• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.42.2-42.2
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• 2010

Transparent conducting oxides (TCOs) are of significant importance for their applications in various devices, such as light-emitting diodes, thin-film solar cells, organic light-emitting diodes, liquid crystal displays, and so on. In order for TCOs to contribute to the performance improvement of these devices, TCOs should have high transmittance and good electrical properties simultaneously. Sn-doped $In_2O_3$ (ITO) is the most commonly used TCO. However, indium is toxic and scarce in nature. Thus, ZnO has attracted a lot of attention because of the possibility for replacing ITO. In particular, group III impurity-doped ZnO showed the optoelectronic properties comparable to those of ITO electrodes. Al-doped ZnO exhibited the best performance among various doped ZnO films because of the high substitutional doping efficiency. However, in order for the Al-doped ZnO to replace ITO in electronic devices, their electrical and optical properties should further significantly be improved. In this connection, different ways such as a variation of deposition conditions, different deposition techniques, and post-deposition annealing processes have been investigated so far. Among the deposition methods, RF magnetron sputtering has been extensively used because of the easiness in controlling deposition parameters and its fast deposition rate. In addition, when combined with post-deposition annealing in a reducing ambient, the optoelectronic properties of Al-doped ZnO films were found to be further improved. In this presentation, we deposited Al-doped ZnO (ZnO:$Al_2O_3$ = 98:2 wt%) thin films on the glass and sapphire substrates using RF magnetron sputtering as a function of substrate temperature. In addition, the ZnO samples were annealed in different conditions, e.g., rapid thermal annealing (RTA) at $900^{\circ}C$ in $N_2$ ambient for 1 min, tube-furnace annealing at $500^{\circ}C$ in $N_2:H_2$=9:1 gas flow for 1 hour, or RTA combined with tube-furnace annealing. It is found that the mobilities and carrier concentrations of the samples are dependent on growth temperature followed by one of three subsequent post-deposition annealing conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.725-732
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• 2017

Weight reduction is one of the important issues in the automotive industry and the development of internal combustion engines vehicles, future vehicles, and eco-friendly vehicles for improving fuel efficiency. The objective of this study is to investigate the improvement of driving performance by weight reduction and optimum design for a formula-type self-designed on-road vehicle. This study is divided into the four steps. Firstly, the engine room was replaced and designed with a lighter engine. Secondly, an optimization study was conducted to simplify and lighten the vehicle components with the design of the frame. Thirdly, the structure design was optimized and the suspension was analyzed with the design of the frame. Finally, the design of an upright and hub with reduced weight was carried out using lighter parts. As a result, we reduced the weight of the vehicle by 48.5kg compared to the previous year (19.5%) and increased the acceleration from 6.8 s to 5.8 s.s.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.417-428
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• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.8
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• pp.759-769
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• 2013

Electronic equipment has been applied to virtually every area associated with commercial, industrial, and military applications. Specifically, electronics have been incorporated into avionics components installed in aircraft. This equipment is exposed to dynamic loads such as vibration, shock, and acceleration. Especially, avionics components installed in a helicopter are subjected to simultaneous sine and random base excitations. These are denoted as sine on random vibrations according to MIL-STD-810F, Method 514.5. In the past, isolators have been applied to avionics components to reduce vibration and shock. However, an isolator applied to an avionics component installed in a helicopter can amplify the vibration magnitude, and damage the chassis, circuit card assembly, and the isolator itself via resonance at low-frequency sinusoidal vibrations. The objective of this study is to investigate the dynamic characteristics of an avionics component installed in a helicopter and the structural dynamic modification of its tray plate without an isolator using both a finite element analysis and experiments. The structure is optimized by dynamic loads that are selected by comparing the vibration, shock, and acceleration loads using vibration and shock response spectra. A finite element model(FEM) was constructed using a simplified geometry and valid element types that reflect the dynamic characteristics. The FEM was verified by an experimental modal analysis. Design parameters were extracted and selected to modify the structural dynamics using topology optimization, and design of experiments(DOE). A prototype of a modified model was constructed and its feasibility was evaluated using an FEM and a performance test.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.69-80
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• 1999

Recently, social requirements of personal identification techniques are rapidly expanding in a number of new application ares. Especially fingerprint recognition is the most important technology. Fingerprint recognition technologies are well established, proven, cost and legally accepted. Therefore, it has more spot lighted among the any other biometrics technologies. In this paper we propose a new on-line fingerprint recognition algorithm for non-inked type live scanner to fit their increasing of security level under the computing environment. Fingerprint recognition system consists of two distinct structural blocks: feature extraction and feature matching. The main topic in this paper focuses on the feature matching using the fingerprint minutiae (ridge ending and bifurcation). Minutiae matching is composed in the alignment stage and matching stage. Success of optimizing the alignment stage is the key of real-time (on-line) fingerprint recognition. Proposed alignment algorithm using clique shows the strength in the search space optimization and partially incomplete image. We make our own database to get the generality. Using the traditional statistical discriminant analysis, 0.05% false acceptance rate (FAR) at 8.83% false rejection rate (FRR) in 1.55 second average matching speed on a Pentium system have been achieved. This makes it possible to construct high performance fingerprint recognition system.

• Journal of IKEEE
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• v.23 no.1
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• pp.193-199
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• 2019

In this study, we have proposed a floating metal guard ring structure based on TCAD simulation in order to enhance the breakdown voltage characteristics of gallium oxide ($Ga_2O_3$) vertical high voltage switching Schottky barrier diode. Unlike conventional guard ring structures, the floating metal guard rings do not require an ion implantation process. The locally enhanced high electric field at the anode corner was successfully suppressed by the metal guard rings, resulting in breakdown voltage enhancement. The number of guard rings and their width and spacing were varied for structural optimization during which the current-voltage characteristics and internal electric field and potential distributions were carefully investigated. For an n-type drift layer with a doping concentration of $5{\times}10^{16}cm^{-3}$ and a thickness of $5{\mu}m$, the optimum guard ring structure had 5 guard rings with an individual ring width of $1.5{\mu}m$ and a spacing of $0.2{\mu}m$ between rings. The breakdown voltage was increased from 940 V to 2000 V without degradation of on-resistance by employing the optimum guard ring structure. The proposed floating metal guard ring structure can improve the device performance without requiring an additional fabrication step.

• Elastomers and Composites
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• v.46 no.4
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• pp.277-283
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• 2011

In this study, engineering grade and high intensity reflective sheets were prepared with glass beads and their reflection performance and physical properties were investigated. The reflective sheets prepared by using glass beads are divided into enclosed or encapsulated lens type, depending on whether the glass beads are open in air or not. Because of an extra layer on the glass bead surface, the enclosed lens type reflective sheets show very little change in the properties by bad weather conditions, compared to encapsulated lens type reflective sheets. Optimization of the amount of glass beads on the surface was carried out, which determines the retroreflective properties. Enclosed and encapsulated lens type reflective sheets with various colors were prepared and their coefficients of retroreflection were determined. The encapsulated type reflective sheet with white color shows a coefficient of retroreflection of $210.4cd/1x{\cdot}m^2$, which is higher than the enclosed type ($74cd/1x{\cdot}m^2$). Effect of washing on the reflective property and adhesive power of the reflective sheets was investigated, and it is found that the number of glass beads decreased with washing and the aluminum layer deposited was damaged extensively in the encapsulated lens type reflective sheets.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.