• Asia pacific journal of information systems
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• v.17 no.4
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• pp.61-95
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• 2007

Small-and-medium sized enterprises(SMEs) represent quite a large proportion of the industry as a whole in terms of the number of enterprises or employees. However researches on information system so far have focused on large companies, probably because SMEs were not so active in introducing information systems as larger enterprises. SMEs are now increasingly bringing in information systems such as ERP(Enterprise Resource Planning Systems) and some of the companies already entered the stage of ongoing use. Accordingly, researches should deal with the use of information systems by SME s operating under different conditions from large companies. This study examined factors and mechanism inducing faithful appropriation of information systems, in particular integrative systems such as ERP, in view of individuals` active feedback-seeking behavior. There are three factors expected to affect end users` feedback-seeking behavior for faithful appropriation of information systems. They are management support, peer IT champ support, and IT staff support. The main focus of the study is on how these factors affect feedback-seeking behavior and whether the feedback-seeking behavior plays the role of mediator for realizing faithful appropriation of information systems by end users. To examine the research model and the hypotheses, this study employed an empirical method based on a field survey. The survey used measurements mostly employed and verified by previous researches, while some of the measurements had gone through minor modifications for the purpose of the study. The survey respondents are individual employees of SMEs that have been using ERP for one year or longer. To prevent common method bias, Task-Technology Fit items used as the control variable were made to be answered by different respondents. In total, 127 pairs of valid questionnaires were collected and used for the analysis. The PLS(Partial Least Squares) approach to structural equation modeling(PLS-Graph v.3.0) was used as our data analysis strategy because of its ability to model both formative and reflective latent constructs under small-and medium-size samples. The analysis shows Reliability, Construct Validity and Discriminant Validity are appropriate. The path analysis results are as follows; first, the more there is peer IT champ support, the more the end user is likely to show feedback-seeking behavior(path-coefficient=0.230, t=2.28, p<0.05). In other words, if colleagues proficient in information system use recognize the importance of their help, pass on what they have found to be an effective way of using the system or correct others' misuse, ordinary end users will be able to seek feedback on the faithfulness of their appropriation of information system without hesitation, because they know the convenience of getting help. Second, management support encourages ordinary end users to seek more feedback(path-coefficient=0.271, t=3.06, p<0.01) by affecting the end users' perceived value of feedback(path-coefficient=0.401, t=6.01, p<0.01). Management support is far more influential than other factors that when the management of an SME well understands the benefit of ERP, promotes its faithful appropriation and pays attention to employees' satisfaction with the system, employees will make deliberate efforts for faithful appropriation of the system. However, the third factor, IT staff support was found not to be conducive to feedback-seeking behavior from end users(path-coefficient=0.174, t=1.83). This is partly attributable to the fundamental reason that there is little support for end users from IT staff in SMEs. Even when IT staff provides support, end users may find it less important than that from coworkers more familiar with the end users' job. Meanwhile, the more end users seek feedback and attempt to find ways of faithful appropriation of information systems, the more likely the users will be able to deploy the system according to the purpose the system was originally meant for(path-coefficient=0.35, t=2.88, p<0.01). Finally, the mediation effect analysis confirmed the mediation effect of feedback-seeking behavior. By confirming the mediation effect of feedback-seeking behavior, this study draws attention to the importance of feedback-seeking behavior that has long been overlooked in research about information system use. This study also explores the factors that promote feedback-seeking behavior which in result could affect end user`s faithful appropriation of information systems. In addition, this study provides insight about which inducements or resources SMEs should offer to promote individual users' feedback-seeking behavior when formal and sufficient support from IT staff or an outside information system provider is hardly expected. As the study results show, under the business environment of SMEs, help from skilled colleagues and the management plays a critical role. Therefore, SMEs should seriously consider how to utilize skilled peer information system users, while the management should pay keen attention to end users and support them to make the most of information systems.

• Journal of The Korean Association For Science Education
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• v.39 no.6
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• pp.777-790
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• 2019

