• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.27-34
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• 2012

Today the energy consumption of ICT networks is about 10% of the worldwide power consumption and will be remarkably increased in the near future. For that reason, the energy saving topics of ICT networks are actually studying in various research institutes. This paper studies the possible energy saving methods assuring the QoS of networks on network level. This paper assumed the energy consumption models according to energy profiles of node and link in IP networks. Especially it formulates the energy problem of a minimum energy consumption with various energy profile models and suggests the methods of energy-aware QoS routing under energy saving and network QoS sustaining condition. It shows the large difference of energy saving according to energy profiles and the possibility of energy saving by using the appropriate energy profile model in the simulation experiment. This paper shows that min_used_path(MP) heuristic of energy-aware QoS routing is the excellent method compared with other heuristic methods as view of reduction ratio of nodes and links and energy saving effect under network QoS sustaining condition. As a result, this paper confirms that the min_used_path(MP) heuristic of energy-aware QoS routing can get energy saving and sustaining of network QoS in IP networks.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.109-123
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• 2004

Corporate financial distress and bankruptcy prediction is one of the major application areas of artificial neural networks (ANNs) in finance and management. ANNs have showed high prediction performance in this area, but sometimes are confronted with inconsistent and unpredictable performance for noisy data. In addition, it may not be possible to train ANN or the training task cannot be effectively carried out without data reduction when the amount of data is so large because training the large data set needs much processing time and additional costs of collecting data. Instance selection is one of popular methods for dimensionality reduction and is directly related to data reduction. Although some researchers have addressed the need for instance selection in instance-based learning algorithms, there is little research on instance selection for ANN. This study proposes a genetic algorithm (GA) approach to instance selection in ANN for bankruptcy prediction. In this study, we use ANN supported by the GA to optimize the connection weights between layers and select relevant instances. It is expected that the globally evolved weights mitigate the well-known limitations of gradient descent algorithm of backpropagation algorithm. In addition, genetically selected instances will shorten the learning time and enhance prediction performance. This study will compare the proposed model with other major data mining techniques. Experimental results show that the GA approach is a promising method for instance selection in ANN.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.113-124
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• 2006

Among tile traffic safety facilities, median barriers are installed above 4-lane national roads due to the awareness of haying an effect on preventing the front collision. Studies about the installation effect analysis of median harrier have been carried out through both at home and outside, mainly indicating total accident reduction effect on pertinent sections. In sum, study about how the accident occurrence form is changed at the point classified by the accident type or severity is insignificant. In the case of outside the country, calculating the accident reduction effect according to the type of median barriers is main research and in domestic, though there is a part of researches assessing reduction effect by accident types, it is not reliable in the view or statistics because of using only 1year's before-aftev data installing the facility, So in this Paper. it is the main purpose to presume the accident conversion effect. For this, we conduct an investigation and collect data about 7-year's accident data containing before-after Project, safety facilities foundation records and index of road alignment on the subject of 4-1ane national roads(108.6km) existing median barrier. Next. using the empirical bayes method, we estimate a model construction and accident conversion effect of accident type severity. We expect the result or this Paper will be applied for a policy execution and Presentation of facility standard related to median barrier from now on.

• Korean Journal of Environment and Ecology
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• v.27 no.5
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• pp.571-578
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• 2013

This study generated regression models through a direct harvesting method to estimate carbon storage and uptake by Pinus densiflora and Pinus koraiensis, the major evergreen tree species in urban landscape, and established essential information to quantify carbon reduction by urban trees. Open-grown landscape tree individuals for each species were sampled reflecting various diameter sizes at a given interval. The study measured biomass for each part including the roots of sample trees to compute the total carbon storage per tree. Annual carbon uptake per tree was quantified by analyzing radial growth rates of stem samples at breast height. The study then derived a regression model easily applicable in estimating carbon storage and uptake per tree for the two species by using diameter at breast height (DBH) as an independent variable. All the regression models showed high fitness with $r^2$ values of higher than 0.98. While carbon storage and uptake by young trees tended to be greater for P. densiflora than for P. koraiensis in the same diameter sizes, those by mature trees with DBH sizes of larger than 20 cm showed results to the contrary due to a difference in growth rates. A tree of P. densiflora and P. koraiensis with DBH of 25 cm stored 115.6 kg and 130.0 kg of carbon, respectively, and annually sequestered 9.4 kg and 14.6 kg. The study has broken new grounds to overcome limitations of the past studies which quantified carbon reduction of the study species by substituting, due to a difficulty in direct cutting and root digging of landscape trees, coefficients from forest trees such as biomass expansion factors, ratios of below ground/above ground biomass, and diameter growth rates.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.351-363
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• 2020

