• Geotechnical Engineering
• /
• v.11 no.2
• /
• pp.19-28
• /
• 1995

Based on the various test data acquired in the field, the large pressure chamber and the small pressure chamber, uplift behaviors and method of determining the ultimate uplift capacity of pile driven in small pressure chamber were studied. After uplift pile experienced 2 or 3 sudden slip due to increase of uplift load, complete pullout failure was occurred. Thus, it appears that the ultimate uplift capacity could be identified as the load at displacement where first slip occurs. The ultimate uplift capacity might be determined in every test and the disturbance after first uplift test could be recovered by adding one blow of the drop hammer, which had to depend on the model pile capacity.

• Journal of Distribution Science
• /
• v.19 no.8
• /
• pp.69-80
• /
• 2021

Purpose: This study's purpose is to explore the relationship between entrepreneurial marketing orientation on new product development performance via marketing and innovation capabilities. Research design, data, and methodology: This research has applied a survey method which involved 159 respondents from food retailers among Michelin Guide Restaurants in Thailand. The literature's existing measurement scales were used to operationalize the constructs proposed in this study. The analyses were conducted using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to test the hypotheses. Results: The results have shown that new product development performance received positive and direct impacts from entrepreneurial marketing orientation, particularly in three dimensions: customer value orientation, opportunity-driven initiatives, and leveraged resources. Likewise, new product development performance received a positive, indirect impact from opportunity-driven initiatives, risk management, customer value orientation, and innovation that is focused on marketing and innovation capabilities. Conclusions: The results are useful for Thai food retailers as to strategy formulation in order to attract tourists from all over the world to tourist destinations in Thailand. Therefore, this empirical study is extremely important for domestic economic development and the international economy. These findings provide theoretical and managerial contributions for developing competitive strategies which will lead to sustainable business practices, as well as for providing future research directions.

• Annual Conference of KIPS
• /
• 2008.05a
• /
• pp.671-674
• /
• 2008

초기의 Ad-hoc 네트워크의 라우팅 프로토콜들은 Table-driven 알고리즘이 대두되었으나, 많은 문제점으로 이동단말의 이동성을 지원하는 On-demand 방식의 라우팅 프로토콜에 대한 연구가 진행되었다. 최근에는 On-demand 와 Table-driven의 장점을 반영한 AODV(Ad-hoc On-demand Distance Vector)가 널리 이용되고 있다. 그러나 AODV의 장점에도 불구하고 아직까지 AODV 는 노드들의 잦은 이동으로 Ad-hoc 네트워크에 많은 라우팅 패킷을 발생시켜 전체적인 네트워크의 성능 면에서 많은 약점을 보이고 있다. 본 논문에서는 Ad-hoc 네트워크를 구성하는 노드들 사이의 링크에 대한 신뢰성을 위해 노드의 이동경로예측을 기반으로 하는 새로운 경로설정 및 유지기법을 제안한다. 제안하는 기법은 AOMDV를 기반으로 노드의 위치와 이동 정보로 이동되는 방향과 위치를 예측하여 보다 안정적인 경로를 선택할 수 있는 기회를 제공하는 라우팅 기법이다. 또한 AOMDV로 다중경로를 보유하여 데이터의 종류와 특성에 적합한 최적의 경로선택으로 불필요한 경로설정 메시지의 오버헤드를 줄인다.

• Annual Conference of KIPS
• /
• 2024.10a
• /
• pp.553-556
• /
• 2024

Talking Head Generation is a highly practical task that is closely tied to current technology and has a wide range of applications in everyday life. This technology will be of great help in the fields of photography, online conversation as well as in education and medicine. In this paper, the authors proposed a novel approach for Individual Audio-Driven Talking Head Generation by leveraging a sequence of landmarks and employing a diffusion model for image reconstruction. Building upon previous landmark-based methods and advancements in generative models, the authors introduce an optimized noise addition technique designed to enhance the model's ability to learn temporal information from input data. The proposed method outperforms recent approaches in metrics such as Landmark Distance (LD) and Structural Similarity Index Measure (SSIM), demonstrating the effectiveness of the diffusion model in this domain. However, there are still challenges in optimization. The paper conducts ablation studies to identify these issues and outlines directions for future development.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.7
• /
• pp.27-37
• /
• 2020

In this paper, we propose the methodology based on data-driven approach using Natural Language Processing and Machine Learning for classifying requirements into functional requirements and non-functional requirements. Through the analysis of the results of the requirements classification, we have learned that the trained models derived from requirements classification with data-preprocessing and classification algorithm based on the characteristics and information of existing requirements that used term weights based on TF and IDF outperformed the results that used stemming and stop words to classify the requirements into functional and non-functional requirements. This observation also shows that the term weight calculated without removal of the stemming and stop words influenced the results positively. Furthermore, we investigate an optimized method for the study of classifying software requirements into functional and non-functional requirements.

