• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.523-536
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• 2011

The objective of this study is to develop the data driven model for the flood forecasting that are improved the problems of the existing hydrological model for flood forecasting in medium and small streams. Neuro-Fuzzy flood forecasting model which linked the Takagi-Sugeno fuzzy inference theory with neural network, that can forecast flood only by using the rainfall and flood level and discharge data without using lots of physical data that are necessary in existing hydrological rainfall-runoff model is established. The accuracy of flood forecasting using this model is determined by temporal distribution and number of used rainfall and water level as input data. So first of all, the various combinations of input data were constructed by using rainfall and water level to select optimal input data combination for applying Neuro-Fuzzy flood forecasting model. The forecasting results of each combination are compared and optimal input data combination for real-time flood forecasting is determined.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1243-1248
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• 2008

As development in technology of bioinformatics recently mates it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. In this thesis, we used CDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.

• Herbal Formula Science
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• v.32 no.2
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• pp.129-139
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• 2024

Objective : This study aims to investigate core herbs and formulas in Internal Medicine on the Liver system (IML) to enhance the efficiency of teaching IML, Herbalogy, and Formula Science, as well as to increase the integration of these courses. Methods : The study employed the frequency concept, commonly utilized in previous studies, alongside network analysis. Results : This study identified frequently used herbs, herbs with high centrality, frequently combined herbs, and core formulas. The herb with the highest frequency was 'Angelicae Gigantis Radix', while the herb with the highest centrality was 'Citri Unshius Pericarpium', and the most frequent herb combination was 'Zingiberis Rhizoma - Citri Unshius Pericarpium'. The network analysis results revealed a total of 5 herbal combination communities. Conclusion : In this study, we identified core herbs using traditional frequency analysis methods. Additionally, to complement traditional analysis methods, we discovered core herbal combinations, and fundamental herbal prescriptions through network analysis. The results of this study can be utilized as foundational data to enhance the efficiency of education in Internal Medicine on the Liver system (IML), Herbology, and Formula Science courses, as well as to improve coherence and consistency between foundational and clinical subjects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.103-110
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• 2015

This paper planned for the adjustment of unified control points by compared adjusted software for integrated network and the national integrated network. There may be some errors in the survey date and interpretation of data processing due to applying different software each year. To minimize errors, we performed a precision network adjustment by consolidating control points per observation session over years. Prior to perform the integrated network adjustment with the GPS analysis program (GLOBK) for the final integrated network adjustment, the Quasi-Observation Combination Analysis(QOCA), the Global Kalman filter VLBI and the GLOBK were compared and analyzed to perform an integrated network adjustment. The integrated network adjustment result indicates that the RMSE was rather big with ±0.03m along the vertical axis, but ±0.006m along the horizontal, that is not much different from the existing result.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.37-48
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• 1991

In this study, conventional network adjustment and combined network adjustment methods for single network adjustment methods for single network and centric combination network were compared by the analysis of root mean square error and standard error ellipse of observed points. It can be concluded from this study that for conventional surveying methods, the accuracy is in theorder of trilateration, traverse and triangulation, and for the case of combined surveying method, the accuracy is in the order of multilateration surveying, combined traverse and combined triangulation-trilateration surveying. And when establishing new control points, the accuracy can be improved by increasing redundant observations of centric combination network instead of using the single network. Also, in case of combined traverse surveying by adding observable laterals, accuracy level of trilateration could be achieved, and it was found that traverse is effective for large areas where sighting is easy, and combined traverse surveying is effective for urban areas where sighting is difficult.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1193-1209
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• 2015

Network coding is promising to maximize network throughput and improve the resilience to random network failures in various networking systems. In this paper, the problem of providing efficient confidentiality for practical network coding system against a global eavesdropper (with full eavesdropping capabilities to the network) is considered. By exploiting a novel combination between the construction technique of systematic Maximum Distance Separable (MDS) erasure coding and traditional cryptographic approach, two efficient schemes are proposed that can achieve the maximum possible rate and minimum encryption overhead respectively on top of any communication network or underlying linear network code. Every generation is first subjected to an encoding by a particular matrix generated by two (or three) Vandermonde matrices, and then parts of coded vectors (or secret symbols) are encrypted before transmitting. The proposed schemes are characterized by tunable and measurable degrees of security and also shown to be of low overhead in computation and bandwidth.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.500-504
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• 2008

The objective of this paper is to evaluate the feasibility of using an optimization model as a effective way to search conjunctive operation scheme to meet two conditions; one is to minimize the electric cost for pumping and another is to meet the water demand for satisfying customers. The feasibility is confirmed as comparing the best combinations of pumps between multi-regional water supply networks from multiple sources which are obtained through an optimization modeling and EPAnet modeling. KModsim model, a network optimization model, was used to determine conjunctive operation scheme in the pipe system. KModsim, based on Lagrangian Relaxation algorithm, is useful for modeling network system and obtaining simultaneously pump combination and water allocation with given input option such as energy unit cost supplying from a source into a consumer, operating pumping combination. This study develops the procedure of determining optimal conjunctive operation scheme with using KModsim model. As a study region, the water supplying systems of the Geojae-city in the Geongsang Namdo Province was selected and investigated. The EPAnet hydraulic simulation result(Ryu et al, 2007, KSWW) gave input data for optimization model; energy unit price(won/$m^3$), water service available area etc.. It was assured that the combination of pump operation through optimum conjunctive operation is to be optimum scheme to obtain the best economic water allocation with comparison to the hydraulic simulation result such as electric cost and pump combination cases. The results obtained through the study are as follows. First, It was found that a well-allocated water supply scheme, the best combination of pump operation through optimum joint operation, promises to save the electric cost and satisfy all operational goals such as stability and revenues during the period. Second, an application of KModSim, a network model, gave the amount of water allocation from each source to a consumer with consideration of economic supply. Finally, in a service area available to supply through conjunctive operation of existing inter-regional water supply networks within short distance, a conjunctive operation is useful for determining each transmission pipeline's service area and maximizing the effectiveness of optimizations in pumping operation time.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Korean Journal of Hydrosciences
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• v.6
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• pp.107-118
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• 1995

The water distribution system problem consists of finding a minimum cost system design subject to hydraulic and operation constraints. The design of new branchin network in a paddy irrigation system is presented here. The program based on the linear programming formulation is aimed at finding the optimal economical combination of two main factors : the capital cost of pipe network and the energy cost. Two loading conditions and booster pumps for design of pipe network are considered to obtain the least cost design.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1344-1350
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• 1990

A design method of ultra-broadband microwave amplifier is presented. A lossy network is represented as the combination of a serial impedance component and a parallel admittance component, and the realizable ranges of the gain and the reflection coefficients are derived with the components connected to the input, output or interstage network. The matching network has been designed by using the serial and parallel immittance loci which have the constant gain or reflection coefficients within the realizable ranges. Using the proposed method, deisgn examples of ultra-broadband amplifiers operating from dc to 12GHz frequency range are presented.