• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.272-288
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• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.1-10
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• 2024

For the purpose of predicting credit card customer churn accurately through data analysis, a model can be constructed with various machine learning algorithms, including decision tree. And feature importance has been utilized in selecting better input features that can improve performance of data analysis models for several application areas. In this paper, a method of utilizing feature importance calculated from the MDI method and its effects are investigated in the credit card customer churn prediction problem with classification trees. Compared with several random feature selections from case data, a set of input features selected from higher value of feature importance shows higher predictive power. It can be an efficient method for classifying and choosing input features necessary for improving prediction performance. The method organized in this paper can be an alternative to the selection of input features using feature importance in composing and using classification trees, including credit card customer churn prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.545-551
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• 2008

The MOCT(Ministry of Construction & Transportation) gave a order named as "The guideline for computerization and application of geotechnical investigation result" to an affiliated organization in March 2007. Today, pilot project of construction of geotechnical information database is in process to be stable for its system after applying this guideline, and discipline how to input investigated data for related users. We have developed standard for geotechnical investigation result-format, metadata for distribution of geotechnical information and to coordinate based on world geodetic system. Also, We had a introduce to status with respect to use the input system, collect a statistics of input contents. At a result, improvement items of input system is proposed. It was analyzed that most users put to practical use easily as a result of education for making use of on the spot of the developed GIIS. But There were problems with the GIIS as well as complexity of metadata formation, such as error of moving part of information window, and a part of recognition error of install program in accordance with computer OS circumstances. Particularly, to improve some parts of GIIS is needed, because of use of or KNHC (Korea National Housing Corporation)-specific format and difference of input process followed by MOCT's guideline. In this study, it is planning to make up for occurred problems, and improvements when operating and managing the Geotechnical Information DB center in 2008.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2614-2632
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• 2015

A multiple symbol detection (MSD) algorithm is proposed relying on soft information for ultra-wideband systems, where differential space-time block code is employed. The proposed algorithm aims to calculate a posteriori probabilities (APP) of information symbols, where a forward and backward message passing mechanism is implemented based on the BCJR algorithm. Specifically, an MSD metric is analyzed and performed for serving the APP model. Furthermore, an autocorrelation sampling is employed to exploit signals dependencies among different symbols, where the observation window slides one symbol each time. With the aid of the bidirectional message passing mechanism and the proposed sampling approach, the proposed MSD algorithm achieves a better detection performance as compared with the existing MSD. In addition, when the proposed MSD is exploited in conjunction with channel decoding, an iterative soft-input soft-output MSD approach is obtained. Finally, simulations demonstrate that the proposed approaches improve detection performance significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3458-3478
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• 2022

Automatic modulation recognition is the core algorithm in the field of modulation classification in communication systems. Our investigations show that deep learning (DL) based modulation recognition techniques have achieved effective progress for multiple-input multiple-output (MIMO) systems. However, network complexity is always an additional burden for high-accuracy classifications, which makes it impractical. Therefore, in this paper, we propose a low-complexity dimensional interactive lightweight network (DilNet) for MIMO systems. Specifically, the signals received by different antennas are cooperatively input into the network, and the network calculation amount is reduced through the depth-wise separable convolution. A two-dimensional interactive attention (TDIA) module is designed to extract interactive information of different dimensions, and improve the effectiveness of the cooperation features. In addition, the TDIA module ensures low complexity through compressing the convolution dimension, and the computational burden after inserting TDIA is also acceptable. Finally, the network is trained with a penalized statistical entropy loss function. Simulation results show that compared to existing modulation recognition methods, the proposed DilNet dramatically reduces the model complexity. The dimensional interactive lightweight network trained by penalized statistical entropy also performs better for recognition accuracy in MIMO systems.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.259-265
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• 2024

In order to improve the SW convergence capabilities of university undergraduate students, methods to evaluate undergraduate students' code design capabilities should be researched along with the development of related courses. In previous studies, there were qualitative evaluation methods and quantitative relative evaluation methods for code results. In the quantitative relative evaluation method, the number of problem decomposition depth, number of function reuses, and number of functions were measured and evaluated. In this study, an evaluation method that was not presented in previous studies was proposed using the problem of presenting the number of input and output information types when designing code. The evaluation problems proposed in this paper applied up to three types of input information and three types of output information. Through this, five code design evaluation questions were presented and a method to quantitatively calculate code design scores was proposed. Codes from 100 student respondents were collected and analyzed through courses that applied the proposed evaluation method. Through result analysis, the number of problem decomposition depths was proportional to the number of types of input information, the number of function reuses was proportional to the number of types of output information, and the number of functions showed a correlation that was proportional to the total number of types of input and output information. Lastly, by analyzing the distribution of evaluation scores of 100 respondents, we demonstrated that the code design evaluation method according to the five input/output information condition evaluation problems is effective.

• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.181-184
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• 2000

In this paper, we described a design of the input buffer system for efficiently dealing with MPEG audio bitstream to demux header and side information, audio data. In order to overcome the limitations of fixed-word manipulation in bitstream demuxing, we proposed a new variable length bit retrieval system with FSM sequencer supporting MPEG audio frame format, and serial buffer demuxing audio stream, FIFO circular buffer including header and side information.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.250-255
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• 2006

A low cost PDP sustain power supply is proposed based on flyback topology using Boundary Conduction Mode(BCM) to control input current regulation. This method guarantees DCM condition to regulate the input current harmonics under all load conditions. An excessive voltage stress due to the link voltage increase can be suppressed by removing link capacitor and adjusting transformer turns ratios, which makes it possible to be used for universal line applications. The proposed converter is tested with a 400W(200V-2A output) prototype circuit.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.449-455
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.