• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Hospice and Palliative Care
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• v.13 no.1
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• pp.13-23
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• 2010

Purpose: This study was to identify the role of general hospice palliative nurse through job analysis (duties, task, and task elements). Methods: The sample consisted of 136 nurses or professors who were performing duties related to hospice care areas in Korea. A survey method was used, and the questionnaire included frequencies, criticality, and difficulties of task elements in job description by the DACUM method. Descriptive statistics were performed by using SPSS WIN 17.0. Results: The job description of general hospice palliative nurse was identified 8 duties, 36 tasks, and 137 task elements. As for the 8 duties, the average scores of frequency, criticality, and difficulty were 2.94, 3.66, and 2.80, respectively. The role of ‘pain assessment’ was the most important task element among frequency and criticality. The lowest score at the frequency and criticality were ‘manage public finance’ and ‘collect datum through diagnostic test & lab', respectively. Furthermore, the role of 'identify spiritual needs of patients and family' was the most difficult task, whereas the role of 'manage documents and information' was the least. Conclusion: In this study, we could recognize the reality of general hospice palliative nurse's performances. For general hospice palliative nurse, therefore, concrete practice guide lines of psychosocial and spiritual care, communication skills, and bereavement care with qualifying system are critically needed.

• Journal of Korean Home Economics Education Association
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• v.23 no.3
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• pp.69-90
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• 2011

To develop the descriptive evaluation questionnaire with high validity and reliability on the Clothing and Textiles section of the middle school Technology & Hone Economics textbook, apply it to students and analyze its results. We made out a draft for descriptive evaluation questionnaire that was based upon the concrete establishment of the goal and the range of evaluation. We also made a rubric for scoring as well as sample answer-sheets. Finally, we completed a total of twenty three descriptive evaluation questions and we applied it to sixty five 2nd-grade students in two classes in a middle school. Descriptive evaluation questionnaire exhibited the relative high validity on each question. Moreover, three graders gave the same score on each question of descriptive evaluation, suggesting that descriptive evaluation questionnaire has the high inter-grader reliability and the strong correlation. But, low academic achievement was generally observed in the subjects. They had difficulty in describing their knowledge via their own language and drawing up accurate and detailed answers. They recognized the positive aspects of descriptive evaluation questionnaire, but they felt it uncomfortable due to study-burden and description itself. To overcome these limitations, it is required that students should experience various materials related to subject contents in classes as well as textbooks, concentrate themselves on finding solutions for problems, expand their scope, and practice describe them in advance. Therefore, the additional training for description evaluation questionnaire will be necessary for the more efficient and discriminative questionnaire. Also the questionnaire with high validity and reliability should be developed and the aggressive and voluntary participation of teachers will be needed.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.1-14
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• 2008

