• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.527-535
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• 2007

The number of SMEs taking up information systems such as Enterprise Resource Planning has been growing rapidly, and many of those organizations have stepped into the stage of ongoing use at this point. Thus, research which takes into account idiosyncratic nature of SME environment is more important than before. Through an empirical study using survey method, we tried to examine the importance of end user's feedback seeking behavior in SMEs and how environmental factors affecting such behavior reinforce and interact with the feedback seeking behavior itself. The result shows that end user's active role as a voluntary feedback seeker is important in utilizing information systems in accordance with the initial design intention in ongoing use environment. Furthermore, in order to facilitate such feedback seeking behavior in SME environment, it is essential that management's involvement and communicating to its employees the importance of effectively utilizing the information systems as well as the support of peer IT champ.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.152-161
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• 2024

The purpose of this study sought to determine the impact of shared leadership perceived by organizational members on team effectiveness and team learning behavior. For this purpose, the results of the empirical analysis of 206 organizational members are as follows. First, shared leadership was analyzed to improve team effectiveness. Second, shared leadership had a positive effect on team learning behavior. Third, team learning behavior was statistically significantly analyzed for team effectiveness. This study confirmed the importance of shared leadership, which has a positive impact on team effectiveness and team learning behavior. This may require building a new culture that can demonstrate the inherent leadership of organizational members in the influence relationship between shared leadership, team effectiveness, and team learning behavior. In other words, in order to systematically demonstrate and implement shared leadership, the execution ability of executives, managers, and working-level managers is important. To this end, it is necessary to build an organizational culture that matches the characteristics of the organization and develop and continuously implement human resource development systems and programs that can implement this.

• Journal of Engineering Education Research
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• v.22 no.2
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• pp.61-70
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• 2019

This paper deals with a 'Designeer' education program that has a specific objective of educating design to undergraduate students in mechanical engineering with the aim of enhancing their ability of collaboration with designers when they are going to work in the field after graduation. The entire curriculum of the Designeer education program is introduced first, the end of which two-semester Capstone Design Courses for senior students is offered to let them practice all the knowledge and skills in a project-based learning environment. Learning specific matters such as sketching & visual thinking, prototyping and user experience design is one thing and practicing those knowledge and skills into a Capstone Design project is another. At this point, design thinking process needs to be in place to give students a foresight of one-year journey and to ensure that they will produce a desirable, feasible and viable product at the end of the year when they define the right problem at the beginning. Their frustrations and discoveries while applying design thinking throughout the year is explained by taking an example of a Robot Vacuum Cleaner design project. Finally, we provide real examples of effective methods to practice divergent and convergent phases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2505-2528
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• 2023

The field of facial expression recognition (FER) has been actively researched to improve human-computer interaction. In recent years, deep learning techniques have gained popularity for addressing FER, with numerous studies proposing end-to-end frameworks that stack or widen significant convolutional neural network layers. While this has led to improved performance, it has also resulted in larger model sizes and longer inference times. To overcome this challenge, our work introduces a novel lightweight model architecture. The architecture incorporates three key factors: Depth-wise Separable Convolution, Residual Block, and Attention Modules. By doing so, we aim to strike a balance between model size, inference speed, and accuracy in FER tasks. Through extensive experimentation on popular benchmark FER datasets, our proposed method has demonstrated promising results. Notably, it stands out due to its substantial reduction in parameter count and faster inference time, while maintaining accuracy levels comparable to other lightweight models discussed in the existing literature.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Journal of Information Technology Applications and Management
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• v.15 no.3
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• pp.153-167
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• 2008

This study examines the effect of sense of community on the relationship between learner satisfaction and influencing factors related to the online learning environment. Influencing factors related to the online learning environment are derived from previous literature and classified into two groups : social dimension (leader's enthusiasm, offline activities) and system dimension (usefulness, ease of use, enjoyability). Learner satisfaction is defined as the learners' perceived learning gains from taking an online class. Study participants included 250 university students from two different institutions. The participants were divided into 43 groups and asked to complete an online TOEIC preparation module using a commercial cooperative learning system over 4 weeks. Data were collected at three points for each participant, at the beginning, 3 weeks after, and at the end of the online module. Two system factors related to the online learning environment (ease of use, usefulness) directly influenced learner satisfaction, while social factors indirectly influenced learner satisfaction through the mediating role of sense of community.

• The Mathematical Education
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• v.42 no.2
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• pp.121-135
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• 2003

This article retrospects the developmental process of the psychology of learning and its' influence on mathematics education. At the end of the article, brain-based learning science is introduced to examine its possibility to improve the psychology of learning mathematics. Behaviorists points of views such as Skinner, Guthrie, and Gagne were summarized to discuss the influences on the learning and teaching of mathematics. Gestalt' theories and Constructivism are also included in the discussion of developmental process of learning psychology. In elaboration of the brain-based learning science, recent research findings and the possibility of it's impact on mathematics education were discussed. Since mathematics itself is the most abstract subject it could be more challenging to identify the teaming process of mathematics compared with other areas. The possibilities of identifying the teaming process of mathematics are cautiously anticipated with a help of new paradigm.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.258-266
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• 2024

E-learning systems have proliferated in recent years, particularly in the wake of the global COVID-19 pandemic. For kids, there isn't a specific online learning platform available, though. To do this, new conceptual models of training and learning software that are adapted to the abilities and preferences of end users must be created. Young pupils: those in kindergarten, preschool, and elementary school are unique subjects with little research history. From the standpoint of software technology, young students who have never had access to a computer system are regarded as specific users with high expectations for the functionality and interface of the software, social network connectivity, and instantaneous Internet communication. In this study, we suggested creating an electronic learning management system that is web-based and appropriate for primary school pupils. User-centered design is the fundamental technique that was applied in the development of the system that we are proposing. Test findings have demonstrated that students who are using the digital environment for the first time are studying more effectively thanks to the online learning management system.