• Journal of Nutrition and Health
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• v.50 no.6
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• pp.552-564
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• 2017

Purpose: The purpose of this study was to measure energy expenditure (EE) the metabolic equivalents (METs) of 13 common physical activities by using a portable telemetry gas exchange system ($K4b^2$) and to assess the accuracy of the accelerometer (Actigraph $GT3X^+$) by gender in Korean adults. Methods: A total of 109 adults (54 males, 55 females) with normal BMI (body mass index) participated in this study. EE and METs of 13 selected activities were simultaneously measured by the $K4b^2$ portable indirect calorimeter and predicted by the $GT3X^+$ Actigraph accelerometer. The accuracy of the accelerometer was assessed by comparing the predicted with the measured EE and METs. Results: EE (kcal/kg/hr) and METs of treadmill walking (3.2 km/h, 4.8 km/h and 5.6 km/h) and running (6.4 km/h) were significantly higher in female than in male participants (p < 0.05). On the other hand, the accelerometer significantly underestimated the EE and METs for all activities except descending stairs, moderate walking, and fast walking in males as well as descending stairs in females. Low intensity activities had the highest rate of accurate classifications (88.3% in males and 91.3% females), whereas vigorous intensity activities had the lowest rate of accurate classifications (43.6% in males and 27.7% in females). Across all activities, the rate of accurate classification was significantly higher in males than in females (75.2% and 58.3% respectively, p < 0.01). Error between the accelerometer and $K4b^2$ was smaller in males than in females, and EE and METs were more accurately estimated during treadmill activities than other activities in both males and females. Conclusion: The accelerometer underestimated EE and METs across various activities in Korean adults. In addition, there appears to be a gender difference in the rate of accurate accelerometer classification of activities according to intensity. Our results indicate the need to develop new accelerometer equations for this population, and gender differences should be considered.

• Tuberculosis and Respiratory Diseases
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• v.48 no.1
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• pp.54-66
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• 2000

Background: Pneumoconiosis, like other chronic respiratory diseases, is essentially incurable and, for many, progressive. While improved survival time is an important aim of treatment, there is growing recognition that for some people, improving the quality of life is more important than extending the length of life. Currently the measurement of the quality of life is used to assess the efficacy of therapeutic agents. Methods: Sixty-three pnemoconiotics who were admitted to St. Mary's Hospital between April and August 1999 were interviewed using COOP charts, Chronic Respiratory Questionnaire(CRQ) and Pneumoconiotic Respiratory Questionnaire(PRQ), a newly developed questionnaire concerning clinical and socioeconomic features of pneumoconiotics. Also, ILO classification of the chest film, pulmonary function test, and arterial blood gas analysis of the patients were evaluated. The scores between Industrial Accident Compensation Insurance(IACI) covered and uncovered patients and between clinically stable and unstable patients were compared. Results: Domains of CRQ and PRQ showed a high internal consistency reliability($\alpha$=0.86-0.89, 0.77-0.81) except the dyspnea domain($\alpha$=0.63) of CRQ. The scores on the CRQ and PRQ showed statistically significant correlations with the results of COOP charts, pulmonary function test and arterial blood gas analysis. The dyspnea domain and social activity domain of the PRQ showed significant difference between IACI covered and uncovered patients and between clinically stable and unstable patients. Conclusion : Korean translation of the Chronic Respiratory Questionnaire and the newly developed Pneumoconiotic Respiratory Questionnaire are reliable and valid methods and are likely to be useful in measuring the quality of life in patients with the chronic respiratory disease including pneumoconiosis.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.19 no.5
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• pp.446-452
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• 1986

This paper presents the composition of neutral and polar lipids obtained from puple shell, Rapana venosa and the abalone, Haliotis discus hannai. The fatty acid composition and the classification of neutral lipids from two species were determined by gas chromatography (GLC) and thin layer chromatography (TLC). Total lipid contents of samples were $0.5\%$ in purple shell and $0.4\%$ in the abalone. The predominant fatty acids of total lipids were eicosapentaenoic acid ($19.30\%$). eicosenoic acid ($12.10\%$) and palmitic acid ($11.77\%$) in the purple shell, and palmitic acid ($21.29\%$), oleic acid ($14.55\%$) and linoleic acid ($14.21\%$) in the abalone. The lipid composition of non-polar lipid fractions in purple shell and abalone was separated and identified as free sterol, free fatty acid, triglyceride and hydrocarbon & esterified sterol by TLC. The contents of triglyceride from both neutral lipids were shown more abundant than any other subclasses. The main fatty acids of neutral lipids were eicosapentaenoic acid ($18.6\%$), palmitic acid ($14.90\%$) and eicosenoic acid ($14.76\%$) in the purple shell, and palmitic acid ($28.12\%$), oleic acid($20.5\%$) and myristic acid ($12.5\%$) in the abalone. Eicosapentaenoic acid ($17.57\%$), stearic acid ($13.26\%$) and eicosatetraenoic acid ($11.24\%$) were important fatty acids of glycolipid in the purple shell, and myristic acid ($12.75\%$), stearic acid ($12.10\%$) and eicosatetraenoic acid ($10.64\%$) in the abalone. The major fatty acids of phospholipids were eicosapentaenoic acid ($20.18\%$), palmitic acid ($11.26\%$) and eicosenoic acid ($10.90\%$) in the purple shell, and palmitic acid ($21.10\%$), eicosapentaenoic acid ($12.90\%$) and oleic acid($11.13\%$) in the abalone.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1092-1099
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• 2022

