• The Journal of Bigdata
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• v.3 no.1
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• pp.55-62
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• 2018

In this study, we have developed a model for predicting the number of patients (flu, pneumonia, and outbreak) using Big Data, which has been mainly performed overseas. Existing patient number system by government adopt procedures that collects the actual number and percentage of patients from several big hospital. However, prediction model in this study was developed combing a real-time collection of disease-related words and various other climate data provided in real time. Also, prediction number of patients were counted by machine learning algorithm method. The advantage of this model is that if the epidemic spreads rapidly, the propagation rate can be grasped in real time. Also, we used a variety types of data to complement the failures in Google Flu Trends.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.612-623
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• 2022

Estimation of the presence of people in real time is extremely useful for businesses in providing better services. Many companies and researchers have attempted various researches in order to count the number of floating population in a specific space. Recently, as part of smart cities and digital twins, commercialization of measuring floating populations using Wi-Fi signals has become active in the public and private sectors. In this paper we present a method of estimating the floating population based on MAC-address values collected from smartphones. By distinguishing Real MAC-address and Random MAC-address values, we compare the estimated number of smartphone devices and the actual number of people caught on CCTV screens to evaluate the accuracy of the proposed method. And it appeared to have a similar correlation between the two datas. As a result, we present a method of estimating the floating population based on analyzing Wi-Fi Probe Requests.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.24-34
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• 2021

Estimation of the presence of people in real time is extremely useful for businesses in providing better services. Many companies and researchers have attempted various researches in order to count the number of floating population in specific space. Recently, as part of smart cities and digital twins, commercialization of measuring floating populations using Wi-Fi signals has become active in the public and private sectors. This paper explains the floating population measuring system from the perspective of general consumers(non-experts) who uses current population data. Specifically, it presents a method of estimating the floating population based on MAC-address values collected from smartphones. By distinguishing Real MAC-address and Random MAC-address values, we compare the estimated number of smartphone devices and the actual number of people caught on CCTV screens to evaluate the accuracy of the proposed method. And it appeared to have a similar correlation between the two datas. As a result, we present a method of estimating the floating population based on analyzing Wi-Fi Probe Requests

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.72-75
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• 2004

In this paper, the task assignment strategy considering communication delay and the priority of distributed tasks is proposed for the real-time network system in order to maximize the utilization of the system. For the task assignment strategy, the relationship among priority of tasks in network nodes, the calculation time of each task, and the end-to-end response time including the network delay is formulated firstly. Then, the task assignment strategy using the genetic algorithm is proposed to optimize the utilization of the system considering the LCM(Least Common Multiple) period. The effectiveness of proposed strategy is proven by the simulation for estimating the performance such as the utilization and the response time of the system in case of changing the number of tasks and the number of network nodes.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.5
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• pp.218-225
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• 2001

This paper analyses the performance of network system using the CAN(Controller Area Network) protocol. Given messages are assumed to be scheduled by the DMS(Deadline Monotonic Scheduling) algorithm. The mathematical models for time-delay that can be occurred between CAN nodes are defined. The effectiveness of modeling is shown by comparing the difference of time-delay between simulations and practical experiments. We analyse the results according to the variation of factors, such as the number of nodes, the transmission speed, the message size and the number of aperiodic messages through simulation and confirm the real-time performance of lower priority messages. We also investigate the real-time performance of periodic messages when aperiodic message generates.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.1
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• pp.49-65
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• 2000

A method for inverting the Laplace transform is presented, using a finite series of the classical Legendre polynomials. The method recovers a real-valued function f(t) in a finite interval of the positive real axis when f(t) belongs to a certain class ${\mathcal{W}}_{\beta}$ and requires the knowledge of its Laplace transform F(s) only at a finite number of discrete points on the real axis s > 0. The choice of these points will be carefully considered so as to improve the approximation error as well as to minimize the number of steps needed in the evaluations. The method is tested on few examples, with particular emphasis on the estimation of the error bounds involved.

• 한국연소학회:학술대회논문집
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• 2012.11a
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• pp.33-35
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• 2012

This study has been mainly motivated to numerically model the supercritical mixing and combustion processes encountered in the liquid propellant rocket engines. In the present approach, turbulence is represented by the extended k-e model. To account for the real fluid effects, the propellant mixture properties are calculated by using generalized cubic equation of state. In order to realistically represent the turbulence-chemistry interaction in the turbulent nonpremixed flames, the flamelet approach based on the real fluid flamelet library has been adopted. Based on numerical results, the detailed discussions are made for the effects of swirl number on flame structure of supercritical kerosene/LOx double swirl coaxial injector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4098-4116
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• 2020

Mobile devices such as smartphones are very attractive targets for augmented reality (AR) services, but their limited resources make it difficult to increase the number of objects to be recognized. When the recognition process is scaled to a large number of objects, it typically requires significant computation time and memory. Therefore, most large-scale mobile AR systems rely on a server to outsource recognition process to a high-performance PC, but this limits the scenarios available in the AR services. As a part of realizing large-scale standalone mobile AR, this paper presents a solution to the problem of accuracy, memory, and speed for large-scale object recognition. To this end, we design our own basic feature and realize spatial locality, selective feature extraction, rough pose estimation, and selective feature matching. Experiments are performed to verify the appropriateness of the proposed method for realizing large-scale standalone mobile AR in terms of efficiency and accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1711-1725
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• 2014

Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1335-1349
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• 2019

Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.