• The Journal of Society for e-Business Studies
• /
• v.17 no.2
• /
• pp.149-163
• /
• 2012

Logistics systems provide their service to customers by coordinating the resources with limited capacity throughout the underlying processes involved to each other. To maintain the high level of service under such complicated condition, it is essential to carry out the real-time monitoring and continuous management of logistics processes. In this study, we propose a method of estimating the service completion time of key processes based on process-state information collected in real time. We first identify the factors that influence the process completion time by modeling and analyzing an influence diagram, and then suggest algorithms for quantifying the factors. We suppose the container terminal logistics and the process of discharging and loading containers to a vessel. The remaining service time of a vessel is estimated using a decision tree which is the result of machine-learning using historical data. We validated the estimation model using container terminal simulation. The proposed model is expected to improve competitiveness of logistics systems by forecasting service completion in real time, as well as to prevent the waste of resources.

• Journal of Korean Society on Water Environment
• /
• v.24 no.1
• /
• pp.91-98
• /
• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Proceedings of the Korea Database Society Conference
• /
• 1999.10a
• /
• pp.183-189
• /
• 1999

오늘의 기업에게 정보기술이란 필수요소이다. 정보기술을 효과적으로 활용하기 위해서는 IT 인프라가 체계적으로 구축되어 있어야 한다. 해당 조직에 적합한 정보기술 기반구조를 설계하고 이의 도입을 위한 구체적인 계획을 수립하기 위해서는 체계적이고 효과적인 정보계획 수립(Information System Planning: ISP)이 필요하다. 그러나 정보계획수립 프로젝트의 상당수가 실패로 그치고 있다. 특히 정보기술의 지속적인 변화 때문에 수립한 정보기술 기반구조 계획안이 실제 구현되지 못하고 계획으로만 남는 경향이 있다. 이러한 ISP의 어려움을 해결하기 위해서는 정보기술 참조모델(reference model)을 적극적으로 활용할 필요가 있다. 즉, 조직의 정보시스템에 공통적으로 적용할 수 있는 IT 인프라나 표준 아키텍쳐를 바탕으로 정보계획수립을 수행해 나가는 방식이 필요하다. 이와 같은 참조모델 기반의 정보계획 수립은 새로운 아키텍쳐를 추출하고 표준화를 이룸으로써 프로젝트의 생산성을 높일 수 있다는 장점을 가지고 있다. 기존의 ISP 연구는 ISP의 필요성, 과정, 성공요인 등에 국한되어 왔으며, 방법론에 대한 연구는 미비한 편이다. 최근들어 ISP의 체계적인 분류나 참조모델 기반 계획수립의 필요성이 제기되었다. 그러나 아직까지 이와같은 접근에서 참조 모델을 어떻게 구축하고 활용해 나갈 것인가에 대한 연구는 부족한 실정이다. 따라서 본 논문에서는 참조모델을 구축하기 위한 다양한 접근방법과 각각의 특징을 제시한다. 나아가서 해당 조직의 상황이나 요구수준에 따라 적합한 접근방법을 선택할 수 있게 해주는 방안을 제시한다.타냈으며, 평가결과에 대해 여러 가지 방법으로 분석하였다. 첫째, 동종제품간 평가분석을 통하여 각각의 제품을 비교하였으며, 둘째 소프트웨어 종류별 평가로 제품을 응용소프트웨어, 응용개발도구, 시스템 소프트웨어로 분류하여 평균값으로 비교하였다. 셋째, 국내외 제품별 평가분석으로 전체 제품을 국내제품과 국외제품으로 분류하여 비교하였으며, 마지막으로 총괄분석을 통해 가중치를 적용하여 전 제품의 점수를 비교하였다. 여기에서는 각 제품의 평균점수에 대한 차이를 95%의 유의수준으로 T-Test를 실시하였다.uted to the society, and what the socioeconomic impacts are resulted from the program. It would be useful for the means of (ⅰ) fulfillment of public accountability to legitimate the program and to reveal the expenditure of pubic fund, and (ⅱ) managemental and strategical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation mod

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.1
• /
• pp.13-21
• /
• 2020

Surrogate models have been used for the rapid estimation of six-DOF aerodynamic coefficients in the context of the design and control of a missile. For this end, we may generate highly accurate surrogate models with a multitude of aerodynamic data obtained from wind tunnel tests (WTTs); however, this approach is time-consuming and expensive. Thus, we aim to swiftly predict aerodynamic coefficients via co-Kriging using a few WTT data along with plenty of computational fluid dynamics (CFD) data. To demonstrate the excellence of co-Kriging models based on both WTT and CFD data, we first generated two surrogate models: co-Kriging models with CFD data and Kriging models without the CFD data. Afterwards, we carried out numerical validation and examined predictive trends to compare the two different surrogate models. As a result, we found that the co-Kriging models produced more accurate aerodynamic coefficients than the Kriging models thanks to the assistance of CFD data.

