• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.151-159
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• 2012

This paper proposes to develop multi-currencies portfolio strategy using principal component analysis (PCA) and logistic regression (LR) in foreign exchange market. While there is a great deal of literature about the analysis of exchange market, there is relatively little work on developing trading strategies in foreign exchange markets. There are two objectives in this paper. The first objective is to suggest portfolio allocation method by applying PCA. The other objective is to determine market timing which is the strategy of making buy or sell decision using LR. The results of this study show that proposed model is useful trading strategy in foreign exchange market and can be desirable solution which gives lots of investors an important investment information.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.495-502
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• 2006

This paper describes how to implement reporting system for web-based information system. Reporting system is the S/W module that user is able to produce output of data in formal format. In Client/Server system, remote user can make formal output with client-side reporting component module. This client reporting component is usually system dependent. With web based information system, intranet, evolving, this client/server system need to he migrated to web-based reporting system. Reporting system component support multi-processing and real-time text/graphic output of server data. Ant Client can save or pint web page of client module. Especially, for real test its function and user interface, this reporting component is adopted in real network management system. The result shows that this reporting system component is very smart and excellent for real time web based monitoring system.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.378-381
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• 2003

The Channel Coding Unit (CCU) is an integral component of Payload Data Transmission System (PDTS) for the Multi-Spectral Camera (MSC) data. The main function of the CCU is channel coding and encryption. CCU has two channels (I ＆ Q) for data processing. The input of CCU is the output of DCSU (Data Compression ＆ Storage Unit). The output of CCU is the input of QTX which modulate data for RF communication. In this paper, there are the overview, short H/W description and operation concept of CCU.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.397-402
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• 2013

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.177-187
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• 2002

In this paper, we consider a multi-factor, multi-cause decision making problem of supply chain. And we show how to measure the operational efficiency of the components in supply chain and also how to improve the efficiency of each component and whole supply chain. As a methodology, the data envelopment analysis (DEA) is adopted to measure the efficiency by considering weight factors such as flexibility, information sharing, logistics level, etc. The proposed algorithm allows whole supply chain to have the improved efficiency rate.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1091-1110
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• 2016

Hybrid deployment under current cloud data centers is a combination of online and offline services, which improves the utilization of the cluster resources. However, the performance of the cluster is often affected by the online services in the hybrid deployment environment. To improve the response time of online service (e.g. search engine), an effective scheduling algorithm based on Storm is proposed. At the component level, the algorithm dispatches the component with more influence to the optimal performance node. Inside the component, a reasonable resource allocation strategy is used. By searching the compressed index first and then filtering the complete index, the execution speed of the component is improved with similar accuracy. Experiments show that our algorithm can guarantee search accuracy of 95.94%, while increasing the response speed by 68.03%.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.393-404
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• 2007

A concept of multi-layered database is proposed for the integrated operation of bridge information in this study. The multi-layered database is a logically integrated database composed of standardized information layers. The standardized information layers represent the data sets that can be unified, and they are defined by standardized information models. Classification system of bridge component was used as a basis of the multi-layered database, and code system based on the classification system was employed as a key integrator to manipulate the distributed data located on the different information layers. In addition, data level indicating priorities of information layers was defined to support strategic planning of the multi-layered database construction. As a proof of concept, a prototype of multi-layered database for object-oriented 3-D shape information and structural calculation document was built. Data consistency check of the semantically same data in the two different information layer was demonstrated, It is expected that the proposed concept can assure the integrity and consistency of information in the bridge information management.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.2
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• pp.143-151
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• 2001

In the recent day, fatigue life prediction techniques play a major role in the design of components in the ground vehicle industry. Full scale durability testing in the laboratory is an essential of any fatigue life evaluation of components or structure of the automotive vehicle. Component testing is particularly important in todey's highly competitive industries where the design to reduce weight and production costs must be balanced with the necessity to avoid expensive service failure. Generally, Multi-axis durability testing device is used to carry out the fatigue test. In this paper, The operation software for simultaneously driving Multi-axis vibration testing device is developed and the input and output data are displayed in windows of PC controller with real time. Moteover the characteristics of the displacement and the load of Multi-axis actuators are calibrated separately.

• Computers and Concrete
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• v.10 no.6
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• pp.557-573
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• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.