• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.5
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• pp.108-114
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• 2010

The injection nozzle of an electro-hydraulic injector is being opened and closed by movement of a injector's needle which is balanced by pressure at the nozzle seat and at the needle control chamber, at the opposite end of the needle. In this study, the effects of needle movement in a piezo-driven injector on unsteady cavitating flows behavior inside nozzle were investigated by cavitation numerical model based on the Eulerian-Lagrangian approach. Aimed at simulating the 3-D two-phase flow behavior, the three dimensional geometry model along the central cross-section regarding of one injection hole with real design data of a piezo-driven diesel injector has been used to simulate the cavitating flows for injection time by at fully transient simulation with cavitation model. The cavitation model incorporates many of the fundamental physical processes assumed to take place in cavitating flows. The simulations performed were both fully transient and 'pseudo' steady state, even if under steady state boundary conditions. As this research results, we found that it could analyze the effect the pressure drop to the sudden acceleration of fuel, which is due to the fastest response of needle, on the degree of cavitation existed in piezo-driven injector nozzle.

• The Journal of Information Systems
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• v.20 no.3
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• pp.1-24
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• 2011

Recently, six sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a six sigma methodology based on data mining for effectively and efficiently processing massive data in driving six sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a "P" steel company for improvement of heat efficiency through reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.264-270
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• 2011

A new method for creating a logical data model is proposed. The logical data model developed by the method defines table, primary key, foreign key, and fields. The framework of the logical data model is constructed by modeling the relationships between events and their related entity types. The proposed method consists of a series of objective and quantitative decisions such as maximum cardinality of relationships and functional dependency between the primary key and attributes. Even beginners to database design can use the methology as long as they understand such basic concepts about relational databases as primary key, foreign key, relationship cardinality, parent-child relationship, and functional dependency. The simple and systematic approach minimizes decision errors made by a database designer. In practial database design the method creates a logical data model in Boyce-Codd normal form unless the user of the method makes a critical decision error, which is very unlikely.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.886-890
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• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.892-908
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• 2010

Services in SOA are typically perceived as black-box to service consumers, and can be dynamically evolved at runtime, and run on a number of unknown and heterogeneous environments. Because of these characteristics of the services, effective and efficient monitoring of various aspects on services is an essential functionality for autonomous management of service. But the problem with or limitation in conventional or existing approaches is, that they focus on services themselves, ignoring the effects by business processes. Consequently, there is a room for service monitoring which provides more useful information of business level by acquisition of only external monitoring data that depend on specific BPEL engine and middleware. Moreover, there is a strong demand to present effective methods to reduce monitoring overhead which can degrade quality of services. EDA can cope with such limitations in SOA by collecting and analyzing events efficiently. In this paper, we first describe EDA benefits in service monitoring, and classify monitorring target, and present an appropriate monitoring method for each monitoring target. Also to provide the applicability of our approach, an event meta-model is defined, and event processing model and architecture based on the meta-model are proposed. And, with the proposed architecture and method, we implement a prototype of an event-driven dynamic monitoring framework which can collect and process internal and external data at runtime. Finally, we present the result of a case study to demonstrate the effectiveness and applicability of the proposed approach.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.27 no.6
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• pp.787-793
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• 1994

It has been demonstrated for the finite-difference ocean circulation model that the problem of uncertain forcing and input data can be tackled with an optimization techniques. The uncertainty problem in interesting flow properties is exploring a finite difference ocean circulation model due to the uncertainty in the driving boundary conditions. The mathematical procedure is based upon optimization method by the conjugate gradient method using the simulated data and a simple barotropic model. An example for the ocean circulation model is discussed in which wind forcing and the steady-state circulation are determined from a simulated stream function.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.385-402
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• 2010

Most of the data warehouse (DW) requirements engineering approaches have not distinguished the early requirements engineering phase from the late requirements engineering phase. There are very few approaches seen in the literature that explicitly model the early & late requirements for a DW. In this paper, we propose an AGDI (Agent-Goal-Decision-Information) model to support the early and late requirements for the development of DWs. Here, the notion of agent refers to the stakeholders of the organization and the dependency among agents refers to the dependencies among stakeholders for fulfilling their organizational goals. The proposed AGDI model also supports three interrelated modeling activities namely, organization modeling, decision modeling and information modeling. Here, early requirements are modeled by performing organization modeling and decision modeling activities, whereas late requirements are modeled by performing information modeling activities. The proposed approach has been illustrated to capture the early and late requirements for the development of a university data warehouse exemplifying our model's ability of supporting its decisional goals by providing decisional information.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.3
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• pp.457-465
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• 2018

This study proposes a novel approach to explore a "travel time budget (TTB)" using a mobile phone signaling data (MPSD), which are passively generated between a mobile phone and a base station. The data analyzied in this study were provided from KT for 8 days (from May 19 to 26 in 2016). They were about 45 million signals passively generated from users whose stay area during night was classified as three areas in Mapo-gu, Seoul and in the city of Sejong. The estmation of TTB was implemented with various pre-processing techniques on the MPSD data in a data-driven analysis. As a result, the TTBs of Mapo-gu, Seoul and Sejong were 82.94 and 80.70 minutes, respectively. The results in this study were also compared with those based on the traditional methods. The authors expect that this result will help transport experts improve the use of MPSD.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.224-227
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• 2015

Data transmission via the car driver's tethered smart phone may have a volume-dependent billing in case car driver' phone transmits data in real-time to the remote data center. The on-board diagnosis data generated are temporary stored locally to mobile remote diagnosis application on the car driver's phone, and then transmit to the data center later when car driver connects to the Internet. To increase the easiest of using the remote vehicle application without blocking other tasks to be executing on the cloud, node.js stands as a suitable candidate for handling tasks of data storage on the cloud via mobile network. We demonstrate the effectiveness of the proposed architecture by simulating a preliminary case study of an android application responsible of real time analysis by using a vehicle-to- smart phones applications interface approach that considers the smart phones to act as a remote user which passes driver inputs and delivers output from external applications. In this paper, we propose a study on development of Remote Vehicle fault diagnostic system features web server architecture based event loop approach using node.js platform, and wireless communication to handle vehicle diagnostics data to a data center.

• Journal of Distribution Science
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• v.17 no.8
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• pp.25-34
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• 2019

Purpose - Investigate the association of internal and external factors of consumers and analysing the role of moderating comparative marketing aspects, especially the part of YouTuber and celebgram in influencing purchase decisions. Apart from that, it provides an overview of the pattern of purchase decision making in forming Millennials and Y generation consumer culture Research design, data, and methodology - This study uses a quantitative research approach with descriptive, predictive, and prospective data analysis on 300 eligible Millennials and Y aged 20-35 years who are bachelor-educated. Data collection using online surveys with final statistical analysis using the Partial Least Square (PLS) approach Results - All hypothesis are declared accepted, indirect testing the dominant internal consumer factors have a positive and significant effect on consumers' purchase decisions. Through testing Moderating, aspect marketing comparative is also authoritative able to moderate internal consumer factors towards purchase decision making. Conclusions - Digital technology is changing the paradigm and perceptions of the millennials and Y generations in terms of behaving as a generation of technology connoisseurs who also influence and shape the culture of that generation and the generations to come in the future.