According to technology acceptance model(TAN) which is one of the most important research models for explaining IT users' behavior, on intention of using IT is determined by usefulness and ease of use of it. However, TAM wouldn't explain the performance of using IT while it has been considered as a very good model for prediction of the intention. Many people would not be confirmed in the performance of using IT until they can control it at their will, although they think it useful and easy to use. In other words, in addition to usefulness and ease of use as in TAM, controllability is also should be a factor to determine acceptance of IT. Especially, there is a very close relationship between controllability and ease of use, both of which explain the other sides of control over the performance of using IT, so called perceived behavioral control(PBC) in social psychology. The objective of this study is to identify the relationship between ease of use and controllability, and analyse the effects of both two beliefs over performance and intention in using IT. For this purpose, we review the issues related with PBC in information systems studies as well as social psychology, Based on a review of PBC, we suggest a research model which includes the relationship between control and performance in using IT, and prove its validity empirically. Since it was introduced as qa variable for explaining volitional control for actions in theory of planned behavior(TPB), there have been confusion about concept of PBC in spite of its important role in predicting so many kinds of actions. Some studies define PBC as self-efficacy that means actor's perception of difficulty or ease of actions, while others as controllability. However, this confusion dose not imply conceptual contradiction but a double-faced feature of PBC since the performance of actions is related with both self-efficacy and controllability. In other words, these two concepts are discriminated and correlated with each other. Therefore, PBC should be considered as a composite concept consisting of self-efficacy and controllability, Use of IT has been also one of important areas for predictions by PBC. Most of them have been studied by analysis of comparison in prediction power between TAM and TPB or modification of TAM by inclusion of PBC as another belief as like usefulness and ease of use. Interestingly, unlike the other applications in social psychology, it is hard to find such confusion in the concept of PBC in the studies for use of IT. In most of studies, controllability is adapted as PBC since the concept of self-efficacy is included in ease of use explicitly. Based on these discussions, we can suggest perceived use control(PUC) which is defined as perception of control over the performance of using IT and composed of controllability and ease of use as sub-concepts. We suggest a research model explaining acceptance of IT which includes the relationships of PUC with attitude and performance of using IT. For empirical test of our research model, two user groups are selected for surveying questionnaires. In the first group, there are freshmen who take a basic course for Microsoft Excel, and the second group consists of senior students who take a course for analysis of management information by Excel. Most of measurements are adapted ones that have been validated in the other studies, while performance is real score of mid-term in each class. In result, four hypotheses related with PUC are supported statistically with very low significance level. Main contribution of this study is suggestion of PUC through theoretical review of PBC. Specifically, a hierarchical model of PUC are derived from very rigorous studies in the relationship between self-efficacy and controllability with a view of PBC in social psychology. The relationship between PUC and performance is another main contribution.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.12
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• pp.1881-1894
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• 2015

The objective of this study was to provide information on difficulty of performing tasks, degree of task satisfaction, job satisfaction, organizational commitment, and turnover intention as well as investigate correlations among these factors. Data were collected on employees working at Centers for Children's Foodservice Management, which had been operating for over 6 months until December 2013. The recruitment period was from December 16, 2013 to January 30, 2014. A total of 228 employees (79.7%) participated in the study, and 227 completed questionnaires were analyzed. Statistical analyses were performed on the data utilizing the SPSS V20.0 and AMOS V21.0 programs. The main results of this study were as follows: task satisfaction of employees in charge of 'visiting-teaching' for children was highest (4.24 points), whereas that of employees in charge of financial management was lowest (2.92 points). In terms of evaluation of job satisfaction factors, the score of 'co-worker' was highest (3.99 points) while that of 'payment' was lowest (2.45 points). Average scores of general job satisfaction, organizational commitment, and turnover intention were 3.56 points, 3.54 points, and 3.07 points, respectively. Job achievement was the most significant influencing factor on general job satisfaction, organizational commitment, and turnover intention. According to the path analysis results, the degree of task satisfaction affected job satisfaction. Organizational commitment had a more significant effect on turnover intention than job satisfaction and mediate both job satisfaction and turnover intention. Although employees of CCFSMs endeavor to improve the quality of child-care facility foodservice, some facilities do not. Controlling turnover intention of employees is especially critical for CCFSMs since it is important for each employees to form strong bonds with child-care facilities as well as to shorten the time required to train new employees. Thus, job satisfaction, which is related to organizational commitment and turnover intention, can be improved by considering poorly scored job satisfaction factors such as wage or workload.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.9-15
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• 2005

