• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• 융합경영연구
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• 제11권4호
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• pp.31-39
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• 2023

Purpose: This study attempted to systematize a support system that can enhance teaching core competencies by establishing a scale for diagnosing teaching core competencies at University A. Research design, data and methodology : To this end, the first Delphi was conducted With six experts related to university core competency modeling research by extracting factors and designing structured questionnaires through a literature review process that collects and analyzes prior research related to domestic and foreign university teaching competency. The derived questions were diagnosed on 27 professors, and independent sample t-verification and ANOVA were conducted using SPSS 24.0 for analysis by key teaching competency factors. Result: What is the standard suitability of KMO. It was shown as 929 (KMO standard conformity value is close to 1), and Barlett's sphericity verification showed χ2=5773.295, df=1081, p<.It appeared as 001 and confirmed that it was suitable for conducting factor analysis. Conclusions: The core competencies of A University teachers were set based on the educational goals of A University, such as basic teaching competency, creative teaching competency, practical teaching competency, and communication teaching competency. This means that the concept and factors of the core competency of professors are likely to change, and in the end, continuous efforts to upgrade and apply research on core competency of professors are essential to quickly and organically respond to changes in competency required to increase the competitiveness of universities.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2007년도 International Conference
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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.

• 인터넷정보학회논문지
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• 제22권6호
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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.

• 대한원격탐사학회지
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• 제33권4호
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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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• 제13권1호
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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.

• 공학교육연구
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• 제22권2호
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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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• 제17권9호
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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.

• 제어로봇시스템학회논문지
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• 제6권12호
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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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• 제15권3호
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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.