• Science of Emotion and Sensibility
• /
• v.22 no.3
• /
• pp.3-20
• /
• 2019

With advancements in technology of virtual reality, it is used for various purposes in many fields such as medical care and healthcare, but as the same time there are also increasing reports of nausea, eye fatigue, dizziness, and headache from users. These symptoms of motion sickness are referred to as cybersickness, and various researches are under way to solve the cybersickness problem because it can cause inconvenience to the user and cause adverse effects such as discomfort or stress. However, there is no official standard for the causes and solutions of cybersickness at present. This is also related to the absence of tools to quantitatively measure the cybersickness. In order to overcome these limitations, this study proposed quantitative and objective cybersickness evaluation method. We measured 128-channel EEG waves from ten participants experiencing visually stimulated virtual reality. We calculated the relative power of delta and alpha in 11 regions (left, middle, right frontal, parietal, occipital and left, right temporal lobe). Multiple regression models were obtained in a stepwise manner with the motion sickness susceptibility questionnaire (MSSQ) scores indicating the susceptibility of the subject to the motion sickness. A multiple regression model with the highest under the area ROC curve (AUC) was derived. In the multiple regression model derived from this study, it was possible to distinguish cybersickness by accuracy of 95.1% with 11 explanatory variables (P_D.MF, P_D.LP, P_D.MP, P_D.RP, P_D.MO, P_A.LF, P_A.MF, P_A.RF, P_A.LP, P_A.RP, P_A.MO). In summary, in this study, objective response to cybersickness was confirmed through 128 channels of EEG. The analysis results showed that there was a clearly distinguished reaction at a specific part of the brain. Using the results and analytical methods of this study, it is expected that it will be useful for the future studies related to the cybersickness.

• Korean Journal of Remote Sensing
• /
• v.34 no.2_1
• /
• pp.203-211
• /
• 2018

Dam discharge is carried out for the management of rivers and area around rivers due to rainy season or drought. Dam discharge should be based on an accurate understanding of the flow rate that can be accommodated in the river. Therefore, understanding the allowable storage capacity of river is an important factor in the management of the environment around the river. However, the methods using water level meters and images, which are currently used to determine the allowable flow rate of rivers, show limitations in terms of accuracy and efficiency. In order to solve these problems, this paper proposes a method to automatically calculate the allowable storage capacity of river based on the images taken by drone. In the first step, we create a 3D model of the river by using the drone images. This generation process consists of tiepoint extraction, image orientation, and image matching. In the second step, the allowable storage capacity is calculated by cross section analysis of the river using the generated river 3D model and the road and river layers in the target area. In this step, we determine the maximum water level of the river, extract the cross-sectional profile along the river, and use the 3D model to calculate the allowable storage capacity for the area. To prove our method, we used Bukhan river's data and as a result, the allowable storage volume was automatically extracted. It is expected that the proposed method will be useful for real - time management of rivers and surrounding areas and 3D models using drone.

• Journal of Bio-Environment Control
• /
• v.21 no.4
• /
• pp.466-477
• /
• 2012

This study aimed to develop the comprehensive indicators which can be used for evaluating greenhouse for tomato cultivation. To achieve this aim, the study developed the evaluation indicators composed of evaluation items, grades and criteria by extracting preliminary evaluation items through analyzing the related papers and preceding studies, and conducting Delphi survey on an expert group. During the three surveys, the questions of closed-ended type were given to a panel of 100 experts - professors related to tomato cultivation and facilities, researchers and farmers (practical users). As a result, the finally established evaluation indicators consist of 4 categories and 39 specific evaluation items. The 4 categories are the structural factor of greenhouse, equipment factor of greenhouse, cultivation factor, and infrastructure factor. These factors consist of specific evaluation items of 9, 15, 7 and 8, respectively. In addition, on 39 specific evaluation items, weighted values were calculated and grades and criteria were established by collecting opinions of the experts. The newly developed evaluation indicators through this study will play an important role in developing new greenhouse models, considering things that should be complemented preferentially regarding in-use facilities, and improving the efficiency of projects supported by the government.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.327-351
• /
• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.39 no.1
• /
• pp.97-107
• /
• 2002

In this paper, we propose the realization of on-line handwritten Chinese character recognition for mobile personal digital assistants (PDA). We focus on the development of an algorithm having a high recognition performance under the restriction that PDA requires small memory storage and less computational complexity in comparison with PC. Therefore, we use index matching method having computational advantage for fast recognition and we suggest a unit reconstruction method to minimize the memory size to store the character models and to accomodate the various changes in stroke order and stroke number of each person in handwriting Chinese characters. We set up standard model consisting of 1800 characters using a set of pre-defined units. Input data are measured by similarity among candidate characters selected on the basis of stroke numbers and region features after preprocessing and feature extracting. We consider 1800 Chinese characters adopted in the middle and high school in Korea. We take character sets of five person, written in printed style, irrespective of stroke ordering and stroke numbers. As experimental results, we obtained an average recognition time of 0.16 second per character and the successful recognition rate of 94.3% with MIPS R4000 CPU in PDA.

