• Journal of Communications and Networks
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• v.18 no.5
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• pp.818-825
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• 2016

Network management and anomaly detection are challenges in high-speed networks due to the high volume of packets that has to be analysed. Flow-based analysis is a scalable method which reduces the high volume of network traffic by dividing it into flows. As sampling methods are extensively used in flow generators such as NetFlow, the impact of sampling on the performance of flow-based analysis needs to be investigated. Monitoring using sampled traffic is a well-studied research area, however, the impact of sampling on flow-based anomaly detection is a poorly researched area. This paper investigates flow sampling methods and shows that these methods have negative impact on flow-based anomaly detection. Therefore, we propose an efficient probabilistic flow sampling method that can preserve flow traffic distribution. The proposed sampling method takes into account two flow features: Destination IP address and octet. The destination IP addresses are sampled based on the number of received bytes. Our method provides efficient sampled traffic which has the required traffic features for both flow-based anomaly detection and monitoring. The proposed sampling method is evaluated using a number of generated flow-based datasets. The results show improvement in preserved malicious flows.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• Journal of Korean Elementary Science Education
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• v.32 no.4
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• pp.379-392
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• 2013

The purpose of this study was to examine the effects of animation-based instruction on elementary students' level of understanding and interest on a plant's structure and function, using "Magic School Bus(Joanna Cole, Bruce Degen, 1986)". The understanding and interest measurements were administered to 99 fifth grade students at a elementary school located in Seoul. The study examined the changes in understanding and interest through pre-test and post-test of the two groups. Intensive interviews were conducted to find factors that enhance understanding and interest. Three key findings were revealed from the results. First, the animation-based instruction enhanced the level of understanding in the experimental group compared to the control group. Second, animation-based instruction made high-achieving students enhance their interest in topic component and attitude component. And, animation-based instruction did not led low-achieving students to increase their interest in the topic component and attitude component. We suggest that animation-based instruction positively influences students' understanding, but its effects on students' interest are dependent on their conceptual understanding of the topic.

• The Mathematical Education
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• v.48 no.1
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• pp.61-80
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• 2009

The purpose of this study was to develop case-based pedagogy in mathematics for elementary school teachers and to investigate how they participate in the course employing case-based pedagogy. The 13 cases were developed and employed in pre-service teacher education. As such, the cases covered all content areas across grades, and included detailed description of mathematics instruction, questions for discussion, theoretical review related to each case, focus analysis and additional analysis, etc. This paper describes in what ways the participant teachers discussed cases, how case-based pedagogy had an influence on the teachers' own instruction during the practicum period, and how they assessed case-based pedagogy. This paper provides issues and suggestions for the professional development of mathematics teachers on the basis of empirical background.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1263-1279
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• 2020

Activating appropriate roles for a session in the role-based access control (RBAC) model has become challenging because of the so-called role explosion. In this paper, factors and issues related to user-driven role management are analysed, and a session role activation (SRA) problem based on reasonable assumptions is proposed to describe the problem of such role management. To solve the SRA problem, we propose an extended RBAC model with context-based role filtering. When a session is created, context conditions are used to filter roles that do not need to be activated for the session. This significantly reduces the candidate roles that need to be reviewed by the user, and aids the user in rapidly activating the appropriate roles. Simulations are carried out, and the results show that the extended RBAC model is effective in filtering the roles that are unnecessary for a session by using predefined context conditions. The extended RBAC model is also implemented in the Apache Shiro framework, and the modifications to Shiro are described in detail.

• Earthquakes and Structures
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• v.13 no.5
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• pp.455-464
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• 2017

This paper develops a new method for analyzing the structural seismic behavior of single-layer reticulated shells based on exponential strain energy density (ESED). The ESED method reveals a characteristic point from a relationship between ESED sum and peak seismic acceleration. Then, the characteristic point leads to an updated concept of structural failure and an ESED-based criterion for predicting structural failure load. Subsequently, the ESED-based criterion and the characteristic point are verified through numerical analysis of typical single-layer reticulated shells with different configurations and a shaking table test of the scale shell model. Finally, discussions further verify the rationality and application of the ESED-based criterion. The ESED method might open a new way of structural analysis and the ESED-based criterion might indicate a prospect for a unified criterion for predicting seismic failure loads of various structures.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.346-348
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• 2022

Media Contents Recommendation Web Service (service name 'mobodra') is a web service that analyzes media types and genre tastes for each user and recommends content accordingly. Users select some of the works randomly provided on the web when signing up for membership and analyze their tastes based on this. Based on this analysis, preferred content for each user is recommended. In this paper, we implement a content recommendation algorithm through item-based collaborative filtering. When the user's activity data or preference is re-examined, the above process is executed again to update the user's taste.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.85-92
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• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.499-513
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• 2020

Among the many studies of carbon-based nanomaterials, carbon-based dots (CDs) have attracted considerable interest owing to their large surface area, intrinsic low-toxicity, excellent biocompatibility, high solubility, and low-cost with environmentally friendly routes, as well as their ability for modification with other nanomaterials. CDs have several applications in biosensing, photocatalysis, bioimaging, and nanomedicine. In addition, the fascinating electrochemical properties of CDs, including high active surface area, excellent electrical conductivity, electrocatalytic activity, high porosity, and adsorption capability, make them potential candidates for electrochemical sensing materials. This paper reviews the recent developments and synthesis of CDs and their composites for the proposed electrochemical sensing platforms. The electrochemical principles and future perspective and challenges of electrochemical biosensors are also discussed based on CDs-nanocomposites.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1272-1275
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• 2002

Watermark detection plays a crucial role in multimedia copyright protection and has traditionally been tackled using correlation-based algorithms. However, correlation-based detection is not actually the best choice, as it does not utilize the distributional characteristics of the image being marked. Accordingly, an efficient watermark detection scheme for DWT coefficients is proposed as optimal for non-additive schemes. Based on the statistical decision theory, the proposed method is derived according to Bayes' decision theory, the Neyman-Pearson criterion, and the distribution of the DWT coefficients, thereby minimizing the missed detection probability subject to a given false alarm probability. The proposed method was tested in the context of robustness, and the results confirmed the superiority of the proposed technique over conventional correlation-based detection method.