• The Journal of Korean society of community based occupational therapy
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• v.10 no.1
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• pp.51-61
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• 2020

Objectives : The purpose of this study is to investigate the effect of object size and reaching distance on kinematic factors of the upper limb while performing arm reaching for normal subjects. Methods : The subjects of this study were 30 university students who were in D university in Busan, and the measuring tool was CMS-70P(Zebris Medizintechnik Gmbh, Germany), a three-dimensional motion analyzer. The task had six conditions. The average velocity of motion, average acceleration, maximum velocity, and the velocity definite number of movements were measured according to changes in object size(2cm, 10cm) and reaching distance(15%, 37.5%, 60%) when they performed arm reaching. The general characteristics of the subject were technical statistics. One-way ANOVA measurement was used to compare variables when the arm reaching task was performed from two object sizes to three reaching distance, and the post-test was conducted with Tukey test. In addition, an independent t-test was used to analyze the kinematic differences according to the two object sizes at three reaching distances. A two-way ANOVA measurement (3×2 Two-way ANOVA measurement) was performed to identify the interaction of the reaching distance(15%, 37.5%, 60%) and the object size(2cm, 10cm). The statistical significance level α was set to .05. Results : When the size of the object increased, the velocity and maximum velocity also increased, but the definite number of velocity decreased. When the reaching distance increased, the velocity and maximum velocity increased, whereas the definite number of velocity decreased. Conclusion : The clinical significance of this study could be utilized as the baseline data for grading object size and reaching distances when the reaching training is implemented for patients whose central nervous system was damaged.

• Journal of the Korea Concrete Institute
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• v.25 no.3
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• pp.339-345
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• 2013

This study is to derive from the required performances and the optimum mix proportion of the roof concrete placed in the in-ground LNG storage tank with a capacity of 200000 $m^3$, and propose the actual data for site concrete work. The concrete placing work without sliding and segregation in the fresh concrete condition is very important because the slope of domed roof is varied in the large range by its curvature. Also the control of hydration heat and the strength development at test ages are classified with massive section about 1.4 m thick and considered to the pre-stressing work and removal of air support after concrete placing work. Considering above condition, slump range is selected $100{\pm}25$ mm under the slope $20^{\circ}$ and $150{\pm}25$ mm over the slope $20^{\circ}$ s until 60 minutes of elapsed time. Also, the roof concrete is satisfied with compressive strength range including design strength at 91 days (30 MPa), pre-stressing work at 7 days (10 MPa), air support removal work at 21 days (14 MPa). Replacement ratio of limestone powder is determined by confined water ratio test and main design factors include water-cement ratio (W/C), sand-aggregate ratio and dosage of admixture. As test results, the optimum mix proportion of the roof concrete used low heat cement is as followings. 1) Replacement ratio of limestone powder 25% by confined water ratio test 2) Water-cement ratio 57.8% 3) Sand-aggregate ratio 42.0%. Also, test results for the adiabatic temperature rising test is satisfied with its criteria and shown the lower value compared to preceding storage tank (TK-13, 14). These required performances and the optimum mix proportion is to apply the actual construction work.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.245-256
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• 2021

This study was conducted to identify the applicability of a LiDAR sensor to forest resources inventories by comparing data on a tree's position, height, and DBH obtained by the sensor with those by existing forest inventory methods, for the tree species of Criptomeria japonica in Jeolmul forest in Jeju, South Korea. To this end, a backpack personal LiDAR (Greenvalley International, Model D50) was employed. To facilitate the process of the data collection, patterns of collecting the data by the sensor were divided into seven ones, considering the density of sample plots and the work efficiency. Then, the accuracy of estimating the variables of each tree was assessed. The amount of time spent on acquiring and processing the data by each method was compared to evaluate the efficiency. The findings showed that the rate of detecting standing trees by the LiDAR was 100%. Also, the high statistical accuracy was observed in both Pattern 5 (DBH: RMSE 1.07 cm, Bias -0.79 cm, Height: RMSE 0.95 m, Bias -3.2 m), and Pattern 7 (DBH: RMSE 1.18 cm, Bias -0.82 cm, Height: RMSE 1.13 m, Bias -2.62 m), compared to the results drawn in the typical inventory manner. Concerning the time issue, 115 to 135 minutes per 1ha were taken to process the data by utilizing the LiDAR, while 375 to 1,115 spent in the existing way, proving the higher efficiency of the device. It can thus be concluded that using a backpack personal LiDAR helps increase efficiency in conducting a forest resources inventory in an planted coniferous forest with understory vegetation, implying a need for further research in a variety of forests.

