• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.9-16
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• 2021

In this paper, we propose a building exploration drone that can be used for a digital twin-based building control system. The existing building control system using a fixed position sensor box has a problem that a management blind spot occurs. And because people patrol themselves, it takes a lot of human resources. In this paper, a drone equipped with a temperature and humidity sensor and a gas leak detection sensor is used to search the internal path of the building centering on the control blind spot. It also aims to solve the problem of the building control system by transmitting information in real time along with the video. In addition, it has a stable hovering function using an optical floor sensor and can be applied to an existing digital twin-based building control system. The results of this study are believed to be of great help in improving the quality of digital twin control systems using drones.

• Toxicological Research
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• v.35 no.3
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• pp.257-270
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• 2019

Silver nanoparticles (AgNPs) have been widely used in a variety of applications in innovative development; consequently, people are more exposed to this particle. Growing concern about toxicity from AgNP exposure has attracted greater attention, while questions about nanosilver-responsive genes and consequences for human health remain unanswered. By considering early detection and prevention of nanotoxicology at the genetic level, this study aimed to identify 1) changes in gene expression levels that could be potential indicators for AgNP toxicity and 2) morphological phenotypes correlating to toxicity of HepG2 cells. To detect possible nanosilver-responsive genes in xenogenic targeted organs, a comprehensive systematic literature review of changes in gene expression in HepG2 cells after AgNP exposure and in silico method, connection up- and down-regulation expression analysis of microarrays (CU-DREAM), were performed. In addition, cells were extracted and processed for transmission electron microscopy to examine ultrastructural alterations. From the Gene Expression Omnibus (GEO) Series database, we selected genes that were up- and down-regulated in AgNPs, but not up- and down-regulated in silver ion exposed cells, as nanosilver-responsive genes. HepG2 cells in the AgNP-treated group showed distinct ultrastructural alterations. Our results suggested potential representative gene data after AgNPs exposure provide insight into assessment and prediction of toxicity from nanosilver exposure.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.163-182
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• 2019

Analyzing the frequency and correlation of the news keywords in the modern society that are becoming complicated according to the time flow is a very important research to discuss the response and solution to issues. This paper analyzed the relationship between the flow of social keyword and major issues through the analysis of news big data for 10 years (2009~2018). In this study, political issues, education and social culture, gender conflicts and social problems were presented as major issues. And, to study the change and flow of issues, it analyzed the change of the issue by dividing it into five years. Through this, the changes and countermeasures of social issues were studied. As a result, the keywords (economy, police) that are closely related to the people's life were analyzed as keywords that are very important in our society regardless of the flow of time. In addition, keyword such as 'safety' have decreased in increasing rate compared to frequency in recent years. Through this, it can be inferred that it is necessary to improve the awareness of safety in our society.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.7-13
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• 2017

This study will provide a practical plan of engineering education through the study of major activities connected with the production of works to accomplish the graduation conditions by completing the comprehensive design subject and the result of the performance. The designed subject is to measure the minimum safety distance during driving using the obstacle detection function of the ultrasonic sensor and to perform the avoidance algorithm based on the measurement value of the acceleration gyro sensor. It is proposed an access surveillance system that minimizes the damage of drones, surrounding objects, and people, and improves air mobility. Experimental results show that the obstacles around the drone are detected by five ultrasonic sensors and the difference of output value is applied to each motor of the drone and obstacle avoidance is confirmed. In addition, the content and level of the data for measuring the achievement of learning achievement in the engineering education certification program were used and the results were confirmed to be consistent with the description of the engineering problem level required for the graduates of 4-year engineering college.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1337-1342
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• 2019

Medical institutions face the challenge of securing competitiveness among medical institutions due to the rapid spread of ICT convergence, and managing data that is growing at an enormous rate due to the emergence of big data and the emergence of the Internet of Things. The big data paradigm of the medical community is not just about large data or tools and processes for processing and analyzing it, but also means a computerized shift in the way people live, think and study. As the medical data is recently released, the demand for the use of medical data is increasing. Therefore, the research on disease detection system based on public data using open source that can help rational and efficient decision making was conducted. As a result of the experiment, unlike a simple disease inquiry or a symptom inquiry about a single disease provided by a public institution, related diseases are searched by a symptom or a cause.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.49-54
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• 2021

