• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.185-192
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• 2013

The development of hardware, popularization of 3D graphics software could get to easily use 3d graphics tool in the school. And learning difficulties of a shape section increased through more being enforced a shape section of an elementary school. Thus we try to improve learning effectiveness in a shape section using Sketech Up software. To do this, we analyzed existing studies, classified 3D graphics software, provided the selection criteria of vector graphics software. And we explained how to select 3D graphics software. We selected and reorganized the shape contents to use Sketch Up, which make and rotate 3D figures, understand aspects of a shape. And we inserted the content about piling 3D figures in the beginning state of the curriculum. we composed 10 periods and practiced our reorganized curriculum to the teaching and learning using Sketch Up. And we conducted before & after survey to check out t-verified. And we acquired meaningful results statistically. Thus applying Sketch Up to the shape learning can be analyzed effectively.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.33-39
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• 2011

In modern society, in business decisions of our customers are continually increasing in importance, and owing to the development of information and communication technology effectively on a computer to measure the preferences of key customer techniques are being studied. However, this preference reflects significantly on personal ideas, and therefore, it is difficult to commercialize a measure calculated according to the ambiguous results. In this paper, by using biometric information that has been measure; we have configured the multi-sensitivity models based on customer preferences to evaluate the proposed system. This system consists of multiple biometric information of multi-dimensional vector model for learning through the use of structured emotional to apply the same criteria to evaluate customer preferences. In addition, by studying the specific subject-specific emotion model, it is shown to improve accuracy with further experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1768-1775
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• 2015

Recent major domestic facilities, such as nuclear power plants, many control cables are installed and are degraded by long-term use, but research on deterioration diagnosis is lacking. In the event of a fault in the cable due to deterioration can be developed into a major accident such as the main plant is stopped, so the deterioration diagnostic techniques of high reliability for the cable is required. In this paper, proposes a methodology using a cable resonance that can effectively diagnose the deterioration of the cable. Prior to the test, we developed a setup for stable measuring the characteristics of the cable and it verified the suitable of the measurement set-up in terms of interactivity and reliability, also measured S-parameters applying verified measurement set-up to the cables that deterioration degree is different. Then, we had amplified the difference in resonance frequency between the healthy state and the deteriorated state using connection in a series of measured S-parameters. In a result from the method, we have verified that the more deteriorate the cables is, the more decrease the resonance frequency is. Measured results are justified by inducing the resonance frequency calculation of the cable from the S- parameters represented by the hyperbolic function formula. VNA(Vector Network Analyzer) for S-parameter measurements used in this study is Agilent E5061B and shielded twisted-pair cables was used for deterioration diagnostic test.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2261-2266
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• 2009

A novel adaptive phase calibration method is proposed for nonlinear amplifiers. Based on the adaptive process of simple phase vector calculations, the AM/PM distortion can be significantly reduced for various input power. The performance of the proposed method is evaluated for up to 80 % improvements in AM/PM distortions, compared with the distortion of a conventional amplifier. Moreover, by means of an additional envelope-compensation technique, the improvement of the adjacent channel power ratio (ACPR) is presented.

• Journal of KIISE:Information Networking
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• v.29 no.2
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• pp.156-164
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• 2002

IPSec is a protocol which provides data encryption, message authentication and data integrity on public and open network transmission. In IPSec, ESP protocol is used when it needs to provide data encryption, authentication and Integrity In real transmission packets. ESP protocol uses DES-CBC encryption mode when sender encrypts packets and receiver decrypts data through this mode IV is used at that time. This value has many tasks of attack during transmission by attacker because it is transferred clean and opened. If IV value is modified, then decryption of ESP data is impossible and higher level information is changed. In this paper we propose a new algorithm that it encrypts IV values using DES-ECB mode for preventing IV attack and checks integrity of whole ESP data using message authentication function. Therefore, we will protect attacks of IV and data, and guarantee core safe transmission on the public network.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.127-135
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• 2021

Raising large numbers of animals in very narrow environments such as laying hens house can be very damaged from small environmental change. Previously researched about laying hens sound analysis system has a problem for applying to the laying hens house because considering only the limited situation of laying hens house. In this paper, to solve the problem, we propose a new laying hens sound analysis model using MFCC feature vector. This model can detect 7 situations that occur in actual laying hens house through 9 kinds of laying hens sound analysis. As a result of the performance evaluation of the proposed laying hens sound analysis model, the average AUC was 0.93, which is about 43% higher than that of the frequency feature analysis method.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.