• Journal of Digital Convergence
• /
• v.15 no.5
• /
• pp.13-25
• /
• 2017

The purpose of this study is to suggest the direction of O2O industry policy for solving the conflict problems with the traditional industry stakeholder and for enhancing the regulations as new industry development is inevitable. We make use of TAIDA that is one of scenario methods to accomplish the purpose and suggest the direction of policy. First, it is needed to prepare directly by government the environment that new business models are able to emerge easily with various consulting services and information supports like public system servers and IT infra, it is practical support policy. Second, positive legal application for new business and making the law for new business are needed in legal issues situation as soon as possible. Third, the conflicts with old and new industry would be managed to the direction of "predictable" progressively. Incongruity among laws, safety and security problems, and the conflict of stakeholder are urgent. Because of the limit in this study, it is expected that O2O industry is categorized in detail aligned to the characteristics and that new policies along to the separate industry areas are developed by the following study.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.7
• /
• pp.58-65
• /
• 2019

Polymer concrete is expected to be widely used as a building material because it has a shorter hardening time and excellent compression, tensile, bending, bond strength, frictional resistance and abrasion loss compared to general concrete. The polymer concrete has excellent vibration damping performance and research on the use of various reinforcing materials is being conducted. However, in order to completely replace the general concrete and the general anti-vibration reinforcement, such polymer concrete requires an overall review of vibration reduction performance considering physical properties, dynamic properties, productivity and field applicability. In this study, the physical and dynamic properties of polymer concrete by epoxy mixing ratio were compared with those of general concrete. It was appeared that compression, tensile, bending and bond strengths of polymer concrete by epoxy mixing were significantly higher than those of general concrete. Especially, the tensile strength was more than 4 ~ 6.5 times. Based on the basic physical properties of polymer concrete, the damping ratio, which is a dynamic characteristic according to the epoxy mixing ratio, was derived through analytical models and experiments. As a result, the dynamic stiffness of polymer concrete was 20% higher than that of general concrete and the loss rate was about 3 times higher.

• Convergence Security Journal
• /
• v.21 no.4
• /
• pp.109-115
• /
• 2021

Currently, with the development of 5G and IoT technology, it is being used in connection with the things used in real life through a network. However, attempts to use networked computers for malicious purposes are increasing, and attacks using malicious codes that infringe the confidentiality and integrity of user information are becoming more intelligent. As a countermeasure to this, research is being conducted on a method of detecting malicious packets using a security control system and AI technology, supervised learning. The cyber security control system is being operated inefficiently in terms of manpower and cost. In addition, in the era of the COVID-19 pandemic, remote work has increased, making it difficult to respond immediately. In addition, malicious code detection using the existing AI technology, supervised learning, does not detect variant malicious code, and has an inaccurate malicious code detection rate depending on the quantity and quality of data. Therefore, in this study, by converging malicious packet detection technologies through various machine learning and deep learning models, the accuracy of malicious packet detection is increased, the false positive rate and the false positive rate are reduced, and a new type of malicious packet can be efficiently detected when intrusion. We propose a malicious packet detection technology.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.132-144
• /
• 2018

3D Geo-spatial information models have been widely used in the field of Civil Engineering, Medical, Computer Graphics, Urban Management and many other. Especially, in surveying and geo-spatial field, the demand for high quality 3D geospatial information and indoor spatial information is so highly increasing. However, it is so difficult to provide a low-cost and high efficiency service to the field which demand the highest quality of 3D model, because pre-constructed spatial data are composed of different formats and storage structures according to the application purpose of each institutes. In fact, the techniques to construct a high applicable 3D geo-spatial model is very expensive to collect and analyze geo-spatial data, but most demanders of 3D geo-spatial model never want to pay the high-cost to that. This study, therefore, suggest the effective way to construct 3D geo-spatial model with low-cost of construction. In general, the effective way to reduce the cost of constructing 3D geo-spatial model as presented in previous studies is to combine the raw data obtained from point cloud observatory and UAV imagery, however this method has some limitation of usage from difficulties to approve the use of raw data because of those have been managed separately by various institutes. To solve this problem, we developed the linking & management system for unifying a high-Resolution raw geo-spatial data based on the point cloud DB and apply this system to extract the basic database from 3D geo-spatial mode for the road database registration. As a result of this study, it can be provided six contents of main entries for road registration by applying the developed system based on the point cloud DB.

• The Journal of Bigdata
• /
• v.4 no.1
• /
• pp.63-72
• /
• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.