• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.99-120
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• 2017

In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.

• Journal of KIISE
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• v.44 no.8
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• pp.803-812
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• 2017

T-commerce is technology-fusion service on which the user can purchase using data broadcasting technology based on bi-directional digital TVs. To achieve the best revenue under a limited environment in regard to the channel number and the variety of sales goods, organizing broadcast programs to maximize the expected sales considering the selling power of each product at each time slot. For this, this paper proposes a method to predict the sales of goods when it is assigned to each time slot. The proposed method predicts the sales of product at a time slot given the week-in-year and weather of the target day. Additionally, it combines a statistical predict model applying SVD (Singular Value Decomposition) to mitigate the sparsity problem caused by the bias in sales record. In experiments on the sales data of W-shopping, a T-commerce company, the proposed method showed NMAE (Normalized Mean Absolute Error) of 0.12 between the prediction and the actual sales, which confirms the effectiveness of the proposed method. The proposed method is practically applied to the T-commerce system of W-shopping and used for broadcasting organization.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.141-166
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• 2018

Information technology improves the efficiency of humanities research. In humanities research, information technology can be used to analyze a given topic or document automatically, facilitate connections to other ideas, and increase our understanding of intellectual history. We suggest a method to identify and automatically analyze the relationships between arguments contained in unstructured data collected from humanities writings such as books, papers, and articles. Our method, which is called history mining, reveals influential relationships between arguments and the philosophers who present them. We utilize several classification algorithms, including a deep learning method. To verify the performance of the methodology proposed in this paper, empiricists and rationalism - related philosophers were collected from among the philosophical specimens and collected related writings or articles accessible on the internet. The performance of the classification algorithm was measured by Recall, Precision, F-Score and Elapsed Time. DNN, Random Forest, and Ensemble showed better performance than other algorithms. Using the selected classification algorithm, we classified rationalism or empiricism into the writings of specific philosophers, and generated the history map considering the philosopher's year of activity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.45-51
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• 2022

Fear of contact exists due to the prevention of the spread of infectious diseases such as COVID-19. When using the common entrance door of an apartment, access is possible only if the resident enters a password or obtains the resident's permission. There is the inconvenience of having to manually enter the number and password for the common entrance door to enter. Also, contactless entry is required due to COVID-19. Due to the development of ICT, users can be easily identified through the development of face recognition and voice recognition technology. The proposed method detects a visitor's face through a CCTV or camera attached to the common entrance door, recognizes the face, and identifies it as a registered resident. Then, based on the registered information of the resident, it is possible to operate without contact by interworking with the elevator on the server. In particular, if face recognition fails with a hat or mask, the visitor is identified by voice or additional authentication of the visitor is performed based on the voice message. It is possible to block the spread of contagiousness without leaving any contactless function and fingerprint information when entering and exiting the front door of an apartment house, and without the inconvenience of access.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.449-456
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• 2022

This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Network) based crop yield prediction model suitable for the domestic crop environment. The existing model has two characteristics. The first is that it replaces the original yield with the average yield of the year, and the second is that it trains the data of the predicted year. The new model uses the original field value to ensure accuracy, and the network structure has been improved so that it can train only with data prior to the year to be predicted. The proposed model predicted the yield per unit area of autumn cabbage for kimchi by region based on weather, soil, soil suitability classes, and yield data from 1980 to 2020. As a result of computing and predicting data for each of the four years from 2018 to 2021, the error amount for the test data set was about 10%, enabling accurate yield prediction, especially in regions with a large proportion of total yield. In addition, both the proposed model and the existing model show that the error gradually decreases as the number of years of training data increases, resulting in improved general-purpose performance as the number of training data increases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.49-57
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• 2022

The government is making great efforts to prevent crimes such as missing children by using public CCTVs. However, there is a shortage of operating manpower, weakening of concentration due to long-term concentration, and difficulty in tracking. In addition, applying real-time object search, re-identification, and tracking through a deep learning algorithm showed a phenomenon of increased parameters and insufficient memory for speed reduction due to complex network analysis. In this paper, we designed the network to improve speed and save memory through the application of Yolo v4, which can recognize real-time objects, and the application of Batch and TensorRT technology. In this thesis, based on the research on these advanced algorithms, OSNet re-ranking and K-reciprocal nearest neighbor for re-identification, Jaccard distance dissimilarity measurement algorithm for correlation, etc. are developed and used in the solution of CCTV national safety identification and tracking system. As a result, we propose a solution that can track objects by recognizing and re-identification objects in real-time within situation of a Korean public multi-CCTV environment through a set of algorithm combinations.