e.g., pertaining to visibility and navigation- may be articulated as design constraints to be embedded directly within state of the art parametric design tools (e.g., Grasshopper); and (2). embedded design precedents lead to the (parametric) generation of a number of morphologies that satisfy people-centred design criteria (in this case, pertaining to wayfinding). Our research presents an exemplar for the integration of cognitively motivated design goals with parametric design-space exploration methods. We posit that this opens-up a range of technological challenges for the engineering and development of next-generation computer aided architecture design systems.

• CELLMED
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• v.6 no.4
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• pp.23.1-23.6
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• 2016

With the rapid developments in nanotechnology, an increasing number of nanomaterials have been applied in various aspects of our lives. Recently, pharmaceutical nanotechnology with numerous advantages has growingly attracted the attention of many researchers. Zinc oxide nanoparticles (ZnO-NPs) are nanomaterials that are widely used in many fields including diagnostics, therapeutics, drug-delivery systems, electronics, cosmetics, sunscreens, coatings, ceramic products, paints, and food additives, due to their magnetic, catalytic, semiconducting, anti-cancer, anti-bacterial, anti-inflammatory, ultraviolet-protective, and binding properties. The present review focused on the recent research works concerning role of ZnO-NP on inflammation. Several studies have reported that ZnO-NP induces inflammatory reaction through the generation of reactive oxygen species by oxidative stress and production of inflammatory cytokines by activation of nuclear factor-${\kappa}B$ ($NF-{\kappa}B$). Meanwhile, other researchers reported that ZnO-NP exhibits an anti-inflammatory effect by inhibiting the up-regulation of inflammatory cytokines and the activation of $NF-{\kappa}B$, caspase-1, $I{\kappa}B$ $kinase{\beta}$, receptor interacting protein2, and extracellular signal-regulated kinase. Previous studies reported that size and shape of nanoparticles, surfactants used for nanoparticles protection, medium, and experimental conditions can also affect cellular signal pathway. This review indicated that the anti-inflammatory effectiveness of ZnO-NP was determined by the nanoparticle size as well as various experimental conditions. Therefore, the author suggests that pharmaceutical therapy with the ZnO-NP is one of the possible strategies to overcome the inflammatory reactions. However, further studies should be performed to maximize the anti-inflammatory effect of ZnO-NP to apply as a potential agent in biomedical applications.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.835-838
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• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.237-241
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• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.35-40
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• 2017

A most important progress in civilization was the introduction of mass production. One of main methods for mass production is die-casting molds. Due to the high velocity of the liquid metal, aluminum die-casting is so complex where flow momentum is critical matter in the mold filling process. Actually in complex parts, it is almost impossible to calculate the exact mold filling performance with using experimental knowledge. To manufacture the lightweight automobile bodies, aluminum die-castings play a definitive role in the automotive part industry. Due to this condition in the design procedure, the simulation is becoming more important. Simulation can make a casting system optimal and also elevate the casting quality with less experiment. The most advantage of using simulation programs is the time and cost saving of the casting layout design. For a die casting mold, generally, the casting layout design should be considered based on the relation among injection system, casting condition, gate system, and cooling system. Also, the extent or the location of product defects was differentiated according to the various relations of the above conditions. In this research, in order to optimize the casting layout design of an automotive Oil Pan_BR2E, Computer Aided Engineering (CAE) simulation was performed with three layout designs by using the simulation software (AnyCasting). The simulation results were analyzed and compared carefully in order to apply them into the production die-casting mold. During the filling process with three models, internal porosities caused by air entrapments were predicted and also compared with the modification of the gate system and overflows. With the solidification analysis, internal porosities occurring during the solidification process were predicted and also compared with the modified gate system.