• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Journal of Korean Academy of Nursing Administration
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• v.9 no.4
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• pp.599-613
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• 2003

Purpose: The ultimate aim of the inquiry was to discover the essence of nurse's experience and promote understanding. Method : Guided by Colaizzi's method - 1. Description of the phenomena of interest by the reader. 2. collection of subject's description of the phenomena, 3. Reading all the subject's descriptions of the phenomenon, 4. Returning the original transcripts and extracting significant statements, 5. Trying to spell out the meaning of each significant statements, 6. Organizing the aggregate formalized meanings into clusters of themes. 7. Writing an exhaustive description, 8. Returning to the subjects for validation of the description, 9. If new data fare revealed during the validations incorporating them into an exhaustive description. The participants in this study were eight are nurses working for clinics. This strategies for data collecting were needed : deep face to face interview. Results : 6 cluster of themes are : 1. the heavy pressure, 2. the pride and the royal summons, 3. the powerlessness, 4. the hope, 5. the tiresome. 6. the distressed feeling. Conclusion : The results of the this study would help us to understand nurses in clinics, make direction for nursing education, and identify need for continuing inquiry.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.109-116
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• 2020

The study examines the nexus between financial development and economic growth in India during Q1: 1996 to Q3: 2018. This study employs time-series data of real GDP and ratio of broad money to GDP as a proxy for economic and financial development, respectively. The data are obtained from RBI database on the Indian economy. All variables are seasonally adjusted using X12-arima technique and expressed in natural logarithm form. Non-linear Autoregressive Distributed Lag (NARDL) bound test has been used to check for cointegrating relationship of these two variables. Empirical findings suggest that, unlike in the short run, in the long run financial development does impact economic growth positively. Further, a symmetric effect of positive and negative components of financial development is found for the Indian economy, whereas the effect of control variable like exchange rate and trade openness is in consonance with common economic intuition. Exchange rate is in consonance with intuitive economic logic that a fall in exchange rate makes exports cheaper and increases the quantity of export, which improves the balance of payment and leads to a rise in aggregate demand, hence improves economic growth. This paper contributes to the existing literature on India by breaking down financial indicator into positive and negative components to examine the finance-growth relationship.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.18-26
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• 2005

The balanced scorecard(BSC) overcomes the limit of traditional financial statement that focuses on only financial performance. BSC is widely used in government and industry because of the clear representation of the relationship and logic between the key performance indicators(KPI) of 4 perspectives - financial, customer, internal process, and loaming and growth. However, traditional BSC does not consider evaluating the difference between the results measured by BSC. By using relatively small number of inputs and outputs In comparing decision-making units, data envelopment analysis(DEA) can aggregate multiple performance measures. In this research, we propose a methodology named CDB(Combined DEA and BSC) to evaluate the performance of organization considering financial and non-financial perspectives. CDB uses KPI of cause-and-effect relationship on BSC as inputs and outputs of DEA method. In addition, this research proposes a method of converting the KPI of BSC to the input and output variables of DEA, and enhancing discrimination power using the limit number of variables. We illustrate the methodology by giving an example of evaluating aquisition-unit efficiency in a supply chain.

• Geomechanics and Engineering
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• v.10 no.1
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• pp.49-57
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• 2016

The high permeability-high strength concrete belongs to the typical of porous materials. It is mainly used in underground engineering for cold area, it can act the role of heat preservation, also to be the bailing and buffer layer. In order to establish a suitable model to predict the thermal conductivity and directly applied for engineering, according to the structure characteristics, the thermal conductivity predicting model was built by resistance network model of parallel three-phase medium. For the selected geometric and physical cell model, the thermal conductivity forecast model can be set up with aggregate particle size and mixture ratio directly. Comparing with the experimental data and classic model, the prediction model could reflect the mixture ratio intuitively. When the experimental and calculating data are contrasted, the value of experiment is slightly higher than predicting, and the average relative error is about 6.6%. If the material can be used in underground engineering instead by the commonly insulation material, it can achieve the basic requirements to be the heat insulation material as well.

• Computers and Concrete
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• v.19 no.5
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• pp.555-566
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• 2017

Recently some efforts have been performed to combine the advantages of light-weight and self-compacting concrete in one package called Light-Weight Self-Compacting Concrete (LWSCC). Accurate prediction of hardened properties from fresh state characteristics is vital in design of concrete structures. Considering the lack of references in mixture design of LWSCC, investigating the proper mixture components and their effects on mechanical properties of LWSCC can lead to a reliable basis for its application in construction industry. This study utilizes wide range of existing data of LWSCC mixtures to study the individual and combined effects of the components on the compressive strength. From sensitivity of compressive strength to the proportions and interaction of the components, two equations are proposed to estimate the LWSCC compressive strength. Predicted values of the equations are in good agreement with the experimental data. Application of lightweight aggregate to reduce the density of LWSCC may bring some mixing problems like segregation. Reaching a higher strength by lowered density is a challenging problem that is investigated as well. The results show that, the compressive strength can be improved by increasing the of mixture density of LWSCC, especially in the range of density under $2000Kg/m^3$.

• Computers and Concrete
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• v.21 no.1
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• pp.21-30
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• 2018

In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3112-3127
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• 2018

Since time series clustering is performed without prior information, it is used for exploratory data analysis. In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications. However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters. The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large. In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series. In the data preprocessing step, we normalize the time series using z-transformation. Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series. In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster. We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series. In our experiments, our method showed better results than the existing method.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.147-158
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• 2020

We apply Return Dispersion Model by calculating CSAD (Cross-sectional standard deviation of return) and State Space Model to identify herding behavior in the period of pandemic (H1N1 and COVID-19). Employing data from TEJ and Data Stream, this paper examines whether the herding behavior is existing in Vietnam and Taiwan stock market, especially during pandemic influenza. We compare the differences in herding behavior between frontier and emerging markets by examining different industries across Vietnam and Taiwan stock market approaches. The results indicate solid evidence for investor herd configuration in the various industries of Vietnam and Taiwan. The herding impact in the industries will be greater than with the aggregate market. The different industries respond differently to influenza pandemic panics through uptrend and downtrend demonstrations. Up to 12 industries were found to have herding in Vietnam, while Taiwan had only 5 of 17 industries classified. Taiwan market, an emerging and herding-level market, has changed due to the impact of changing conditions such as epidemics, but not as strongly as in Vietnam. From there, we see that the disease is a factor that, not only creates anxiety from a health perspective, but also causes psychological instability for investors when investing in the market.