• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.430-435
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• 2022

In this paper, we consider the non-orthogonal multiple access (NOMA) technique in the heterogeneous network (HetNET) consisting of a single macro base station (BS) and multiple small BSs, where the perfect successive interference cancellation is assumed for the NOMA signals. In this paper, we propose a deep learning-based user association and power allocation scheme to maximize the data rate in the NOMA-based HetNET. In particular, the proposed scheme includes the deep neural network (DNN)-based user association process for load balancing and the DNN-based power allocation process for data-rate maximization. Through the simulation assuming path loss and Rayleigh fading channels between BSs and users, the performance of the proposed scheme is evaluated, and it is compared with the conventional maximum signal-to-interference-plus-noise ratio (Max-SINR) scheme. Through the performance comparison, we show that the proposed scheme provides better sum rate performance than the conventional Max-SINR scheme.

• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• 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 the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.259-266
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• 2022

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.95-103
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• 2022

Quick and accurate understanding of the situation in a severe accident is essential for conducting the appropriate accident management and response using the accident diagnosis information. This study employed deep learning technology to diagnose severe accidents through the major safety parameters transferred from a nuclear power plant (NPP) to AtomCARE. After selecting the major accident scenarios to consider, a learning database was established for particular scenarios affiliated with major scenarios by performing a large number of severe accident analyses using MAAP5 code. The severe accident diagnosis technology, which classifies detailed accident scenarios using the major safety parameters from NPPs, was developed by training it with the established database . Verification and validation were conducted by blind test and principal component analysis. The technology developed in this study is expected to be extended and applied to all severe accident scenarios and be utilized as a base technology for quick and accurate severe accident diagnosis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.83-95
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• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.1-13
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• 2023

Predicting earthquakes is difficult due to the complexity of the systems underlying tectonic phenomena and incomplete understanding of the interactions among tectonic settings, tectonic stress, and crustal components. The Korean Peninsula is located in a stable intraplate region with a low average seismicity of M 2.3. As public interest in the earthquake grows, we analyzed earthquakes on the Korean Peninsula by attempting to predict spatio-temporal earthquake patterns and magnitudes using Facebook's Prophet model based on deep learning, and here we discuss seismic distribution zones using DBSCAN, a cluster analysis method. The Prophet model predicts future earthquakes in Chungcheongbuk-do, Gyeonggi-do, Seoul, and Gyeongsangbuk-do.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.39-48
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• 2023

Because a railway bridge is designed over a long period of time and covers a large site, it involves various environmental factors and uncertainties. For this reason, design changes often occur, even if the design was thoroughly reviewed in the initial design stage. In particular, design changes of large-scale facilities, such as railway bridges, consume significant time and cost, and it is extremely inefficient to repeat all the procedures each time. In this study, a technique that can improve the efficiency of learning after design change was developed by utilizing the learning result before design change through transfer learning among deep-learning algorithms. For analysis, scenarios were created, and a database was built using a previously developed railway bridge deep-learning-based prediction system. The proposed method results in similar accuracy when learning only 1000 data points in the new domain compared with the 8000 data points used for learning in the old domain before the design change. Moreover, it was confirmed that it has a faster convergence speed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.329-335
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• 2023

Avocado, a superfood selected by Time magazine and one of the late ripening fruits, is one of the foods with a big difference between local prices and domestic distribution prices. If this sorting process of avocados is automated, it will be possible to lower prices by reducing labor costs in various fields. In this paper, we aim to create an optimal classification model by creating an avocado dataset through crawling and using a number of deep learning-based transfer learning models. Experiments were conducted by directly substituting a deep learning-based transfer learning model from a dataset separated from the produced dataset and fine-tuning the hyperparameters of the model. When an avocado image is input, the model classifies the ripeness of the avocado with an accuracy of over 99%, and proposes a dataset and algorithm that can reduce manpower and increase accuracy in avocado production and distribution households.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.55-62
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• 2017

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.