• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.9
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• pp.1265-1273
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• 2012

The purpose of this study was to investigate the BMI, intake of snacks, and perceptions and use of food and nutrition labels by middle school students (144 boys and 189 girls) in Chuncheon area. The average height and weight of boys were $171.0{\pm}6.4$ cm and $61.0{\pm}11.4$ kg, respectively, whereas those of girls were $160.0{\pm}4.8$ cm and $50.8{\pm}6.6$ kg, respectively. Average body mass index (BMI) of boys and girls were $20.8{\pm}3.3$ and $19.8{\pm}2.4$, respectively (p<0.01). Dietary intake attitude score of girls ($34.39{\pm}5.66$) was higher than that of boys ($33.92{\pm}5.40$) (p<0.05). Subjects bought and ate snacks 1 to 3 times per week (40.2%) by themselves, and most consumed snacks were cookies (23.1%), instant noodles (16.2%), ice cream (13.2%), and candy and chocolates (13.2%). The most important factor in purchasing of snacks was 'taste' ($4.49{\pm}0.67$). When subjects bought processed foods, the rates of reading food labels was 86.6%. The most important factor of the food labels was 'expiration date' (42.9%). The degree of reading food labels on processed foods by girls ($22.70{\pm}5.72$) was higher than that of boys ($20.96{\pm}5.35$) (p<0.01). Of the 13.2% of subjects that did not read food labels, the reason why was that they were not interested (50.0%). Of the 78.4% of subjects that read nutrition labels, the most important component of the nutrition labels was 'calories' (75.9%). The main reason for reading nutrition labels was 'to control weight' (45.6%). In general, use of food labels correlated positively with dietary intake attitude score (p<0.05) and use of nutrition labels (p<0.01). Using multiple regression analysis, we found that 'usefulness of dietary life' was the most significant variable that affects the importance of food and nutrition labels. Therefore, development of an educational program on food and nutrition labels for adolescents will be effective in improving dietary life.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1041-1043
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• 1993

Fuzzy logic based Control Theory has gained much interest in the industrial world, thanks to its ability to formalize and solve in a very natural way many problems that are very difficult to quantify at an analytical level. This paper shows a solution for treating membership function inside hardware circuits. The proposed hardware structure optimizes the memoried size by using particular form of the vectorial representation. The process of memorizing fuzzy sets, i.e. their membership function, has always been one of the more problematic issues for the hardware implementation, due to the quite large memory space that is needed. To simplify such an implementation, it is commonly [1,2,8,9,10,11] used to limit the membership functions either to those having triangular or trapezoidal shape, or pre-definite shape. These kinds of functions are able to cover a large spectrum of applications with a limited usage of memory, since they can be memorized by specifying very few parameters ( ight, base, critical points, etc.). This however results in a loss of computational power due to computation on the medium points. A solution to this problem is obtained by discretizing the universe of discourse U, i.e. by fixing a finite number of points and memorizing the value of the membership functions on such points [3,10,14,15]. Such a solution provides a satisfying computational speed, a very high precision of definitions and gives the users the opportunity to choose membership functions of any shape. However, a significant memory waste can as well be registered. It is indeed possible that for each of the given fuzzy sets many elements of the universe of discourse have a membership value equal to zero. It has also been noticed that almost in all cases common points among fuzzy sets, i.e. points with non null membership values are very few. More specifically, in many applications, for each element u of U, there exists at most three fuzzy sets for which the membership value is ot null [3,5,6,7,12,13]. Our proposal is based on such hypotheses. Moreover, we use a technique that even though it does not restrict the shapes of membership functions, it reduces strongly the computational time for the membership values and optimizes the function memorization. In figure 1 it is represented a term set whose characteristics are common for fuzzy controllers and to which we will refer in the following. The above term set has a universe of discourse with 128 elements (so to have a good resolution), 8 fuzzy sets that describe the term set, 32 levels of discretization for the membership values. Clearly, the number of bits necessary for the given specifications are 5 for 32 truth levels, 3 for 8 membership functions and 7 for 128 levels of resolution. The memory depth is given by the dimension of the universe of the discourse (128 in our case) and it will be represented by the memory rows. The length of a world of memory is defined by: Length = nem (dm(m)＋dm(fm) Where: fm is the maximum number of non null values in every element of the universe of the discourse, dm(m) is the dimension of the values of the membership function m, dm(fm) is the dimension of the word to represent the index of the highest membership function. In our case then Length=24. The memory dimension is therefore 128*24 bits. If we had chosen to memorize all values of the membership functions we would have needed to memorize on each memory row the membership value of each element. Fuzzy sets word dimension is 8*5 bits. Therefore, the dimension of the memory would have been 128*40 bits. Coherently with our hypothesis, in fig. 1 each element of universe of the discourse has a non null membership value on at most three fuzzy sets. Focusing on the elements 32,64,96 of the universe of discourse, they will be memorized as follows: The computation of the rule weights is done by comparing those bits that represent the index of the membership function, with the word of the program memor . The output bus of the Program Memory (μCOD), is given as input a comparator (Combinatory Net). If the index is equal to the bus value then one of the non null weight derives from the rule and it is produced as output, otherwise the output is zero (fig. 2). It is clear, that the memory dimension of the antecedent is in this way reduced since only non null values are memorized. Moreover, the time performance of the system is equivalent to the performance of a system using vectorial memorization of all weights. The dimensioning of the word is influenced by some parameters of the input variable. The most important parameter is the maximum number membership functions (nfm) having a non null value in each element of the universe of discourse. From our study in the field of fuzzy system, we see that typically nfm 3 and there are at most 16 membership function. At any rate, such a value can be increased up to the physical dimensional limit of the antecedent memory. A less important role n the optimization process of the word dimension is played by the number of membership functions defined for each linguistic term. The table below shows the request word dimension as a function of such parameters and compares our proposed method with the method of vectorial memorization[10]. Summing up, the characteristics of our method are: Users are not restricted to membership functions with specific shapes. The number of the fuzzy sets and the resolution of the vertical axis have a very small influence in increasing memory space. Weight computations are done by combinatorial network and therefore the time performance of the system is equivalent to the one of the vectorial method. The number of non null membership values on any element of the universe of discourse is limited. Such a constraint is usually non very restrictive since many controllers obtain a good precision with only three non null weights. The method here briefly described has been adopted by our group in the design of an optimized version of the coprocessor described in [10].