• The Mathematical Education
• /
• v.58 no.3
• /
• pp.383-401
• /
• 2019

This study started with the idea that it is necessary to focus on common concepts and ideas among the subjects when conducting integrated education in high school. This is a preliminary study for developing materials that can be taught in mathematics in the context of already learning scientific concepts in high school. For this purpose, Mendel 's law of genetics was studied among the contents of biological subjects which are known to have relatively little connection with mathematics. The more common links between the two subjects are, the better, in order to integrate math and other subjects and develop materials for teaching. Therefore, in this study, we investigated not only the probability domain but also the concept of statistical domain. We have been wondering if there is a more abundant idea to connect between 'Mendel's law' and 'probability and statistics'. Through these anxieties, we could find that concepts such as 'likely equality' and 'permutation and combination' including 'a large number of laws' can be a link between two subjects. Based on this, we were able to develop class materials that correspond to classes. This study is expected to help with research related to development of integrated education support materials, focusing on mathematics.

• Journal of Korea Technology Innovation Society
• /
• v.22 no.3
• /
• pp.340-361
• /
• 2019

This study analyzes the technological structure of artificial intelligence (AI) and technological capabilities of AI companies based on patent information. 2589 AI patents registered in USPTO from 2007 to 2017 were collected and analyzed by the Latent Dirichlet Allocation (LDA) to derive 20 AI technology topics. Analysis of technology development trends by AI technology reveals that visual understanding, data analysis, motion control, and machine learning are growing, while language understanding and speech technology are sluggish. In addition, we also investigated leading companies in each sub-field of AI as well as core competencies of global IT companies. The findings of this study are expected to be fruitfully used for formulation and implementation of technology strategy of AI companies.

• The Journal of the Korea Contents Association
• /
• v.18 no.12
• /
• pp.218-228
• /
• 2018

The purpose of this study was to present a educational content developed for training of dental extraction skill in a virtual environment. The development of the content consists of five steps: learning content analysis, draw a design model, development, test of validity, rectification and complete of the content. We developed the virtual reality (VR) simulator with producing an animation of surgical stages on the 3D models of human face for simulating dental extraction procedure. The results of validity tests for the content were mean 4.81 (SD, 0.72) for interface validity and mean 4.66 (SD, 0.71) for content validity, which represents strong evidence for the validity of the content. The data of the study indicates that the educational content developed for training of dental extraction skill using VR technology can be suitable to improve surgical skill of dental extraction in clinical field. We expect that further development of the education contents based on the VR technology to improve various surgical skills in clinical field will be addressed in the future.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.4
• /
• pp.27-36
• /
• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• Journal of Environmental Science International
• /
• v.32 no.8
• /
• pp.595-611
• /
• 2023

The purpose of this study is to improve the daily prediction results of PM2.5 from the air quality diagnosis and evaluation system operated by the Busan Institute of Health and Environment in real time. The air quality diagnosis and evaluation system is based on the photochemical numerical model, CMAQ (Community multiscale air quality modeling system), and includes a 3-day forecast at the end of the model's calculation. The photochemical numerical model basically has limitations because of the uncertainty of input data and simplification of physical and chemical processes. To overcome these limitations, this study applied DNN (Deep Neural Network), a deep learning technique, to the results of the numerical model. As a result of applying DNN, the r of the model was significantly improved. The r value for GFS (Global forecast system) and UM (Unified model) increased from 0.77 to 0.87 and 0.70 to 0.83, respectively. The RMSE (Root mean square error), which indicates the model's error rate, was also significantly improved (GFS: 5.01 to 6.52 ug/m³ , UM: 5.76 to 7.44 ug/m³ ). The prediction results for each concentration grade performed in the field also improved significantly (GFS: 74.4 to 80.1%, UM: 70.0 to 77.9%). In particular, it was confirmed that the improvement effect at the high concentration grade was excellent.

• Advances in nano research
• /
• v.16 no.3
• /
• pp.231-249
• /
• 2024

When the amplitude of the vibrations is equivalent to that clearance, the vibrations for small amplitudes will really be significantly nonlinear. Nonlinearities will not be significant for amplitudes that are rather modest. Finally, nonlinearities will become crucial once again for big amplitudes. Therefore, the concrete panel system may experience a big amplitude in this work as a result of the high temperature. Based on the 3D modeling of the shell theory, the current work shows the influences of the von Kármán strain-displacement kinematic nonlinearity on the constitutive laws of the structure. The system's governing Equations in the nonlinear form are solved using Kronecker and Hadamard products, the discretization of Equations on the space domain, and Duffing-type Equations. Thermo-elasticity Equations. are used to represent the system's temperature. The harmonic solution technique for the displacement domain and the multiple-scale approach for the time domain are both covered in the section on solution procedures for solving nonlinear Equations. An effective data-driven solution is often utilized to predict how different systems would behave. The number of hidden layers and the learning rate are two hyperparameters for the network that are often chosen manually when required. Additionally, the data-driven method is offered for addressing the nonlinear vibration issue in order to reduce the computing cost of the current study. The conclusions of the present study may be validated by contrasting them with those of data-driven solutions and other published articles. The findings show that certain physical and geometrical characteristics have a significant effect on the existing concrete panel structure's susceptibility to temperature change and GPL weight fraction. For building construction industries, several useful recommendations for improving the thermo-mechanics' behavior of structural concrete panels are presented.