• Journal of the Korean Society of Radiology
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• v.13 no.2
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• pp.261-270
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• 2019

The separation permeation characteristics of $N_2-O_2$ gas in air at cell membrane model of skin which irradiated by high energy electron(linac 6 MeV) were investigated. The cell membrane model of skin used in this experiment was a sulfonated polydimethyl siloxane(PDMS) non-porous membrane. The pressure range of $N_2$ and $O_2$ gas were appeared from $1kg_f/cm^2$ to $6kg_f/cm^2$. In this experiment(temperature $36.5^{\circ}C$), the permeation change of $N_2$ and $O_2$ gas in non-porous membrane by non-irradiation were found to be $1.19{\times}10^{-4}-2.43{\times}10^{-4}$, $1.72{\times}10^{-4}-2.6{\times}10^{-4}cm^3(STP)/cm^2{\cdot}sec{\cdot}cmHg$, respectively. That of $N_2$ and $O_2$ gas in non-porous membrane by irradiation were found to be $0.19{\times}10^{-4}-0.56{\times}10^{-4}$, $0.41{\times}10^{-4}-0.76{\times}10^{-4}cm^3(STP)/cm^2{\cdot}sec{\cdot}cmHg$, respectively. The irradiated membrane was significantly decreased about 4-10 times than membrane which was not irradiated. And ideal separation factor of $N_2$ and $O_2$ gas by non-irradiation was found to be from 1.32 to 0.42 and that of $N_2$ and $O_2$ gas by irradiation was found to be from 0.237 to 0.125. The irradiated membrane was significantly decreased about 4-5 times than membrane which was not irradiated. When the operation change(cut) and pressure ratio(Pr) by non-irradiation were about 0, One was increased to the oxygen enrichment and the other was decreased to the oxygen enrichment. The irradiated membrane was significantly decreased about 4-19 times than membrane which was not irradiated. As the pressure of $N_2$ and $O_2$ gas was increased, the selectivity was decreased. As separation permeation characteristics of $N_2-O_2$ gas in cell membrane model of skin were abnormal, cell damages were appeared at cell.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.83-105
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• 2016

Measuring an individual's subjective wellbeing in an accurate, unobtrusive, and cost-effective manner is a core success factor of the wellbeing support system, which is a type of medical IT service. However, measurements with a self-report questionnaire and wearable sensors are cost-intensive and obtrusive when the wellbeing support system should be running in real-time, despite being very accurate. Recently, reasoning the state of subjective wellbeing with conventional sentiment analysis and unstructured data has been proposed as an alternative to resolve the drawbacks of the self-report questionnaire and wearable sensors. However, this approach does not consider contextual polarity, which results in lower measurement accuracy. Moreover, there is no sentimental word net or ontology for the subjective wellbeing area. Hence, this paper proposes a method to extract keywords and their contextual polarity representing the subjective wellbeing state from the unstructured text in online websites in order to improve the reasoning accuracy of the sentiment analysis. The proposed method is as follows. First, a set of general sentimental words is proposed. SentiWordNet was adopted; this is the most widely used dictionary and contains about 100,000 words such as nouns, verbs, adjectives, and adverbs with polarities from -1.0 (extremely negative) to 1.0 (extremely positive). Second, corpora on subjective wellbeing (SWB corpora) were obtained by crawling online text. A survey was conducted to prepare a learning dataset that includes an individual's opinion and the level of self-report wellness, such as stress and depression. The participants were asked to respond with their feelings about online news on two topics. Next, three data sources were extracted from the SWB corpora: demographic information, psychographic information, and the structural characteristics of the text (e.g., the number of words used in the text, simple statistics on the special characters used). These were considered to adjust the level of a specific SWB. Finally, a set of reasoning rules was generated for each wellbeing factor to estimate the SWB of an individual based on the text written by the individual. The experimental results suggested that using contextual polarity for each SWB factor (e.g., stress, depression) significantly improved the estimation accuracy compared to conventional sentiment analysis methods incorporating SentiWordNet. Even though literature is available on Korean sentiment analysis, such studies only used only a limited set of sentimental words. Due to the small number of words, many sentences are overlooked and ignored when estimating the level of sentiment. However, the proposed method can identify multiple sentiment-neutral words as sentiment words in the context of a specific SWB factor. The results also suggest that a specific type of senti-word dictionary containing contextual polarity needs to be constructed along with a dictionary based on common sense such as SenticNet. These efforts will enrich and enlarge the application area of sentic computing. The study is helpful to practitioners and managers of wellness services in that a couple of characteristics of unstructured text have been identified for improving SWB. Consistent with the literature, the results showed that the gender and age affect the SWB state when the individual is exposed to an identical queue from the online text. In addition, the length of the textual response and usage pattern of special characters were found to indicate the individual's SWB. These imply that better SWB measurement should involve collecting the textual structure and the individual's demographic conditions. In the future, the proposed method should be improved by automated identification of the contextual polarity in order to enlarge the vocabulary in a cost-effective manner.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.25-34
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• 2015

In this research, we applied a procedure of quality control (QC) to the agro-meteorological data measured at the Suwon weather station of Korea Meteorological Administration (KMA). The QC was conducted through six steps based on the KMA Real-time Quality control system for Meteorological Observation Data (RQMOD) and four steps based on the International Soil Moisture Network (ISMN) QC modules. In addition, we set up our own empirical method to remove erroneous data which could not be filtered by the RQMOD and ISMN methods. After all these QC procedures, a well-refined agro-meteorological dataset was complied at both air and soil temperatures. Our research suggests that soil moisture requires more detailed and reliable grounds to remove doubtful data, especially in winter with its abnormal variations. The raw data and the data after QC are now available at the NCAM website (http://ncam.kr/page/req/agri_weather.php).