With the recent development of smart healthcare technology, interest in daily diseases is increasing. However, healthcare data has an imbalance between positive and negative data. This is caused by the difficulty of collecting data because there are relatively many people who are not patients compared to patients with certain diseases. Data imbalances need to be adjusted because they affect performance in ongoing learning during disease prediction and analysis. Therefore, in this paper, We replace missing values through multiple imputation in detection models to determine whether they are prevalent or not, and resolve data imbalances through over-sampling. Based on AutoML using preprocessed data, We generate several models and select top 3 models to generate ensemble models.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.17-26
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• 2021

Recently, COVID-19 has spread and time to stay at home has been increasing in accordance with quarantine guidelines of the government such as recommendations to refrain from going out. As a result, the number of single-person households staying at home is also increasingsingle-person households are less likely to be notified to the outside world in times of emergency than multi-person households. This study collects various situations occurring in the home with lidar, image, and voice sensors and analyzes the data according to the sensors through their respective algorithms. Using this method, we analyzed abnormal patterns such as emergency situations and conducted research to detect abnormal signs in humans. Artificial intelligence algorithms that detect abnormalities in people by each sensor were studied and the accuracy of anomaly detection was measured according to the sensor. Furthermore, this work proposes a fusion method that complements the pros and cons between sensors by experimenting with the detectability of sensors for various situations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.9
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• pp.771-780
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• 2021

The space Debris laser ranging system is called to be a definite type of satellite laser ranging system that measures the distance to satellites. It is a system that performs POD (Precise Orbit Determination) by measuring time of flight by firing a laser. Distance precision can be measured in mm-level units, and it is the most precise system among existing systems. Currently, KASI has built SLR in Sejong and Geochang, and utilized SLR data to verify the precise orbits of the STSAT-2C and KOMASAT-5. In recent years, due to the fall or collision of space debris, its satellites have been threatened, and in terms of security, laser tracking of space objects is receiving great interest in order to protect their own space assets and protect the safety of the people. In this paper, a 1.5m-class main mirror was applied for the system design of a multipurpose laser tracking system that considers satellite laser ranging and space object laser tracking. System preliminary performance analysis was performed based on Link Budget analysis considering specifications of major components.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.33 no.1
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• pp.16-23
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• 2022

Objectives: Early detection of developmental issues in infants and necessary intervention are important. To identify the comorbid conditions, a comprehensive evaluation is required. The study's objectives were to 1) generate scale items by identifying and eliciting concepts relevant to young children (12-71 months) with developmental delays, 2) develop a comprehensive screening tool for developmental delay and comorbid conditions, and 3) assess the tool's validity and cut-off. Methods: Multidisciplinary experts devised the "Infant Comprehensive Evaluation for Neurodevelopmental Delay (ICEND)," an assessment method that comes in two versions depending on the age of the child: 12-36 months and 37-71 months, through monthly seminars and focused group interviews. The ICEND is composed of three parts: risk factors, resilience factors, and clinical scales. In parts 1 and 2, there were 41 caretakers responded to the questionnaires. Part 3 involved clinicians evaluating ten subscales using 98 and 114 questionnaires for younger and older versions, respectively. The Child Behavior Checklist, Strengths and Difficulties Questionnaire, Infant-Toddler Social Emotional Assessment, and Korean Developmental Screening Test for Infants and Children were employed to analyze concurrent validity with the ICEND. The analyses were performed on both typical and high-risk infants to identify concurrent validity, reliability, and cut-off scores. Results: A total of 296 people participated in the study, with 57 of them being high-risk (19.2%). The Cronbach's alpha was positive (0.533-0.928). In the majority of domains, the ICEND demonstrated a fair discriminatory ability, with a sensitivity of 0.5-0.7 and specificity 0.7-0.9. Conclusion: The ICEND is reliable and valid, indicating its potential as an auxiliary tool for assessing neurodevelopmental delay and comorbid conditions in children aged 12-36 months and 37-71 months.