• Journal of Industrial Technology
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• v.26 no.B
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• pp.183-189
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• 2006

Ad hoc network does not need any preexisting network infrastructure, and it has been developed as temporal networks in the various fields. Infostation is an efficient system to transfer informations which are not sensitive to delay. In this paper, we propose a disaster emergency management system using sensors attached to animals, that is combined with infostation system. We also analyze the performance of the proposed system by simulation. From the performance analysis results, we expect that the proposed system will be very useful to early detect big forest fires which occur frequently in Korea mountain areas.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.454-459
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• 2006

There are two main streams for pattern classification technology One is the method using PCA (Principal Component Analysis) and the other is the method using Neural network. Both of them have merits and demerits. In general, using PCA is so simple while using neural network can improve algorithm continually. Algorithm using neural network needs so many calculations rendering very slow response. In this work, an attempt is made to develop algorithms adopting both PCA and neural network merits for simpler, but faster and smarter.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.425-431
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• 2005

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• New & Renewable Energy
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• v.7 no.1
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• pp.36-44
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• 2011

Technology is developed from the efficient interaction with other technology files while building up its own research field. This study analyzes the structure of solar cell research area and describes its paths of the technology development in terms of interdisciplinary diversity using the Co-Classification method during 1979-2009. As a results, 1,380 studies are determined as the interdisciplinary among the 2,605 studies. It shows that 52.98% of the solar cell researches have interdisciplinary relationships with two or more research fields. In addition, we show that the research area of solar cell technology is composed by Material Science, Multidisciplinary and Energy & Fuel, Physics, Applied, Chemistry, Physical from the Co-Classification matrix and network analysis. It means the complexity of the technological knowledge production increased with the concept of interdisciplinary. The results can be used for the planning of the efficient solar cell technology development.

• Journal of Korean Library and Information Science Society
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• v.50 no.2
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• pp.43-60
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• 2019

Since information technology such as database or network technology was brought into the information and library science fields, the functions and services of libraries have drastically changed. To cope with the changes of fields, library schools have been improving curricula. This study collected curricula of library and information science in US and Korea and selected classes related to information technology. It also investigated the title keywords and keywords of class description statistically. As a result, 'system, 'database', 'network', 'programing', 'web' are major topic keywords for both countries, but 'library'shows high frequency pnly in Korea.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.247-250
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• 2007

There are three main kinds of service standards for 3G(Third-Generation) wireless communication as WCDMA, CDMA2000 and TD-SCDMA(Time Division-Synchronous Code Division Multiple Access). Compare with WCDMA and CDMA2000, TD-SCDMA system has distinguished technical characters. It is a TDD(Time Division Duplexing) based technology and deploys several advanced but in some respects complex technologies such as smart antenna, joint-detection and baton-handoff, etc. Therefore to analyze and design TD-SCDMA wireless network, it needs more efficient and systematic simulation tool. General simulation tool has so many analysis functions including path loss prediction, capacity and coverage analysis. For more suitable for TD-SCDMA, new additional technologies have to be implemented in simulation tool. Especially as the wireless network highly advancing focused on data service, it more needs to research and develop on the reliability of the simulation tool. In this paper, to give the concrete process and skill about how to implement TD-SCDMA simulation tool, we define the kinds of simulation tool and list basic analysis functions available for TD-SCDMA network design at first. And then we explain how to consider the effects of new technologies of TD-SCDMA and give the solutions about theses considerations.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.179-187
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• 2020

In a social environment where population aging is rapidly progressing, the healthcare service market is growing fast with the increasing interest in health and quality of life based on rising income levels and the evolution of technology. In this study, after keywords were extracted from Korean and US patent data published on KIPRIS from 2000 to October 2019, frequency analysis, time series analysis, and keyword network analysis were performed. Through this, the change of technology trends were identified, which keywords related to healthcare was shifted from traditional medical words to ICT words. In addition, although the keywords in Korean patents are 55% similar to those in the US, they show an absolute gap in patent production volume. In the next study, we will analyze various data such as domestic and international research and can obtain meaningful implications in the global market on the identified keywords.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.893-898
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• 2014

Recently, the interior permanent synchronous motor (IPMSM) has been applied to an integrated starter and generator (ISG) for hybrid electric vehicles. In the design of such a motor, thermal analysis is necessary to maximize the power density because the loss is proportional to the power of a motor. Therefore, a cooling device as a heat sink is required internally. Generally, a cooling system designed with a water jacket structure is widely used for electric motors because it has advantages of simple structure and cooling effectiveness. An effective approach to analyze an electric machine with a water jacket is a thermal equivalent network. This network is composed of thermal resistance, a heat source, and thermal capacitance that consider the conduction, convection, and radiation. In particular, modeling of the cooling channel in a network is challenging owing to the flow of the coolant. In this paper, temperature prediction using a thermal equivalent network is performed in an ISG that has a water cooled system. Then, an experiment is conducted to verify the thermal equivalent network.