Purpose : This study attempted to investigate the effects of supply of highly concentrated $(30\%)$ oxygen on human ability of visuospatial cognition using time course data analysis of functional Magnetic Resonance Imaging (fMRI). Materials and Methods : To select an item set in the visuospatial performance test, two questionnaires with similar difficulty were developed through group testing. A group test was administered to 263 college students. Two types of questionnaire containing 20 questions were developed to measure the ability of visuospatial cognition. Eight college students (right-handed male, average age of 23.5 yrs) were examined for fMRI study. The experiment consisted of two runs of the visuospatial cognition testing, one with $21\%$ level of oxygen and the other with $30\%$ oxygen level. Each run consisted of 4 blocks, each containing control and visuospatial items. Functional brain images were taken from 37 MRI using the single-shot EPI method. Using the subtraction procedure, activated areas in the brain during visuospatial tasks were color-coded by t-score. To investigate the time course data in each activated area from brain images, 4 typical regions (cerebellum, occipital lobe, parietal lobe, and frontal lobe) were selected. Results : The average accuracy was $50.63{\pm}8.63$ and $62.50{\pm}9.64$ for $21\%\;and\;30\%$ oxygen respectively, and a statistically significant difference was found in the accuracy between the two types of oxygen (p<0.05). There were more activation areas observed at the cerebellum, occipital lobe, parietal lobe and frontal lobe with $30\%$ oxygen administration. The rate of increase in the cerebellum, occipital lobe and parietal lobe was $17\%$ and that of the frontal lobe, $50\%$. Especially, there were increase of intensity of BOLD signal at the parietal lobe with $30\%$ oxygen administration. The increase rate of the left parietal lobe was $1.4\%$ and that of the right parietal lobe, $1.7\%$. Conclusion : It is concluded that while performing visuospatial tasks, high concentrations of oxygen administration make oxygen administration sufficient, thus making neural network activate more, and the ability to perform visuospatial tasks increase.

• Journal of Animal Environmental Science
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• v.17 no.1
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• pp.39-48
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• 2011

The present survey was conducted to provide basic information on automatic milking system (AMS) in relation to purchase motive, milk yield and quality, customer satisfaction, difficulties of operation and customer suggestions, etc. Purchase motives of AMS were insufficient labor (44%), planning of dairy experience farm (25%), better performance of high yield cows (19%) and others (6%), respectively. Average cow performance after using AMS was 30.9l/d for milk yield, 3.9% for milk fat, 9,100/ml for bacterial counts. Sixty-eight percentage of respondents were very positive in response to AMS use for their successors but 18% were negative. The AMS operators were owner (44%), successor (44%), wife (6%) and company worker (6%), respectively. The most difficulty (31%) in using AMS was operating the system and complicated program manual. The rate of response to system error and breakdown was 25%. The reasons for culling cow after using AMS were mastitis (28%), reproduction failure (19%), incorrect teat placement (12%), metabolic disease (7%) and others (14%), respectively. Fifty-six percentages of the respondents made AMS maintenance contract and 44% did not. Average annual cost of the maintenance contract was 6,580,000 won. Average score for AMS satisfaction measurement (1 to 5 range) was 3.2 with decrease of labor cost 3.7, company A/S 3.6, increase of milk yield 3.2 and decrease of somatic cell count 2.8, respectively. Suggestions for the higher efficiency in using AMS were selecting cows with correct udder shape and teat placement, proper environment, capital and land, and attitude for continuous observation. Systematic consulting was highly required for AMS companies followed by low cost for AMS setup and systematization of A/S.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.3
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• pp.1-10
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• 2021

This study aimed to evaluate preferences of Bukhansan dulegil using sentiment analysis, a natural language processing technique, to derive preferred and non-preferred factors. Therefore, we collected blog articles written in 2019 and produced sentimental scores by the derivation of positive and negative words in the texts for 21 dulegil courses. Then, content analysis was conducted to determine which factors led visitors to prefer or dislike each course. In blogs written about Bukhansan dulegil, positive words appeared in approximately 73% of the content, and the percentage of positive documents was significantly higher than that of negative documents for each course. Through this, it can be seen that visitors generally had positive sentiments toward Bukhansan dulegil. Nevertheless, according to the sentiment score analysis, all 21 dulegil courses belonged to both the preferred and non-preferred courses. Among courses, visitors preferred less difficult courses, in which they could walk without a burden, and in which various landscape elements (visual, auditory, olfactory, etc.) were harmonious yet distinct. Furthermore, they preferred courses with various landscapes and landscape sequences. Additionally, visitors appreciated the presence of viewpoints, such as observation decks, as a significant factor and preferred courses with excellent accessibility and information provisions, such as information boards. Conversely, the dissatisfaction with the dulegil courses was due to noise caused by adjacent roads, excessive urban areas, and the inequality or difficulty of the course which was primarily attributed to insufficient information on the landscape or section of the course. The results of this study can serve not only serve as a guide in national parks but also in the management of nearby forest green areas to formulate a plan to repair and improve dulegil. Further, the sentiment analysis used in this study is meaningful in that it can continuously monitor actual users' responses towards natural areas. However, since it was evaluated based on a predefined sentiment dictionary, continuous updates are needed. Additionally, since there is a tendency to share positive content rather than negative views due to the nature of social media, it is necessary to compare and review the results of analysis, such as with on-site surveys.

