• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.289-295
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• 2021

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.366-368
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• 2021

최근 프로그램은 그 크기와 복잡도가 나날이 증가하고 있어, 프로그램 행위 모델링에 대한 중요성은 다양한 분야에서 증대되고 있다. 프로그램의 동적 분석은 런타임에 생성되는 데이터가 너무나 많아, 많은 데이터를 활용하기 용이한 딥러닝 기술이 사용되고 있다. 하지만, 기존의 연구들은 연산과 연산의 매개변수 중 매개변수에 대한 고려가 충분치 않았다. 이는 매개변수가 딥러닝에 알맞은 입력으로 표현되기 어렵기 때문인데, 우리는 이를 해결하기 위해 매개변수로 인해 발생되는 런타임 행위에서 특징적인 값들을 추출하는 것으로 대체하였다. 또한, 연산과 특징적인 값들이 여러 개의 LSTM-RNN 으로 처리됨을 보이고, 이 결과를 시각화 하여 효과적임을 보였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.418-418
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• 2021

신월 빗물저류배수시설은 2010년 집중호우로 침수피해가 발생한 강서구 및 양천구의 저지대 침수문제 해결을 위해서 양천구의 지하 50m 깊이에 설치한 직경 10m, 길이 3.6km, 저류량 32만톤 규모의 지하 대심도 저류 터널이다. 해당 시설은 강우 발생시 유역의 중상류 하수관에서 횡월류 수문을 통해 우수를 저류터널로 유입 및 저류하고, 하류에 위치한 목동 빗물펌프장과 연계하여 배수할 수 있도록 구성되어 있다. 현재 시설의 운영은 유입부 인근에 설치된 수위계를 통해 수문 가동 여부를 판단하고 있으며, 운영 기준 및 매뉴얼은 서울기술연구원에서 지속적인 모니터링을 통해 고도화하고 있다. 본 연구의 목적은 실측 수위 기반의 신월 빗물저류배수시설 운영을 자동화하기 위한 방편으로, 딥러닝 기반의 RNN, LSTM, GRU 등의 알고리즘을 이용하여 유입부 관거 수위를 예측하는 모델을 개발하는 것을 목표로 하였다. 모델의 개발 및 검·보정을 위해 2010년부터 유역 내 구축되어 있는 강우 및 하수관 수위 자료와 목동 빗물펌프장 운영자료를 활용하였다. 현재 신월 빗물저류배수시설은 2020년 5월 준공되어 절대적인 자료 축적 기간이 부족하기 때문에, 향후 지속적인 강우-수위 모니터링을 통해 모델을 고도화하여 시설의 운영에 활용할 수 있도록 개선해 나갈 예정이다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.144-145
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• 2023

육상, 철도, 항공 등 타 교통분야에서 지속적으로 사고와 인명피해가 줄어든 반면, 해양분야는 해양사고가 증가하며 실효적 해양교통안전관리에 대한 필요성이 대두되고 있다. 최근 3년간 국내 해상에서 발생한 충돌사고 중 어선을 포함한 충돌사고가 전체의 약 84%를 차지하며, 해상교통의 주요 변수인 어선을 포함한 국가의 해상교통량 파악은 반드시 필요한 실정이다. 본 연구에서는 현 정부 국정과제인 '디지털 해상 교통망 구축'과 더불어 해양교통안전관리체계 마련의 일환으로 국내 전체 선박위치발신장치(AIS, V-PASS) 데이터를 활용하여 해상교통량을 분석하고 예측 모델을 개발한다. 이를 위해 선박 밀집도를 통한 그리드별 공간가산분석과 항적 데이터 전처리 및 선형화, 선박 길이에 따른 점용면적 산정을 통한 단위 그리드별 해상교통량을 분석한다. 또한, 과거 교통량 데이터는 딥러닝 기반의 시계열 특성을 지닌 RNN과 LSTM 모델을 활용하여 교통량 예측 모형을 개발한다. 본 연구의 결과는 해상교통량과 해양사고의 연관성 분석 및 속력제한구역 등 해상정책 수립의 정량적 근거를 제공하며, 국민에게 해상교통정보 제공을 통해 교통복지 증진에 기여할 수 있다.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.127-133
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• 2021

A recent study of people physically falling focused on analyzing the motions of the falls using a recurrent neural network (RNN) and a deep learning approach to get good results from detecting 2D human poses from a single color image. In this paper, we investigate a detection method for estimating the position of the head and shoulder keypoints and the acceleration of positional change using the skeletal keypoints information extracted using PoseNet from an image obtained with a low-cost 2D RGB camera, increasing the accuracy of judgments about the falls. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion-analysis method. A public data set was used to extract human skeletal features, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than a conventional, primitive skeletal data-use method.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.449-454
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• 2020

This paper proposes a machine learning-based emotion analysis system that detects a user's depression through their SNS posts. We first made a list of keywords related to depression in Korean, then used these to create a training data by crawling Twitter data - 1,297 positive and 1,032 negative tweets in total. Lastly, to identify the best machine learning model for text-based depression detection purposes, we compared RNN, LSTM, and GRU in terms of performance. Our experiment results verified that the GRU model had the accuracy of 92.2%, which is 2~4% higher than other models. We expect that the finding of this paper can be used to prevent depression by analyzing the users' SNS posts.