• Physical Therapy Korea
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• v.28 no.2
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• pp.123-131
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• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.

• Journal of the Korean Institute of Navigation
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• v.20 no.4
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• pp.81-98
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• 1996

The purpose of this study was analysis of the muscle action in physical exercise in the rolling machine. The rolling machine moved by eletric power-driven was made to keep the constant cycle and size of rolling. The subjects of this study consist of 4 seaman(SM) and 4 landman (LM). The experiment analyzed the muscle power of lower and upper limbs by Intergrated Electromyogram(IEMG). The measurement was made on the ground, and 6 and 8 degrees of rolling separately. This study concludes as follows ; including analysis of IEMG of heavy exercise in two hands curl, a standstill walking and just standing. 1. IEMG of the lower limbs when standing. 1) In 6 degrees of rolling, for the landman(LM), vastus medialis m.(9.73), vastus lateralis m.(9.55), and rectus femores m.(8.73) acted more. As for the seaman(SM), tibialis anterior m.(5.38), biceps femores m.(5.05), and gastrocnemius m.(4.47) acted more. 2) In 8 degrees of rolling, in common, for both LM and SM, it were vastus medialis m.(11.20 and 8.97), vastus lateralis m.(16.20 and 4.63), and tibialis anterior m.(5.13 and 4.47). 3) It was showed that IEMG of LM was larger than that of SM. 2. IEMG of the lower limbs when walking. 1) On the ground, for the LM, gastrocnemius m.(7.08), vastus medialis m.(6.65), and vastus latralis m.(6.60) acted more. As for the SM, vastus lateralis m.(7.08), vastus medialis m.(6.58) and restus femores m.(5.10) acted more. 2) In both 8 and 6 degrees of rolling, vastus medials m.(14.50 and 11.98), vastus lateralis m.(10.10 and 14.10), and gastrocnemius m.(11.75 and 7.10) acted more in two groups. 3) It was showed that IEMG of LM was larger than that of SM. 3. IEMG of the lower limbs when heavy exercise(two hands curl). 1) On the ground, for the LM, vastus lateralis m.(21.68), vastus medialis m.(16.08), and rectus femores m.(14.08) acted more. As for the SM, tibialis anterior m.(16.08), vastus medialis m.(14.58), and vastus lateralis m.(8.78) acted more. 2) In 8 and 6 dgrees of rolling, it were vastus medialis m.(17.05 and 12.45), vastus lateralis m.(37.98 and 17.08), and tibialis anterior m.(19.83 and 13.20). 3) It was showed that IEMG of LM was larger than that of SM. 4. IEMG of the upper limbs when heavy exercise. 1) On the ground, the brachialis m.(44.30 and 17.80), and biceps brachii m.(13.40 and 25.10) acted more in two groups. 2) In both 6 and 8 degrees of rolling, the brachialis m.(37.60 and 24.35), and biceps brachii m.(11.38 and 7.97) acted more in two groups. 3) It was showed that IEMG of SM was larger than that of LM.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.854-861
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• 2005

The functional behavior of a portable consumer electronic (PCE) product is nearly all expressed with human-machine interaction (HMI) tasks. Although physical prototyping and computer aided design (CAD) software can show the appearance of the product, they cannot properly reflect its functional behavior. In this paper, we propose a virtual prototyping (VP) system that incorporates virtual reality and HMI functional simulation in order to enables users to capture not only the realistic look of a PCE product but also its functional behavior. We obtain geometric part models of the product and their assembly and kinematics information with the help of CAD and reverse engineering tools, and visualize them with various display tools. We adopt state transition methodology to capture the HMI functional behavior of the product into a state transition chart, which is later used to construct a finite state machine (FSM) for the functional simulation of the product. The FSM plays an important role to control the transition between states of the product. The proposed VP system receives input events such as mouse clicks on buttons and switches of the virtual prototype model, and it reacts to the events based on the FSM by activating associated activities. The VP system provides the realistic visualization of the product and the vivid simulation of its functional behavior. It can easily allow users to perform functional evaluation and usability testing. Moreover, it can greatly reduce communication errors occurring in a typical product development process. A case study about VP of an MP3 player is given to show the usefulness of the proposed VP system.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.572-577
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• 2017

Virtualization allows multiple virtual machines to share the resources of a physical machine in order to utilize idle resources. The purpose of virtualization is the efficient allocation of resources among virtual machines. However, the efficient allocation of resources is difficult because the workload characteristics of each virtual machine cannot be understood in the current virtualization environment. This causes performance interference among virtual machines, which leads to performance degradation of the virtual machine. Previous works have been carried out to develop a method of solving such performance interference. This paper introduces a representative method, a CPU scheduling method that guarantees I/O performance by using feedback control to solve performance interference. In addition, we compare and analyze a model-based feedback control method and a dynamic feedback control method.

• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.165-172
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• 2021

Breaking wave is one of the important design factors in the design of coastal and port structures as they are directly related to various physical phenomena occurring on the coast, such as onshore currents, sediment transport, shock wave pressure, and energy dissipation. Due to the inherent complexity of the breaking wave, many empirical formulas have been proposed to predict breaker indices such as wave breaking height and breaking depth using hydraulic models. However, the existing empirical equations for breaker indices mainly were proposed via statistical analysis of experimental data under the assumption of a specific equation. In this study, a new Munk-type empirical equation was proposed to predict the height of breaking waves based on a representative linear supervised machine learning technique with high predictive performance in various research fields related to regression or classification challenges. Although the newly proposed breaker height formula was a simple polynomial equation, its predictive performance was comparable to that of the currently available empirical formula.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.995-1003
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• 2021

As the need for artificial intelligence (AI) education, which will become the core of the upcoming intelligent information society rises, the national level is also focusing attention by including artificial intelligence-related content in the curriculum. In this study, the PASPA education program was presented to enhance students' creative problem-solving ability in the process of solving problems in daily life through supervised machine learning. And Micro:bit, a physical computing tool, was used to enhance the learning effect. The teaching and learning process applied to the PASPA education program consists of five steps: Problem Recoginition, Argument, Setting data standard, Programming, Application and evaluation. As a result of applying this educational program to students, it was confirmed that the creative problem-solving ability improved, and it was confirmed that there was a significant difference in knowledge and thinking in specific areas and critical and logical thinking in detailed areas.

• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.