• Journal of the Korea Society of Computer and Information
• /
• v.26 no.2
• /
• pp.27-37
• /
• 2021

In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.6
• /
• pp.766-773
• /
• 2021

This paper presents an implementation result and performance evaluation of pavilion management services that does not only provide real-time status of the pavilion in the forest but also prediction services through machine learning. The developed hardware prototype detects whether the pavilion is occupied using a motion detection sensor and then sends it to a cloud database along with location information, date and time, temperature, and humidity data. The real-time usage status of the collected data is provided to the user's mobile application. The performance evaluation confirms that the average response time from the hardware module to the applications was 1.9 seconds. The accuracy was 99%. In addition, we implemented a pavilion availability prediction service that applied a machine learning-based SVM (Support Vector Model) model to collected data and provided it through mobile and web applications.

• Korean Journal of Acupuncture
• /
• v.38 no.4
• /
• pp.267-274
• /
• 2021

Objectives : Acupoints education is important in that it can determine the clinical competency of Korean Medicine Doctors (KMDs). Accordingly, we aimed to develop a practical simulator for acupoints education, acupoints training, acupoints practice, and acupoints evaluation. Methods : Korean Medicine (KM) SMART Table can be divided into hardware, server and components, and is organically linked. We develop KM SMART Table that combines the hardware of a human-sized table with a UHD display capable of multi-touch in two cases and software that can teach acupoints. We make Augmented Reality (AR) contents linked with KM SMART Table contents and develop applications that can use contents using mobile devices. By developing an AR image tracking module to react with KM SMART Table, it enables acupoint learning according to the mobile device platform and human anatomy. Results : The current system is a prototype where some 3D technology has been implemented, but the AR function will be produced later. New learning using 3D and AR will be required during acupoints education and acupoints practice. It will be used a lot in OSCE (Objective Structured Clinical Examination) practices for strengthening the competency of KMDs, and it will be of great help not only in KM education as a unique simulator of KM, but also in the practice of acupuncture and chuna for musculoskeletal diseases. Conclusions : The KM SMART Table is a technology that combines 3D and AR to learn acupoints, and to conduct acupoints OSCE practice, and we suggest that it can be usefully used for educational evaluation.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.35 no.5
• /
• pp.259-266
• /
• 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 the Korea Institute of Information and Communication Engineering
• /
• v.27 no.1
• /
• pp.27-33
• /
• 2023

Demands for eco-friendly food materials are increasing rapidly because of increased interest in well-being and health care, deterioration of air quality due to fine dust, and various soil and water pollution. Aquaponics is a system that can solve various problems such as economic activities, environmental problems, and safe food provision of the elderly population. However, techniques for deriving the optimal growth environment should be preceded. In this paper, we intend to design an intelligent plant growth measurement system that considers the characteristics of existing aquaponics. In particular, we would like to propose a module configuration plan for learning data and judgment systems when providing a uniform growth environment, focusing on designing systems suitable for production sites that do not have high-performance processing resources among intelligent aquaponics production management modules. It is believed that the proposed system can effectively perform deep learning with small analysis resources.

• Journal of Broadcast Engineering
• /
• v.27 no.4
• /
• pp.538-547
• /
• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.7
• /
• pp.4198-4211
• /
• 2014

The purpose of this study was to integrate the general education activities in the preschool developmental delay regarding the effect on the social skills of infant and young children. The participants of this research were young children with a developmental delay, who were enrolled in a school for the physically challenged in Gim-Hae City, South Kyongsangnamdo in Korea. An experimental qualitative module was selected to observe the social consequences after taking the extensive educational programs on young children with a developmental delay. The data was based on the observations of a researcher regarding the child's behavior and videotaped material was analyzed qualitatively in terms of the occasions and their social behaviors while extensive education was performed. The qualitative analysis explains the lower arena of the learning social skill including the quantitative changes in the communicational skill, and self-control skills.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.4
• /
• pp.43-52
• /
• 1998

We extract a hand region by using color information, which is an important feature for human vision to distinguish objects. Because pixel values in images are changed according to the luminance and lighting source, it is difficult to extract a hand region exactly without previous knowledge. We generate a hand skin model at learning stage, and extract a hand region from images by using the model. We also use a Kalman filter to consider changes of pixel values in a hand skin model. A Kalman filter restricts a search area for extracting a hand region at next frame also. The validity of the proposed method is proved by implementing the hand-region acquisition module.

• Journal of Broadcast Engineering
• /
• v.25 no.2
• /
• pp.192-199
• /
• 2020

Recently, the utilization of the object tracking algorithm based on the deep learning model is increasing. A system for tracking multiple objects in an image is typically composed of a chain form of an object detection algorithm and an object tracking algorithm. However, chain-type systems composed of several modules require a high performance computing environment and have limitations in their application to actual applications. In this paper, we propose a method that enables real-time operation in low-performance computing environment by adjusting the computational process of object detection module in the object detection-tracking chain type system.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2003.10a
• /
• pp.86-94
• /
• 2003

Electronically available biological literature has been accumulated exponentially in the course of time. So, researches on automatically acquiring knowledge from these tremendous data by text mining technology become more and more prosperous. However, most of the previous researches are technology oriented and are not well focused in practical extraction target, hence result in low performance and inconvenience for the bio-researchers to actually use. In this paper, we propose a more biology oriented target domain specific text mining system, that is, POSTECH bio-text mining system (POSBIOTM), for signal transduction pathway extraction, especially for G protein-coupled receptor (GPCR) pathway. To reflect more domain knowledge, we specify the concrete target for pathway extraction and define the minimal pathway domain ontology. Under this conceptual model, POSBIOTM extracts interactions and entities of pathways from the full biological articles using a machine learning oriented extraction method and visualizes the pathways using JDesigner module provided in the system biology workbench (SBW) [14]