• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.565-572
• /
• 2018

Recently, UAVs (Unmanned Aerial Vehicles) or drones have gained popularity for the engineering surveying and mapping because they enable the rapid data acquisition and processing as well as their operation cost is low. The applicable fields become much wider including the topographic monitoring, agriculture, and forestry. It is reported that the high geospatial accuracy is achievable with the drone photogrammetry for many applications. However most studies reported the best achievable mapping results using well-distributed ground control points though some studies investigated the impact of control points on the accuracy. In this study, we focused on the drone mapping of corridors such as roads and pipelines. The distribution and the number of control points along the corridor were diversified for the accuracy assessment. In addition, the effects of the camera self-calibration and the number of the image strips were also studied. The experimental results showed that the biased distribution of ground control points has more negative impact on the accuracy compared to the density of points. The prior camera calibration was favored than the on-the-fly self-calibration that may produce poor positional accuracy for the case of less or biased control points. In addition, increasing the number of strips along the corridor was not helpful to increase the positional accuracy.

• Resources Recycling
• /
• v.27 no.6
• /
• pp.23-29
• /
• 2018

Recently rapid economic growth and technological development have led to an increase in the generation of waste electrical and electronic equipment (WEEE). As the amount of electric and electronic waste generated increases, the importance of processing waste printed circuit boards (PCB) is also increasing. Various studies have been conducted to recycle various valuable metals contained in a waste PCB in an environmentally friendly and economical manner. To get anode slime containing Ag and Au, Anode copper prepared from PCB scraps was used by means of electro-refining. Ag and Au recovery was conducted by leaching, direct reduction, and ion exchange method. In the case of silver, the anode slime was leached at 3 M $HNO_3$, 100 g/L, $70^{\circ}C$, and Ag was recovered by precipitation, alkali dissolution, and reduction method. In the case of gold, the nitrate leaching residues of the anode slime was leached at 25% aqua regia, 200 g/L, $70^{\circ}C$, and Au was recovered by pH adjustment, ion exchange resin adsorption, desorption and reduction method. The purity of the obtained Au and Ag were confirmed to be 99.99%.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.10
• /
• pp.285-290
• /
• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.12
• /
• pp.155-163
• /
• 2020

This study developed a system that can collect, manage, and utilize the travel routes of individuals who tested positive for coronavirus disease 2019 (COVID-19) based on the data standardization and quality management principles and presented the analysis data collected from the existing system. Unlike many other countries in the world, Korea demonstrated a rapid response by conducting epidemiological investigations. Further, the local governments have actively shared the travel routes of individuals who tested positive for COVID-19 to facilitate proactive prevention of the infectious disease per the Infectious Disease Control and Prevention Law. However, currently, there is no standard protocol for the local governments to share the information, thus complicating the process of sharing, managing, and utilizing the collected data. Therefore, this study developed a system that can facilitate sharing of the travel routes of individuals who tested positive for COVID-19 by establishing database construction procedures and using the travel route of COVID-19 patients as per the Disaster & Safety Information Sharing Platform and developing a data processing guideline, a data entry system with default templates, and Open API. Although this sharing system was designed to communicate the travel routes of COVID-19 patients, it can also be utilized in case of other infectious diseases. Therefore, it can be used as a response strategy for future outbreaks of infectious diseases.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.7
• /
• pp.217-224
• /
• 2022

Today, with the development of technology and industry, fire accidents in special buildings are increasing as special buildings increase. However, despite the rapid development of information and communication technology, human casualties are steadily occurring due to the underdeveloped and ineffective indoor fire alarm system. In this study, we confirmed that the existing indoor fire alarm system using acoustic alarm could not deliver a sufficiently large alarm to the in-room personnel. To improve this, we designed and implemented a fire alarm system using edge computing and beacons. The proposed improved fire alarm system consists of terminal sensor nodes, edge nodes, a user application, and a server. The terminal sensor nodes collect indoor environment data and send it to the edge node, and the edge node monitors whether a fire occurs through the transmitted sensor value. In addition, the edge node continuously generate beacon signals to collect information of smart devices with user applications installed within the signal range, store them in a server database, and send application push-type fire alarms to all in-room personnel based on the collected user information. As a result of conducting a signal valid range measurement experiment in a university building with dense lecture rooms, it was confirmed that device information was normally collected within the beacon signal range of the edge node and a fire alarm was quickly sent to specific users. Through this, it was confirmed that the "blind spot problem of the alarm" was solved by flexibly collecting information of visitors that changes time to time and sending the alarm to a smart device very adjacent to the people. In addition, through the analysis of the experimental results, a plan to effectively apply the proposed fire alarm system according to the characteristics of the indoor space was proposed.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.10
• /
• pp.373-380
• /
• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• The Journal of KAIRB
• /
• v.4 no.1
• /
• pp.16-22
• /
• 2022

Purpose: The traditional ethical study only suggests a blurred insight on the research using medical big data, especially in this rapid-changing and demanding environment which is called "4th Industry Revolution." Current institutional/ethical issues in big data research need to approach with the thoughtful insight of past ethical study reflecting the understanding of present conditions of this study. This study aims to examine the ethical issues that are emerging in recent health care big data research. So, this study aims to survey the public perceptions on of health care big data as part of the process of public discourse and the acceptance of the utility and provision of big data research as a subject of health care information. In addition, the emerging ethical challenges and how to comply with ethical principles in accordance with principles of the Belmont report will be discussed. Methods: Survey was conducted from June 3th August to 6th September 2020. The online survey was conducted through voluntary participation through Internet users. A total of 319 people who completed the survey (±5.49%P [95% confidence level] were analyzed. Results: In the area of the public's perspective, the survey showed that the medical information is useful for new medical development, but it is also necessary to obtain consents from subjects in order to use that medical information for various research purposes. In addition, many people were more concerned about the possibility of re-identifying personal information in medical big data. Therefore, they mentioned the necessity of transparency and privacy protection in the use of medical information. Conclusion: Big data on medical care is a core resource for the development of medicine directly related to human life, and it is necessary to open up medical data in order to realize the public good. But the ethical principles should not be overlooked. The right to self-determination must be guaranteed by means of clear, diverse consent or withdrawal of subjects, and processed in a lawful, fair and transparent manner in the processing of personal information. In addition, scientific and ethical validity of medical big data research is indispensable. Such ethical healthcare data is the only key that will lead to innovation in the future.

• Geophysics and Geophysical Exploration
• /
• v.25 no.1
• /
• pp.26-37
• /
• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Journal of Internet Computing and Services
• /
• v.22 no.6
• /
• pp.141-150
• /
• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.2
• /
• pp.281-290
• /
• 2022

In Korea, the number of national facilities for which a safety inspection is mandatory is increasing, and a safer safety inspection method is needed. This study aimed to increase the efficiency of the bridge safety inspection by enabling rapid exterior inspection while securing the safety of inspectors by using drones to perform the safety inspections of bridges, which had mainly relied on visual inspections. For the research, the Youngjong Grand Bridge in Incheon was selected as a test bed and was divided into four parts: the warren truss, suspension bridge main cable, main tower, and pier. It was possible to establish a five-step standard procedure for drone safety inspections. The step-by-step contents of the standard procedure obtained as a result of this research are: Step 1, facility information collection and analysis, Step 2, analysis of vulnerable parts and drone flight planning, Step 3, drone photography and data processing, Step 4, condition evaluation by external inspection, Step 5, building of external inspection diagram and database. Therefore, if the safety inspections of civil engineering facilities including bridges are performed according to this standard procedure, it is expected that these inspection can be carried out more systematically and efficiently.