Research on exhaust aftertreatment devices to reduce air pollutants and greenhouse gas emissions is being actively conducted. However, in the case of the particulate matters/nitrogen oxides (PM/NOx) simultaneous reduction device for ships, the problem of back pressure on the diesel engine and replacement of the filter carrier is occurring. In this study, for the optimal design of the integrated device that can simultaneously reduce PM/NOx, an appropriate standard was presented by studying the flow inside the device and change in back pressure through the inlet/outlet pressure. Ansys Fluent was used to apply porous media conditions to a diesel particulate filter (DPF) and selective catalytic reduction (SCR) by setting porosity to 30%, 40%, 50%, 60%, and 70%. In addition, the ef ect on back pressure was analyzed by applying the inlet velocity according to the engine load to 7.4 m/s, 10.3 m/s, 13.1 m/s, and 26.2 m/s as boundary conditions. As a result of a computational fluid dynamics analysis, the rate of change for back pressure by changing the inlet velocity was greater than when inlet temperature was changed, and the maximum rate of change was 27.4 mbar. This was evaluated as a suitable device for ships of 1800kW because the back pressure in all boundary conditions did not exceed the classification standard of 68mbar.

• Journal of the Korean Chemical Society
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• v.67 no.5
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• pp.378-392
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• 2023

The purpose of this study is to analyze the pre-service chemistry teachers' cognition of the nature of model in process of designing and developing models using technology. For this purpose, 19 pre-service chemistry teachers' in the 3rd grade of a education college located in the central region observe experimental phenomena related to Boyle's law presented in the 7th grade science textbook and researchers required the design and development of a model related to the observed experimental results using technology. Based on previous studies, the nature of model were classified into two aspect: 'Representational aspect' and 'Explanatory aspect'. The 'Representational aspect' was classified into 'Representation', 'Abstraction', and 'Simplification', and the 'Explanatory aspect' was classified into 'Analysis', 'Interpretation', 'Reasoning', 'Explanation', and 'Quantification'. The pre-service chemistry teachers' cognition were analyzed by the classification. As a result of the study, the 'Representation' of the 'expressive aspect' was uniformized in the form of space that changes in volume, and the pressure was expressed as the Brightness inside the cylinder or frequency of color change of particles for 'Abstraction'. In the case of 'Simplification', the particle collision was expressed as a perfectly elastic collision, but there was a group that could not simply indicate the type of particle. In the 'Explanatory aspect', in the case of 'Analysis', volume was classified as a manipulated variable, and in the case of 'Interpretation', most groups analyzed the change in pressure through the collision of gas particles. However, the cognition involved in 'Reasoning' was not observed much. In the case of 'Explanation', there were groups that did not succeed in explanation because the area where the particles collided was not set or incorrectly set, and in the case of 'Quantification', there was a group that formulated the number of collisions per unit time, and on the contrary, there was a group that could not quantify the number of collisions because they could not be expressed in numbers.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of Nutrition and Health
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• v.48 no.2
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• pp.180-191
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• 2015

Purpose: There is a lack of data on the energy cost of children's everyday activities, adult values are often used as surrogates. In addition, the influence of body weight on the energy cost of activity when expressed as metabolic equivalents (METs) has not been vigorously explored. Methods: In this study 20 elementary school students 9~12 years of age completed 18 various physical activities while energy expenditure was measured continuously using a portable telemetry gas exchange system ($K_4b^2$, Cosmed, Rome, Italy). Results: The average age was 10.4 years and the average height and weight was 145.1 cm and 43.6 kg, respectively. Oxygen consumption ($VO_2$), energy expenditure and METs at the time of resting of the subjects were 5.41 mL/kg/min, 1.44 kcal/kg/h, and 1.5 METs, respectively. METs values by 18 physical activities were as follows: Homework and reading books (1.6 METs), playing game with a mobile phone or video while sitting (1.6 METs), watching TV while sitting on a comfortable chair (1.7 METs), playing video game or mobile phone game while standing (1.9 METs), sweeping a room with a broom (2.7 METs) and playing a board game (2.8 METs) belong to light intensity physical activities. By contrary, speedy walking and running were 6.6 and 6.7 METs, respectively, which belong to high intensity physical activities over 6.0 METs. When the effect of body weight on physical activity energy expenditure was determined, $R^2$ values increased with 0.116 (playing a game at sitting), 0.176 (climbing up and down stairs), 0.246 (slow walking), and 0.455 (running), which showed that higher activity intensity increased explanation power of body weight on METs value. Conclusion: This study is important for direct evaluation of energy expenditure by physical activities of children, and it could be used directly for revising and complementing the existing activity classification table to fit for children.