• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.39-39
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• 2019

인구의 도시 집중화로 인하여 다량의 생활용수의 사용에 따라 하천의 자정능력을 초과하여 오염을 유발시키고 있다. 이에 도시하천들의 오염은 점점 심해져 경제적으로 많은 문제를 유발하고 있다. 이러한 하천오염 문제를 과학적으로 대응하기 위해서는 오염물질의 농도 측정 및 데이터 축척을 통한 오염예측이 필수적이라 할 수 있으며, 부산광역시 보건환경정보 공개시스템에서는 하천수질 자동측정망을 설치하여 시간 단위로 오염물질을 측정하고 있다. 그러나 온천천의 하천수질 데이터는 계속 쌓여가고 있는데 이 데이터를 활용해서 하천수질 인자 예측이 거의 이뤄지지 않고 있다. 본 연구에서는 순환신경망 알고리즘을 활용하여 일 단위의 하천수질 인자 예측을 시도하였다. 순환신경망은 인공신경망의 발전된 형태인 시계열 학습에 강한 RNN, LSTM 알고리즘을 활용한 일단위 하천수질 인자 예측을 하고자 하였다. 연구에 앞서 시간 단위로 쌓여있는 데이터를 평균 내어 일 단위로 변경하였고 이 데이터를 가지고 일 단위 하천수질 인자 예측을 진행하였다. 연구에는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 DO, 탁도 등 항목을 예측하였다. 하천오염의 학습과 예측을 위해 대상지로는 부산지역 온천천의 부곡교, 세병교, 이섭교 관측소를 선택하였다. 연구를 위해 DO, 탁도 등 자료 수집은 부산광역시 보건환경정보 공개시스템의 자료를 활용하였다. 모형의 학습을 위해 입력자료로는 하천수질 인자 자료를 이용하였고, 자료의 학습에는 2014년~2017년 4년간의 자료를 학습자료로 사용하였고, 2018년 1년간의 자료는 모형의 검증을 위해 사용하였다. RNN, LSTM 알고리즘을 활용하여 분석 시 은닉층의 개수, 반복시행횟수, sequence length 등의 값을 조절하여 하천수질 인자 예측을 하였다. 모형의 검증을 위해 $R^2$(r square)와 RMSE(root mean square error)을 이용하여 통계분석을 실시하였다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.179-184
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• 2021

In this paper, we propose the deep learning-based neural network model to predict bunker price. In the shipping industry, since fuel oil accounts for the largest portion of ship operation costs and its price is highly volatile, so companies can secure market competitiveness by making fuel oil purchasing decisions based on rational and scientific method. In this paper, short-term predictive analysis of HSFO 380CST in Singapore is conducted by using three recurrent neural network models like RNN, LSTM, and GRU. As a result, first, the forecasting performance of RNN models is better than LSTM and GRUs using long-term memory, and thus the predictive contribution of long-term information is low. Second, since the predictive performance of recurrent neural network models is superior to the previous studies using econometric models, it is confirmed that the recurrent neural network models should consider nonlinear properties of bunker price. The result of this paper will be helpful to improve the decision quality of bunker purchasing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.27-32
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• 2019

Research that implements the classification process through Deep Learning algorithm, one of the outstanding human abilities, includes a unimodal model, a multi-modal model, and a multi-modal method using music videos. In this study, the results were better by suggesting a system to analyze each song's spectrum into short samples and vote for the results. Among Deep Learning algorithms, CNN showed superior performance in the category of music genre compared to RNN, and improved performance when CNN and RNN were applied together. The system of voting for each CNN result by Deep Learning a short sample of music showed better results than the previous model and the model with Softmax layer added to the model performed best. The need for the explosive growth of digital media and the automatic classification of music genres in numerous streaming services is increasing. Future research will need to reduce the proportion of undifferentiated songs and develop algorithms for the last category classification of undivided songs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.177-183
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• 2022

Recently, interesting of home training is getting bigger due to COVID-19. Accordingly, research on applying HAR(human activity recognition) technology to home training has been conducted. However, existing paper of HAR proposed static activity instead of dynamic activity. In this paper, the deep learning model where dynamic exercise posture can be analyzed and the accuracy of the user's exercise posture can be shown is proposed. Fitness images of AI-hub are analyzed by blaze pose. The experiment is compared with three types of deep learning model: RNN(recurrent neural network), LSTM(long short-term memory), CNN(convolution neural network). In simulation results, it was shown that the f1-score of RNN, LSTM and CNN is 0.49, 0.87 and 0.98, respectively. It was confirmed that CNN is more suitable for human activity recognition than other models from simulation results. More exercise postures can be analyzed using a variety learning data.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.174-179
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• 2023