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.11
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• pp.1649-1657
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• 2016

The purpose of this study was to investigate the body mass index (BMI), dietary intake attitudes, and nutrient intake status according to sugar-containing food intake frequency of 409 college students in Gyeonggi-do. Subjects were categorized into three groups according to sugar-containing food intake frequency: rare intake group (n=113), average intake group (n=195), and frequent intake group (n=101). The average height and weight (P<0.001) of each group were $163.8{\pm}0.11cm$ and $52.9{\pm}8.6kg$, $164.4{\pm}0.1cm$ and $56.2{\pm}6.4kg$, and $167.9{\pm}0.1cm$ and $68.0{\pm}15.7kg$, respectively. The average BMIs of the groups were $19.6{\pm}2.3$, $20.7{\pm}0.8$, and $24.0{\pm}2.7$, respectively (P<0.001). Dietary intake attitude scores of the groups were $30.73{\pm}5.68$, $30.11{\pm}5.51$, and $28.00{\pm}5.31$, respectively (P<0.001). As a result of nutrient intake status, daily averages of energy and carbohydrate intake of the frequent intake group were significantly higher than those of the rare intake group (P<0.05). On the other hand, vitamin A, vitamin $B_1$, and vitamin C intakes of the rare intake group were significantly higher than those of the frequent intake group (P<0.05). Using multiple regression analysis, we found that BMI was the most significant variable affecting sugar-containing food intake. Therefore, nutrition education is necessary to improve nutrient intake while considering sugar intake for maintenance of healthy weight.

• Journal of agricultural medicine and community health
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• v.30 no.2
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• pp.167-182
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• 2005

Objectives: This study was performed to investigate the prevalence of knee osteoarthritis according to the criteria of diagnosing knee osteoarthritis in rural women and the factors related with this disease. Methods: The data obtained from 200 women older than 40 years of age residing in 5 Ri's in Goryeong-gun. Gyeongsanbuk-do by random cluster sampling from September to October 2002. Knee osteoarthritis was determined positive according to the Kellgren and Lawrence classification and knee pain. Results: Among these subjects, 71.0% showed more than grade 2 in radiologic finding and the rate of knee pain according to the survey was 67.0%. The rate of subjects meeting the criteria of knee osteoarthritis was 54.0%. According to univariate analysis, the prevalence of knee osteoarthritis increased with age and those farming people and people working in household industry was significantly high at 58.9% compared with others. The prevalence of knee osteoarthritis showed a significant relationship with the family history and past history of knee injury and knee surgery(p<0.01), and diabetes mellitus(p<0.05). The score of ADL was significantly different in the subjects with knee osteoarthritis compared with normal group(p<0.05). When the presence of knee osteoarthritis and the period of the life style of seating down on the floor were compared, a significant difference was present between the osteoarthritis group and normal group. As for metabolic factors, the blood sugar level, bone density, and body mass index(BMI) were significantly different in the osteoarthritis group compared with normal group. When multiple logistic regression analysis was performed with the presence of knee osteoarthritis as the dependent variable, the prevalence of knee osteoarthritis was significantly affected by older age, subjects farming or working in household industry, the history of knee injury, the history of surgery, higher blood sugar level, and higher BMI. Conclusions: These subjects need an intervention through self-care programs such as exercise for preventing osteoarthritis, weight control programs, other exercise programs strengthening knee joints, and guidelines when working in vinyl houses.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.327-340
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• 2017

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.

