• 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.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.847-878
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• 2020

In the era of disruptive change, a data-driven approach is vital to Human Resource Management (HRM) of any leading organization, for it is used to gain a competitive advantage. HR analytics (HRA) has emerged as innovative technologies since advanced analytics, i.e., predictive or prescriptive analytics, were widely used in the High Performing Organizations (HPOs). Therefore, many organizations elevate themselves to become HPOs through Data Science on the "people side." This paper proposes a systematic literature review using the Literature Weighted Scoring (LWS) to develop a conceptual framework based on three adoption theories, which are the Technology-Organization-Environment (TOE), Diffusion of Innovation (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that a total of 13 theory-derived factors are determined as influential factors affecting HRA adoption, and the top three factors are "Quantitative Self-Efficacy," "Top Management Support," and "Data Availability." The conceptual framework with hypotheses is proposed to provide a foundation for further studies on organizational HRA adoption.

• Journal of KIBIM
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• v.6 no.4
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• Journal of Information Technology Applications and Management
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• v.9 no.3
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• pp.129-139
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• 2002

There exist a number of information systems planning (ISP) methodologies. Historically these methodologies have been evolving to reflect new technologies and business requirements. In fact, it is an uneasy task to select a methodology that fits a business need. Though there have been a number of studies proposing new ISP approaches, we are unable to find much research doing a comparative analysis on existing ISP methodologies. Our study, therefore, is to present a classification scheme for ISP approaches and to provide a guideline framework for selecting an approach most suitable to a particular firm's need. Our classification utilizes types of components covered in ISP deliverables and the peculiarity of these components. Such classification scheme and selection framework would help derive an IT-driven new enterprise model more effectively.

• Advances in aircraft and spacecraft science
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• v.4 no.2
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• pp.185-202
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• 2017

In a flexible airvehicle, an assessment of the structural coupling levels through analysis and experiments provides structural data for the design of notch filters which are generally utilized in the flight control system to attenuate the flexible response pickup. This is necessitated as during flight, closed loop control actuation driven with flexible response inputs could lead to stability and performance related problems. In the present work, critical parameters influencing servoelastic response have been identified. A sensitivity study has been carried out to assess the extent of influence of each parameter. A multi-parameter tuning approach has been implemented to achieve an enhanced analytical model for improved predictions of aircraft servoelastic response. To illustrate the model updation approach, initial and improved test analysis correlation of lateral servoelastic responses for a generic flexible airvehicle are presented.

• ETRI Journal
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• v.45 no.4
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• pp.690-703
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• 2023

Multistage amplifiers have become appropriate choices for high-speed electronics and data conversion. Because of the large number of high-impedance nodes, frequency compensation has become the biggest challenge in the design of multistage amplifiers. The new compensation technique in this study uses two differential stages to organize feedforward and feedback paths. Five Miller loops and a 500-pF load capacitor are driven by just two tiny compensating capacitors, each with a capacitance of less than 10 pF. The symbolic transfer function is calculated to estimate the circuit dynamics and HSPICE and TSMC 0.18 ㎛. CMOS technology is used to simulate the proposed five-stage amplifier. A straightforward iterative approach is also used to optimize the circuit parameters given a known cost function. According to simulation and mathematical results, the proposed structure has a DC gain of 190 dB, a gain bandwidth product of 15 MHz, a phase margin of 89°, and a power dissipation of 590 ㎼.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.4
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• pp.345-355
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• 2014

This paper focuses on aerodynamic and inertial modeling of the propeller for its applications in flight dynamics analyses of a propeller-driven airplane. Unsteady aerodynamic and inertial loads generated by the propeller are formulated using the blade element method, where the local velocity and acceleration vectors for each blade element are obtained from exact kinematic relations for general maneuvering conditions. Vortex theory is applied to obtain the flow velocities induced by the propeller wake, which are used in the computation of the aerodynamic forces and moments generated by the propeller and other aerodynamic surfaces. The vortex lattice method is adopted to obtain the induced velocity over the wing and empennage components and the related influence coefficients are computed, taking into account the propeller induced velocities by tracing the wake trajectory trailing from each of the propeller blades. Aerodynamic forces and moments of the fuselage and other aerodynamic surfaces are computed by using the wind tunnel database and applying strip theory to incorporate viscous flow effects. The propeller models proposed in this paper are applied to predict isolated propeller performances under steady flight conditions. Trimmed level forward and turn flights are analyzed to investigate the effects of the propeller on the flight characteristics of a propeller-driven light-sports airplane. Flight test results for a series of maneuvering flights using a scaled model are employed to run the flight dynamic analysis program for the proposed propeller models. The simulations are compared with the flight test results to validate the usefulness of the approach. The resultant good correlations between the two data sets shows the propeller models proposed in this paper can predict flight characteristics with good accuracy.