• Journal of Korea Society of Industrial Information Systems
• /
• v.24 no.2
• /
• pp.65-80
• /
• 2019

With the advent of the ICT-based 4th industrial revolution, the convergence of the manufacturing industry and ICT seems to be the new breakthrough for achieving the company's competitiveness and play a role on the key element for accelerating the revival of the manufacturing industry. When the smart factory is implemented, each plant can analyze the quantity of data collected, build the data-driven operation systems which can make decisions, and ultimately discover the correlation among many events in the manufacturing sites. As the customers' needs become diversified more and more, it is required for the company to change its operating method from large quantity batch production systems to customizable and flexible manufacturing systems. For performing this requirements, it is essential for the company to adopt the smart factory. Based on technology acceptance model (TAM), this study investigates the factors influencing continuous use intention and switching intention of the smart factory. To do so, a questionnaire survey is conducted both online and offline. 122 samples are used for the study analysis. The results of this study will provide many implications with many researchers and practitioners relevant smart factories.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1586-1591
• /
• 2022

Kirchhoff-Love plate (KLP) equation is a well established theory for a description of a deformation of a thin plate under certain outer source. Meanwhile, analysis of a vibrating plate in a frequency domain is important in terms of obtaining the main frequency/eigenfunctions and predicting the vibration of plate. Among various modal analysis methods, dynamic mode decomposition (DMD) is one of the efficient data-driven methods. In this work, we carry out DMD based modal analysis for KLP where thin plate is under effects of sine-type outer force. We first construct discrete time series of KLP solutions based on a finite difference method (FDM). Over 720,000 number of FDM-generated solutions, we select only 500 number of solutions for the DMD implementation. We report the resulting DMD-modes for KLP. Also, we show how DMD can be used to predict KLP solutions in an efficient way.

• Journal of Industrial Convergence
• /
• v.21 no.9
• /
• pp.23-32
• /
• 2023

Interest in Serious Games for Healthcare (SGHs) that can improve health through games is increasing. Digital Therapeutics (DTx) is a treatment that must be approved for effectiveness and safety, so it should follow the traditional drug distribution method, but SGHs are wellness products that are more flexible in terms of adoption and diffusion than DTx. SGHs are effective because it can provide customized services through continuous monitoring and feedback. When SGHs are applied to cognitive impairment treatment or behavioral correction, malfunctions and side effects are minor. This study developed research model based on the Valence Framework, gathered data from 142 undergraduates, and demonstrated that only the perceived benefits have a statistically significant positive (+) effect on SGHs acceptance intentions. Based on these results, this study suggests that SGHs companies should promote benefits in accepting SGHs for general users and they need for a distribution and analytics platform strategy based on a data-driven approach.

• Journal of Korea Multimedia Society
• /
• v.19 no.8
• /
• pp.1587-1596
• /
• 2016

This paper presents an interactive locomotion controller using motion capture data and inverted pendulum model. Most of the data-driven character controller using motion capture data have two kinds of limitation. First, it needs many example motion capture data to generate realistic motion. Second, it is difficult to make natural-looking motion when characters navigate dynamic terrain. In this paper, we present a technique that uses dimension reduction technique to motion capture data together with the Gaussian process dynamical model (GPDM), and interpolates the low-dimensional data to make final motion. With the low-dimensional data, we can make realistic walking motion with few example motion capture data. In addition, we apply the inverted pendulum model (IPM) to calculate the root trajectory considering the real-time user input upon the dynamic terrain. Our method can be used in game, virtual training, and many real-time applications.

• Journal of Korea Water Resources Association
• /
• v.51 no.6
• /
• pp.503-514
• /
• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.