• Journal of the Korean Wood Science and Technology
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• v.44 no.1
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• pp.85-95
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• 2016

In this study, furfural, which is one of the value-added chemicals, was produced from the hydrolyzate of Quercus mongolica using dilute acid pretreatment, and the optimal pretreatment condition was determined by Response Surface Methodology (RSM) to obtain high yield of furfural. Based on Central Composite Design, the pretreatment experiment was designed with parameters such as reaction temperature ($X_1$), acid concentration ($X_2$), and reaction time ($X_3$) as independent variables, while dependent variable was furfural concentration (Y), and furfural yield (Z) was shown as percentage of Y per a dry weight basis. According to results of RSM, it was confirmed that reaction temperature ($X_1$) was the most influence factor and reaction temperature ($X_1$)-acid concentration ($X_2$) was the most significant interaction factor on furfural yield. Also, the optimal condition for the highest furfural yield was predicted at reaction temperature of $184^{\circ}C$, acid concentration of 1.17%, and reaction time of 5 min by RSM, and expected maximum yield of furfural was 6.37%. Experimentally, the maximum yield of furfural produced at above optimal condition was 6.21%, and it was considerably similar with the predicted value, and therefore the model for furfural production from the hydrolyzate of Quercus mongolica during dilute acid pretreatment could be built using RSM.

• Journal of Korean Orthopaedic Sports Medicine
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• v.4 no.1
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• pp.18-28
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• 2005

Purpose: The purpose of this study is to evaluate the clinical, radiological and histopathological results after microfracture surgery for degenerative arthritis of the knee. Materials and Methods: From Oct. 1997 to Dec. 1998, 48 knees in 46 patients were treated by microfracture technique. Their mean age at the time of operation was 56 years(range, 40-75 years) and mean period of follow-up study was one year(range, 7-20 months). For 24 knees in 22 patients, 'second-look' arthroscopies and biopsies were performed at 6 months following microfracture. At the last follow up clinical results were evaluated with Baumgaertner's scale. The specimens of 24 cases were stained with H-E, Safranin-O, and Masson's trichrome. Eighteen of 24 cases were stained immunohistochemically and the Western blotting test was performed on 12 cases for type II collagen. We analyzed the relationship of the Western blotting for type II collagen with clinical score, preoperative varus deformity, joint space widening in radiological result, extent of repaired articular cartilage in '2nd-look' arthroscopic findings, patient's age and weight. Results: Clinical results were excellent in 90% and good in 10%. Among the 24 knees, more than 80% of areas of chondral defect were covered with regenerated cartilage in 21 knees Histologically, the repaired tissue appears to be a hybrid of hyaline cartilage and fibrocartilage. Repaired cartilage contains variable amounts of type II collagen with immunohistochemical staining. The results of the Western blotting test were similar. The amounts of type II collagen formation had positive correlation with the extent of repaired cartilage and preoperative varus deformity. Conclusion: 'Second-look' showed that the chondral defect areas were covered with newly grown grayish white tissue. Articular cartilage repair was confirmed with histological and immunohisto-chemical study qualitatively, and the amount of type II collagen was calculated with the Western blotting test quantitatively. The exact nature and fate of repaired cartilagenous tissues need further long term follow-up study. The results of this study provide the rationale to select osteoarthritic patients indicated for microfracture surgery.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Journal of Sasang Constitutional Medicine
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• v.12 no.1
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• pp.120-135
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• 2000

Objective ; The purpose of this study is to find out the constitutional symptomatic pattern of Soyangin and Taeumin through investigation of difference between two groups in stroke. Method ; 70 inpatients(Soyangin 31, Taeumin 39) in stroke, admitted into Kyung-Hee Oriental Hospital from 1. July. 1999 to 20. Sept. 1999, were investigated through questionnare which consists of 16 parts 155 questions, and the problems which have significant difference between Soyangin and Taeumin group were analyzed statistically. Result 1. The analysis of general characteristic difference between Soyangin and Taeumin group (1) The number of Soyangin patients is 31, Taeumin 39 and the number of Male patients 43, Female 27. (2) The mean weight of Taeumin group is heavier than that of Soyangin group and the degree of obesity of Taeumin group higher. (3) The number of patients who have smoking history is more at Soyangin group. (4) In neurological problem, dizziness is more complained of in Soyangin group. 2. The analysis about the result of Questionnare (1) In problems related with 'Sleeping', the time of going to bed and getting up and the duration of sleeping are more irregular in Taeumin group. (2) In problems related with 'Defecation', the shape of stool is more variable in Taeumin group. (3) In problems related with 'Urination', Soyangin group have more complaint. (4) In problems related with 'The condition of digestion', Taeumin group have a good appetite and more compalint in lower abdominal region, but on the contrary Soyangin group lose appetite and have more complaint in upper abdominal region. (5) In problems related with 'Head and Face', Soyangin group have more complaint. (6) In problems related with 'Eye, Ear and Nose', Soyangin group have more complaint at eye and ear. (7) In promlems related with 'Chest region', Soyangin group easily feel choked up and Taeumin group heart throbs. (8) In problems related with 'Skin', Taeumin group easily feel change of color tone at skin and Soyangin group dry.