Launch vehicles are subject to airborne acoustic loads during atmospheric flight and these effects become pronounced especially in transonic region. As the vibration due to the acoustic loads can cause malfunction of payloads, it is essential to predict and reduce the acoustic loads. In this study, a complete process has been developed for predicting airborne vibro-acoustic environment inside the payload pairing and subsequent noise reduction procedure employing acoustic blankets and Helmholtz resonators. Acoustic loads were predicted by Reynolds-Averaged Navier-Stokes (RANS) analysis and a semi-empirical model for pressure fluctuation inside turbulent boundary layer. Coupled vibro-acoustic analysis was performed using VA One SEA's Finite Element Statistical Energy Analysis (FE-SEA) hybrid module and ANSYS APDL. The process has been applied to a hammerhead launch vehicle to evaluate the effect of acoustic load reduction and accordingly to verify the effectiveness of the process. The presently developed process enables to obtain quick analysis result with reasonable accuracy and thus is expected to be useful in the initial design phase of a launch vehicle.

• Journal of Dental Rehabilitation and Applied Science
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• v.35 no.4
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• pp.220-227
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• 2019

Purpose: The purpose of this study was to compare the degree of tooth preparation abilities of students according to three self-assessment methods. Materials and Methods: forty-eight sophomores in Kyungpook National University College of Dentistry were divided into three experimental groups. Students performed tooth preparation of the left mandibular first molar for full gold crown. They performed self-assessment using the three methods (visual, digital, and putty index self-assessment group), and reperformed tooth preparation. An intraoral scanner was used to scan each tooth model (prepared tooth and unprepared tooth), and data were acquired in standard tessellation language (STL) file format. The STL files of prepared tooth and unprepared tooth were superimposed using the 3-dimensional analysis software (Geomagic control X). And the reduction amount was measured. In the statistical analysis, all values of reduction amount were analyzed with the Wilcoxon signed rank test and Kruskal-Wallis test (α = 0.05). Results: The three self-assessment methods showed statistically significant differences (P < 0.001). The putty index self-assessment group showed the highest reduction in error than the digital self-assessment method. Conclusion: Within limitations of this study, students showed significant differences in improvement of tooth preparation ability according to the three self-evaluation methods.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.653-658
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• 2010

In this paper, we propose a word spacing system which can be performed with just a small memory. We focus on significant memory reduction while maintaining the performance of the system as much as the latest studies. Our proposed method is based on the theory of Hidden Markov Model. We use only probability information not adding any rule information. Two types of features are employed: 1) the first features are the spacing patterns dependent on each individual syllable and 2) the second features are the values of transition probability between the two syllable-patterns. In our experiment using only the first type of features, we achieved a high accuracy of more than 91% while reducing the memory by 53% compared with other systems developed for mobile application. When we used both types of features, we achieved an outstanding accuracy of more than 94% while reducing the memory by 76% compared with other system which employs bigram syllables as its features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2527-2533
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• 2011

In this paper we propose a new pre-processing algorithm applied to multi-view video coding using color compensation algorithm based on image features. Multi-view images have a difference between neighboring frames according to illumination and different camera characteristics. To compensate this color difference, first we model the characteristics of cameras based on frame's feature from each camera and then correct the color difference. To extract corresponding features from each frame, we use Harris corner detection algorithm and characteristic coefficients used in the model is estimated by using Gauss-Newton algorithm. In this algorithm, we compensate RGB components of target images, separately from the reference image. The experimental results with many test images show that the proposed algorithm peformed better than the histogram based algorithm as much as 14 % of bit reduction and 0.5 dB ~ 0.8dB of PSNR enhancement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.396-404
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• 2011

Recently, the wide spread popularity of smart phones causes the advent of various mobile applications base on the location information. Since previous pedestrian navigations are applied by extending car navigations, these are not only difficult to provide the appropriate route information, but also raise limitations in the efficient query processing by data structures of car road networks. In addition, these increase the power consumption caused by the growth of I/O frequency. In this paper, we propose a pedestrian road network model for the accurate route information and a storage structure for the pedestrian road network based on POI to reduce the I/O frequency. The proposed method enables efficient route searches over POI reflecting the characteristics and requirements of pedestrian roads. Also, a reduction of query processing costs for the route searching by a data structure considered with POI can save the power consumption more than previous approaches.