• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5819-5840
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• 2018

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

• Korean journal of medical education
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• v.30 no.4
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• pp.317-326
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• 2018

Purpose: The study examines changes in students' self-assessment of their general communication (GC) and medical communication (MC) competencies, as well as perceptions of MC concepts. Methods: Participants included 108 second year medical students enrolled at a Korean medical school studying an MC curriculum. It was divided into three sections, and participants responded to questionnaires before and after completing each section. To assess perceived GC and MC competency, items based on a 7-point Likert scale were employed; a single open-ended item was used to examine students' perceptions of MC. Statistical analysis was conducted to gauge GC and MC competency, whereas semantic network analysis was used to investigate students' perceptions of MC. Results: Students perceived their GC competency to be higher than MC. Perceived MC competency differed significantly across the three sections, whereas no differences were found for GC. There were no statistically significant differences after completing the curriculum's second and third sections; however, the vocabulary students used to describe MC concepts became more scholarly and professional. In the semantic networks, the link structure between MC-related words decreased in linearity and looseness, becoming more complex and clustered. The words 'information' and 'transfer' proved integral to students' perceptions; likewise, 'empathy' and 'communication' became closely connected in a single community from two independent communities. Conclusion: This study differed from prior research by conducting an in-depth analysis of changes in students' perceptions of MC, and its findings can be used to guide curriculum development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3258-3273
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• 2021

Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.23-32
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• 2018

The purpose of this study is to establish a walking energy weighted ERAM model that can predict the pedestrian volume by the connection structure of the vertical and horizontal spaces within a three-dimensional building. The process of building a walking-energy weighted ERAM model is as follows. First, the spatial graph was used to reproduce three-dimensional buildings with vertical and horizontal spatial connection structures. Second, the walking energy was measured on the spatial graph. Third, ERAM model was used to apply weights with spatial connection properties in random walking environment, and the walking energy weights were applied to the ERAM model to calculate the walk energy weighted ERAM values and visualize the distribution of pedestrian flow. To verify the validation of the established model, existing and proposed spatial analysis models were compared to real space. The results of this study are as follows : The model proposed in this study showed as much elaborated estimation of pedestrian traffic flow in real space as in traditional spatial analysis models, and also it showed much higher level of forecasting pedestrian traffic flow in real space than existing models.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.11
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• pp.73-79
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• 2018

In this study, for the prediction of energy consumption in the ventilator, one of the components of the air conditioning system, the predicted results were analyzed and accurate by the change in the number of neurons and inputs. The input variables of the prediction model for the energy volume of the fan were the supply air flow rate, the exhaust air flow rate, and the output value was the energy consumption of the fan. A predictive model has been developed to study with the Levenbarg-Marquardt algorithm through 8760 sets of one-minute resolution. Comparison of actual energy use and forecast results showed a margin of error of less than 1% in all cases and utilization time of less than 3% with very high predictability. MBE was distributed with a learning period of 1.7% to 2.95% and a service period of 2.26% to 4.48% respectively, and the distribution rate of ${\pm}10%$ indicated by ASHRAE Guidelines 14 was high.8.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.9
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• pp.207-214
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• 2019

Natural ventilation through openings such as windows in school buildings is an efficient resource for natural cooling during the intermediate season of the year. Because the natural ventilation uses the wind outside the building, the amount of ventilation will depend not only on the wind speed and wind direction but also on the window layout and open window ratio. Therefore, in this study, the natural ventilation plans of school classroom windows are divided into 4 types and 8 cases as shown in Table 1. The characteristics of cooling effect by natural ventilation are simulated by applying Energyplus's Airflow Network Model and the comfort of the occupants is evaluated by the number of hours included in the 80% acceptability range of the ASHRAE Standard 55-2010 adaptive comfort model for the weekdays (Monday-Friday) and the class hours (08: 00-19: 00). Based on the analysis results of the above, this study presents basic data related to classroom cooling plan using intermediate season natural ventilation.

• Korea Trade Review
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• v.44 no.6
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• pp.327-344
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• 2019

This paper explores the hub-and-spoke system as the structure of the global economic network that presents obstacles for international cooperation. With its exclusive jurisdiction and control over the hub, a powerful state can employ coercive economic measures to compel and deter unwanted behavior of rogue states and even its allies. Against this backdrop, this study analyzes the cases of the US blocking access to its market by Chinese Huawei as well as the case of Japan in restricting trade for highly advanced goods to South Korea. This analysis reveals that both measures are forms of secondary boycotts, which affect not only the entities within their jurisdiction but also others located in third countries. In addition, this paper extends its findings to free trade agreements and offers implications on the outward processing scheme for the Gaeseong Industrial Complex in the KORUS FTA and the Korea-China FTA. These events result in a gray-risk for South Korea, a country that aims to resolve North Korea's denuclearization and inter-Korean economic cooperation.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.757-770
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• 2022

Palmprint recognition has drawn increasingly attentions in the past decade due to its uniqueness and reliability. Traditional palmprint recognition methods usually use high-resolution images as the identification basis so that they can achieve relatively high precision. However, high-resolution images mean more computation cost in the recognition process, which usually cannot be guaranteed in mobile computing. Therefore, this paper proposes an improved low-resolution palmprint image recognition method based on residual networks. The main contributions include: 1) We introduce a channel attention mechanism to refactor the extracted feature maps, which can pay more attention to the informative feature maps and suppress the useless ones. 2) The ResStage group structure proposed by us divides the original residual block into three stages, and we stabilize the signal characteristics before each stage by means of BN normalization operation to enhance the feature channel. Comparison experiments are conducted on a public dataset provided by the Hong Kong Polytechnic University. Experimental results show that the proposed method achieve a rank-1 accuracy of 98.17% when tested on low-resolution images with the size of 12dpi, which outperforms all the compared methods obviously.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.8-14
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• 2022

In this study reports experiments were conducted to determine the quality of weld beads of different materials, Al and Cu. Among the lasers used to make battery cells for electric vehicles, non-destructive testing was performed using deep learning to determine the quality of beads welded with the ARM laser. Deep learning was performed using AlexNet algorithm with a convolutional neural network structure. The results of quality identification were divided into good and bad, and the result value was derived that all the results were in agreement with 94% or more. Overall, the best welding quality was obtained in the experiment for the fixed ring beam output/variable center beam output, in the case of the fixed beam (ring beam) 500W and variable beam (center beam) 1,050W; weld bead failure was seldom observed. The tensile force test to confirm the reliability of welding reported an average tensile force of 2.5kgf/mm or more in all sections.