The purpose of this study is to investigate the experience of teachers who apply Smart-technology in elementary school STEAM class and the reasons, difficulties when applying the technology and required support. Semi-structured in-depth interviews were conducted with six elementary school teachers with specialized knowledge in STEAM education who have experienced STEAM lessons several times before. The research findings are as follows: First, research participants utilized a variety of Smart-technology in STEAM class, most of which were experiential or interactive technology. Among the STEAM learning criteria, the Smart-technology in 'Creative Design' course was most often applied. Second, they adopted Smart Technology in STEAM class to encourage students to feel interested, actively participate in the class, enjoy indirect experience, and nurture interest in state-of-the-art technology. They used it to prepare for future societies and organize classes that are suitable for STEAM learning criteria. They also used Smart-technology because it was easy to use. Third, they found it difficult to find, secure, and use suitable Smart-technology when applying Smart-technology in the STEAM class. They also had trouble restructuring the curriculum. In addition, there were difficulties in using Smart-technology in the class such as lack of class hours, increased level of activity, insufficient physical environment and unexpected malfunction of Smart-technology, thus interrupted the class. After the class, it was hard to manage Smart-technology and also, there were difficulties in assessment, record, and negative awareness of surrounding people. Fourth, they mentioned that's suggesting education guidelines, develop, and distribute educational materials are required to enable 'Creative Design,' reduce educational content, provide training, secure Smart-technology equipment and provide Wi-Fi, support teacher's club and communities and create an atmosphere to emotionally support teachers in order to activate using Smart-technology in STEAM class.

• Journal of Korean Academy of Nursing
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• v.4 no.2
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• pp.17-31
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• 1974

Today, over seventy five percent of nursing in Korea provide a psychiatric experience in the basic curriculum. The psychiatric affiliation presents numerous major problems of adjustment to the student. The Importance of positive attitude toward the nursing care of psychiatric patients is recognized by the nursing profession. I have fined out the unfavorable attitude of non collegiate nursing students toward psychiatric nursing affiliation by previous research. This study was undertaken in response to a felt need to explore the use of several devices which might yield information about attitudes toward psychiatric nursing as a basis for future planning of the program offered at a selected hospital. This study is designed to meet the following objectives; (1) In order to find out the expressed attitudes of fifty·three collegiate nursing students toward their psychiatric affiliation. (2) To compare responses given by selected group of collegiate and non collegiate nursing students to same questionnaire (3) To determine the relationship between the attitudes of nursing students toward psychiatric nursing and the type of instructions where experience was obtained. A questionnaire, a Korean translation of the "Psychiatric Nursing Attitude Questionnaire" by Moldered Elizabeth fletcher, was administered to fifty-three collegiate nursing students who had completed a four-week psychiatric affiliation in a S hospital psychiatric ward during May 7, 1973 to Dec. 16, 1973. - The questionnaire of 100 statements was administered in the following way; (1) Part Ⅰ, Preconceptions, was, given in individual conferences with each subject, during the first few days of their affiliation, and again during the final week of affiliation. The responses to Part I were oral. (2) Part Ⅱ, Expectations, Part Ⅲ, Personal Relations, Part Ⅳ, Personal Feelings, and Part V, Attitudes and Activities of Patients were given to all of the subjects in a group meeting during the second week of the affiliation, and again, during the fourth week at the termination of the affiliation. Responses to Parts Ⅱ, Ⅲ, Ⅳ·, and V, were written. Each of the 100 statements of the questionnaire was considered to be either Positive or Negative. A favorable response was assigned the positive value of 1 and an unfavorable response was assigned the Negative value of O. The coefficient of correlation was computed between the two sets of scores for the fifty-three nursing students, The mean score, the standard deviation, and the differences in the means on each of the five parts of the questionnaire were computed and the relationships calculated by at-test. The results of the study were as follows; 1. There was no significant correlation between the two sets of the scores for the fifty-three nursing students during the four-week psychiatric affiliation. (r= 0.36) 2. There was no significant difference in the mean scores between the first and final tests for any of the questionnaire. 3. The Part Ⅰ, Preconceptions, data indicated collegiate nursing students have positive attitudes in preconceptions than non collegiate nursing students and preconceptions toward the psychiatric affiliation which affect their psychiatric nursing experience. 4. The Part Ⅱ, Expectations, data indicated more appropriate expectations of collegiate nursing students related to pre psychiatric affiliation orientation and sufficient theory learning than non-collegiate nursing students. 5. The Part Ⅲ, Personal relations, data indicated some students have negative attitudes in personal relations with normal people in respect to psychological security and social responsibilities. 6. The Part Ⅳ, Personal feelings, data indicated nursing students have psychological insecurity & inappropriateness. 7. The Part V, Attitudes and activities of patients, data indicated collegiate nursing students have more positive attitudes to the psychotic behavior of certain situations due to sufficient theory learning. 8. The data indicated collegiate·nursing students have more positive attitude than non-collegiate nursing students. 5. The Part Ⅲ, Personal relations, data indicated some students have negative attitudes in personal relations with normal people in respect to psychological security and social responsibilities. 6. The Part Ⅳ, Personal feelings, data indicated nursing students have psychological insecurity & inappropriateness. 7. The Part V, Attitudes and activities of patients, data indicated collegiate nursing students have more positive attitudes to the psychotic behavior of certain situations due to sufficient theory learning. 8. The data indicated collegiate·nursing students have more positive attitude than non-collegiate nursing students through psychiatric affiliation.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• 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 the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4884-4890
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• 2015

