• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.681-686
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• 2016

Software defect prediction is an important factor in efficient project management and success. The severity of the defect usually determines the degree to which the project is affected. However, existing studies focus only on the presence or absence of a defect and not the severity of defect. In this study, we proposed an ensemble model using FCM based on defect severity. The severity of the defect of NASA data set's PC4 was reclassified. To select the input column that affected the severity of the defect, we extracted the important defect factor of the data set using Random Forest (RF). We evaluated the performance of the model by changing the parameters in the 10-fold cross-validation. The evaluation results were as follows. First, defect severities were reclassified from 58, 40, 80 to 30, 20, 128. Second, BRANCH_COUNT was an important input column for the degree of severity in terms of accuracy and node impurities. Third, smaller tree number led to more variables for good performance.

• Tunnel and Underground Space
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• v.26 no.1
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• pp.46-58
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• 2016

In this study, a LIDAR laser scanner was used to scan a rock slope around Mt. Gwanak and to produce point cloud from which directional information of rock joint surfaces shall be extracted. It was analyzed using two different algorithms, i.e. Ball Pivoting and Wrap algorithm, and four sampling intervals, i.e. raw, 2, 5, and 10 cm. The results of Fuzzy K-mean clustering were analyzed on the stereonet. As a result, the Ball Pivoting and Wrap algorithms were considered suitable for extraction of rock surface orientation. In the case of 5 cm sampling interval, both triangulation algorithms extracted the most number of the patch and patched area.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.205-211
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• 2015

Recently, a mobile communication network and the wireless terminal is suddenly develop, mobile office service is more and more the sportlight. However, the user may receive an attack from a malicious third party if the up/download the data in the remote to perform the work in a mobile office environment. In this paper, we propose scheme to manage the information lost due to theft smartphone that contain spill prevention personal information and company information from the mobile office environment (call history, incoming messages, phonebook, calendar, location information, banking information, documents, etc.). The proposed scheme using the number of triangular fuzzy information about the state of the personal information and business intelligence to implement a pair-wise comparison matrix. In particular, the proposed scheme is to prevent the value obtained by constructing a pair-wise comparison matrix for personal information and business intelligence and pair your smartphone is lost when a third party not allow access to personal information and corporate information is leaked to the outside.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.635-638
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• 2005

The purpose of video segmentation is to segment video sequence into shots where each shot represents a sequence of frames having the same contents, and then select key frames from each shot for indexing. There are two types of shot changes, abrupt and gradual. The major problem of shot change detection lies on the difficulty of specifying the correct threshold, which determines the performance of shot change detection. As to the clustering approach, the right number of clusters is hard to be found. Different clustering may lead to completely different results. In this thesis, we propose a video segmentation method using a color-X$^2$ intensity histogram-based fuzzy c-means clustering algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.29-43
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• 2019

In recent years, as the network traffic in the WSN(Wireless Sensor Network) has been increased by the growing number of IoT wireless devices, SDWSN(Software-Defined Wireless Sensor Network) and its security that aims a secure SDN(Software-Defined Networking) for efficiently managing network resources in WSN have received much attention. In this paper, we study on how to efficiently and securely design a PUF(Physical Unclonable Function)-assisted group key distribution scheme for the SDWSN environment. Recently, Huang et al. have designed a group key distribution scheme using the strengths of SDN and the physical security features of PUF. However, we observe that Huang et al.'s scheme has weak points that it does not only lack of authentication for the auxiliary controller but also it maintains the redundant synchronization information. In this paper, we securely design an authentication process of the auxiliary controller and improve the vulnerabilities of Huang et al.'s scheme by adding counter strings and random information but deleting the redundant synchronization information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.36-46
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• 2022

It takes a relatively long time for construction companies that lack the ability to finance to adapt to construction policy in the construction industry. However, financial institutions rarely provide financial products to construction companies, particularly small and medium-sized construction companies, because their security capacity and credit rating are low. This study investigates the financial products needed for small and medium construction companies to adapt to policy changes. The demand of small and medium construction companies for financial products is analyzed by experts' advise and survey. And, when the investigated financial products for the construction industry are introduced, the legal systems in need of revision are analyzed. Based on the analyzed demand and the number of legal systems needing revision, the priority for the introduction of financial products to the construction industry is analyzed. Among the financial products investigated, the priority of "Expert consultation, such as accountant, tax accountant, lawyer, etc." is the highest. In future studies, the criteria and method of financial product development for high-priority financial products could be researched.

• Computers and Concrete
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• v.29 no.3
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• pp.187-199
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• 2022

The usability of waste materials as raw materials is necessary for sustainable production. This study investigates the effects of different powder materials used to replace cement (0%, 5% and 10%) and standard sand (0%, 20% and 30%) (basalt, limestone, and dolomite) on the compressive strength (f_c), flexural strength (f_r), and ultrasonic pulse velocity (UPV) of mortars exposed to freeze-thaw cycles (56, 86, 126, 186 and 226 cycles). Furthermore, the usability of artificial intelligence models is compared, and the prediction accuracy of the outputs is examined according to the inputs (powder type, replacement ratio, and the number of cycles). The results show that the variability of the outputs was significantly high under the freeze-thaw effect in mortars produced with waste powder instead of those produced with cement and with standard sand. The highest prediction accuracy for all outputs was obtained using the adaptive-network-based fuzzy inference system model. The significantly high prediction accuracy was obtained for the UPV, f_c, and f_r of mortars produced using waste powders instead of standard sand (R² of UPV, f_c and f_f is 0.931, 0.759 and 0.825 respectively), when under the freeze-thaw effect. However, for the mortars produced using waste powders instead of cement, the prediction accuracy of UPV was significantly high (R²=0.889) but the prediction accuracy of f_c and f_r was low (R²f_c=0.612 and R²f_f=0.334).

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.273-280
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• 2003

The purpose of this study is to identify and evaluate the competitiveness of ports in ASEAN(Association of Southeast Asian Nations), which plays a leading role in basing the hub of international logistics strategies as a countermeasure in changes of international logistics environments. This region represents most severe competition among Mega hub ports in the world in terms of container cargo throughput at the onset of the 21 st century. The research method in this study accounted for overlapping between attributes, and introduced the HFP method that can perform mathematical operations. The scope of this study was strictly confined to the ports of ASEAN. which cover the top 100 of 350 container ports that were presented in Containerization International Yearbook 2002 with reference to container throughput. The results of this study show Singapore in the number one position. Even compared with major ports in Korea (after getting comparative ratings and applying the same data and evaluation structure), the number one position still goes to Singapore and then Busan(2) and Manila(2), followed by Port Klang(4), Tanjugn Priok(5), Tanjung Perak(6), Bangkok(7), Inchon(8), Laem Chabang(9) and Penang(9). In terms of the main contributions of this study, it is the first empirical study to apply the combined attributes of detailed and representative attributes into the advanced HFP model which was enhanced by the KJ method to evaluate the port competitiveness in ASEAN. Up-to-now, none have comprehensively conducted researches with sophisticated port methodology that has discussed a variety of changes in port development and terminal transfers of major shipping lines. Moreover, through the comparative evaluation between major ports in Korea and ASEAN, the presentation of comparative competitiveness for Korea ports is a great achievement in this study. In order to reinforce this study, it needs further compensative research, including cost factors which could not be applied to modeling the subject ports by lack of consistently qualified in ASEAN.