Bulk trailers, used for the transportation of powdered materials, such as cement and fly ash, are crucial in the construction industry. The speedy exhaustion of powdered materials stored in the tank of bulk trailers is relevant to improving transportation efficiency and reducing transportation costs. The exhaust time can be reduced by developing an automatic control system to replace the manual exhaust operation. The instantaneous or accumulated exhausts of powdered materials must be measured for automatic control of the bulk trailer exhaust system. Accordingly, we previously proposed a recurrent neural network (RNN) model that estimated the instantaneous exhaust based on low-cost pressure sensor signals without an expensive flowmeter for powders. Although our previous study utilized only an RNN model, models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are also widely utilized for time-series estimation. This study compares the performance of three neural network models (MLP, CNN, and RNN) in estimating instantaneous and accumulated exhausts. In terms of the instantaneous exhaust estimation, the difference in the performance of neural network models was insignificant (that is, 8.64, 8.62, and 8.56% for the MLP, CNN, and RNN, respectively, in terms of the normalized root mean squared error). However, in the case of the accumulated exhaust, the performance was excellent in the order of CNN (1.67%), MLP (2.03%), and RNN (2.20%).

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• Convergence Security Journal
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• v.24 no.3
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• pp.95-104
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• 2024

The performance capability of the future battlefield depends on whether the next-generation technology of the Fourth Industrial Revolution, called ABCMS (AI, Bigdata, Cloud, Mobile, Security), can be applied to secure innovative defense capabilities It is no exaggeration to say. In addition, the future military operation environment is rapidly changing into a net work-oriented war (NCW) in which all weapon systems mutually share battlefield information and operate in real-time within a single integrated information and communication network based on the network and is expanding to the scope of operation of the manned and unmanned complex combat system. In particular, communication networks responsible for high-speed and hyperconnectivity require high viability and efficiency in power operation based on multi-tier (defense mobile, satellite, M/W, wired) networks for the connection of multiple combat elements and smooth distribution of information. From this point of view, this study is different from conventional single-media, single-path transmission with fixed specifications, It is an artificial intelligence-based transmission technology using RNN (Recurrent Neural Networks) algorithm and load distribution during traffic congestion using available communication wired and wireless infrastructure multimedia simultaneously and It is the development of MMMP-Multi-Media Multi-Path adaptive network technology.

• ETRI Journal
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• v.40 no.2
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• pp.257-265
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• 2018

Pronouns are frequently dropped in Korean sentences, especially in text messages in the mobile phone environment. Restoring dropped pronouns can be a beneficial preprocessing task for machine translation, information extraction, spoken dialog systems, and many other applications. In this work, we address the problem of dropped pronoun recovery by resolving two simultaneous subtasks: detecting zero-pronoun sentences and determining the type of dropped pronouns. The problems are statistically modeled by encoding the sentence and classifying types of dropped pronouns using a recurrent neural network (RNN) architecture. Various RNN-based encoding architectures were investigated, and the stacked RNN was shown to be the best model for Korean zero-pronoun recovery. The proposed method does not require any manual features to be implemented; nevertheless, it shows good performance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.45-48
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• 2016

오늘날의 고객은 다양한 정보를 통해 넓은 선택의 기회를 가진다. 이러한 상황에서 기업들은 고객과의 지속적인 관계를 유지하기 어려워짐에 따라 고객 유지와 신규 고객 유치를 위한 마케팅 비용을 천문학적으로 지출하고 있다. 기업들이 이탈하는 고객의 속성을 분석하고 이탈 시점을 예측할 수 있다면 마케팅에 사용되는 비용과 노력을 최소화할 수 있을 것으로 예측된다. 이를 위해 본 논문에서는 효과적인 고객 이탈 예측을 위한 딥러닝 기반의 이탈 예측 모델을 제안한다. 이 모델은 모바일 RPG 게임 고객의 시계열적인 행동 패턴을 이용하여 이탈을 예측하는 모델로, 예측을 위한 학습을 할 때 모델링된 고객 데이터를 분석하여 이탈 고객의 특성을 파악할 수 있게 한다. 실험을 통해 이탈 고객과 미 이탈 고객의 모델링된 값이 각각 특정 속성에 치중되어 있는 것을 확인하였고, 제안 모델이 합리적으로 고객의 이탈을 예측하는 것을 보였다.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.57-62
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• 2017

In this paper, we applied long short term memory(LSTM) for classifying political polarity in cyber public sphere. The data collected from the cyber public sphere is transformed into word corpus data through word embedding. Based on this word corpus data, we train recurrent neural network (RNN) which is connected by LSTM's. Softmax function is applied at the output of the RNN. We conducted our proposed system to obtain experimental results, and we will enhance our proposed system by refining LSTM in our system.