• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.209-216
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• 2013

RFID/USN, are recognized as the new growth engines for the future, regardless of the advanced and developing countries. RFID, in particular, already entered practical stage by global companies. EPCIS, which is the one of the EPC golabal network components, makes a huge load on the system due to the large amount of entering data by following events. In this study, a data model developed based on ER-WIN by collecting four kind information that occurred in the EPC global networks. This model supports both in processing of high performance and huge capacity of data by a considerable storage capacity and input speed. A simulation was developed in order to verify the performance. Each model tested several times and results were compared.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Journal of Sasang Constitutional Medicine
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• v.33 no.4
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.97-104
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• 2022

The purpose of the existing work is to inspect the impact of knowledge management on organizational performance. Business experts now appreciate how important knowledge management is for organizational performance. Earlier studies have investigated the research model with causal linkages, however, only a few of them have considered sample-selecting bias problems when analyzing the model of knowledge management on organizational performance. The number of 312 executives related to knowledge management from 312 enterprises that have been approved with quality management systems offered suitable responses for analyses. The data was employed to investigate the effect of knowledge management on organizational performance, considering sample-selecting bias. The empirical outcomes indicate that sample-selecting bias exists in the causal impact of knowledge management on organizational performance. The empirical findings are helpful to scholars of knowledge management as well as business executives by giving an insight into the casual effect of knowledge management on organizational performance with the intervention of sample-selecting bias. The acceptance of knowledge management should be tailored to improve competitive advantages that will lead to better organizational performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.935-944
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• 2023

In this study, the Inverse-type (: Inverse-Exponential, Inverse-Rayleigh) life distribution, which is known to be suitable for reliability research, was applied to a software development cost model based on finite failure NHPP(: Nonhomogeneous Poisson Process), and then the attributes that determine the model's performance were analyzed. Additionally, to evaluate the efficiency of the model, it was compared with the Goel-Okumoto basic model. The performance of the model was analyzed using failure time data, and MLE (: Maximum Likelihood Estimation) was applied to calculate the parameters. In conclusion, first, as a result of analyzing m(t), which determines the development cost, the Inverse-Exponential model was efficient due to its small error in the true value. Second, as a result of analyzing the release time along with the development cost, the Inverse-Rayleigh model was confirmed to be the best. Third, as a result of comprehensive evaluation of the attributes (m(t), cost, and release time) of the proposed model, the Inverse-Rayleigh model had the best performance. Therefore, if software developers can effectively utilize this research data in the early process, they will be able to proactively explore and analyze attributes that affect cost.

• The Journal of Bigdata
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• v.4 no.1
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• pp.11-27
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• 2019

There is gradually being a decrease in earnings rate of the container terminals because of worsened business environment. To enhance global competitiveness of terminal, operators of the container terminal have been attempting to deal with problems of operations through analyzing overall the terminal operations. For improving operations of the container terminal, the operators try to efforts about analyzing and utilizing data from the database which collects and stores data generated during terminal operation in real time. In this paper, we have analyzed the characteristics of operating processes and defined the event log data to generate container processes and CKO processes using stored data in TOS (terminal operating system). And we have explained how imperfect event logs creating non-normal processes are refined effectively by analyzing the container and CKO processes. We also have proposed the framework to refine the event logs easily and fast. To validate the proposed framework we have implemented it using python2.7 and tested it using the data collected from real container terminal as input data. In consequence we could have verified that the non-normal processes in the terminal operations are greatly improved.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• Korea Trade Review
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• v.41 no.4
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• pp.91-110
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• 2016

AEO certification has a positive effect on firm's performance. Therefore, this study analyzed the effect of AEO certification on firm's performance using panel data analysis for firm to have international competitiveness. It uses the Hausman-Taylor test for effective solutions of endogenous matter. In terms of the result of analysis, AEO certification has a positive effect on domestic and foreign sales, especially direct benefit and business process improvement of AEO certification have a positive effect on domestic and foreign sales through the improvement of international logistics flow. In conclusion, this study proposes the policy of AEO certification by analyzing the effect of AEO certification on firm's performance by using the panel data analysis.

• Convergence Security Journal
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• v.4 no.3
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• pp.1-8
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• 2004

Large distributed systems such as computational and data grids require that a substantial amount of monitoring data be collected for various tasks such as fault detection, performance analysis, performance tuning, performance prediction, security analysis and scheduling. to cope with this problem, they are needed network monitoring architecture which can collect various network characteristic and analyze network security state. In this paper, we suggest network performance and security analysis system based on network monitoring. The System suggest that users can see distance network state with tuning network parameters.