• 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.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.113-118
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• 2022

Motivated by people highlighting important phrases while reading or taking notes we propose a neural network training method by highlighting defective pixel areas to classify effectively defect types of images with complex background textures. To verify our proposed method we apply it to the problem of classifying the defect types of tire band fabric images that are too difficult to classify. In addition we propose a backlight highlighting technique which is tailored to the tire band fabric images. Backlight highlighting images can be generated by using both the GradCAM and simple image processing. In our experiment we demonstrated that the proposed highlighting method outperforms the traditional method in the view points of both classification accuracy and training speed. It achieved up to 13.4% accuracy improvement compared to the conventional method. We also showed that the backlight highlighting technique tailored for highlighting tire band fabric images is superior to a contour highlighting technique in terms of accuracy.

• The Korean Journal of Pain
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• v.35 no.4
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• pp.468-474
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• 2022

Background: The new type of corona virus has a wide range of symptoms. Some people who have COVID-19 can experience long-term effects from their infection, known as post-COVID conditions. The authors aimed to investigate prolonged musculoskeletal pain as a symptom of the post-COVID-19 condition. Methods: This is a descriptive study on the patients who were diagnosed with COVID-19 in a university hospital, between March 2020 and March 2021. Patient records and an extensive questionnaire were used to obtain relevant demographic and clinical characteristics, including hospitalization history, comorbidities, smoking history, duration of the pain, the area of pain, and the presence of accompanying neuropathic symptoms. Results: Of the diagnosed patients, 501 agreed to participate in the study. Among the participants, 318 had musculoskeletal pain during COVID-19 infection, and 69 of them reported prolonged pain symptoms as part of their a post-COVID condition which could not be attributed to any other cause. The mean duration of pain was 4.38 ± 1.73 months, and the mean pain level was 7.2 ± 4.3. Neuropathic pain symptoms such as burning sensation (n = 16, 23.2%), numbness (n = 15, 21.7%), tingling (n = 10, 14.5%), stinging (n = 4, 5.8%), freezing (n = 1, 1.4%) were accompanied in patients with prolonged musculoskeletal pain. Conclusions: Patients with COVID-19 may develop prolonged musculoskeletal pain. In some patients, neuropathic pain accompanies it. Awareness of prolonged post-COVID-19 pain is crucial for its early detection and management.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.336-344
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• 2021

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.91-96
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• 2022

At the current camera-based technology level, sensor-based basic life pattern recognition technology has to suffer inconvenience to obtain accurate data, and commercial band products are difficult to collect accurate data, and cannot take into account the motive, cause, and psychological effect of behavior. the current situation. In this paper, radar technology for life pattern recognition is a technology that measures the distance, speed, and angle with an object by transmitting a waveform designed to detect nearby people or objects in daily life and processing the reflected received signal. It was designed to supplement issues such as privacy protection in the existing image-based service by applying it. For the implementation of the proposed system, based on TI IWR1642 chip, RF chipset control for 60GHz band millimeter wave FMCW transmission/reception, module development for distance/speed/angle detection, and technology including signal processing software were implemented. It is expected that analysis of individual life patterns will be possible by calculating self-management and behavior sequences by extracting personalized life patterns through quantitative analysis of life patterns as meta-analysis of living information in security and safe guards application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.