• Journal of Korean Home Economics Education Association
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• v.24 no.2
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• pp.117-134
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• 2012

The purpose of this study was to identify the stage of concern, the level of use, and the innovation configuration of Home Economics teachers regarding creativity and personality education in Home Economics(HE) classes. The survey questionnaires were sent through mails and e-mails to middle-school HE teachers in the whole country selected by systematic sampling and convenience sampling. Questionnaires of the stages of concern and the levels of use developed by Hall(1987) were used in this study. 187 data were used for the final analysis by using SPSS/window(12.0) program. The results of the study were as following: First, for the stage of concerns of HE teachers on creativity and personality education, the information stage of concerns(85.51) was the one with the highest response rate and the next high in the following order: the management stage of concerns(81.88), the awareness stage of concerns(82.15), the refocusing stage of concerns(68.80), the collaboration stage of concerns(61.97), and the consequence stage of concerns(59.76). Second, the levels of use of HE teachers on creativity and personality education was highest with the mechanical levels(level 3; 21.4%) and the next high in the following order: the orientation levels of use(level 1; 20.9%), the refinement levels(level 5; 17.1%), the non-use levels(level 0; 15.0%), the preparation levels(level 2; 10.2%), the integration levels(level 6; 5.9%), the renewal levels(level 7; 4.8%), the routine levels(level 4; 4.8%). Third, for the innovation configuration of HE teachers on creativity and personality education, more than half of the HE teachers(56.1%) mainly focused on personality education in their HE classes; 31.0% of the HE teachers performed both creativity and personality education; a small number of teachers(6.4%) focused on creativity education; the same number of teachers(6.4%) responded that they do not focus on neither of the two. Examining the level and type of performance HE teachers applied, the average score on the performance of creativity and personality education was 3.76 out of 5.00 and the mean of creativity component was 3.59 and of personality component was 3.94, higher than standard. For the creativity education, openness/sensitivity(3.97) education was performed most and the next most in the following order: problem-solving skill(3.79), curiosity/interest(3.73), critical thinking(3.63), problem-finding skill(3.61), originality(3.57), analogy(3.47), fluency/adaptability(3.46), precision(3.46), imagination(3.37), and focus/sympathy(3.37). For the personality education, the following components were performed in order from most to least: power of execution(4.07), cooperation/consideration/just(4.06), self-management skill(4.04), civic consciousness(4.04), career development ability(4.03), environment adaptability(3.95), responsibility/ownership(3.94), decision making(3.89), trust/honesty/promise(3.88), autonomy(3.86), and global competency(3.55). Regarding what makes performing creativity and personality education difficult, most HE teachers(64.71%) chose the lack of instructional materials and 40.11% of participants chose the lack of seminar and workshop opportunity. 38.5% chose the difficulty of developing an evaluation criteria or an evaluation tool while 25.67% responded that they do not know any means of performing creativity and personality education. Regarding the better way to support for creativity and personality education, the HE teachers chose in order from most to least: 'expansion of hands-on activities for students related to education on creativity and personality'(4.34), 'development of HE classroom culture putting emphasis on creativity and personality'(4.29), 'a proper curriculum on creativity and personality education that goes along with students' developmental stages'(4.27), 'securing enough human resource and number of professors who will conduct creativity and personality education'(4.21), 'establishment of the concept and value of the education on creativity and personality'(4.09), and 'educational promotion on creativity and personality education supported by local communities and companies'(3.94).