• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.89-92
• /
• 2022

본 논문에서는 딥러닝 모델 기반 보행자의 GPS 경로를 예측하는 시스템을 제안한다. 다양한 경로 예측 방식들 중 본 논문은 GPS 데이터 기반 경로 예측 연구이다. 시계열 데이터인 보행자의 GPS 경로를 학습하여 다음 경로를 예측하도록 하는 딥러닝 모델 기반 연구이다. 본 논문에서는 보행자의 GPS 경로를 딥러닝 모델이 학습할 수 있도록 데이터 구성 방식을 제시하였으며, 예측 범위에 큰 제약이 없는 예측 딥러닝 모델을 제안한다. 본 논문의 딥러닝 모델에 적합한 파라메터들을 제시하였으며, 우수한 예측 성능을 보이는 결과를 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.601-602
• /
• 2013

경로손실 예측 모델은 무선 네트워크 설계를 위한 기본 척도이며, 적용 환경과 적용 시스템에 영향을 받는 특징이 있다. 대부분의 기존 연구는 도심, 교외와 같은 육상 환경을 기반으로 한다. 일반적으로 해수면 경로 손실은 전파의 잦은 굴절로 인해 지표면 보다 크며, 주파수에 비례한 경로손실이 있다고 알려져 있다. 하지만 해상 환경의 관련 연구는 자유공간에 적용하기 때문에 예측 모델의 정확성을 낮춘다. 본 논문은 해상 무선통신 서비스를 위한 2.4 GHz 대역의 해수면 경로손실 예측 모델을 제안한다. 이를 위해 육상과 해상에서 각각 수신신호 세기를 측정하고, 다양한 예측 모델과 비교 분석하여 실용성과 정확성을 입증한다.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.61-68
• /
• 2022

In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model's test performance are presented.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.7
• /
• pp.579-584
• /
• 2003

All of propagation path loss prediction models, which have been presented up to date, are oかy for ground living space. In reality, sea surface free space is different from ground living space in physical hierarchical structure. If the propagation path prediction model for ground living space is applied to the sea surface free space, propagation path loss will be smaller than actual value, while the maximum service straight line will become shorter. Thus this paper proposed and simulated the propagation path loss prediction model for predicting propagation path loss more accurately in sea surface free space, with its focus on CDMA mobile communication frequency band. Then the simulation results were compared to actual survey to verify its practicality.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.1-10
• /
• 2024

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.1-9
• /
• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.93-95
• /
• 2014

In this paper, we proposed efficient forwarding path computing method using Context-Awareness Mobility Prediction Model. Context-Awareness Mobility Prediction Model is storing and classifying node's previous velocity and direction according to time in the hierarchical cluster structure. To overcome environment which node-to-node connection is broken off easily, the proposed algorithm calculate the connectivity formed matrix structure by comparing predicted velocity and direction, and use masking operation for selecting relay moving to destination. The proposed algorithm identified to show short delay by utilizing forwarding path which is continue node-to-node connection in the unstable situation.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.112-114
• /
• 2004

오늘날 웹을 이용하는 사용자들의 웹 검색 형태를 저장한 웹 로그 데이터들은 데이터 마이닝을 위한 중요한 자료가 되고 있다. 이들 웹 로그들로부터 사용자의 현재 행동을 기반으로 사용자가 다음에 요청할 요구를 예측할 수 있는 예측 모델을 만들 수 있다. 하지만 이들 웹 로그들은 크기가 매우 크고 분석하기가 어렵다. 이런 문제를 해결하기 위해 이미 않은 방법이 제안되었다. 그 중에서 효과적으로 예측할 수 있도록 제안된 순차적 분류 기반에 연관법칙을 적용한 예측 기법이 있다. 본 논문에서는 전방향 참조 경로 탐사 패턴 알고리즘을 적용하여 연관규칙에 기반 한 웹 문서 예측 기법을 향상시키는 모델을 제안한다.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.2
• /
• pp.1-12
• /
• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.

• Electronics and Telecommunications Trends
• /
• v.11 no.2 s.40
• /
• pp.17-29
• /
• 1996

본 고에서는 이동통신 시스템의 전파특성을 이해하기 위해 800~950MHz 대역에 적용되는 전파경로손실 예측모델을 소개한다. 또한 미연방통신위원회(Federal Communications Commission; FCC)의 미연방통신법전(Code of Federal Regulations; CFR) Part 90에서 규정하고 있는 전계강도 기준을 전계강도 예측모델로서 분석해보고 그 타당성을 제시한다.