• The Journal of the Society of Korean Medicine Diagnostics
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• v.14 no.2
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• pp.43-50
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• 2010

Background and Objectives: Cardiff Acne Disability Index(CADI) is one tool used internationally to measure the quality of life of acne sufferers. There, however, is a necessity of developing Korean version of CADI, as the questionnaires of the original one are written in English, making it hard to apply for Korean patients. So as a first step, we conducted a cross-cultural translation of CADI into Korean and verification of face validity. Methods: After properly translating CADI questionnaires into Korean up to guidelines for cross-cultural adaptation of health related quality-of-life measures, we conducted a survey with 122 undergraduates to get face validity, using the translated questionnaires. Results: About the translated CADI questionnaires, 86 out of 107 undergraduates replied that they had no difficulty understanding them, while 21 offered ideas about ambiguous expressions of them. Upon further examination of two oriental doctors, two sentences were additionally modified in the translated version. Conclusions: Firstly, we created the Korean version of CADI, one of the most effective methods in the world to measure acne sufferers' quality of life, by properly translated the original version into Korean. Then we conducted a survey for face validity with the translated questionnaires and gathered opinions from those questioned. After going through some examining and correcting procedures based on the opinions, we finalized the Korean version of CADI. It will also require a follow-up verification process to prove credibility and validity of the final version of Korean CADI.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.74-85
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• 2019

The purpose of this study was to analyze the vocational rehabilitation service environment of the mentally facilities. Focus group interview (FGI) was conducted for obtain the data. According to the interview results, first, in providing vocational rehabilitation services for people with mental illness, comprehensive services should be provided considering various needs. Second, the facilities are not recognized as vocational rehabilitation facilities for people with disabilities. Therefore, it is operated according to the regulations on mental rehabilitation facilities in the Mental Health Welfare Act. Third, they need management supports. Finally, It raised the necessity of revising laws and systems that are not systematized without reflecting reality. Based on the results of this study, some suggestions are presented.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.613-625
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• 2019

The rise of the Internet of Things (IoT) devices have greatly influenced many industries and one of them is healthcare where wearable devices started to track all your daily activities for better health monitoring accuracy and even down to tracking daily food intake in some cases. With the amounts of data that are being tracked and shared between from these devices, questions were raised on how to uphold user's data privacy when data is shared between these IoT devices and third party. With the blockchain platforms started to mature since its inception, the technology can be implemented according to a variety of use case scenarios. In this paper, we present a system architecture based on the healthcare system and IoT network by leveraging on multiple blockchain networks as the medium in between that should enable users to have direct authority on data accessibility of their shared data. We provide proof of concept implementation and highlight the results from our testing to show how the efficiency and scalability of the healthcare system improved without having a significant impact on the performance of the Electronic Medical Record (EMR) that mostly affected by the previous solution since these solutions directly connected to a public blockchain network and which resulted in significant delays and high cost of operation when a large amount of data or complicated functions are involved.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.93-102
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• 2019

As interest in wellness grows, There is a lot of research about monitoring individual health using wearable devices. Accordingly, a variety of methods have been studied to distinguish exercise from daily activities using wearable devices. Most of these existing studies are machine learning methods. However, there are problems with over-fitting on individual person's learning, data discontinuously recognition by independent segmenting and fake activity. This paper suggests a detection method for exercise activity based on the physiological response principle of heart rate up and down during exercise. This proposed method calculates activity intensity and heart rate from triaxial and photoplethysmography sensor to determine a heart rate recovery, then detects exercise by estimating activity intensity or detecting a heart rate rising state. Experimental results show that our proposed algorithm has 98.64% of averaged accuracy, 98.05% of averaged precision and 98.62% of averaged recall.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.11-17
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• 2020

In order to guarantee safe storage and transportation of a flammable liquid solution, it is very important to know its flash point information. In this paper, flash points of n-octane+n-nonane system and n-nonane+n-decane system were measured by Seta flash apparatus and an empirical equation is proposed for the accurate estimation of flash point. Empirical equation is used to predict flash point of n-octane+n-nonane system and n-nonane+n-decane system, which were also compared to Unifac-based model. Absolute average errors of flash point data predicted by Unifac-based model are 0.7℃ and 0.6℃ for n-octane+n-nonane system and n-nonane+n-decane system, respectively. Absolute average errors of flash point data predicted by empirical equation are 0.2℃ and 0.4℃ for n-octane+n-nonane system and n-nonane+n-decane system, respectively. In conclusion, empirical equation proposed in this paper, presented the most satisfactory.

• Nutrition Research and Practice
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• v.15 no.2
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• pp.235-247
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• 2021

BACKGROUND/OBJECTIVES: Increasing the consumption of vegetables and fruits in Mexico remains a challenge. Promoting sustainable food production systems through schools may be an effective way to educate young people about food and nutrition issues. A study of nutritional education in adolescents, based on the school garden, is necessary in order to evaluate its effects on the consumption of fruits and vegetables among middle- and upper-income segments of the population. The objective of this study was to evaluate the effect of an educational intervention, accompanied by a school garden as an educational teaching tool, to improve vegetable and fruit consumption by Mexican teenagers attending a private middle/high school. SUBJECTS/METHODS: Teenagers between 12 and 18 years of age (n = 126) attending a private middle/high school in Queretaro, Mexico participated in a 3-arm, controlled, comparative impact study using a vegetable and fruit consumption frequency questionnaire, food consumption diaries, a psychosocial factor assessment questionnaire of vegetable and fruit consumption, and structured interviews. The participants were randomized into 3 experimental groups: 1) food education + school garden (FE + SG), 2) FE only, and 3) control group (CG). RESULTS: The FE + SG and FE groups significantly increased the frequency and daily intake of vegetables and fruits compared to the CG. The FE + SG group showed greater understanding of, reflection upon, and analysis of the information they received about vegetable and fruit consumption, as well as a greater willingness to include these in their daily diet. CONCLUSIONS: FE accompanied by a SG as a teaching tool is more effective at promoting vegetable and fruit consumption than either education alone or control in teenagers in middle-upper income segments of the population.

• PNF and Movement
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• v.18 no.3
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• pp.365-373
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• 2020

Purpose: The COVID-19 pandemic has changed how healthcare is delivered worldwide and has affected the environment of the healthcare. Physical therapy in traditional healthcare systems can be difficult in unusual circumstances, such as the COVID-19 pandemic. Tele-physical therapy, defined as "the delivery of the physical therapy at a distance using electronic information and telecommunication technologies," will be a solution for this healthcare crisis. Thus, in this study, we proposed a mobile application for tele-physical therapy. Methods: This study used the Chrome Browser version 83.0.4 based on the Windows 10 64Bit operating system to use the App Inventor. To operate the mobile application, we used the Samsung Galaxy Note 9. The design of the mobile application was based on the review of a system used in the physical therapy department. Results: The graphical user interface (GUI) of the mobile application was displayed on three screens: selecting a painful joint (1st screen of the GUI); selecting a painful movement of the joint (2nd screen of the GUI); a self-manual therapy method and movie (3rd screen of the GUI). The proposed mobile application showed the stable repeatability of the self-manual therapy movie. Conclusion: The results of this study demonstrated that the proposed mobile application using the App Inventor for android will be able to create easy to use and reliable tele-physical therapy.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.