• Electronics and Telecommunications Trends
• /
• v.35 no.1
• /
• pp.80-88
• /
• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.5
• /
• pp.412-421
• /
• 2013

Motion picture industry is one of the most representative fields in the cultural industry and has experienced constant growth both worldwide and within domestic markets. However, little research has been undertaken for diffusion patterns of motion pictures, whereas various issues such as demand forecasting and success factor analysis have been widely explored. To analyze diffusion patterns, we adopted extended Bass model to reflect the potential demand of movies. Four clusters of selected movies were derived by k-means clustering method with criteria of opening strength and profitability and then compared by their diffusion patterns. Results indicated that movies with high profitability and medium opening strength are most significantly influenced by word of mouth effect, while low profitability movies display nearly monotonic decreasing diffusion patterns with noticeable initial adoption rates and relatively early peak points in their runs.

• Architectural research
• /
• v.10 no.1
• /
• pp.41-48
• /
• 2008

Construction projects have frequently exceeded their schedule despite reliable estimates at the start of a project. This problem was attributed to unpredictable causes at the beginning and to shortage of proper tools to accurately predict project completion date. To supplement this difficulty, project managers need a comprehensive system that can be employed to monitor the progress of an ongoing project and to evaluate potential delay for achieving the goal on time. This paper proposed a progressive-based expert system for quantitative assessments of project delay at the early stages of the execution. Furthermore, the system is used to inspect the change of the uncertainty on completion date and its magnitude. The proposed expert system is helpful for furnishing project managers a warning signal as a project is going behind schedule and for tracking the changed uncertainty at a desired confidence level. The main objectives of this paper are to offer a new system to overcome the difficulties of conventional forecasting tools and to apply a construction project into the system to illustrate its effectiveness. This paper focuses on construction phase of project development and is intended for the use by project managers.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2005.11a
• /
• pp.789-792
• /
• 2005

Development of high-strength concrete and improved durability has brought new opportunities to the construction industry. However, some attention was given to characteristics of such concrete, in particular with respect to their cracking sensitivity. It has been argued and demonstrated experimentally that a low water/cement ratio concrete undergoes shrinkage due to self-desiccation. This so-called autogenous shrinkage cracking is a major concern for concrete durability. One possible method to reduce cracking due to autogenous shrinkage is the addition of expansive additive. Tests conducted by many researches have shown the beneficial effects of addition of expansive for reducing the risk of shrinkage-introduced cracking. This paper aimed at forecasting deformation of high strength cement paste with expansive additive for early age.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2010.04a
• /
• pp.395-397
• /
• 2010

AWS that exist in Pusan is watching local meteorological phenomena established in place that the weather observatory does not exist by real time, and is used usefully to early input data of numerical weather forecasting model. I wished to display downtown of Pusan and air temperature change of peripheral area using this AWS data. Analyzed volatility using AWS observation data for 5 years to recognize air temperature change of Pusan area through data about temperature among them. Drew air temperature distribution chart by season of recapitulative Pusan area applying IDW linear interpolation with this.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.55
• /
• pp.25-31
• /
• 2000

To make a better decision about when to shutdown a nuclear power plant, we build a decision model using influence diagrams. We proceed the analysis adopting a bayesian approach. Firstly, an accident arrival rate is assumed to be known and this assumption is relaxed later. We perform our analysis on the cases of exponential time to accidents, and gamma distribution for the arrival rate. An optimal shutdown time is obtained considering the trade-off between the costs incurred by an accident due to late shutdown and the possible loss of revenues due to the early shutdown. We also derive the upper bound of the failure rate where we may operate the plant.

• Proceedings of the National Institute of Ecology of the Republic of Korea
• /
• v.1 no.1
• /
• pp.9-21
• /
• 2020

Ecological communities adapt the concept of informatics in the late 20 century and develop rapidly in the early 21 century to form Ecoinformatics as the new approach of ecological research. The new approach takes into account the data-intensive nature of ecology, the precious information content of ecological data, and the growing capacity of computational technology to leverage complex data as well as the critical need for informing sustainable management of complex ecosystems. It comprehends techniques for data management, data analysis, synthesis, and forecasting on ecological research. The present paper attempts to review the development history, studies and application cases of ecoinformatics in ecological research especially on Long Term Ecological Research (LTER). From the applications show that the ecoinformatics approach and management system have formed a new paradigm in ecological research.

• Korean Medical Education Review
• /
• v.16 no.3
• /
• pp.141-146
• /
• 2014

This study focused on investigating the future medicine and health care industry paradigm shift and suggesting the right direction for the medical education system in order for students to be better prepared in the near future. Here, I will discuss four issues related to the future of medicine: Health 2.0, digital health, personalized medicine, and innovations of the public healthcare system. Every issue has lessons for medical education and teaching for students who major in health professions. However, it is obvious that the present is an important period of time as, currently, we are at an early stage in the future health care environment. Recently, there have been rapid transformations in various fields of medicine. Therefore, if we fail to lead medical education in the right direction, medical students will suffer from major problems in coping with these changes.

• Journal of Construction Engineering and Project Management
• /
• v.4 no.4
• /
• pp.9-16
• /
• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.2
• /
• pp.240-249
• /
• 2014

This study aims to identify an assessment system based on multiple patent indices that can predict the likelihood of success in the commercialization of a patented technology in advance. In addition, we examine the effectiveness of our predictive model in identifying valuable technologies early on. We analyzed 3,063 secondary battery technologies patented in the US over the past 10 years. Our analysis identified 22 of the 25 most promising patented technologies, corresponding with the top 50% of industry-patented technologies that directly and indirectly succeeded in commercialization. These results support our claim that it is possible to identify attributes for the assessment of patent commercial potential to a significant degree. Our system presents a useful assessment index in the forecasting and determination of potential commercial success of patented technologies.