• Journal of Internet Computing and Services
• /
• v.20 no.2
• /
• pp.109-118
• /
• 2019

The purpose of this study is to deliver both market-practical and civil-centric political implication for sharing economy by investigating the nature of consumer-adoption for car-sharing service. With the global interest and market proliferation of the sharing economy, various service models for sharing idle resources have also been released in Korea. Particularly, in case of car sharing service, public - private partnership projects are spreading rapidly in various local governments including Seoul, along with the growing demand for alternative transportation system centering on the urban area. This study conducted an empirical study on the process of accepting the car sharing service by analyzing the data collected from users of the car sharing service "Sharing Car(Nanum Car)" of Seoul Metropolitan Government. A survey was conducted on 281 users in their twenties who are in the age of main use among the experienced users of the "Sharing Car(NaNum)" residing in Seoul. The result of analysis on the relationship between these users' perceived value and intention to use the vehicle sharing service would provide implications for establishing consumer(citizen)-centeric policies as well as market implications.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.4
• /
• pp.87-96
• /
• 2021

Determining design rainfall is the first step to plan an agricultural drainage facility. The objective of this study is to evaluate whether the current method for parameter estimation is reasonable for computing the design rainfall. The current Gumbel-Kendall (G-K) method was compared with two other methods which are Gumbel-Chow (G-C) method and Probability weighted moment (PWM). Hourly rainfall data were acquired from the 60 ASOS (Automated Synoptic Observing System) stations across the nation. For the goodness-of-fit test, this study used chi-squared (𝛘²) and Kolmogorov-Smirnov (K-S) test. When using G-K method, 𝛘² statistics of 18 stations exceeded the critical value (𝑥²_a=0.05,df=4=9.4877) and 10, 3 stations for G-C method, PWM method respectively. For K-S test, none of the stations exceeded the critical value (D^a=0.05_n=0.19838). However, G-K method showed the worst performances in both tests compared to other methods. Subsequently, this study computed design rainfall of 48-hour duration in 60 ASOS stations. G-K method showed 5.6 and 6.4% higher average design rainfall and 15.2 and 24.6% higher variance compared to G-C and PWM methods. In short, G-K showed the worst performance in goodness-of-fit tests and showed higher design rainfall with the least robustness. Likewise, considering the basic assumptions of the design rainfall estimation, G-K is not an appropriate method for the practical use. This study can be referenced and helpful when revising the agricultural drainage standards.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.12 no.6
• /
• pp.17-28
• /
• 2002

XTR is a new method to represent elements of a subgroup of a multiplicative group of a finite field GF( $p^{6m}$) and it can be generalized to the field GF( $p^{6m}$)$^{[6,9]}$ This paper progress optimal extention fields for XTR among Galois fields GF ( $p^{6m}$) which can be aplied to XTR. In order to select such fields, we introduce a new notion of Generalized Opitimal Extention Fields(GOEFs) and suggest a condition of prime p, a defining polynomial of GF( $p^{2m}$) and a fast method of multiplication in GF( $p^{2m}$) to achieve fast finite field arithmetic in GF( $p^{2m}$). From our implementation results, GF( $p^{36}$ )longrightarrowGF( $p^{12}$ ) is the most efficient extension fields for XTR and computing Tr( $g^{n}$ ) given Tr(g) in GF( $p^{12}$ ) is on average more than twice faster than that of the XTR system on Pentium III/700MHz which has 32-bit architecture.$^{［6,10］/ ［6,10］/6,10］}$

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.13-21
• /
• 2020

The Android author identification study can be interpreted as a method for revealing the source in a narrow range, but if viewed in a wide range, it can be interpreted as a study to gain insight to identify similar works through known works. The problem found in the Android author identification study is that it is an important code on the Android system, but it is difficult to find the important feature of the author due to the meaningless codes. Due to this, legitimate codes or behaviors were also incorrectly defined as malicious codes. To solve this, we introduced the concept of survival network to solve the problem by removing the features found in various Android apps and surviving unique features defined by authors. We conducted an experiment comparing the proposed framework with a previous study. From the results of experiments on 440 authors' identified apps, we obtained a classification accuracy of up to 92.10%, and showed a difference of up to 3.47% from the previous study. It used a small amount of learning data, but because it used unique features without duplicate features for each author, it was considered that there was a difference from previous studies. In addition, even in comparative experiments with previous studies according to the feature definition method, the same accuracy can be shown with a small number of features, and this can be seen that continuously overlapping meaningless features can be managed through the concept of a survival network.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.3
• /
• pp.141-148
• /
• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.123-132
• /
• 2020