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.6
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• pp.155-164
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• 2018

Many malicious programs have been compressed or encrypted using various commercial packers to prevent reverse engineering, So malicious code analysts must decompress or decrypt them first. The OEP (Original Entry Point) is the address of the first instruction executed after returning the encrypted or compressed executable file back to the original binary state. Several unpackers, including PinDemonium, execute the packed file and keep tracks of the addresses until the OEP appears and find the OEP among the addresses. However, instead of finding exact one OEP, unpackers provide a relatively large set of OEP candidates and sometimes OEP is missing among candidates. In other words, existing unpackers have difficulty in finding the correct OEP. We have developed new tool which provides fewer OEP candidate sets by adding two methods based on the property of the OEP. In this paper, we propose two methods to provide fewer OEP candidate sets by using the property that the function call sequence and parameters are same between packed program and original program. First way is based on a function call. Programs written in the C/C++ language are compiled to translate languages into binary code. Compiler-specific system functions are added to the compiled program. After examining these functions, we have added a method that we suggest to PinDemonium to detect the unpacking work by matching the patterns of system functions that are called in packed programs and unpacked programs. Second way is based on parameters. The parameters include not only the user-entered inputs, but also the system inputs. We have added a method that we suggest to PinDemonium to find the OEP using the system parameters of a particular function in stack memory. OEP detection experiments were performed on sample programs packed by 16 commercial packers. We can reduce the OEP candidate by more than 40% on average compared to PinDemonium except 2 commercial packers which are can not be executed due to the anti-debugging technique.

• Korean journal of food and cookery science
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• v.19 no.3
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• pp.286-294
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• 2003

The purpose of this study was to analyze the food service management practices and productivity in 49 elementary schools in the Daegu and Gyeongsangbuk-do areas. Survey questionnaires were used to obtain a variety of quantitative and qualitative information, including general food service management and productivity, on elementary school food service systems. Descriptive analysis, $\div$2-test, t-test and one-way ANOVA analysis were used as the statistical methods in this study. Eighteen elementary schools were located in urban areas, 13 in provincial areas and 18 in isolated areas. The average number of meals was 565.1, with a significant difference (p＝0.001) between the areas. The average cost per a meal was 1151.0 Won, with a significant difference (p＝0.001) between the areas. The productivity Index (meal/hour) was 13.5, with a significant difference (p＝0.001) between the areas. There was a significant positive correlation of the productivity Index between the total number of meal, the number of employees, the total food cost, meals per employee and the employee's working period. There was a significant negative correlation between the productivity Index and the number of side dishes, the lost per meal, the labor cost per meal and the employee's job satisfaction degree index (JDI).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.33-50
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• 2022

While the number of female entrepreneurs has been increasing, and female entrepreneurship has been increasingly perceived as a driving force of sustainable economic development, there is a lack of studies of female entrepreneurship, particularly in the non-Western regions. This study aims to explore current levels of entrepreneurial competences of female college students in four Asian countries (i.e., Indonesia, Korea, Philippines, and Vietnam), differences in the competences between countries, and factors affecting their entrepreneurial competences. Using online surveys, the present study collected data from 516 female Asian college students and examined their entrepreneurial competences in six dimensions-entrepreneurship, sensibility, business management, relationship management, strategic management, and multi-tasking. This study also investigated effects of four variables (i.e., entrepreneurship course taking experiences, on-campus entrepreneurship experiences, off-campus entrepreneurship experiences, and entrepreneurial intentions) on the six aspects of entrepreneurial competences. Data analysis reveals that female Asian college students as a whole group possess quite high levels of entrepreneurial competences while the Filipino students show the biggest competence in all the six dimensions measured. As regards affecting factors, this study finds that, in the total sample, regression equations are significant in all the six dimensions of entrepreneurial competences. On-campus experiences have significantly positive effects on those six dimensions while course taking experiences and entrepreneurial intentions positively affect three different dimensions each. However, out-of-campus experiences turn out to be negative though their effects are insignificant. Meanwhile, in individual samples, different factors affect different dimensions of entrepreneurial competences. Based on these findings, the present study suggests some actions for promoting female entrepreneurship and for conducting future studies.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.