These days most of people possesses an average of one to two mobile devices in the world and a wireless network market is gradually expanding. Wi-Fi preference are increasing in accordance with the use growth of mobile devices. A number of areas such as public agencies, health care, education, learning, and content, manufacturing, retail create new values based on Wi-Fi, and the global network is built and provides complex services. However, There exist some attacks and vulnerabilities like wireless radio device identifier vulnerability, illegal use of network resources through the MAC forgery, wireless authentication key cracking, unauthorized AP / devices attack in the next generation radio network environment. In addition, advanced security technology research, such as authentication Advancement and high-speed secure connection is not nearly progress. Therefore, this paper designed a secure communication system for message protection in next-generation wireless network environments by device identification and, designing content classification and storage protocols. The proposed protocol analyzed safeties with respect to the occurring vulnerability and the securities by comparing and analyzing the existing password techniques in the existing wireless network environment. It is slower 0.72 times than existing cypher system, WPA2-PSK, but enforces the stability in security side.

• Journal of Science Education
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• v.36 no.2
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• pp.313-328
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• 2012

This study was conducted for the purpose of making the better utilization of MBL in class, based on 2009 curriculum which emphasizes research activities and recommends the direct use of the MBL. We investigated primary, secondary and pre-service teachers' satisfaction and perception level after conducting training about making good use of MBL. The satisfaction level of the training turned out to be high, level of applicability of MBL, expected improvement in learning skills of students and the will to apply it in class was high. The answer that they expect MBL to increase students' curiosity and interest in science was the highest among the survey results, which means that MBL could be used as a solution to lack of students' interest in science. Besides, primary teachers than secondary and pre-teachers, long careered teachers than short careered teachers and MBL-experienced teachers than inexperienced teachers showed more satisfaction and the will to adapt MBL overall. Primary and pre-teachers hoped MBL training to be more related to STEAM education, whereas secondary teachers wanted the training to have more to do with increasing creativity If advanced MBL training program is opened. The price was chosen as the best obstacle to MBL class' application, and the lack of manual for experiment and education to teacher was also pointed out secondly. In conclusion, if MBL is fully equipped in school and training on how to take advantage of it is provided continually, It is expected that MBL could increase the utilization in the field of science education. The results of this paper can be used when you configure the MBL utilization training program.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.57-64
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• 2017

Short message service (SMS) is one of the most important communication methods for people who use mobile phones. However, illegal advertising spam messages exploit people because they can be used without the need for friend registration. Recently, spam message filtering systems that use machine learning have been developed, but they have some disadvantages such as requiring many calculations. In this paper, we implemented a spam message filtering system using the set-based POI search algorithm and sentence similarity without servers. This algorithm can judge whether the input query is a spam message or not using only letter composition without any server computing. Therefore, we can filter the spam message although the input text message has been intentionally modified. We added a specific preprocessing option which aims to enable spam filtering. Based on the experimental results, we observe that our spam message filtering system shows better performance than the original set-based POI search algorithm. We evaluate the proposed system through extensive simulation. According to the simulation results, the proposed system can filter the text message and show high accuracy performance against the text message which cannot be filtered by the 3 major telecom companies.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.562-567
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• 2009

As a key mechanism of the human emotion interaction, Facial Expression is a powerful tools in HRI(Human Robot Interface) such as Human Computer Interface. By using a facial expression, we can bring out various reaction correspond to emotional state of user in HCI(Human Computer Interaction). Also it can infer that suitable services to supply user from service agents such as intelligent robot. In this article, We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

• Journal of the Korean School Mathematics Society
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• v.12 no.2
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• pp.309-325
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• 2009

The purpose of the study was to investigate the differences between Korean mathematics textbooks and CPMP textbooks in the view of conceptual network, structure of mathematical contents, instructional design, and teaching and learning environment to explore the implications for mathematics education in Korea. According to the results, Korean textbooks emphasized the mathematical structures and conceptual network, on the other hand, CPMP textbooks focused on making connections between mathematical concepts and corresponding real life situations as well as mathematical structures. And generalizing mathematical concepts at the symbolic level was very important objective in Korean textbooks, but in the CPMP textbooks, investigating mathematical ideas and solving problems in diverse contexts including real- life situations were considered very important. Teachers using Korean textbooks preferred an explanatory teaching method with the use of concrete manipulatives and student worksheet, however, teachers using CPMP textbooks emphasized collaborative group activities to communicate mathematical ideas and encouraged students to use graphing calculators when they explore mathematical concepts and solve problems.