The EDucation-research Integration through Simulation On the Net (EDISON) platform, which is a web-based platform that provides computational science and engineering simulation execution environments, can offer various analysis environments to students, general users, as well as computational science and engineering researchers. To expand the user base of the simulation environment services, the EDISON platform holds a challenge every year and attempts to increase the competitiveness and excellence of the platform by analyzing the user requirements of the various simulation environment offered. The challenge platform system in the field of computational science and engineering is provided to users in relation to the simulation service used in the existing EDISON platform. Previously, EDISON challenge servicesoperated independently from simulation services, and hence, services such as end-user review and intermediate simulation results could not be linked. To meet these user requirements, the currently in-service challenge platform for computational science and engineering is linked to the existing computational science and engineering service. In addition, it was possible to increase the efficiency of service resources by providing limited services through various analyses of all users participating in the challenge. In this study, by analyzing the simulation and usage environments of users, we provide an improved challenge platform; we also analyze ways to improve the simulation execution environment.

• Journal of Advanced Navigation Technology
• /
• v.25 no.4
• /
• pp.280-285
• /
• 2021

The significance of pilots' fatigue and the attributed risk management had continuously increased over time as the airline industry expanded. Research and legislation efforts associated with pilot fatigue are being taking place actively all over the world. In the developed world such as the United States and European Union etc., the airline pilot fatigue is already being managed by considering the show up time, the number of take offs and landings made, resting period, jet lag etc., when computing flight duty time. In Korea, the flight duty time is only limited by the total number of hours per given period regardless of the flight conditions and environment. Such lack of regulation demand development of a fatigue management program. According to the survey taken from the airline pilots in Korea, it has been found that acquiring foreign policies directly may in turn, increase the risk of fatigue. This research suggest future studies regarding fatigue management program adapted exclusively to Korean domestic flight environment and culture.

• Journal of the Society of Disaster Information
• /
• v.17 no.4
• /
• pp.897-903
• /
• 2021

Purpose: Recently, ransomware damage that encrypts victim's data through hacking and demands money in exchange for releasing it is increasing domestically and internationally. Accordingly, research and development on various response technologies and solutions are in progress. Method: A secure storage area and a general storage area were created in the same virtual environment, and the sample data was saved by registering the access process. In order to check whether the stored sample data is infringed, the ransomware sample was executed and the hash function of the sample data was checked to see if it was infringed. The access control performance checked whether the sample data was accessed through the same name and storage location as the registered access process. Result: As a result of the experiment, the sample data in the secure storage area maintained data integrity from ransomware and unauthorized processes. Conclusion: Through this study, the creation of a secure storage area and the whitelist-based access control method are evaluated as suitable as a method to protect important data, and it is possible to provide a more secure computing environment through future technology scalability and convergence with existing solutions.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.101-125
• /
• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of the Korea Computer Graphics Society
• /
• v.27 no.5
• /
• pp.1-11
• /
• 2021

The purpose of our research is to efficiently deform a 3D models which is composed of a triangular mesh and a skeleton. We designed a novel inverse kinematics (IK) solver that calculates the updated positions of mesh vertices with fewer computing operations. Through our user interface, one or more markers are selected on the surface of the model and their target positions are set, then the system updates the positions of surface vertices to construct a deformed model. The IK solving process for updating vertex positions includes many computations for obtaining transformations of the markers, their affecting joints, and their parent joints. Many of these computations are often redundant. We precompute those redundant terms in advance so that the 3-nested loop computation structure was improved to a 2-nested loop structure, and thus the computation time for a deformation is greatly reduced. This novel IK solver can be adopted for efficient performance in various research fields, such as handling 3D models implemented by LBS method, or object tracking without any markers.