• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.45-52
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• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.157-163
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• 2023

The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through end-users. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1299-1303
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• 2005

Over the last decade the research of end-to-end behavior on computer network has grown by orders but it has few researching in hop-by-hop behavior. We think if we know hop-by-hop behavior it can make better understanding in network behavior. This paper represent ICMP time stamp request and time stamp reply as tool of network study for learning in hop-by-hop behavior to estimate link bandwidth and link structure. We describe our idea, experiment tools, experiment environment, result and analysis, and our discussion in our observative.

• Educational Technology International
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• v.12 no.2
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• pp.47-70
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• 2011

The purpose of this paper was to develop evaluation procedures for blended instruction, focusing on the courses that are currently offered in the university. This study analyzed current evaluation procedures and instruments and suggested redesign the evaluation process for blended instruction. The evaluation procedures are designed based on the combination of objective-oriented and consumer-oriented evaluation approaches. It includes three stages: front-end (screening), formative evaluation, and summative evaluation. During the front-end evaluation stage, information regarding students' technology skills and attitudes towards online instruction and classroom instruction are suggested to collect and plan the instructional strategies accordingly. The formative evaluation is conducted during the semester to collect students' opinions about the course and instructors modify their instruction based on the evaluation results. At the end of semester, summative evaluation is to be conducted to collect the data to improve the course. Evaluation questions and components for each stage are developed to collect the data such as students' perceptions of the course, the usefulness of online instructional materials, the effectiveness of blended learning strategies, and students' satisfaction with the course.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.141-154
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• 2016

Objectives: The purpose of the study was to identify factors influencing on nursing students' end-of-life care attitude. Methods: A sample of convenience of 147 nursing students, Instrument included death anxiety, death attitude, Self-esteem, Life satisfaction, end-of-life care attitude. Results: A significant negative correlation was found among end-of-life care attitude, death anxiety, death attitude. Death anxiety(${\beta}$=-.392), self-esteem(${\beta}$=.179) & experience of learning(${\beta}$=-.227) about death were significant predictive variables. This variables accounted for 18.7% of the variance in end-of-life care attitude. Conclusions: Based on the Findings of this study, it can be used to develop educational programs for end-of-life care.

• Knowledge Management Research
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• v.1 no.1
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• pp.65-80
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• 2000

The main purpose of this study is to answer a question that how a company can be a learning organization producing useful knowledge by applying neuro-cybernetics approach. This approach borrows its working principles from the human body systems. The current study urges that the principles can be applied to build a learning organization. System 1 to 5, the core parts of neuro-cybernetics, are explained. And it is explored that how these systems can be designed for a company to be a learning organization. Limitations of the current study are discussed at the end of the paper.

• Health Policy and Management
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• v.4 no.2
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• pp.111-125
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• 1994

In developing total hospital information system, large amount of time and expense are to be spent while its results are likely to lead itself to end-users' dissatisfaction. Some of the main complaints on the part of end-users come from insufficient consideration of end-users environment as well as inappropriate representation of their requirement in the system alalysis and design. This papre addresses some advantages of Essential Logical Modeling Process for better analysis and design, explaining by example the developmental process of the Patent Management and Accounting System for a tertiary care hospital. In the case, the Essential Model, suggested by McMenamin and Palmer, proved to be an effective tool for clear separation of analysis and design phase and for better communication among system developers and with end-users. The modeling process itself contributed to better program modularity as well, shown in a Structured Chart. Difficulties in learning how to identify' essential activities' for the modeling practice were experienced in the beginnins stage, which were, however, overcome by elaborating some heuristic guideling and by rdferring to necessary tools including State Transition Diagram, Control Flow Diagram, and so many. While full evaluation of the Essential Model usag remains to wait till the completion of the case project, its strengt in making clear distinction between analysis and design phase was enough to be attractive to system analysts. The model concepts are open to many further application fields, particularly such areas as business re engineering, process remodeling, office automation, and organizational restructuring.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.244-247
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• 1993

In this paper, Fuzzy-Neural Network Control system that has the characteristic of fuzzy control to be controlled easily end the good characteristic of a artificial neural network to control the plant due to its learning is presented. A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which ere created by a expert. To adaptivity, the more precise modeling is implemented by error beck-propagation learning of adjusting the link-weight of fuzzy membership function in Fuzzy-Neural Network. The more classified fuzzy rule is used to include the property of Dual Mode Method. To test the effectiveness of the algorithm presented above, the simulation for position end velocity of DC servo motor is implemented.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.